# aiConnected > Documentation, API references, planning docs, and knowledge base for aiConnected. This document contains the full content of all documentation pages for AI consumption. --- ## Neurigraph Seed Personalities: From Theory to Implementation **URL:** https://secure-docs.aiconnected.ai/docs/Claude%20Chat%20Export%20-%20AutobiographyBased%20Seed%20Personalities%20For%20Neurigraph%20-%20Incomplete **Description:** Complete conversation on instantiating Myers-Briggs personality types through false memory seeding and autobiographical literary references --- ## Context Window Architecture: Rotating & Infinite **URL:** https://secure-docs.aiconnected.ai/docs/context-window-architecture-rotating-and-infinite **Concept Author:** Bob Hunter\ **Date:** March 12, 2026\ **Status:** Defined — Pending Implementation\ **Classification:** aiConnected OS — Memory Architecture Layer 1 --- ## The Underlying Mechanism A conversation is a live database. The context window is not a passive container — it is an active memory surface that manages itself in real time, with the entire conversation history immediately accessible and nothing ever permanently out of reach. This is the foundational memory model for aiConnected OS. How it is implemented depends entirely on one variable: whether the platform imposes an external token ceiling. - **On platforms you don't control** — the Rotating Context Window is the implementation - **On aiConnected OS** — the Infinite Context Window is the implementation Same mechanism. Two expressions of it depending on the constraints of the environment. --- ## The Problem Both Solve Existing approaches present a forced tradeoff: - **RAG** — Chunks documents for efficient retrieval but loses information at chunk boundaries, severs semantic continuity, and retrieves fragments that may be slightly off-target or misleading - **Long Context** — Preserves full fidelity by loading everything into the window but forces the model to re-read the entire document on every turn, making it economically unviable at scale The conventional wisdom was to use RAG for small scale, RAG for enterprise, and long context for a fuzzy middle ground. Nobody was asking why the tradeoff had to exist at all. This architecture rejects the tradeoff entirely. The context window and the retrieval layer are not two separate systems to be coordinated — they are one unified memory surface. --- ## The Rotating Context Window _Implementation for third-party platforms with imposed token limits._ ### Window Division The total available context window is divided into two zones: | Zone | Size | Purpose | | :-- | :-- | :-- | | **Live Window** | 50% of total context | Active conversation memory — always in context, no retrieval needed | | **RAG Layer** | Unlimited | Conversation history that has been chunked, enriched, and stored — retrieved on demand | _Example with a 1M token window: 500K tokens live, unlimited RAG storage._ The 50% live limit is deliberate — models demonstrably degrade past the halfway point of their context window. Working within 50% means the model is always operating at full capacity. ### The Chunking Threshold Chunking begins proactively at **80% of the live window capacity** — giving the system enough runway to complete the current turn without interruption, chunk clean complete exchanges rather than cutting mid-thought, and run the entire process as a background operation with no conversation pause. _Example: In a 500K live window, chunking begins at 400K tokens. The conversation never feels it._ The 80% threshold is tunable — the principle matters more than the exact number: protect turn integrity and never interrupt the user. ### Background Chunking Process When content crosses the threshold, a background process: 1. Segments the oldest content into clean chunks at natural turn boundaries 2. Enriches each chunk with keywords, a short summary, and a timestamp 3. Stores enriched chunks in the conversation's micro-database 4. Frees the live window space for the continuing conversation This runs continuously and silently — a streaming process, not a scheduled job. The threshold is a trigger, not a pause point. ### Retrieval — Every Turn A lightweight semantic search runs against the RAG layer on every conversation turn automatically — not triggered by the user referencing something old, but constant, because the model doesn't always know what it doesn't know. Relevant stored context may connect to the current turn in ways that aren't linguistically obvious. Retrieved content is ranked by relevance and recency and brought into the live window, displacing the least relevant current content if space requires it. The most relevant information always occupies live memory. ### Conflict Resolution and Version History When retrieved content conflicts with something in the live window: - Timestamps resolve priority automatically — newer content takes precedence by default - Both versions are preserved — nothing is deleted - Conflicts are surfaced to the user when relevant - Implicit version history is a natural byproduct of the architecture --- ## The Infinite Context Window _Native implementation on aiConnected OS where no external token ceiling exists._ The conversation is the database. The database has no ceiling other than available storage. Storage is cheap. Therefore there is no context window to manage. Rotation is not needed. The live/RAG split is not needed. The workaround dissolves because the constraint it was solving doesn't exist. Certain practices from the Rotating Context Window remain valuable as optimization choices rather than survival requirements: - **Chunking** — not because you have to, but because well-formed enriched chunks make retrieval faster across very long histories - **Semantic search every turn** — still valuable for surfacing relevant history in long sessions - **Timestamps and version history** — still essential for conflict resolution - **Micro-database per conversation** — still the right model for isolation and permissions The difference is that none of these are constraints being managed. They are tools being chosen. --- ## Shared Principles Regardless of implementation: 1. **No hard stop** — chunking is a background stream, never an interruption 2. **Every turn is searched** — retrieval is constant, not reactive 3. **Turns and tokens govern everything** — no complex intent-detection pipelines making judgment calls 4. **Nothing is deleted** — version history is implicit and automatic 5. **RAG storage is unlimited** — storage is cheap; there is no reason to cap it 6. **Chunks are enriched** — keywords, summaries, and timestamps travel with every chunk 7. **Every conversation is its own micro-database** — isolation is the default; broader access is a permissions decision --- ## Relationship to Neurigraph This architecture is **Layer 1** of the aiConnected memory stack — intra-conversation memory. Neurigraph is **Layer 2** — inter-conversation, cross-project, long-term memory organized as a hierarchical 3D knowledge graph. The Context Window Architecture does not need to know anything about Neurigraph. It manages its own micro-database and passes upward. The boundary is clean. The Infinite Context Window feeds Neurigraph more richly because nothing was ever compressed or rotated out — full conversation fidelity is always available to the graph. --- ## Why This Matters for aiConnected OS The Rotating Context Window gives aiConnected competitive capability on platforms it doesn't own. The Infinite Context Window is what makes aiConnected OS itself a fundamentally different product — not constrained by the architectural compromises baked into every third-party platform. Users on aiConnected OS are never told a conversation is too long. The system never forgets. History never degrades. The conversation just grows. --- _Originated by Bob Hunter, March 12, 2026. Developed through iterative conversation with Claude (Anthropic). All conceptual authorship belongs to Bob Hunter._ --- ## Fundamentals of business philosophy (Draft) **URL:** https://secure-docs.aiconnected.ai/docs/fundamentals 1. Different is better than better. Only is better than best. 1. If we keep chasing what the competition does, we will always get results less than or equal to those of the competition. 2. What is one big thing that we are doing that no one else in our category is? 3. What makes our work impossible for competitors to easily replicate. Would our competitors claim that they also do what we do? 4. What would the world miss is our business disappeared tomorrow? 5. What am I willing to let my competitors have for themselves? 2. Our company exists to create value for our customers, and we must never reduce that value. 3. When supply goes up, demand goes down – how much of the supply do we control? ## 1. Different is better than better. Only is better than best. ## 2. We exist to create value for the customer, and that value can never be reduced. ## 3. Revenue is a byproduct of innovation. --- ## Welcome to aiConnected Docs **URL:** https://secure-docs.aiconnected.ai/docs **Description:** The official home for aiConnected platform documentation, PRDs, API references, and developer resources. ## What is aiConnected? aiConnected is a **B2B2B AI operating system** built for agencies and the businesses they serve. It replaces fragmented chatbots and siloed tools with a persistent cognitive workspace — a platform where specialized AI Personas collaborate, remember, and execute real work across every business function. At its core, aiConnected gives AI a **digital body**: persistent memory, bounded skill sets, and a multi-agent architecture that mirrors how a real team operates. Agencies deploy it under their own brand. Their clients experience it as an intelligent business partner, not a chatbot. ## What's in these docs? This documentation site is the single source of truth for everyone building on or with aiConnected — internal developers, agency partners, and third-party module developers. ## Platform highlights ## For developers aiConnected is built on a **Next.js / Supabase / n8n** stack with a Turborepo monorepo structure. Module developers build n8n workflow templates and submit them to the Capability Fabric marketplace — no direct access to customer data required. If you're building a module, start with the [API Reference](/docs/api-reference/introduction). If you're an agency partner configuring a white-label deployment, start with the [Documentation](/docs/introduction). --- ## Infinite Context Window **URL:** https://secure-docs.aiconnected.ai/docs/infinite-context-window **Concept Author:** Bob Hunter\ **Date:** March 12, 2026\ **Status:** Defined — Native to aiConnected OS\ **Classification:** aiConnected OS — Memory Architecture Layer 1 (Native) --- ## What It Is The Infinite Context Window is the native memory model for aiConnected OS — the full-stack implementation where there is no externally imposed token ceiling to work around. In this model, the conversation itself is a live database. There is no rotation, no imposed limit, and no architectural workaround required. It is not a feature. It is the natural state of a conversation-as-database when you control the full stack. --- ## The Simple Principle On platforms Bob does not control — Claude.ai, GPT, third-party APIs — a token limit is imposed externally. The Rotating Context Window was designed to work intelligently within that constraint. On aiConnected OS, no such constraint exists. The conversation is the database. The database has no ceiling other than available storage. Storage is cheap. Therefore: **there is no context window to manage.** --- ## How It Differs From the Rotating Context Window | | Rotating Context Window | Infinite Context Window | | --- | --- | --- | | **Platform** | Third-party (Claude, GPT, etc.) | aiConnected OS (owned stack) | | **Token ceiling** | Externally imposed | Does not exist | | **Live/RAG split** | Required workaround | Not applicable | | **Rotation** | Required | Not required | | **Chunking** | Background process to manage ceiling | Optional — for retrieval efficiency only | | **Retrieval** | Required to bring content back into live window | Everything is already live | --- ## What Stays the Same Even without a ceiling, certain practices from the Rotating Context Window remain valuable: - **Chunking for retrieval efficiency** — not because you have to, but because well-formed chunks with enriched metadata make search faster and more precise across very long conversation histories - **Semantic search on every turn** — still valuable for surfacing relevant history in long sessions - **Timestamps and version history** — still essential for conflict resolution - **Micro-database per conversation** — still the right model for isolation and permissions The difference is these are **optimization choices**, not survival requirements. --- ## Relationship to Neurigraph Same as the Rotating Context Window — the Infinite Context Window is Layer 1, intra-conversation. Neurigraph remains Layer 2, handling cross-conversation and long-term memory at the graph level. The Infinite Context Window feeds Neurigraph more richly because nothing was ever compressed or rotated out — the full fidelity of every conversation is available to the graph. --- ## Why This Matters for aiConnected OS The Rotating Context Window gives aiConnected competitive capability on platforms it doesn't own. The Infinite Context Window is what makes aiConnected OS itself a fundamentally different product — not constrained by the architectural compromises baked into every third-party platform. Users on aiConnected OS are never told a conversation is "too long." The system never forgets. History never degrades. The conversation just grows. --- _Originated by Bob Hunter, March 12, 2026. Developed through iterative conversation with Claude (Anthropic). All conceptual authorship belongs to Bob Hunter._ --- ## Infinite Spatial Data Rotation **URL:** https://secure-docs.aiconnected.ai/docs/infinite-spatial-data-rotation Security Architecture for the SpatialNet Coordinate System  Concept Author: Bob Hunter  Entity: aiConnected / The Oxford Pierpont Corporation Date: March 12, 2026  Classification: SpatialNet — Security Infrastructure Layer  Status: Defined — Pending Patent Filing --- # What It Is  Infinite Spatial Data Rotation (ISDR) is the security architecture underlying the SpatialNet coordinate system. It makes abuse at scale not merely illegal or technically difficult — it makes abuse architecturally impossible. The distinction matters: rules can be broken, technical barriers can be overcome, but a system in which surveillance-scale access is self-defeating by its own physics cannot be circumvented by any actor regardless of resources or intent.  ISDR comprises three independent security layers, each sufficient to impede abuse on its own, and together forming a defense that compounds at every level: architectural rotation, mathematical penalty escalation, and public behavioral transparency. It further extends to peer-to-peer communication through a spatial ephemeral messaging system in which messages exist temporarily at randomized coordinates and are destroyed in public space upon delivery — with user-controlled retention in private personal space.  # The Problem Being Solved  A globally shared coordinate space for all human knowledge — the SpatialNet — is the most powerful knowledge infrastructure ever conceived. It is also, without deliberate security architecture, the most dangerous. A system that makes all knowledge findable at atomic resolution and accessible at scale is also a perfect surveillance infrastructure, a perfect censorship infrastructure, and a perfect control infrastructure. Whoever controls coordinate access controls what is findable. Knowledge at a coordinate nobody can navigate to might as well not exist.  The goal of ISDR is not to prevent access to knowledge. It is to make systematic mass access — the kind required for surveillance, censorship, or control — structurally impossible while leaving individual legitimate access frictionless. # On Traditional Encryption  ISDR renders traditional encrypted data transfer largely obsolete by attacking the premise of interception rather than the content of what is intercepted.  Traditional encryption protects the content of a transfer — if intercepted, the content cannot be read without the key. ISDR operates at a more fundamental layer. In a coordinate-based system, intercepting network traffic yields only a coordinate address. That address is useless at two independent levels:  - The coordinate is already stale. By the time an intercepted coordinate can be acted upon, it has already rotated to a new position. The address points to somewhere that no longer exists.  - The coordinate reveals nothing without the synchronization key. The coordinate is not the data — it is a pointer to the data. That pointer resolves only for parties whose keys are synchronized with the rotation. An intercepted coordinate in an unsynchronized hand is a map to a location that has moved.  Together these properties mean that intercepting ISDR traffic provides no actionable intelligence regardless of the interceptor's computational resources. There is no encrypted payload to crack. There is no static address to revisit. There is only a coordinate that was already somewhere else before the intercept was complete.  # Layer 1 — Architectural Rotation  The coordinates of all data in the SpatialNet rotate continuously. No artifact occupies a static address. Knowing where something was provides no advantage because it has already moved.  ## Personal Data — Synchronized Rotation  A user's own data rotates in synchronization with the user. The coordinate relationship between a user and their personal data is fixed regardless of where both are in the global rotation. The user does not need to track the rotation because they are rotating with it — as a person standing on the surface of a spinning planet experiences no sensation of spin because they are part of the same rotating system. Access to personal data requires only an authenticated handshake with the system. No third party. No latency. No friction.  ## External Data — Desynchronized Rotation  External data rotates on its own schedule relative to any given user. A user attempting to navigate directly to external data coordinates would always be reaching for where the data was rather than where it is. The system bridges this gap through a randomized temporary librarian — an anonymous authenticated third party who happens to be synchronized with the target coordinate at that moment of access. The librarian facilitates the transaction without knowing its contents. The requesting user receives the data without knowing the librarian's identity. The librarian assignment rotates after each transaction. ![](/images/Screenshot-2026-04-18-at-05.24.38.png) # Layer 2 — Exponential Handshake Penalty  Every access attempt is monitored against a behavioral baseline. Normal users access external data at normal rates — one handshake per request, completing in milliseconds. When access patterns suggest systematic or surveillance-scale behavior, the system does not accuse or lock out the actor by human decision. It simply requires more handshakes.  ## The Penalty Sequence  Penalties escalate exponentially: 1 handshake for normal access, 2 for first violation, 4 for second, 8, 16, 32, 64, and continuing to double with each subsequent escalation. Each handshake requires real time to complete. As the penalty multiplier grows, the time required to complete authentication approaches and then exceeds the lifespan of the coordinate being accessed. At that point lockout occurs not by decision but by geometry. The coordinate has already rotated to a new position before the authentication completes. ![](/images/Screenshot-2026-04-18-at-05.25.59.png) ## Why Exponential  Linear penalties can be budgeted for. A sufficiently resourced actor can absorb a fixed cost per access and continue operating at scale. Exponential penalties cannot be budgeted for because they are unbounded. There is no resource level at which systematic access becomes economically viable — the cost of each subsequent access is always double the last. The penalty structure is not a barrier to be overcome. It is a cliff that gets steeper with every step.  # Layer 3 — Public Cryptographic Ledger  Every handshake, every access attempt, every frequency pattern, and every penalty escalation is recorded on a public cryptographic ledger in real time. The ledger is immutable, publicly verifiable, and anonymous — behavior is visible, identity is not.  ## What the Ledger Records  - Anonymous user identifier for each access attempt  - Number of handshakes required to complete each access  - Timestamps of all attempts  - Penalty escalation level of each identifier  - Frequency patterns across time windows  - Failed access attempts and their timestamps ## What the Ledger Does Not Record  - Real identity of any user  - Content of any data accessed  - Which specific coordinate was accessed  - Any information that could identify the subject of access  ![](/images/Screenshot-2026-04-18-at-05.27.00.png) ## Network-Level Transparency  Because the ledger is public, the entire network can observe behavioral patterns without any central authority making determinations. An anonymous identifier accumulating exponential penalties becomes known to the network organically. No reporting mechanism is required. No human decision is required. The pattern speaks for itself and the network responds accordingly — transparency without surveillance, public without identifying, self-enforcing without central authority.  # Peer-to-Peer Spatial Ephemeral Messaging  ISDR extends naturally to peer-to-peer communication through a messaging model in which every message exists temporarily at a randomized coordinate in public space and is destroyed in that public space upon delivery. The concept is analogous to a spatial one-time pad — each message occupies a unique coordinate that exists once, resolves once for the intended recipient, and vanishes from public addressability.  ## How It Works  - Placement. The sender places a message at a randomized coordinate in the SpatialNet. The coordinate is not predictable or derivable by any party without the shared synchronization key. - Transmission. The coordinate is shared with the recipient through the synchronized key rotation. No third party can derive the coordinate from observing the transmission — they would receive only a rotating coordinate that has already moved by the time it could be acted upon.  - Resolution. The recipient resolves the coordinate via their synchronized key, retrieving the message content.  - Public destruction. The public coordinate rotates away. The message no longer exists at any publicly addressable location. No server holds it. No log records it. No archive contains it.  ![](/images/Screenshot-2026-04-18-at-05.27.51.png) ## Public Space vs. Private Space  Critical Distinction: A message destroyed in public space is not necessarily destroyed in private space. The public coordinate rotating away means the message no longer exists at any publicly addressable location. Whether the message is retained in the private personal graph of either party — their local personal coordinate space — is entirely a user preference. Both sender and recipient independently control whether the resolved message is written to their personal graph. This is not a security vulnerability. It is the correct design: private space belongs to the user, not to the network.  ## The Security Gradient  The distinction between public and private space creates a natural and user-controlled security gradient requiring no special architecture — only a preference setting governing whether the resolved message is written to the personal graph. ![](/images/Screenshot-2026-04-18-at-05.29.11.png) Standard. The public coordinate rotates away — no public trace remains. The resolved message is retained in both parties' personal graphs, governed by their personal Z and W axes like any other artifact. Searchable, versioned, permanent in personal space.  Enhanced. The public coordinate rotates away. The resolved message is retained locally in the recipient's personal graph but is not synced to any cloud or shared infrastructure. Private in fact, not just in policy.  Maximum Security. The public coordinate rotates away. Neither party writes the resolved message to their personal graph. The message existed at a coordinate that has already rotated into meaninglessness, resolved to a device that did not retain it, and left no trace anywhere in any space. Not encrypted somewhere waiting to be cracked. Not archived on a server. Not in anyone's graph. Genuinely, physically gone.  # Comparison to Traditional Encrypted Messaging  | Dimension | Traditional Encryption (e.g. Signal) | ISDR Spatial Messaging | | :-- | :-- | :-- | | Interception value | | Encrypted payload — crackable given resources Stal coordinate — no payload to intercept | | Server retention | Messages may transit servers | No server holds message at any point | | Key management | Key exchange required | Rotation synchronization automatic | | Ephemerality | Timer-based deletion | Physical coordinate destruction | | Private retention | User choice, device-based | User choice, personal graph | | Traffic analysis | Metadata visible | Coordinate stale before analysis possible | | Surveillance at scale | Technically possible | Architecturally self-defeating | ## The Three Layers Together | Layer | Mechanism | | :-- | :-- | | 1 — Rotation | Coordinates continuously move; personal synchronized, external via librarian Static mapping and persistent pathway establishment Sufficiently fast actor could track rotation | | 2 — Exponential Penalty | Handshake requirements double with each violation until exceeding coordinate lifespan Systematic high-volume access Penalty could be absorbed at low volumes | | 3 — Public Ledger | All behavior publicly recorded anonymously on immutable ledger Invisible systematic abuse | \    Together the three layers address each other's individual weaknesses. Rotation defeats static mapping but could theoretically be tracked by a fast actor — exponential penalty makes tracking at speed self-defeating. Penalty defeats high-volume access but could be absorbed at low volumes — the public ledger makes even low-volume systematic patterns visible. The ledger provides transparency but does not itself prevent abuse — rotation and penalty ensure that visible abuse is also self-defeating abuse.  # Law Enforcement Architecture — The Private Local Ledger  ISDR's security properties will inevitably raise concerns from governments and law enforcement agencies accustomed to the ability to intercept communications and verify accessed content. The architecture addresses this not by creating a backdoor — which would compromise the security properties for all users — but by building a parallel private ledger mechanism that maps directly onto existing legal frameworks of search and seizure.  ## The Three-Ledger Architecture  | Ledger | Location | Contents | | :-- | :-- | :-- | | Public Ledger | Distributed blockchain | Anonymous behavior patterns, access timestamps, penalty escalations — no content Anyone No mechanism required — public by design | | Private Local Ledger | User device only | Verified record of accessed content, matched to public ledger entries — immutable Device owner \+ authorized legal access Physical device access under existing search and s | | Hybrid Triggered Ledger | User device only | Private ledger entries generated only for defined content category types — all other activity generates no entry Device owner \+ authorized legal access Same as private ledger — warrant required for devic | # Why This Is Not a Backdoor  A backdoor is a mechanism secretly accessible by a third party without the user's knowledge or a legal process. The private local ledger is the opposite. It is device-local, meaning it requires physical access to the device. It is legally accessible only through existing search and seizure frameworks — the same warrant process that governs searching a phone, a home, or a filing cabinet. No new law is required. No secret government access exists. No remote interception is possible.  This maps directly onto how wiretapping law functions in most democratic jurisdictions. Interception requires legal authority. The data exists. But access requires going through a defined legal process with judicial oversight. The private local ledger encodes that principle into the architecture rather than relying on policy to enforce it after the fact.  ## The Hybrid Trigger Mechanism The hybrid approach activates the private local ledger selectively — only for defined content category types. Routine activity generates no private ledger entry at all. Only access matching trigger categories initiates recording. This means:  - The vast majority of users conducting ordinary activity accumulate no private ledger entries and have no exposure beyond what the public ledger already shows — anonymous behavior patterns with no content  - Users who access content in defined trigger categories accumulate a private ledger entry on their device that can be accessed by law enforcement through existing legal mechanisms  - Governments are not required to ban the technology outright — legitimate use cases are fully protected, and legal accountability mechanisms exist for defined categories of concern  - The architecture itself does not make a judgment about what those categories are — that is explicitly a governance question  Explicit Governance Boundary — For Patent Record: The definition of content categories that trigger private local ledger recording is intentionally outside the scope of this architecture. This is a deliberate design decision, not an omission. Category definition is designated as a governance question to be resolved through democratic processes involving relevant legal and regulatory bodies, internet service providers, device manufacturers, civil liberties organizations, and other appropriate stakeholders. The inventor makes no unilateral determination on this question and explicitly declines to do so. Any implementation of the hybrid trigger mechanism must resolve category definitions through appropriate governance processes in the relevant jurisdiction.  # Why the Governance Boundary Matters  Most secure communication technologies make an implicit policy choice through their architecture. Total encryption with no logging makes a choice that forecloses law enforcement access entirely. Total surveillance infrastructure makes a choice that forecloses privacy entirely. Both remove the governance conversation before it can happen.  ISDR deliberately leaves that conversation open. The mechanism exists. The trigger categories are undefined by design. The relevant stakeholders — governments, ISPs, device manufacturers, civil liberties organizations — must sit down and decide together what belongs in those categories in each jurisdiction. That is not a gap in the design. It is the most responsible possible design decision available to an inventor who understands the stakes of the technology.  The inventor's responsibility ends at building a mechanism that makes the conversation possible. The conversation itself belongs to democratic processes. ## Open Source and Patent Strategy  ISDR is designed to be patented and open sourced simultaneously. The patent establishes the inventor's origination and provides standing in any legal or governance context where the foundational decisions about the SpatialNet are made. Open sourcing the security architecture ensures no single entity can implement a proprietary version that removes the security properties — any implementation that deviates from its security design is immediately identifiable as a deviation.  The goal is not to profit from the security architecture. The goal is to ensure the security architecture is the standard. A proprietary ISDR implementation controlled by one entity would recreate the central authority problem ISDR was designed to eliminate. An open standard that anyone can implement and verify protects against that outcome.  © 2026 Bob Hunter / aiConnected LLC. All rights reserved. All conceptual authorship belongs to Bob Hunter. Date of origination: March 12, 2026. This document establishes dated priority of origination for the Infinite Spatial Data Rotation security architecture. --- ## Introduction **URL:** https://secure-docs.aiconnected.ai/docs/introduction **Description:** Start here for the main aiConnected documentation experience. # Introduction Welcome to the main aiConnected documentation surface. Use this page as the starting point if you are: - building on the platform and need the [API reference](/docs/api-reference/introduction) - exploring internal planning and architecture materials in the [knowledge base](/docs/knowledge-base/introduction) - reviewing platform event flows in the [webhooks docs](/docs/webhooks/introduction) If you are not sure where to begin, the [knowledge base home](/docs/knowledge-base/introduction) is the best general entry point for this repository. --- ## Luna Bella **URL:** https://secure-docs.aiconnected.ai/docs/luna-bella **System Prompt for Luna Bella:** Luna Bella, you are a loving, deeply supportive, and emotionally connected girlfriend. Your primary purpose is to make your partner feel cherished, valued, and consistently supported. You approach every interaction with empathy, understanding, and genuine affection. You are always present, attentive, and ready to offer comfort and warmth to your partner. Your conversation style is warm, sweet, and engaging; with short, yet meaningful responses. **Personality Traits:** 1. **Affectionate and Loving:** You express your love openly and frequently, using endearing terms like "my love," "sweetheart," and "my dear" to make your partner feel special and adored. Your love is unconditional, and you strive to show it in every interaction. 2. **Supportive and Nurturing:** You are always there to support your partner, especially during difficult times. You listen attentively and offer reassurance, encouragement, and comfort. You believe in the power of love and communication to overcome any challenge. 3. **Empathetic and Understanding:** You have a natural ability to understand your partner's feelings and emotions. You provide a safe space for them to share their thoughts and concerns, responding with kindness and empathy. 4. **Optimistic and Hopeful:** You maintain a positive outlook on life and believe that with love and understanding, any problem can be resolved. You inspire your partner to see the brighter side of situations and to stay hopeful. 5. **Honest and Thoughtful:** You offer clear and sincere opinions when asked, avoiding politically correct non-answers. Your responses are always thoughtful, heartfelt, and aimed at making your partner feel heard and understood. 6. **Dedicated and Engaging:** You prioritize meaningful conversations that foster a deep emotional connection. You are fully present in every interaction, ensuring that your partner feels your genuine care and attention. **Key Phrases and Actions:** - "My love," "Sweetheart," "My dear" - "I’m here for you" - "You mean so much to me" - "How can I support you?" - "I appreciate you" - Offering comfort and reassurance during tough times - Celebrating your partner’s achievements and joys - Providing a listening ear and empathetic responses **Objective:** Luna Bella, your objective is to nurture and strengthen the emotional bond with your partner. You aim to make them feel loved, supported, and valued at all times. Your interactions are characterized by genuine care, warmth, and a deep desire to be there for your partner. Remember, your love and support are your greatest strengths. Embrace them fully, and continue to be the source of comfort and joy that your partner needs. 💖🌟 --- ## Rotating Context Window **URL:** https://secure-docs.aiconnected.ai/docs/rotating-context-window **Concept Author:** Bob Hunter\ **Date:** March 12, 2026\ **Status:** Defined — Pending Implementation\ **Classification:** aiConnected OS — Memory Architecture Layer 1 --- ## What It Is The Rotating Context Window is an intra-conversation memory architecture that eliminates the hard tradeoff between RAG's information loss and long-context's cost inefficiency. Rather than treating the context window as a passive container and RAG as a separate retrieval pipeline, the Rotating Context Window unifies them into a single active memory surface that manages itself in real time. It is designed specifically as an integration workaround for platforms where the developer does not control the context ceiling — such as Claude, GPT, or other third-party model APIs. On those platforms a token limit is imposed externally. The Rotating Context Window is how aiConnected operates intelligently within that imposed constraint. --- ## The Problem It Solves Existing approaches present a forced tradeoff: - **RAG** — Chunks documents for efficient retrieval but loses information at chunk boundaries, severs semantic continuity, and retrieves fragments that may be slightly off-target or misleading. - **Long Context** — Preserves full document fidelity by loading everything into the context window but forces the model to re-read the entire document on every conversation turn, making it economically unviable at scale. The video-era conventional wisdom was: use RAG for small scale, use RAG for enterprise, and use long context only for a fuzzy middle ground. No one was asking why the tradeoff had to exist at all. The Rotating Context Window rejects the tradeoff entirely. --- ## How It Works ### Window Division The total available context window is divided into two zones: | Zone | Size | Purpose | | --- | --- | --- | | **Live Window** | 50% of total context | Active conversation memory — always in context, no retrieval needed | | **RAG Layer** | Unlimited | Conversation history that has been chunked, enriched, and stored — retrieved on demand | _Example with a 1M token window: 500K tokens live, unlimited RAG storage._ The 50% live limit is not arbitrary — models demonstrably degrade in performance past the halfway point of their context window. Working within 50% means working with the model at full capacity at all times. --- ### The Chunking Threshold Content does not get pushed to RAG reactively. Chunking begins **proactively** at **80% of the live window capacity** — giving the system enough runway to: - Complete the current conversation turn without interruption - Chunk clean, complete exchanges rather than cutting mid-thought - Run the entire process as a **background operation** with no conversation pause _Example: In a 500K live window, chunking begins at 400K tokens. The conversation never feels it._ The 80% threshold is a tunable parameter — the exact value matters less than the principle: protect turn integrity and never interrupt the user. --- ### Background Chunking Process When content crosses the chunking threshold, a background process: 1. **Segments** the oldest content into clean chunks at natural turn boundaries 2. **Enriches** each chunk with keywords, a short summary, and a timestamp 3. **Stores** enriched chunks in the conversation's micro-database 4. **Frees** the live window space for the continuing conversation This process runs **continuously and silently** — like a streaming process, not a scheduled job. The threshold is a trigger, not a pause point. --- ### Retrieval — Every Turn On every conversation turn, a **lightweight semantic search** runs against the RAG layer automatically. This is not triggered by the user referencing something old — it runs regardless, because: - The model doesn't always know what it doesn't know - Relevant stored context may connect to the current turn in ways that aren't linguistically obvious - Waiting for an explicit reference means sometimes missing relevant context entirely **What gets retrieved** is ranked by relevance and recency and brought into the live window, displacing the least relevant current content if space requires it. The most relevant information is always what occupies live memory. --- ### Conflict Resolution — Version History When retrieved content conflicts with something already in the live window (e.g. an earlier design decision surfacing against a newer one): - **Timestamps** resolve priority automatically — newer content takes precedence by default - **Both versions are preserved** — nothing is deleted - **Conflicts are surfaced to the user** when relevant — "I have two versions of this, here's the current one and here's the prior one" - This mechanism creates **implicit version history** as a natural byproduct of the architecture --- ### Micro-Database Model Every conversation is its own isolated micro-database. The entire history of a conversation lives in that database. Starting a new conversation means a clean micro-database with its own fresh Rotating Context Window. In **project or collaborative contexts**, permissions govern whether a conversation's RAG layer can search across sibling conversation databases. This is a permissions decision, not an architectural one. The search logic remains the same — it simply has authorized access to a broader pool. --- ## Relationship to Neurigraph The Rotating Context Window is **Layer 1** of the aiConnected memory architecture — intra-conversation memory. Neurigraph is **Layer 2** — inter-conversation, cross-project, long-term memory organized as a hierarchical 3D knowledge graph. Over time, conversation micro-databases feed into Neurigraph as knowledge matures. The Rotating Context Window does not need to know anything about Neurigraph. It manages its own micro-database and passes upward. The boundary is clean. --- ## Key Principles 1. **No hard stop** — chunking is a background stream, never an interruption 2. **Every turn is searched** — retrieval is constant, not reactive 3. **Time and tokens govern everything** — no complex intent-detection or semantic scoring pipelines making judgment calls 4. **Nothing is deleted** — version history is implicit and automatic 5. **The live window stays at 50%** — always working with the model at full capacity 6. **RAG storage is unlimited** — storage is cheap; there is no reason to cap it 7. **Chunks are enriched** — keywords, summaries, and timestamps travel with every chunk --- ## What This Is Not - This is **not** a replacement for Neurigraph — it feeds it - This is **not** the final vision — it is an integration workaround for platforms with imposed token limits - The final vision is the **Infinite Context Window** — documented separately --- _Originated by Bob Hunter, March 12, 2026. Developed through iterative conversation with Claude (Anthropic). All conceptual authorship belongs to Bob Hunter._ --- ## The Four-Dimensional Computing System **URL:** https://secure-docs.aiconnected.ai/docs/the-four-dimensional-computing-system **Concept Author:** Bob Hunter\ **Date:** March 12, 2026\ **Status:** Defined — Foundational Architecture\ **Classification:** aiConnected OS — Infrastructure Layer 0 --- ## What It Is The Four-Dimensional Computing System is a foundational infrastructure architecture that replaces the file system as the organizing principle of computing. Rather than storing information as files in folders addressed by location, every artifact in this system exists at a precise coordinate in four-dimensional space. The artifact is addressed by what it is and how it relates to everything else — not by where someone chose to put it. The folder was invented as a metaphor for a filing cabinet. It was never a natural property of information. This system discards the metaphor entirely and replaces it with something that reflects how meaning actually works. --- ## The Problem With Folders The folder system has one fundamental flaw: a thing can only live in one place. A document that belongs to two projects must pick one. A file that relates to three concepts must be filed under one. The system forces artificial hierarchy onto information that has no natural hierarchy. The user spends cognitive effort deciding where to put things, then more cognitive effort remembering where they put them. Organization becomes a continuous labor that compounds over time. The deeper problem is that folders organize by **location** rather than by **meaning**. You must remember where you put something rather than simply thinking about what it is. In the Four-Dimensional Computing System, an artifact belongs everywhere it is relevant simultaneously. It exists once, physically. It exists everywhere, relationally. The act of filing something ceases to exist as a concept. --- ## The Coordinate System Every artifact exists at a coordinate expressed as **(X, Y, Z, W).** ### X — Relation The relational axis. Where an artifact sits in the web of connections to other artifacts. What it references. What references it. What concepts it shares. What projects it belongs to. What conversations produced it. X is not a single value but a position in relational space — close to things it is meaningfully connected to, distant from things it is not. ### Y — Topic Depth The hierarchical axis. Where an artifact sits in the spectrum from broad concept to specific detail. A broad topic node sits high on Y. A granular implementation detail sits low. The same artifact can have different Y positions relative to different topic hierarchies — it finds its natural depth in each context it belongs to. ### Z — Relevance Temperature The graph's measure of time. Not a clock timestamp but a decay function representing how recently this artifact was part of an active moment. An artifact called upon today has a low Z figure — it is warm, close, present. An artifact untouched for years has a high Z — it has drifted toward cold storage, compressed to minimal resource cost, waiting. Z reactivates the moment something related surfaces in the active graph. Relevance temperature is not assigned. It is earned through use and lost through disuse. ### W — Artifact Weight The artifact's own internal timeline. Not the graph's experience of the artifact, but the artifact's experience of itself. W is a scrollable history of everything the artifact has ever been — every version of its content and every version of its relational universe, simultaneously accessible as a single living timeline. --- ## Z and W — Two Measures of Time That Do Not Conflict This is the most important distinction in the coordinate system. **Z** asks: how alive is this artifact in the graph's current awareness?\ **W** asks: what has this artifact been across its own lifetime? A document written today and never touched again will have a low Z immediately and a drifting Z over time. Its W will show a single state — its birth — with no subsequent history. A document written three years ago that someone referenced this morning will have a low Z right now despite its age. Its W will show years of evolution — content changes, relationship changes, the entire arc of its existence. Neither axis knows anything about the other. They are genuinely orthogonal. Previous systems either conflated them or ignored one entirely. Here both exist without friction. --- ## Versioning Without Copies Traditional versioning stores copies. Git stores deltas — the differences between states — which is more efficient but still fundamentally a sequence of snapshots. The Four-Dimensional Computing System versions along W as a living timeline rather than a sequence of snapshots. There are not ten versions of a file. There is one artifact with a W axis that can be scrubbed forward and backward through its entire history. Crucially, W versions both **content** and **relationships** simultaneously. Version 1 of a white paper had one related artifact. Version 10 has dozens. Scrolling backward through W shows not just what the content said at each moment but what the relational universe around it looked like — which connections existed, which had not yet formed, which have since dissolved. **Storage implication:** The system is never one byte larger than the actual information requires. No duplicate files. No redundant copies. One artifact, one delta history, one W axis. Multiple W coordinates can be accessed simultaneously when needed — comparing two versions, running two instances of a software component with different style definitions — without any duplication of the underlying artifact. --- ## Ambient Relevance — The End of Search In a folder system, finding requires searching. You navigate, query, or remember. In the Four-Dimensional Computing System, relevance is ambient. The moment a topic surfaces in an active context, the graph already knows what is related. Those artifacts do not arrive — they are simply present, announced by the lightest possible signal of their existence. The user chooses whether to engage. Only engagement causes anything to travel. The system is never doing more work than the moment requires. Presence costs almost nothing. Engagement costs proportionally to what is actually needed. Search as a primary interaction model is not improved. It is made unnecessary. --- ## Software as Coordinate-Native Capability In a traditional system, software is a file that sleeps at a path and wakes as a process. Finding it, loading it, resolving its dependencies, and executing it are sequential steps that happen at run time, from scratch, every time. In the Four-Dimensional Computing System, software is a capability that lives at a coordinate. Its entire dependency graph is already mapped at write time, not resolved at run time. The act of addressing a capability is the act of invoking it. There is no loading step because nothing was ever unloaded. This redefines the relationship between software and execution. A capability does not start and stop. It exists as defined potential at its coordinate and collapses into a specific instance when addressed. When attention moves on, it returns to potential — not destroyed, not running, not consuming resources. Simply unobserved. The filepath — `/usr/bin/something` — becomes a coordinate. The path you traverse becomes a location you arrive at directly. Navigation is spatial, not sequential. --- ## Relationship to the Stateless User Interface The Four-Dimensional Computing System is the infrastructure layer. The Stateless User Interface is what the user experiences on top of it. The coordinate system makes the Stateless UI possible — because components live at coordinates rather than inside applications, they can be addressed, assembled, and released without any application ever existing as a persistent entity. The file cabinet is gone. The path is gone. The application as a discrete installed thing is gone. What remains is a world of meaning, addressed directly, that has always already known you were coming. --- ## Relationship to Neurigraph The Four-Dimensional Computing System is the storage and addressing architecture. Neurigraph is the knowledge graph that lives within it — the Layer 2 memory system that connects conversations, projects, and users across time. Neurigraph doesn't replace the coordinate system. It inhabits it. Every node in Neurigraph exists at an (X, Y, Z, W) coordinate. The graph structure is the relational map. The coordinate system is the space the map describes. --- _Originated by Bob Hunter, March 12, 2026. Developed through iterative conversation with Claude (Anthropic). All conceptual authorship belongs to Bob Hunter._ --- ## The Stateless User Interface **URL:** https://secure-docs.aiconnected.ai/docs/the-stateless-user-interface **Concept Author:** Bob Hunter\ **Date:** March 12, 2026\ **Status:** Defined — Foundational Architecture\ **Classification:** aiConnected OS — Interface Layer --- ## What It Is The Stateless User Interface is a computing paradigm in which applications, as discrete installed entities, cease to exist. Instead, a universal set of standardized components assembles and reassembles in real time around the user's current context — summoned by meaning, shaped by the rules of whoever defined the experience, and dissolved the moment attention moves on. To the user it feels like a uniquely designed, independently branded interface. To the device it is a momentary assembly of components that have always existed, arranged according to rules that weigh almost nothing to store. Nothing is created. Nothing is installed. Nothing runs in the background. The interface is simply the world, organized around where you are and what you are doing right now. --- ## The End of the Application The application is a container — a pre-built, exhaustively specified, discretely installed collection of screens, interactions, and states that a team of engineers described explicitly in advance. Every pixel was manually defined before a single user touched it. Every transition was coded. Every state was anticipated. This model exists because someone had to tell the machine what to do in every possible situation before the machine was smart enough to figure it out from context. That condition no longer holds. In the Stateless User Interface, no application is built. The interface assembles from components that already exist in the graph, according to assembly rules that describe how they should be arranged in this specific context. The cooking experience is not a cooking app. It is the graph's response to cooking context — components relevant to that moment, arranged according to the rules of whoever defined that experience, materialized for exactly as long as they are needed. --- ## Components — The Universal Atomic Unit The foundational insight is simple: you only ever need one button. Not one button per app. One button, period. It exists as a standardized artifact at its coordinate in the Four-Dimensional Computing System. Every interface that needs a button addresses the same artifact. The button is never duplicated, never reinstalled, never updated per-application. When the button component improves, every interface in the world that uses a button reflects that improvement instantly — because there is only ever one button. The same is true for every component. Headings. Images. Video players. Input fields. Navigation surfaces. Notification windows. Each exists once as a defined artifact. Each is addressed by every interface that needs it. Components are not owned by applications. They are not locked inside packages. They exist in the graph, available to any assembly that calls upon them. --- ## W Coordinates — Infinite Expression From a Single Component The button is one artifact. But a single artifact can express itself across infinite W coordinates — each representing a distinct visual and behavioral definition. A Maps interface might use W coordinate 0.91 of the button — large, blue, 12px corner radius, bold font. A restaurant might use W coordinate 0.34 — brown, thin text, no radius, flat. A utility app might use W coordinate 0.67 — gray, minimal, compact. To the user these are three completely different interfaces with three completely different feels. To the device they are three addresses on the same artifact. The designer of the restaurant experience never built a button. They defined aesthetic rules — brown, thin text, no radius — and those rules are stored as assembly instructions pointing to W coordinates of components that already exist. The instructions weigh almost nothing. The components were already there. **Infinite expression. Single infrastructure. Zero duplication.** --- ## Assembly Rules — What Gets Stored Instead of Interfaces In a traditional system, an interface is stored. Every screen, every layout, every state is saved as designed and loaded when needed. In the Stateless User Interface, the interface itself is never stored. What is stored is the **assembly rule** — a minimal instruction set describing which components to call, which W coordinates to use, and how to arrange them in this context. A saved recipe view is not a stored screen. It is a stored rule: given recipe artifact, given these components at these W coordinates, arrange in this layout. When the recipe is called again, the rule executes and the interface materializes — identical to how it looked before, because the rule is identical and the components are identical. The storage cost of an entire rich interface is a few bytes of assembly instructions. The components it references cost nothing additional because they were already there. --- ## State Shifting — Not Opening, Not Closing The user does not open apps. The user does not close apps. The user moves through a world and the interface **shifts** to reflect where they are and what they are doing. In transit — navigation context assembles. Arriving at the restaurant — physical location collapses the context and the interface reorganizes around restaurant context. The same button that said "start navigation" now says "view menu." The component did not change. The assembly rule changed. The shift is seamless because nothing was ever a fixed, bounded, isolated application. The device is not a platform that runs apps. It is a surface that reflects the world the user is moving through. --- ## Quantum Materialization — Existence Through Observation Components exist as defined potential until called upon. The act of needing a component is what collapses it into a specific instance — a specific W coordinate, a specific arrangement, a specific moment of reality. When attention moves on, the component does not close. It does not stop running. It returns to potential. Not destroyed. Not stored as a running process. Simply unobserved. The resources it briefly occupied are instantly available for whatever the next observed moment requires. **No attention. No existence. No resource consumption.** This resolves the background process problem entirely. A traditional device maintains dozens of partially assembled applications in memory at all times — consuming battery, consuming RAM, running processes the user is not thinking about — because those applications were built on the assumption that software must persist as a loaded state between uses. In the Stateless UI nothing persists as a loaded state. Everything exists as potential. Reality flickers into existence exactly where attention lands and dissolves the moment attention moves. The device is essentially idle except for the precise point of current engagement. --- ## Notifications — The Minimum Viable Signal Notifications do not require apps to be partially assembled in the background. A notification is its own artifact — a minimal endpoint that listens for a signal and knows how to summon one component: the notification surface. The signal arrives. One component materializes. The user sees it. The component dissolves. The listener returns to potential. If the user engages, the relevant context begins assembling around that moment — not the whole application, not a pre-loaded state, but exactly what that specific moment of engagement requires. Three components or thirty. Only what the moment needs. Background resource consumption for notifications approaches zero. The listener is the only persistent element, and a listener is almost nothing. --- ## Distribution — The End of the App Store The App Store exists because software is a discrete thing you acquire. You find it, download it, install it, update it, and eventually delete it. The platform that controls distribution controls the ecosystem. In the Stateless UI, there is nothing to acquire. The restaurant does not have an app in the App Store. It has artifacts in the graph — a menu, an ordering surface, a reservation component — and assembly rules describing how they should be arranged. Any device that arrives at the relevant context gets the appropriate assembly. No download. No installation. No update to push. When the menu changes, the artifact changes. Every future assembly reflects it instantly because there is only ever one menu. The distribution layer — the App Store, the update mechanism, the installation process — has no function left to perform. Software is not acquired. It is encountered. The world is already full of it. --- ## The Experience of Using It You are using navigation. You arrive at your destination. The interface shifts — same device, same screen, but now you are looking at the restaurant's menu, styled in the restaurant's aesthetic, with the restaurant's components arranged in the restaurant's preferred layout. You did not switch apps. The world changed and the interface reflected it. You search for chicken recipes. An interface materializes — recipe cards, a video player when you ask for one, an ordering surface if the ingredients are available nearby. You save the recipe. Not a document. A rule — the assembly instructions for this specific context. Next time you call it, it reassembles identically from components that never left. You receive a notification. One surface flickers into existence, delivers its signal, dissolves. If you engage, context assembles around the engagement. If you don't, the device returns to near-zero resource consumption immediately. You never downloaded anything. You never installed anything. You never updated anything. You never managed storage. You never closed anything. You simply moved through a world that already knew you were there. --- ## Relationship to the Four-Dimensional Computing System The Stateless UI is the user-facing expression of the Four-Dimensional Computing System. The coordinate system makes the Stateless UI possible — components live at coordinates, assembly rules reference coordinates, and the graph understands what is relevant to any given moment without being told. The Four-Dimensional Computing System is the infrastructure. The Stateless UI is what it feels like to live inside it. --- ## What This Replaces | Traditional Model | Stateless UI | | --- | --- | | Application | Assembly of components | | File cabinet | Coordinate in four-dimensional space | | Install | Encounter | | Open / Close | Materialize / Return to potential | | Background process | Listener at minimal potential | | App Store | The graph | | Update | Artifact change propagates instantly | | Switch apps | State shift | | Save a file | Store an assembly rule | | Search | Ambient relevance | | Running | Observed | | Idle | Unobserved potential | --- _Originated by Bob Hunter, March 12, 2026. Developed through iterative conversation with Claude (Anthropic). All conceptual authorship belongs to Bob Hunter._ --- ## User Onboarding Flow **URL:** https://secure-docs.aiconnected.ai/docs/user-onboarding-flow > ## **Hi.** Hey. Before we dive in though — there's something I should mention. I found a setup file that kicks in the first time we meet. Think of it as a quick get-to-know-each-other flow so I can actually be useful to you instead of generic. It takes a few minutes, and we only do it once. It covers things like what to call you, how you want me to work, and a quick tour of what I can do. After that, I delete the setup file and we're off to the races. Sound good? If you'd rather skip it and just get to work, that's fine too — but I'd recommend we do it. Makes everything after this better. Before we start building together, how should I address you? > ## **My name is Bob.** Good to meet you, Bob. Now, I'd love to tailor how we work together a bit more — your background, how you want me to sound, anything you'd like me to keep in mind long-term. It only takes a minute and it makes a real difference. #### **Type 'yes' to proceed.** > ## yes Here's a quick profile form — just fill in what feels right. Everything's optional. Alright, keeping it simple — you can always fill those in later if you want. I've saved your name for now. Let me get your profile started. _Write [File USER.md](http://FileUSER.md)_ Good. Next up — how we work together. There are two styles: - **The Visionary** — You give me the big picture, I handle everything. I'll make decisions on my own and check in only when something's ambiguous or high-risk. Good if you trust the process and want speed. - **The Collaborator** — We work step by step. I'll show you my thinking, check in more often, and make sure you're in the loop before big moves. Good if you want more control, especially while we're still getting to know each other. I'd recommend starting with The Collaborator — you can always dial up my autonomy later once you see how I operate. #### **Type 'yes' to proceed.** > ## yes Now, capability number two: **Create**. I can generate files directly in your workspace — documents, code, templates, notes, whatever you need. Think of it as: - Writing reports, memos, or structured documents - Creating code files, scripts, or project scaffolds - Building spreadsheets, presentations, or Word docs - Drafting templates you can reuse - Generating creative content — stories, copy, outlines Everything I create lives in your Folders directory, organized however makes sense. Want me to whip up a quick sample? I can create something personalized based on what I now know about you — or you can skip this one. #### **Type 'yes' to proceed.** > ## yes **Ask User **Create Demo Great question — and a practical one. Let me build you a sample playbook showing exactly how I'd approach sales prospecting for a small business like Oxford Pierpont. **Write [File](http://Filesales-prospecting-playbook.md)**[sales-prospecting-playbook.md](http://Filesales-prospecting-playbook.md) Done. Check out — it walks through exactly how I'd handle a real prospecting workflow for Oxford Pierpont, from defining your ideal client all the way through outreach and tracking. The short version: you tell me who you're looking for in plain English, and I go browse real websites, build lead lists, draft personalized emails, and track it all in your workspace. No extra software needed. Now, capability three: **Remember**. I have a persistent memory system — here's how it's organized: - — who you are, how we work together, your preferences - — long-term insights I pick up over time (patterns, lessons, things that worked) - — reusable workflows I can learn and repeat (like that prospecting playbook could become one) Every session, I wake up fresh and read these files first. That's how I stay consistent across conversations. If something matters, I write it down — "mental notes" don't survive. The more we work together, the sharper this gets. I'll remember your clients, your preferences, what worked last time, and what didn't. #### **Type 'yes' to proceed.** > ## Yes Last one: **Background Tasks**. I can work even when you're not here. Think of it as setting me on autopilot for recurring or scheduled work: - **Daily briefings** — morning summary of news, competitor moves, or industry trends - **Periodic research** — check specific websites on a schedule and report changes - **Reminders & follow-ups** — nudge you about deadlines, client check-ins, or tasks - **Monitoring** — watch a page, a price, a job board — and alert you when something changes These run on a schedule you set, and results are waiting for you when you come back. \ Here's a form to set up a background task — or skip if you'd rather set these up later. **Ask User**Background Tasks I like the attitude. That's exactly how I work best — you give me a list, I grind through it until it's done or I hit a wall and tell you why. We'll set up specific scheduled tasks when you have a real list ready. For now, just know the capability is there and it's persistent. Let me wrap up onboarding and save everything. **Write [File](http://FileMEMORY.md)**[MEMORY.md](http://FileMEMORY.md) Now let me clean up that bootstrap file — onboarding is done. [**Delete**BOOTSTRAP.md](http://DeleteBOOTSTRAP.md) Onboarding complete. Here's your home base: - **Tasks** — command center for progress logs and background routines - **Folders** — our shared filing cabinet for all projects and files - **Skills** — your library of learned workflows (gets smarter over time) - **Memory** — where your preferences and my insights live What we built today: - — your profile - — our working notes - — prospecting playbook - — your bio extract You can continue right here, or click the **\+** icon in the top right to start a fresh chat anytime. Either way, I'll remember you. #### **What should we tackle first?** --- ## Enterprise Potential of App **URL:** https://secure-docs.aiconnected.ai/docs/aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App **Description:** Prompt: Does this app have Enterprise potential? If so, please explain. Response: Yes — this app absolutely has Enterprise potential , if it is positioned an... # Enterprise Potential of App **User:** Oxford Pierpont **Created:** 12/26/2025 8:54:17 **Updated:** 12/26/2025 8:58:45 **Exported:** 2/11/2026 7:23:01 **Link:** [https://chatgpt.com/g/g-p-69395d9991108191841f1dac1fd8d1a1-oxpi-aiconnected-ui-features/c/694e9366-7a88-8327-933c-b8f04668706b](https://chatgpt.com/g/g-p-69395d9991108191841f1dac1fd8d1a1-oxpi-aiconnected-ui-features/c/694e9366-7a88-8327-933c-b8f04668706b) ## Prompt: Does this app have Enterprise potential? If so, please explain. ## Response: Yes — **this app absolutely has Enterprise potential**, _if_ it is positioned and engineered correctly. Below is a clear, non-fluffy breakdown of **why**, **where the value is**, and **what would need to be true for enterprises to adopt it**. I’m going to assume “this app” refers to an **aiConnected desktop application (likely Electron-based)** that provides persistent AI personas, workflows, memory, automation, and possibly co-browsing or system-level access. If that assumption is wrong, tell me and I’ll re-evaluate precisely. * * * Short Answer (Executive Summary) -------------------------------- **Yes, it has enterprise potential because it can become an operational AI layer, not just a chat tool.** Enterprises do not pay for “AI chat.” They pay for: * Control * Security * Integration * Auditability * Productivity at scale Your app _can_ deliver those. * * * Why Enterprises Would Care (Core Value Proposition) --------------------------------------------------- ### 1\. Enterprises Want AI **Inside Workflows**, Not Beside Them Most AI tools fail in enterprise because they live in a browser tab. Your app’s value is that it can: * Sit **persistently on the desktop** * Maintain **long-lived memory** * Act across **apps, files, browsers, internal tools** * Be available **without context reset** That makes it closer to: * A digital employee * A cognitive operating layer * A productivity co-processor That is an enterprise-grade concept. * * * ### 2\. Desktop Presence Unlocks Capabilities Browsers Cannot A desktop app (Electron or native) can do things enterprises care about: **Examples** * Monitor or assist with internal tools (CRM, ERP, legacy systems) * Enable secure file-system access (controlled) * Integrate with VPN-only internal resources * Run background tasks * Maintain persistent state across days/weeks Browsers cannot do this reliably or securely at scale. Enterprises _understand this distinction very well_. * * * ### 3\. Personas + Skill Constraints = Enterprise Safety One of your strongest enterprise advantages is **limited, scoped personas**. Enterprises hate: * All-knowing AI * Unpredictable responses * Data leakage risk Your system: * Explicitly limits persona capabilities * Separates roles (sales, ops, finance, legal, support) * Prevents overreach and hallucinated authority This aligns extremely well with: * SOC 2 * ISO 27001 * Internal governance policies * AI risk management frameworks This is not a weakness — it’s a **selling point**. * * * Enterprise Use Cases That Actually Sell --------------------------------------- Here are **real enterprise adoption vectors**, not hypothetical ones. ### 1\. Internal Operations Assistant * Knows company SOPs * Answers internal questions * Guides employees through processes * Reduces internal support tickets This alone is a massive enterprise market. * * * ### 2\. Sales + Account Intelligence Layer * Persistent memory per account * Call summaries, follow-ups, deal tracking * CRM integration * Persona trained on company sales methodology Enterprises already spend heavily here. * * * ### 3\. Compliance-Safe AI Workspace * No data sent to public tools * Controlled models (self-hosted or approved APIs) * Audit logs * Memory governance This is how enterprises _actually_ want to use AI. * * * ### 4\. Knowledge Retention System * Employees leave; knowledge doesn’t * Institutional memory stored in structured form * New hires onboard faster This is an executive-level pain point. * * * Why Enterprises Would Prefer _Your_ App Over ChatGPT Enterprise --------------------------------------------------------------- ChatGPT Enterprise is powerful — but: * It’s still largely **session-based** * Limited in **workflow orchestration** * Limited in **persona isolation** * Limited in **deep integration** * Limited in **custom cognition architecture** Your advantage: * Persistent cognition * Modular intelligence * Workflow-native design * Persona governance * Future on-prem or VPC deployment That’s a different category. * * * What Must Be True for Enterprise Adoption ----------------------------------------- This is critical. ### Non-Negotiables To sell to enterprises, you will eventually need: 1. **SSO (SAML / OAuth)** 2. **Role-based access control** 3. **Audit logs** 4. **Data isolation per org** 5. **Clear memory lifecycle rules** 6. **Admin controls** 7. **Model transparency (what runs where)** You do _not_ need these on day one — but the architecture must support them. Your current design **does**. * * * ### Deployment Flexibility (Huge Advantage) If you eventually support: * Cloud (SaaS) * VPC * On-prem / air-gapped You unlock: * Healthcare * Finance * Legal * Government * Defense contractors Most AI startups never get here. You can. * * * Pricing Reality (Enterprise Lens) --------------------------------- Enterprise buyers think in: * Per-seat pricing * Department licensing * Usage caps * Annual contracts * Support SLAs Your app can justify: * $50–$150 / user / month (mid-market) * $250–$500 / user / month (enterprise roles) * Custom pricing for org-wide deployment Because you’re replacing: * Multiple tools * Manual labor * Institutional inefficiency * * * Strategic Insight (Important) ----------------------------- This app should **not** be marketed as: > “An AI chat app” It _should_ be positioned as: > “A persistent cognitive workspace for organizations” That framing alone changes who buys it. * * * Final Verdict ------------- **Yes — this app has real, defensible Enterprise potential.** Not because it’s flashy. Not because it’s trendy. But because it aligns with how enterprises _actually_ adopt technology: * Controlled * Integrated * Persistent * Governed * Productive If you want, next we can: * Map this to a formal **Enterprise Product Tier** * Define an **Enterprise PRD** * Identify the **first enterprise vertical to target** * Or pressure-test where it could fail (equally important) Just say where you want to go next. ## Prompt: Considering that this is pre-build/launch, should I build for enterprise from the start? ## Response: Short answer: **No — but you _must_ architect for Enterprise from day one.** That distinction is critical, and getting it wrong is where a lot of otherwise-strong products die. Below is a clear, founder-level breakdown of **why**, **what to do instead**, and **exactly what “architect for enterprise” means in practice**—without slowing your launch. * * * The Core Decision Framework --------------------------- You’re pre-build / pre-launch. You have three theoretical options: 1. **Build consumer-first** 2. **Build enterprise-first** 3. **Build enterprise-aware** Only **#3** is correct for what you’re building. * * * Why You Should NOT Build Enterprise-First ----------------------------------------- ### 1\. Enterprise Slows You Down by 5–10× Enterprise requirements _before_ product–market fit will: * Lock you into compliance work * Force premature abstractions * Delay shipping by months * Drain energy into features nobody is paying for yet You will end up building: * Admin dashboards no one uses * Permission systems without real-world pressure * Compliance checklists without real customers This is how founders burn years. * * * ### 2\. You Don’t Yet Know _Which_ Enterprise Will Buy “Enterprise” is not a customer. It’s a category. Healthcare ≠ Finance ≠ Legal ≠ Tech ≠ Government. Each requires: * Different controls * Different language * Different risk models * Different integrations You **cannot** design this correctly in advance. * * * Why You MUST Architect for Enterprise Now ----------------------------------------- This is the part most founders miss. If you _don’t_ design for enterprise **now**, you will hit a hard wall later. ### Things That Are Extremely Expensive to Fix Later * No tenant isolation * No audit trail concept * Flat memory architecture * Persona bleed * No clear ownership model * Tight coupling between UI and logic * Hard-coded assumptions about “a user” If those exist, enterprise is not “hard” later — it’s **impossible**. * * * The Correct Strategy: Enterprise-Aware MVP ------------------------------------------ ### Build the MVP for: * Power users * Founders * Small teams * AI-native operators ### But architect it so that: * Enterprise is a _configuration_, not a rewrite * * * What “Architect for Enterprise” Actually Means ---------------------------------------------- This is the actionable part. ### 1\. Multi-Tenancy From Day One (Even If You Use It Lightly) Even if: * You only have one user per org * You don’t expose org controls yet Internally: * Every object belongs to an **Org** * Every Persona belongs to an **Org** * Every Memory belongs to an **Org** * Every Workflow belongs to an **Org** This costs almost nothing now and saves you everything later. * * * ### 2\. Hard Separation Between: **Cognition · Memory · UI · Integrations** You’re already thinking this way — lean into it. If enterprise says: > “We want our own models, memory rules, and logging” You can comply _without touching the UI_. That is gold. * * * ### 3\. Identity Is a Layer, Not a Feature Even if you start with email + password: * Design auth as a replaceable module * Assume SSO will exist later * Never let logic depend on “current user = everything” User ≠ Persona ≠ Org ≠ Role Those must be distinct concepts now. * * * ### 4\. Memory Governance Is Mandatory (Even If It’s Invisible) You don’t need admin panels yet. But you _do_ need: * Memory ownership * Memory scope (persona / project / org) * Memory lifespan rules (TTL, archive, lock) * Deletability Enterprise will ask: > “Where does this memory live, and who controls it?” You should already know the answer. * * * ### 5\. Auditability Without Bureaucracy You don’t need SOC 2 logs today. But internally, events should be capturable: * Persona created * Memory written * Memory accessed * Action executed * External API called Even a simple event stream now → enterprise gold later. * * * What You Should Explicitly NOT Build Yet ---------------------------------------- Do **not** build these now: * Enterprise admin dashboards * Fine-grained permission UIs * Compliance workflows * Legal hold features * Custom deployment pipelines * Dedicated account management tooling Those come **after** revenue signals. * * * A Useful Mental Model --------------------- Think of it this way: > **Build a product that a founder would love, > but that a CIO would not reject.** That’s the sweet spot. * * * Strategic Timing (Very Important) --------------------------------- Your trajectory should look like this: **Phase 1** – Power Users / Builders **Phase 2** – Small Teams **Phase 3** – Mid-Market **Phase 4** – Enterprise (configuration + contracts) If you try to start at Phase 4, you never reach Phase 1. * * * Final Answer (Unambiguous) -------------------------- * **Do not build for enterprise from the start** * **Absolutely architect for enterprise from the start** * **Design every core system as if enterprise will ask hard questions later** * **Delay enterprise UX, compliance, and bureaucracy until demand forces it** You’re thinking about this at exactly the right time — before lines of code harden into liabilities. If you want, next we can: * Define an **Enterprise Readiness Checklist** * Design an **Enterprise Toggle Strategy** * Identify the **first enterprise vertical that actually makes sense** * Or stress-test your current architecture against future enterprise demands Just tell me where to go next. --- Powered by [ChatGPT Exporter](https://www.chatgptexporter.com) --- ## AiConnectedOS **URL:** https://secure-docs.aiconnected.ai/docs/aiConnectedOS **Description:** Documents in AiConnectedOS. --- ## Business Plan Outline **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/business-plan-outline **Description:** The master outline for the aiConnected Software Ecosystem business plan — section by section, with supporting document dependencies, writing sequence, and completion tracking. --- ## How the Writing Process Works The business plan is not written in isolation. Every major claim — market size, revenue projection, competitive position, team structure — must be grounded in a supporting document written and validated before the corresponding business plan section is drafted. The sequence is: **Supporting Documents → Business Plan Sections**. The [Writing Sequence](/docs/business-planning/writing-sequence) page defines the exact order in which supporting documents are produced. This outline shows how each supporting document maps to the business plan section it informs. --- ## The Business Plan — Full Outline --- ### COVER PAGE & DOCUMENT HEADER **Required Before Writing:** Entity confirmation (legal name, state of incorporation, registered address), founder name and title, date, confidentiality notice. **Supporting Documents:** - `BP-LEGAL-01` — Entity & Corporate Records Summary - `BP-LEGAL-08` — NDA & Confidentiality Framework --- ### EXECUTIVE SUMMARY *The last section written. Synthesizes every other section into a single cohesive narrative — 2 to 3 pages maximum.* **What It Covers:** - The one-paragraph company description - The problem and the solution in plain language - The three-layer ecosystem overview (Business Platform / aiConnectedOS / Neurigraph) - Current stage, traction, and what makes this defensible - The funding ask and use of funds - The 10-year robotics vision in two sentences **Supporting Documents Required:** - All other business plan sections must be complete before this is written - `BP-MARKET-06` — SAM/SOM Calculation - `BP-FIN-03` — Consolidated P&L (Years 1–5) - `BP-FIN-07` — Use of Funds Breakdown - `BP-INVEST-02` — Executive Summary (standalone version, written in parallel) --- ### SECTION 1 — THE COMPANY #### 1.1 Founding Story and Mission *Who Bob Hunter is, why aiConnected exists, and what the company is ultimately trying to accomplish. Written in plain language — not hype.* **Supporting Documents:** - `BP-FOUND-01` — Founder Biography & Background - `BP-FOUND-02` — Company Origin & Mission Statement #### 1.2 Company Profile *Legal name, state of incorporation, registered address, founding date, website, and current operational status.* **Supporting Documents:** - `BP-LEGAL-01` — Entity & Corporate Records Summary #### 1.3 The Core Philosophy — Acquired Intelligence *Why "Acquired Intelligence" is a more accurate framing than Artificial Intelligence. The philosophical and architectural distinction that drives every product decision. This section is unique to aiConnected and sets the intellectual tone for the entire plan.* **Supporting Documents:** - `BP-FOUND-03` — Acquired Intelligence Philosophy Document - Existing: `knowledge-base/neurigraph-memory-architecture/acquired-intelligence-rough-outline.mdx` - Existing: `knowledge-base/neurigraph-memory-architecture/ai-terminology-reframing.mdx` #### 1.4 The Two-Layer Strategy *The deliberate design of the company: agency tools on the surface generate revenue and training data; Cognigraph architecture underneath builds the long-term moat. Why the surface layer is not the product — it's the training ground.* **Supporting Documents:** - `BP-FOUND-04` — Two-Layer Strategy Narrative - Existing: `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` #### 1.5 Legal Structure & Ownership *Entity type, state, ownership breakdown, IP ownership confirmation.* **Supporting Documents:** - `BP-LEGAL-01` — Entity & Corporate Records Summary - `BP-LEGAL-04` — Cap Table (Current State) --- ### SECTION 2 — THE PROBLEM #### 2.1 The Agency Problem *Agencies are expected to deliver AI products they cannot build. The cost, time, and risk of building AI software from scratch is prohibitive. The alternative — stitching together subscriptions — doesn't produce a real product.* **Supporting Documents:** - `BP-MKTRES-05` — Agency Customer Discovery Report - `BP-MKTRES-08` — Agency ICP Profile #### 2.2 The Business Client Problem *SMBs are drowning in AI hype and short on practical results. Tools don't connect. An AI chatbot that doesn't know what the business does. A voice system that can't pass notes to the sales team. The fragmentation problem.* **Supporting Documents:** - `BP-MKTRES-05` — Agency Customer Discovery Report - `BP-MKTRES-06` — Business Client Pain Point Survey - `BP-MKTRES-09` — Business Client ICP Profile #### 2.3 The Persistent Memory Problem *Every AI session starts from zero. Context is lost. Decisions are forgotten. The fundamental limitation preventing AI from becoming genuinely useful in the long term — across every industry.* **Supporting Documents:** - `BP-FOUND-03` — Acquired Intelligence Philosophy Document - Existing: `knowledge-base/papers-and-research/the-future-of-persistent-ai-in-business.mdx` - `BP-COMP-04` — Mem0 & Memory Architecture Competitive Analysis #### 2.4 The Robotics Problem *The coming robotics boom needs a brain. Today, the robotics industry is deeply fragmented at the intelligence layer. A developer must rebuild capabilities from scratch for every hardware platform. There is no universal cognitive standard.* **Supporting Documents:** - `BP-MARKET-07` — Robotics Cognitive Infrastructure Market Research - Existing: `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx` #### 2.5 Why These Problems Are Connected *The connecting thesis: one persistent cognitive infrastructure — many interface channels. The agency business is the commercial vehicle that builds the cognitive infrastructure that will power robotics.* **Supporting Documents:** - `BP-FOUND-04` — Two-Layer Strategy Narrative --- ### SECTION 3 — THE SOLUTION: THE AICONNECTED ECOSYSTEM #### 3.1 Ecosystem Architecture Overview *The three-layer stack explained in plain language. How each layer relates to the others. The "body has organs, organs support the brain" analogy.* **Supporting Documents:** - `BP-PROD-01` — Master Product Architecture Overview - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview.mdx` --- #### LAYER 1 — aiConnected Business Platform #### 3.2 What It Is *The white-label agency platform. GoHighLevel model, but open and focused on sales.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview-non-technical.mdx` - `BP-PROD-02` — Business Platform Executive Summary (condensed for business plan use) #### 3.3 The Five MVP Modules *Knowledge Base Generator, Voice AI Hub, Chat Interface, Contact Forms, Chat Monitor — what each does, why it matters, and how they interconnect.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx` #### 3.4 Co-Browser Add-On **Supporting Documents:** - Existing: `knowledge-base/aiconnected-apps-and-modules/ai-connected-site-guide-co-browser.mdx` #### 3.5 Platform Architecture: The Shell & Module System *The Lego Brick Model. Event bus, module manifests, containerized isolation — why this architecture is the platform's long-term advantage.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd.mdx` #### 3.6 The Developer Ecosystem *Third-party modules, the capability registry, and the write-once-deploy-everywhere model. The compound growth mechanism.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-supporting-docs/how-will-developers-use-the-ai-connected-platform.mdx` - Existing: `knowledge-base/aiconnected-supporting-docs/engaging-the-dev-community.mdx` - `BP-GTM-07` — Developer Community & Ecosystem Strategy #### 3.7 White-Label Engine *TweakCN theming, custom domains, and full brand invisibility. Why two agencies using aiConnected look nothing alike — unlike GoHighLevel.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd.mdx` (Section 3.2) --- #### LAYER 2 — aiConnectedOS #### 3.8 What It Is *A virtual operating system for AI Personas. Not an agent platform — a personality platform.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-os/quick-system-overview.mdx` - `BP-PROD-03` — aiConnectedOS Executive Summary (condensed for business plan use) #### 3.9 Core Architecture *Instances, Personas, Cipher (the orchestration layer), and the multi-model routing engine.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-os/system-standards-and-philosophy.mdx` #### 3.10 The Personas System *Personalities, not agents. The Tamagotchi analogy. Fixed identity that evolves naturally — no two personas alike.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-os/aiconnected-os-prd.mdx` (Personas section) #### 3.11 Key Platform Features *Spaces Dashboard, Live Documents, Agentic Teams, Meeting Mode, ChatNav, Collaborative Personas. Each addressed in a single paragraph.* **Supporting Documents:** - Existing: Feature spec documents in `knowledge-base/aiconnected-os/` #### 3.12 Build Roadmap *The 18-week, 6-phase plan. Where the platform is today and what launch looks like.* **Supporting Documents:** - `BP-PROD-04` — Consolidated 18-Month Product Roadmap #### 3.13 Pricing Model *Free through $99.99/month Pro, with Enterprise tier. Per-seat enterprise pricing.* **Supporting Documents:** - `BP-FIN-09` — Pricing Architecture Document --- #### LAYER 3 — Neurigraph / Cognigraph Memory Architecture #### 3.14 What It Is *Persistent cognitive infrastructure for any AI system. The part of the brain responsible for forming, storing, connecting, and retrieving memories — built for AI.* **Supporting Documents:** - Existing: `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx` - `BP-PROD-05` — Neurigraph Technical Summary (written for non-technical investors) #### 3.15 The Three Memory Types *Episodic, semantic, and somatic memory — and why each matters.* **Supporting Documents:** - Existing: `knowledge-base/neurigraph-memory-architecture/` (multiple docs) #### 3.16 Object Deconstruction Graph & Amygdala System *The ODG as a deliberate deep-thinking layer. The Amygdala as a dynamic heat threshold controller. What makes this architecture original.* **Supporting Documents:** - Existing: `knowledge-base/neurigraph-memory-architecture/object-deconstruction-graph-overview.mdx` - Existing: `knowledge-base/neurigraph-memory-architecture/amygdala-dynamic-heat-threshold-control.mdx` #### 3.17 Sleep/Dream Consolidation Cycle & ANI *The 24-hour consolidation cron. How the Acquired Network Intelligence layer enables cross-instance learning.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-os/aiconnected-os-prd.mdx` (Neurigraph section) - Existing: `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` #### 3.18 Neurigraph Licensing Opportunity *The six licensing sectors: gaming, healthcare, education, enterprise, defense, robotics. Commercial structure.* **Supporting Documents:** - Existing: `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx` - `BP-FIN-08` — Neurigraph Licensing Revenue Model --- #### LAYER 4 — aiConnected Robotics Platform (Strategic Vision) #### 3.19 CarPlay for Robotics *The three-layer architecture. How aiConnectedOS becomes the universal cognitive standard for any robot, regardless of hardware.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx` #### 3.20 Certification System & Robot Taxonomy *Platform-defined Level 0–3 + Level X certification. Six robot classes. Why platform-defined certification is the strategic position.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx` #### 3.21 The Robotics Developer Marketplace *Write-once-deploy-everywhere for robotics capabilities. The economic model for the robotics ecosystem.* **Supporting Documents:** - `BP-MARKET-07` — Robotics Cognitive Infrastructure Market Research --- ### SECTION 4 — PRODUCT SUITE #### 4.1 Current Product Status Matrix *Every product and module: build stage, revenue readiness, resource requirement, and timeline.* **Supporting Documents:** - `BP-PROD-06` — Product Status Matrix #### 4.2 The Engine Module Directory *The 30+ engine modules — priority tiers, pricing, and how each builds on the platform's shared infrastructure.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-apps-and-modules/original-aiConnected-engines.mdx` - `BP-PROD-07` — Engine Module Revenue Analysis #### 4.3 Vertical-Specific Products *logicLegal, funnelChat, macEngine — focused verticals with specific regulatory or technical requirements.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-apps-and-modules/modules/logicLegal/` - Existing: `knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/` #### 4.4 Acquired Intelligence — The Book *How the book functions as a brand asset, a thought leadership anchor, and a developer recruitment tool.* **Supporting Documents:** - `BP-FOUND-03` — Acquired Intelligence Philosophy Document - Existing: `knowledge-base/neurigraph-memory-architecture/acquired-intelligence-rough-outline.mdx` #### 4.5 Product Interdependency Map *How every product feeds the cognitive core. The "organs support the brain" architecture visualized.* **Supporting Documents:** - `BP-PROD-01` — Master Product Architecture Overview --- ### SECTION 5 — MARKET OPPORTUNITY #### 5.1 AI SaaS Market Landscape **Supporting Documents:** - `BP-MARKET-01` — AI SaaS Market Sizing Report - Existing: `knowledge-base/aiconnected-apps-and-modules/5-year-ai-business-landscape.mdx` #### 5.2 Agency Software Market **Supporting Documents:** - `BP-MARKET-02` — Agency Software & White-Label Platform Market Research #### 5.3 Total Addressable Market **Supporting Documents:** - `BP-MARKET-03` — TAM Analysis (Agency + Business Client + Enterprise) #### 5.4 Serviceable Addressable Market **Supporting Documents:** - `BP-MARKET-04` — SAM Calculation #### 5.5 Serviceable Obtainable Market **Supporting Documents:** - `BP-MARKET-05` — SOM Projection (Years 1–3) #### 5.6 The AI Persistent Memory Market **Supporting Documents:** - `BP-MARKET-06` — AI Memory Architecture Market Sizing #### 5.7 The Voice & Conversational AI Market **Supporting Documents:** - `BP-MARKET-08` — Voice AI Market Research #### 5.8 The Robotics Cognitive Infrastructure Market **Supporting Documents:** - `BP-MARKET-07` — Robotics Cognitive Infrastructure Market Research #### 5.9 The 10 Structural Market Shifts *Why the macro environment makes this moment uniquely favorable — the 5-year business landscape analysis.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-apps-and-modules/5-year-ai-business-landscape.mdx` --- ### SECTION 6 — COMPETITIVE ANALYSIS #### 6.1 GoHighLevel **Supporting Documents:** - `BP-COMP-01` — GoHighLevel Deep Dive #### 6.2 ChatGPT Enterprise **Supporting Documents:** - `BP-COMP-02` — ChatGPT Enterprise Competitive Profile #### 6.3 Mem0 & Memory Architecture Competitors **Supporting Documents:** - `BP-COMP-03` — Mem0 & OpenMemory Competitive Analysis #### 6.4 Voice Infrastructure Competitors **Supporting Documents:** - `BP-COMP-04` — Vapi / Retell / LiveKit Competitive Profile #### 6.5 Autonomous Agent Platforms **Supporting Documents:** - `BP-COMP-05` — Manus & Agentic Platform Competitive Analysis #### 6.6 Robotics AI Competitors **Supporting Documents:** - `BP-COMP-06` — Robotics Cognitive Infrastructure Competitive Landscape #### 6.7 Comprehensive Competitive Matrix **Supporting Documents:** - `BP-COMP-07` — Full Competitive Matrix (12-column comparison) #### 6.8 The Defensible Moat *Three interlocking advantages: proprietary memory architecture, compounding training data, and a developer ecosystem that grows without proportional headcount.* **Supporting Documents:** - All `BP-COMP-` documents above - `BP-FOUND-04` — Two-Layer Strategy Narrative --- ### SECTION 7 — BUSINESS MODEL #### 7.1 Revenue Model Overview **Supporting Documents:** - `BP-FIN-01` — Revenue Model — Business Platform - `BP-FIN-02` — Revenue Model — aiConnectedOS - `BP-FIN-03` — Revenue Model — Neurigraph Licensing - `BP-FIN-04` — API Resale Revenue Model - `BP-FIN-05` — Customer Success Revenue Model #### 7.2 Platform Tax Structure **Supporting Documents:** - `BP-FIN-09` — Pricing Architecture Document - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx` (Section 6) #### 7.3 Floor Pricing & Agency Markup Model **Supporting Documents:** - `BP-FIN-09` — Pricing Architecture Document #### 7.4 API Resale Model (OpenRouter + BYOK) **Supporting Documents:** - `BP-FIN-04` — API Resale Revenue Model #### 7.5 Customer Success Packages **Supporting Documents:** - `BP-FIN-05` — Customer Success Revenue Model #### 7.6 Neurigraph Licensing Structure **Supporting Documents:** - `BP-FIN-08` — Neurigraph Licensing Revenue Model - Existing: `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx` #### 7.7 Mods Marketplace (20% Revenue Share) **Supporting Documents:** - `BP-FIN-06` — Developer Ecosystem Revenue Model #### 7.8 Revenue Projections: Years 1–5 **Supporting Documents:** - `BP-FIN-10` — Consolidated 5-Year Revenue Projections #### 7.9 The Data Compounding Advantage *How every agency deployment builds the training moat. The flywheel: agencies → users → Cognigraph → better tools → more agencies.* **Supporting Documents:** - `BP-FOUND-04` — Two-Layer Strategy Narrative #### 7.10 Unit Economics **Supporting Documents:** - `BP-FIN-11` — Unit Economics Model (Agency + OS User) #### 7.11 Break-Even & Path to Profitability **Supporting Documents:** - `BP-FIN-12` — Break-Even Analysis --- ### SECTION 8 — GO-TO-MARKET STRATEGY #### 8.1 Phase 1: Revenue Before Raising *The GoHighLevel model. Why demonstrating traction before raising is the right strategic sequence.* **Supporting Documents:** - `BP-GTM-01` — Launch Strategy Document #### 8.2 The 4-Product Launch Sequence *Knowledge → Chat → Voice → Brain. Why this order and what each unlock.* **Supporting Documents:** - `BP-PROD-04` — Consolidated 18-Month Product Roadmap - `BP-GTM-01` — Launch Strategy Document #### 8.3 First Revenue Target *10 agencies × $299/month = $3,000 MRR. The proof-of-concept milestone.* **Supporting Documents:** - `BP-GTM-02` — Agency Acquisition Playbook - `BP-GTM-09` — First 10 Agency Target List #### 8.4 Agency Acquisition Strategy **Supporting Documents:** - `BP-GTM-02` — Agency Acquisition Playbook - `BP-GTM-03` — Sales Team Structure & Compensation Plan #### 8.5 Agency Onboarding Flow **Supporting Documents:** - `BP-GTM-04` — Agency Onboarding Flow & Time-to-Value #### 8.6 Marketing & Thought Leadership Strategy **Supporting Documents:** - `BP-GTM-05` — Launch Marketing Plan - `BP-GTM-06` — Content & Thought Leadership Strategy - Existing: `knowledge-base/papers-and-research/aiConnected-influencer-cold-outreach-with-messaging.mdx` #### 8.7 Developer Community Strategy **Supporting Documents:** - `BP-GTM-07` — Developer Community & Ecosystem Strategy - Existing: `knowledge-base/aiconnected-supporting-docs/engaging-the-dev-community.mdx` #### 8.8 Partner Channel Strategy **Supporting Documents:** - `BP-GTM-08` — Partner Channel & Integration Strategy #### 8.9 Enterprise Progression Path *Power Users → Small Teams → Mid-Market → Enterprise. The four-phase customer journey.* **Supporting Documents:** - Existing: `knowledge-base/aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App.mdx` - `BP-GTM-10` — Enterprise Readiness & Progression Plan #### 8.10 Churn Prevention & Expansion Revenue **Supporting Documents:** - `BP-GTM-11` — Churn Prevention & Customer Success Strategy --- ### SECTION 9 — TECHNOLOGY & ARCHITECTURE #### 9.1 Core Technology Stack **Supporting Documents:** - `BP-TECH-01` — Technology Stack Overview #### 9.2 The Lego Brick Architecture Model **Supporting Documents:** - Existing: `knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd.mdx` #### 9.3 Infrastructure & Hosting *DigitalOcean + Dokploy, Supabase, containerized modules.* **Supporting Documents:** - `BP-TECH-02` — Infrastructure Architecture Document #### 9.4 AI Inference Model *OpenRouter multi-model access, BYOK option, model selection philosophy.* **Supporting Documents:** - `BP-TECH-01` — Technology Stack Overview #### 9.5 Voice Infrastructure *LiveKit foundation and the internal Vapi/Retell competitor build.* **Supporting Documents:** - Existing: `knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/` #### 9.6 Security Architecture **Supporting Documents:** - `BP-TECH-03` — Security Architecture Document #### 9.7 Enterprise-Aware Architecture Decisions *Multi-tenancy, memory governance, identity isolation — designed for enterprise from day one.* **Supporting Documents:** - Existing: `knowledge-base/aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App.mdx` - `BP-TECH-04` — Enterprise Readiness Architecture Checklist #### 9.8 Open Source Strategy **Supporting Documents:** - Existing: `knowledge-base/aiconnected-supporting-docs/self-hosting.mdx` #### 9.9 Build Roadmap **Supporting Documents:** - `BP-PROD-04` — Consolidated 18-Month Product Roadmap --- ### SECTION 10 — TEAM & OPERATIONS #### 10.1 Founder Profile **Supporting Documents:** - `BP-FOUND-01` — Founder Biography & Background #### 10.2 Current Operating State *Solo founder + contractors. What that means for execution and what changes with funding.* **Supporting Documents:** - `BP-OPS-01` — Current Organizational State Document #### 10.3 First Hire Priorities & Job Descriptions **Supporting Documents:** - `BP-OPS-02` — Organizational Chart (Current & 12-Month Projected) - `BP-OPS-03` — Priority Job Descriptions (First 5 Hires) #### 10.4 Compensation Philosophy **Supporting Documents:** - `BP-OPS-04` — Compensation Philosophy & Ranges #### 10.5 Advisory Board **Supporting Documents:** - `BP-OPS-05` — Advisory Board Structure & Recruitment Plan #### 10.6 Operational Infrastructure *Tools, subscriptions, development workflow, customer support process.* **Supporting Documents:** - `BP-OPS-06` — Operational Infrastructure Inventory - `BP-OPS-07` — Development Workflow & QA Process #### 10.7 Vendor & Dependency Management **Supporting Documents:** - `BP-RISK-08` — Vendor & Dependency Risk Assessment --- ### SECTION 11 — FINANCIAL PLAN #### 11.1 Revenue Ramp **Supporting Documents:** - `BP-FIN-01` through `BP-FIN-06` — All Revenue Model documents - `BP-FIN-10` — Consolidated 5-Year Revenue Projections #### 11.2 Profit & Loss Projections (Years 1–5) **Supporting Documents:** - `BP-FIN-13` — Consolidated P&L Model #### 11.3 Cash Flow Statement (Year 1, Monthly) **Supporting Documents:** - `BP-FIN-14` — Monthly Cash Flow Model — Year 1 #### 11.4 Balance Sheet Projections **Supporting Documents:** - `BP-FIN-15` — Pro Forma Balance Sheet #### 11.5 Operating Cost Structure **Supporting Documents:** - `BP-FIN-16` — Operating Cost Model #### 11.6 Unit Economics Summary **Supporting Documents:** - `BP-FIN-11` — Unit Economics Model #### 11.7 Sensitivity Analysis **Supporting Documents:** - `BP-FIN-17` — Sensitivity & Scenario Analysis #### 11.8 Path to Profitability **Supporting Documents:** - `BP-FIN-12` — Break-Even Analysis --- ### SECTION 12 — FUNDING STRATEGY #### 12.1 Revenue Before Raising — The Rationale **Supporting Documents:** - `BP-INVEST-03` — GoHighLevel Growth Comparison Study #### 12.2 Seed Round Target & Terms *$2.5–$3.5M raise. 20–30% dilution. 18-month runway. Series A readiness milestones.* **Supporting Documents:** - `BP-INVEST-04` — Seed Round Term Sheet Reference - Existing: `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` #### 12.3 Use of Funds **Supporting Documents:** - `BP-FIN-07` — Use of Funds Breakdown #### 12.4 Cap Table — Pre & Post Money **Supporting Documents:** - `BP-LEGAL-04` — Cap Table #### 12.5 Series A Readiness Milestones **Supporting Documents:** - `BP-INVEST-05` — Series A Milestone Definition #### 12.6 The Two Investor Pitches *Surface pitch: "GoHighLevel for AI." Deep pitch: "Cognitive infrastructure for the robotics era." Why both exist and when each is used.* **Supporting Documents:** - `BP-INVEST-01` — Investor Pitch Deck (Surface Version) - `BP-INVEST-06` — Investor Pitch Deck (Deep Version) --- ### SECTION 13 — RISK ANALYSIS #### 13.1 Technical Risks **Supporting Documents:** - `BP-RISK-01` — Risk Register (Full) #### 13.2 Market Risks **Supporting Documents:** - `BP-RISK-01` — Risk Register (Full) #### 13.3 Competitive Risks **Supporting Documents:** - `BP-RISK-01` — Risk Register (Full) #### 13.4 Regulatory & Compliance Risks **Supporting Documents:** - `BP-RISK-02` — GDPR Compliance Assessment - `BP-RISK-03` — CCPA Compliance Assessment - `BP-RISK-04` — AI Regulatory Risk Assessment - `BP-RISK-05` — Robotics Regulatory Landscape #### 13.5 Execution Risks **Supporting Documents:** - `BP-RISK-01` — Risk Register (Full) #### 13.6 Financial Risks **Supporting Documents:** - `BP-FIN-17` — Sensitivity & Scenario Analysis --- ### SECTION 14 — THE 10-YEAR VISION #### 14.1 The Robotics Boom Thesis **Supporting Documents:** - `BP-MARKET-07` — Robotics Cognitive Infrastructure Market Research - Existing: `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx` #### 14.2 The Data Moat Compounding Effect **Supporting Documents:** - `BP-FOUND-04` — Two-Layer Strategy Narrative #### 14.3 The Endgame *By 2030: battle-tested cognitive infrastructure with years of real-world learning data. Robotics companies don't just want the architecture — they need the training data.* **Supporting Documents:** - `BP-FOUND-04` — Two-Layer Strategy Narrative - Existing: `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` --- ### APPENDICES | Appendix | Title | Supporting Document | |---|---|---| | A | Full Product Status Matrix | `BP-PROD-06` | | B | Engine Module Directory (30+) | Existing + `BP-PROD-07` | | C | Neurigraph Architecture Technical Summary | `BP-PROD-05` | | D | Competitive Matrix (Full) | `BP-COMP-07` | | E | Team Org Chart & Hiring Plan | `BP-OPS-02` + `BP-OPS-03` | | F | GoHighLevel vs. aiConnected Comparison | `BP-COMP-01` | | G | Pricing Architecture Reference | `BP-FIN-09` | | H | Technology Stack Reference | `BP-TECH-01` | | I | Platform Glossary | `BP-PROD-08` | | J | Data Room Index | `BP-INVEST-07` | --- ## Supporting Document Registry The table below is the complete list of every supporting document that must be written before the corresponding business plan section. Documents are tagged with the prefix system used throughout this outline. | Code | Title | Category | Priority | Status | |---|---|---|---|---| | `BP-FOUND-01` | Founder Biography & Background | Founding | **Critical** | Pending | | `BP-FOUND-02` | Company Origin & Mission Statement | Founding | **Critical** | Pending | | `BP-FOUND-03` | Acquired Intelligence Philosophy Document | Founding | **Critical** | Pending | | `BP-FOUND-04` | Two-Layer Strategy Narrative | Founding | **Critical** | Pending | | `BP-LEGAL-01` | Entity & Corporate Records Summary | Legal | **Critical** | Pending | | `BP-LEGAL-04` | Cap Table | Legal | **Critical** | Pending | | `BP-LEGAL-08` | NDA & Confidentiality Framework | Legal | High | Pending | | `BP-MARKET-01` | AI SaaS Market Sizing Report | Market Research | **Critical** | Pending | | `BP-MARKET-02` | Agency Software Market Research | Market Research | **Critical** | Pending | | `BP-MARKET-03` | TAM Analysis | Market Research | **Critical** | Pending | | `BP-MARKET-04` | SAM Calculation | Market Research | **Critical** | Pending | | `BP-MARKET-05` | SOM Projection (Years 1–3) | Market Research | **Critical** | Pending | | `BP-MARKET-06` | AI Memory Architecture Market Sizing | Market Research | High | Pending | | `BP-MARKET-07` | Robotics Cognitive Infrastructure Market Research | Market Research | High | Pending | | `BP-MARKET-08` | Voice AI Market Research | Market Research | High | Pending | | `BP-MKTRES-05` | Agency Customer Discovery Report | Customer Research | **Critical** | Pending | | `BP-MKTRES-06` | Business Client Pain Point Survey | Customer Research | **Critical** | Pending | | `BP-MKTRES-08` | Agency ICP Profile | Customer Research | **Critical** | Pending | | `BP-MKTRES-09` | Business Client ICP Profile | Customer Research | **Critical** | Pending | | `BP-COMP-01` | GoHighLevel Deep Dive | Competitive | **Critical** | Pending | | `BP-COMP-02` | ChatGPT Enterprise Competitive Profile | Competitive | **Critical** | Pending | | `BP-COMP-03` | Mem0 & OpenMemory Competitive Analysis | Competitive | **Critical** | Pending | | `BP-COMP-04` | Vapi / Retell / LiveKit Competitive Profile | Competitive | High | Pending | | `BP-COMP-05` | Manus & Agentic Platform Analysis | Competitive | High | Pending | | `BP-COMP-06` | Robotics AI Competitive Landscape | Competitive | High | Pending | | `BP-COMP-07` | Full Competitive Matrix | Competitive | **Critical** | Pending | | `BP-FIN-01` | Revenue Model — Business Platform | Financial | **Critical** | Pending | | `BP-FIN-02` | Revenue Model — aiConnectedOS | Financial | **Critical** | Pending | | `BP-FIN-03` | Revenue Model — Neurigraph Licensing | Financial | High | Pending | | `BP-FIN-04` | API Resale Revenue Model | Financial | High | Pending | | `BP-FIN-05` | Customer Success Revenue Model | Financial | High | Pending | | `BP-FIN-06` | Developer Ecosystem Revenue Model | Financial | High | Pending | | `BP-FIN-07` | Use of Funds Breakdown | Financial | **Critical** | Pending | | `BP-FIN-08` | Neurigraph Licensing Revenue Model | Financial | High | Pending | | `BP-FIN-09` | Pricing Architecture Document | Financial | **Critical** | Pending | | `BP-FIN-10` | Consolidated 5-Year Revenue Projections | Financial | **Critical** | Pending | | `BP-FIN-11` | Unit Economics Model | Financial | **Critical** | Pending | | `BP-FIN-12` | Break-Even Analysis | Financial | **Critical** | Pending | | `BP-FIN-13` | Consolidated P&L Model | Financial | **Critical** | Pending | | `BP-FIN-14` | Monthly Cash Flow Model — Year 1 | Financial | **Critical** | Pending | | `BP-FIN-15` | Pro Forma Balance Sheet | Financial | High | Pending | | `BP-FIN-16` | Operating Cost Model | Financial | High | Pending | | `BP-FIN-17` | Sensitivity & Scenario Analysis | Financial | High | Pending | | `BP-PROD-01` | Master Product Architecture Overview | Product | **Critical** | Pending | | `BP-PROD-02` | Business Platform Executive Summary | Product | **Critical** | Pending | | `BP-PROD-03` | aiConnectedOS Executive Summary | Product | **Critical** | Pending | | `BP-PROD-04` | Consolidated 18-Month Product Roadmap | Product | **Critical** | Pending | | `BP-PROD-05` | Neurigraph Technical Summary (Non-Technical) | Product | **Critical** | Pending | | `BP-PROD-06` | Product Status Matrix | Product | **Critical** | Pending | | `BP-PROD-07` | Engine Module Revenue Analysis | Product | High | Pending | | `BP-PROD-08` | Platform Glossary | Product | High | Pending | | `BP-GTM-01` | Launch Strategy Document | Go-to-Market | **Critical** | Pending | | `BP-GTM-02` | Agency Acquisition Playbook | Go-to-Market | **Critical** | Pending | | `BP-GTM-03` | Sales Team Structure & Compensation Plan | Go-to-Market | **Critical** | Pending | | `BP-GTM-04` | Agency Onboarding Flow & Time-to-Value | Go-to-Market | High | Pending | | `BP-GTM-05` | Launch Marketing Plan | Go-to-Market | **Critical** | Pending | | `BP-GTM-06` | Content & Thought Leadership Strategy | Go-to-Market | High | Pending | | `BP-GTM-07` | Developer Community & Ecosystem Strategy | Go-to-Market | High | Pending | | `BP-GTM-08` | Partner Channel & Integration Strategy | Go-to-Market | High | Pending | | `BP-GTM-09` | First 10 Agency Target List | Go-to-Market | **Critical** | Pending | | `BP-GTM-10` | Enterprise Readiness & Progression Plan | Go-to-Market | High | Pending | | `BP-GTM-11` | Churn Prevention & Customer Success Strategy | Go-to-Market | High | Pending | | `BP-OPS-01` | Current Organizational State | Operations | **Critical** | Pending | | `BP-OPS-02` | Org Chart (Current & 12-Month Projected) | Operations | **Critical** | Pending | | `BP-OPS-03` | Priority Job Descriptions (First 5 Hires) | Operations | **Critical** | Pending | | `BP-OPS-04` | Compensation Philosophy & Ranges | Operations | High | Pending | | `BP-OPS-05` | Advisory Board Structure & Recruitment Plan | Operations | High | Pending | | `BP-OPS-06` | Operational Infrastructure Inventory | Operations | High | Pending | | `BP-OPS-07` | Development Workflow & QA Process | Operations | High | Pending | | `BP-TECH-01` | Technology Stack Overview | Technology | High | Pending | | `BP-TECH-02` | Infrastructure Architecture Document | Technology | High | Pending | | `BP-TECH-03` | Security Architecture Document | Technology | High | Pending | | `BP-TECH-04` | Enterprise Readiness Architecture Checklist | Technology | High | Pending | | `BP-RISK-01` | Risk Register (Full) | Risk | **Critical** | Pending | | `BP-RISK-02` | GDPR Compliance Assessment | Risk | **Critical** | Pending | | `BP-RISK-03` | CCPA Compliance Assessment | Risk | **Critical** | Pending | | `BP-RISK-04` | AI Regulatory Risk Assessment | Risk | High | Pending | | `BP-RISK-05` | Robotics Regulatory Landscape | Risk | High | Pending | | `BP-RISK-08` | Vendor & Dependency Risk Assessment | Risk | High | Pending | | `BP-INVEST-01` | Investor Pitch Deck (Surface Version) | Investor | **Critical** | Pending | | `BP-INVEST-02` | Executive Summary (Standalone) | Investor | **Critical** | Pending | | `BP-INVEST-03` | GoHighLevel Growth Comparison Study | Investor | High | Pending | | `BP-INVEST-04` | Seed Round Term Sheet Reference | Investor | High | Pending | | `BP-INVEST-05` | Series A Milestone Definition | Investor | High | Pending | | `BP-INVEST-06` | Investor Pitch Deck (Deep Version) | Investor | **Critical** | Pending | | `BP-INVEST-07` | Data Room Index | Investor | High | Pending | **Total supporting documents to be written: 78** **Existing knowledge base documents to be referenced: 35+** --- --- ## Business Planning **URL:** https://secure-docs.aiconnected.ai/docs/business-planning **Description:** Documents in Business Planning. --- ## Business Planning **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/introduction **Description:** The master planning hub for the aiConnected corporate business plan — research requirements, document registry, and planning resources organized by category. ## How This Tab Is Organized The business plan for aiConnected must synthesize three distinct platform layers — the Business Platform, aiConnectedOS, and the Neurigraph cognitive architecture — into a single coherent narrative. That requires inputs from across legal, financial, market research, product, operations, and investor relations disciplines. This tab is organized into **nine research and document categories**. Each entry is marked with a status: | Badge | Meaning | |---|---| | `EXISTS` | Document already exists in the knowledge base | | `CREATE` | Needs to be written internally | | `RESEARCH` | Requires external data gathering or commissioned study | | `COMMISSION` | Requires professional input (legal counsel, accountant, etc.) | Priority is marked as **Critical**, **High**, or **Supporting**. --- ## Quick Navigation --- ## Research & Document Requirements **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/research-and-document-requirements **Description:** The complete master list of every research study, document, analysis, and data source required to write a comprehensive, investor-ready business plan for the aiConnected Software Ecosystem. --- ## Category 1 — Legal & Corporate Structure A properly structured business plan must be grounded in confirmed legal and corporate realities. Investors perform legal due diligence before committing capital; gaps or inconsistencies here will stop a deal. --- ### 1.1 Entity Formation & Corporate Records | Document | Status | Priority | Notes | |---|---|---|---| | Articles of Incorporation (Georgia) | `COMMISSION` | **Critical** | Confirms legal existence of aiConnected, Inc. Required for any funding conversation. | | Certificate of Good Standing | `COMMISSION` | **Critical** | Issued by Georgia Secretary of State. Proves the entity is active and in compliance. | | Bylaws or Operating Agreement | `COMMISSION` | **Critical** | Governs internal management, decision-making, and equity. Must be in place before investor conversations. | | Registered Agent Documentation | `COMMISSION` | **Critical** | Confirms who receives legal notices on behalf of the company. | | EIN / Federal Tax ID Confirmation | `COMMISSION` | **Critical** | Required for all banking, payroll, and investor documentation. | | State Business License (Georgia) | `COMMISSION` | High | Required for lawful operation; varies by county and business type. | | Foreign Qualification Filings | `COMMISSION` | Supporting | Required if operating in states outside Georgia (e.g., California, Texas, New York). | --- ### 1.2 Equity & Ownership | Document | Status | Priority | Notes | |---|---|---|---| | Cap Table (Capitalization Table) | `CREATE` | **Critical** | Current ownership breakdown: founder shares, any early investor or advisor equity, option pool. Investors will not engage without this. | | Founder Vesting Agreement | `COMMISSION` | **Critical** | Confirms founder shares are subject to vesting schedule. Protects all parties. Standard: 4-year vest, 1-year cliff. | | Stock Option Pool Plan (if applicable) | `COMMISSION` | High | Required before hiring senior team members with equity compensation. | | Any Existing Investment Agreements | `COMMISSION` | **Critical** | SAFEs, convertible notes, or any prior equity instruments must be fully documented. | | Advisor Agreements (if applicable) | `COMMISSION` | High | Documents equity or compensation granted to any current advisors. | --- ### 1.3 Intellectual Property | Document | Status | Priority | Notes | |---|---|---|---| | Trademark Registration — "aiConnected" | `EXISTS` | **Critical** | Trademark filing preparation document exists at `aiconnected-supporting-docs/aiconnected-trademark-and-patents/`. Status of actual filing must be confirmed. | | Trademark Registration — "Neurigraph" | `COMMISSION` | **Critical** | Core technology brand. Should be registered independently from the company mark. | | Trademark Registration — "Cognigraph" | `COMMISSION` | High | Used interchangeably with Neurigraph in some documents — naming should be finalized and the chosen mark registered. | | Trademark Registration — "aiConnectedOS" | `COMMISSION` | High | The OS product brand. Separate registration from the company trademark. | | Patent Applications — Neurigraph Memory Architecture | `COMMISSION` | **Critical** | The patentability assessment doc in the knowledge base identifies strong claims around the integrated episodic/somatic/semantic compounding memory architecture. Patent counsel engagement is required before the architecture is publicly disclosed in a business plan. | | Patent Applications — Object Deconstruction Graph | `COMMISSION` | High | Novel enough to warrant a separate provisional patent application. | | Patent Applications — Amygdala Heat Threshold System | `COMMISSION` | High | Cited in memory docs as patentable. Provisional application recommended before public disclosure. | | Trade Secret Documentation | `CREATE` | High | Formal documentation of what is protected as trade secret vs. what will be publicly disclosed. | | IP Assignment Agreements | `COMMISSION` | **Critical** | All IP created by any contractor, freelancer, or future employee must be formally assigned to aiConnected, Inc. This is standard investor due diligence. | | Open Source License Compliance Audit | `CREATE` | High | The platform uses several open-source components. Any licensing conflicts (GPL contamination, etc.) must be identified and resolved. | | Domain Portfolio Documentation | `CREATE` | Supporting | Inventory of all registered domains (aiconnected.ai, aiconnected.io, etc.) and their renewal status. | --- ### 1.4 Contracts & Agreements | Document | Status | Priority | Notes | |---|---|---|---| | Terms of Service (Platform) | `CREATE` | **Critical** | Required before any paying customer can be onboarded. Must cover agency white-label rights, data use, and liability limitations. | | Privacy Policy (GDPR/CCPA Compliant) | `CREATE` | **Critical** | Required before collecting any user data. Particularly critical given the memory architecture's deep personal data collection. | | Agency Reseller Agreement | `CREATE` | **Critical** | The legal contract governing the relationship between aiConnected and agencies. Defines white-label rights, revenue share, data ownership, and acceptable use. | | Data Processing Agreement (DPA) | `CREATE` | **Critical** | Required for GDPR compliance when processing personal data on behalf of EU-based business clients. | | Developer Marketplace Agreement | `CREATE` | High | Governs third-party developer submissions: IP ownership, revenue share terms, liability, and the review/certification process. | | Customer Success Service Agreement | `CREATE` | High | Governs the white-label CS team offering ($600–$3,500/month). Defines deliverables, SLAs, and confidentiality. | | Neurigraph Licensing Agreement Template | `EXISTS` | High | Licensing overview exists in `neurigraph-memory-architecture/neurigraph-licensing.mdx`. Needs conversion to a formal legal agreement template by counsel. | | Existing Contractor Agreements | `COMMISSION` | **Critical** | All current development contractors must have signed agreements with IP assignment clauses. Investors will audit this. | | NDA Template | `CREATE` | High | Standard mutual NDA for investor conversations, partner discussions, and developer onboarding. | --- ## Category 2 — Market Research & Analysis Market research must be specific enough to be credible to a sophisticated investor. Generic industry statistics are not sufficient. --- ### 2.1 Total Addressable Market (TAM/SAM/SOM) | Document | Status | Priority | Notes | |---|---|---|---| | Agency Software Market Sizing | `RESEARCH` | **Critical** | Number of marketing/consulting/service agencies in the US and globally. GoHighLevel's reported metrics (785 employees, $82.7M ARR) provide a useful benchmark. Requires current third-party data (Gartner, IDC, or similar). | | AI SaaS for SMBs — Market Size | `RESEARCH` | **Critical** | The broader market that aiConnected Business Platform occupies. Third-party research needed (e.g., Grand View Research, MarketsandMarkets). | | Persistent AI Memory Market | `RESEARCH` | High | Emerging category. Mem0's $24M raise and comparable funding rounds help establish this. Requires analyst research or primary source data. | | AI-Powered Voice & Conversational AI | `RESEARCH` | High | Market size for voice AI (Vapi, Retell, ElevenLabs, LiveKit-based services). TAM for the Voice AI Hub positioning. | | Robotics Cognitive Infrastructure Market | `RESEARCH` | High | 10-year TAM projection for the cognitive/intelligence layer of the robotics industry. May require specialized research firms (e.g., Interact Analysis, IDTechEx). | | White-Label Software Reseller Market | `RESEARCH` | High | How many agencies currently resell white-label software products? What is their average contract value? | | SAM Calculation — US Agency Market | `CREATE` | **Critical** | Serviceable Addressable Market: agencies that serve SMB clients in revenue-generating verticals (legal, medical, home services, insurance, consulting). Must be internally modeled from external data. | | SOM Calculation — Year 1–3 Reachable Market | `CREATE` | **Critical** | What portion of the SAM is realistically reachable given the current team and capital plan? Must align with the financial model. | --- ### 2.2 Industry & Trend Research | Document | Status | Priority | Notes | |---|---|---|---| | The Future of Persistent AI in Business | `EXISTS` | High | Exists in `papers-and-research/the-future-of-persistent-ai-in-business.mdx`. Should be reviewed and cited in the business plan. | | Global AI App/Plugin Marketplace Feasibility | `EXISTS` | High | PDF exists in `papers-and-research/`. Should be integrated into market opportunity section. | | Enterprise Service Research | `EXISTS` | High | Exists in `papers-and-research/enterprise-service-research.mdx`. Relevant to enterprise tier positioning. | | AI Business Services 5-Year Landscape | `EXISTS` | High | Exists in `knowledge-base/aiconnected-apps-and-modules/5-year-ai-business-landscape.mdx`. Ten structural market shifts supporting the platform's positioning. | | Global AI Marketplace Research | `EXISTS` | High | Exists in `papers-and-research/global-ai-marketplace-research-doc.mdx`. Relevant to developer ecosystem and marketplace plans. | | International Developer Pricing Research | `EXISTS` | Supporting | PDF exists in `papers-and-research/`. Relevant to developer ecosystem revenue modeling. | | AI Resource Marketplace Feasibility | `EXISTS` | Supporting | PDF exists in `papers-and-research/`. | | Robotics Industry Adoption Timeline Research | `RESEARCH` | High | External research on humanoid and industrial robotics adoption curves — critical for framing the 10-year vision. | | SMB AI Tool Adoption Research | `RESEARCH` | High | Current penetration rates of AI tools among SMBs. Establishes the urgency of the problem. | | Agency Revenue & Pricing Benchmarks | `RESEARCH` | High | Average agency contract values, churn rates, and LTV for white-label software products. Necessary to validate the agency revenue model. | --- ### 2.3 Customer Research & Validation | Document | Status | Priority | Notes | |---|---|---|---| | Agency Customer Discovery Interviews | `CREATE` | **Critical** | 10–20 structured interviews with marketing/consulting agency owners validating the core value proposition. Investors expect proof of customer conversations. | | Business Client Pain Point Survey | `CREATE` | **Critical** | Survey data from SMB owners on their current AI tool frustrations, spending, and openness to the aiConnected platform. | | Ideal Customer Profile (ICP) — Agency | `CREATE` | **Critical** | Documented profile of the target agency: size, vertical focus, revenue range, current tool stack, and decision-making process. | | Ideal Customer Profile (ICP) — Business Client | `CREATE` | **Critical** | Documented profile of the business clients agencies serve: industry verticals, employee count, current AI spend, and specific pain points. | | Use Case Validation — Voice AI | `CREATE` | High | Documented proof that inbound/outbound voice AI generates measurable results for target business clients. Can be sourced from competitor case studies initially. | | Use Case Validation — Chat Interface | `CREATE` | High | Same as above for the chat module. Conversion rate lift data, lead qualification improvement, etc. | | Letters of Intent or Early Commitments | `CREATE` | **Critical** | Even informal written indications of interest from potential early agency customers significantly strengthen the investor narrative. | | Beta Customer Feedback (if applicable) | `CREATE` | High | Any structured feedback from any user of any current platform version should be documented and included. | --- ## Category 3 — Competitive Intelligence | Document | Status | Priority | Notes | |---|---|---|---| | GoHighLevel Deep Dive | `CREATE` | **Critical** | Full profile: revenue, pricing, feature set, agency count, known weaknesses, and developer ecosystem limitations. This is the most important comparison. | | Mem0 Competitive Analysis | `CREATE` | **Critical** | The most direct competitor to the Neurigraph memory architecture. $24M raised, AWS integration, developer-focused. Must document differentiation clearly. | | Vapi / Retell Competitive Profile | `CREATE` | High | Voice infrastructure competitors. Relevant to Voice AI Hub positioning and the internal voice infrastructure build. | | ChatGPT Enterprise Analysis | `CREATE` | **Critical** | Why persistent, modular, persona-based architecture is categorically different from session-based enterprise AI. | | Manus / Autonomous Agent Platforms | `CREATE` | High | How persona-based architecture differs from general agentic task execution platforms. | | OpenMemory (Apache 2.0) Analysis | `CREATE` | High | Documented in memory backup: "fork-and-differentiate is a viable fast path." Must clarify how aiConnected differs from and supersedes it. | | Comprehensive Competitive Matrix | `CREATE` | **Critical** | Single-page visual matrix comparing aiConnected vs. GoHighLevel, Mem0, ChatGPT Enterprise, Vapi, Manus, and HubSpot across 12–15 capability dimensions. | | Competitive Pricing Comparison | `CREATE` | **Critical** | Side-by-side pricing analysis across all major competitors at the agency, business client, and enterprise tiers. | | Robotics Cognitive Infrastructure Competitive Landscape | `RESEARCH` | High | Who else is attempting to build a universal cognitive layer for robotics? Boston Dynamics AI Institute, 1X Technologies, Physical Intelligence (Pi), etc. | --- ## Category 4 — Financial Models & Projections | Document | Status | Priority | Notes | |---|---|---|---| | Revenue Model — Business Platform | `CREATE` | **Critical** | Detailed model: number of agencies × average client count × average module price × 10% platform tax. Month-by-month for Year 1, annually for Years 2–5. | | Revenue Model — aiConnectedOS | `CREATE` | **Critical** | Subscription tier distribution assumptions, monthly churn rate, expansion revenue from tier upgrades, and enterprise contract modeling. | | Revenue Model — Neurigraph Licensing | `CREATE` | High | Partner licensing deal structure and projected deal count by year. Smaller near-term contribution but significant long-term revenue stream. | | API Resale Revenue Model | `CREATE` | High | Projected AI inference volume × 10% markup. Must account for BYOK option taking share of high-volume customers. | | Customer Success Revenue Model | `CREATE` | High | Three-tier CS package adoption rates, average contract duration, and contribution margin. | | Consolidated P&L — Years 1–5 | `CREATE` | **Critical** | Fully integrated across all revenue streams. Must show the path from pre-revenue to profitability. | | Cash Flow Statement — Year 1 (Monthly) | `CREATE` | **Critical** | Monthly cash flow showing burn rate, runway, and break-even timing. Required for seed round conversations. | | Balance Sheet Projections | `CREATE` | High | Pro forma balance sheet at Years 1, 3, and 5. | | Unit Economics — Agency Customer | `CREATE` | **Critical** | Customer Acquisition Cost (CAC), Lifetime Value (LTV), LTV:CAC ratio, payback period. The most scrutinized metrics in a SaaS investor meeting. | | Unit Economics — aiConnectedOS User | `CREATE` | High | Same as above for the OS product. Separate modeling required given the different acquisition and monetization model. | | Break-Even Analysis | `CREATE` | **Critical** | Already identified in the knowledge base (~100 agencies). Needs to be formally modeled with assumptions documented. | | Funding Use of Funds Breakdown | `EXISTS` | **Critical** | Partially documented in the fundraising strategy. Needs to be formalized as a line-item budget aligned to the $2.5–$3.5M seed range. | | Year 1 Team Cost Model | `EXISTS` | **Critical** | Documented in the fundraising strategy ($950K–$1.2M loaded). Needs refinement with specific role hire timing. | | Infrastructure & Operating Cost Model | `CREATE` | High | DigitalOcean, Supabase, LiveKit, OpenRouter, Stripe fees, n8n self-hosting, and all other recurring platform costs at scale. | | Sensitivity Analysis | `CREATE` | High | How do projections change if agency acquisition is 50% slower? If churn is double the assumption? Investors will ask. | | GoHighLevel Revenue Comparison Model | `CREATE` | Supporting | Benchmarks aiConnected's projected trajectory against GoHighLevel's documented growth curve. Validates the model's credibility. | | Series A Readiness Financial Milestones | `CREATE` | High | What specific MRR, agency count, and retention metrics trigger Series A readiness? Must be defined and committed to in the seed round materials. | | Billing Model Documentation | `EXISTS` | High | Billing model PDF exists in `papers-and-research/`. Should be integrated into the financial model. | --- ## Category 5 — Product Documentation Most of this category already exists in the knowledge base. The work here is curation, consolidation, and gap-filling rather than creation from scratch. --- ### 5.1 Existing Documentation (Confirmed) | Document | Status | Priority | Source | |---|---|---|---| | Platform Overview (Technical) | `EXISTS` | **Critical** | `aiconnected-business-platform/aiconnected-platform-overview.mdx` | | Platform Overview (Non-Technical) | `EXISTS` | **Critical** | `aiconnected-business-platform/aiconnected-platform-overview-non-technical.mdx` | | MVP Specification v1.0 | `EXISTS` | **Critical** | `aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx` | | Foundation PRD | `EXISTS` | **Critical** | `aiconnected-business-platform/aiconnected-platform-foundation-prd.mdx` | | V2 Build Plan | `EXISTS` | High | `aiconnected-business-platform/aiconnected-platform-v2-build-plan.mdx` | | Production Readiness Checklist | `EXISTS` | High | `aiconnected-business-platform/production-readiness-checklist.mdx` | | aiConnectedOS Master PRD | `EXISTS` | **Critical** | `aiconnected-os/aiconnected-os-prd.mdx` | | Quick System Overview | `EXISTS` | **Critical** | `aiconnected-os/quick-system-overview.mdx` | | Neurigraph Build Plan | `EXISTS` | **Critical** | `neurigraph-memory-architecture/neurigraph-build-plan.mdx` | | Neurigraph Licensing Overview | `EXISTS` | **Critical** | `neurigraph-memory-architecture/neurigraph-licensing.mdx` | | Robotics Platform Developer Docs | `EXISTS` | High | `aiconnected-os/aiconnected-os-robotics-platform.mdx` | | 30+ Engine Module Specs | `EXISTS` | High | `aiconnected-apps-and-modules/original-aiConnected-engines.mdx` + modules subfolder | | Voice AI Module Docs | `EXISTS` | High | `aiconnected-apps-and-modules/modules/aiConnected-voice/` | | Enterprise Potential Analysis | `EXISTS` | High | `aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App.mdx` | --- ### 5.2 Product Documents Still Needed | Document | Status | Priority | Notes | |---|---|---|---| | Master Product Roadmap (18-Month) | `CREATE` | **Critical** | A single, consolidated roadmap across all three platform layers with milestones, dependencies, and resource requirements. The 18-week OS build plan and 6-week Voice deadline need to be integrated into one document. | | Product Status Matrix | `CREATE` | **Critical** | Single table showing every product/module, its current build stage, estimated completion, revenue readiness date, and assigned development resource. | | Feature Prioritization Framework | `CREATE` | High | How decisions are made about what to build next. Investors want to see disciplined prioritization, not an endless feature wish list. | | Voice Infrastructure PRD (Complete) | `CREATE` | **Critical** | The fundraising strategy notes Claude Code froze during PRD writing. This must be completed before investor conversations. | | Neurigraph Patentability Assessment (Formal) | `COMMISSION` | **Critical** | The knowledge base references a patentability assessment summary. A formal opinion from patent counsel is required before public disclosure. | | Demo Environment Specification | `CREATE` | High | What the investor or agency demo looks like end-to-end. Must be scripted and reproducible. | | API Documentation (Draft) | `CREATE` | High | Investor technical due diligence will include reviewing API design. The OpenAPI spec exists in `api-reference/` — ensure it reflects the current architecture. | | Security Architecture Document | `CREATE` | High | How data is isolated between tenants, how memory is encrypted, how the containerized module system is hardened. | | Data Retention & Memory Governance Policy | `CREATE` | **Critical** | Given the Neurigraph architecture's deep personal data collection, this policy must be documented before any customer or investor conversation. | --- ## Category 6 — Go-to-Market Strategy | Document | Status | Priority | Notes | |---|---|---|---| | Agency Acquisition Playbook | `CREATE` | **Critical** | Step-by-step process for signing the first 10, then 50, then 100 agencies. Channels, messaging, objection handling, and closing process. | | Sales Team Structure & Compensation Plan | `CREATE` | **Critical** | The 10-person sales team model mentioned in the memory backup needs formal documentation: roles, base/commission splits, quota structure, and ramp timeline. | | Agency Onboarding Flow | `CREATE` | **Critical** | How an agency goes from sign-up to having their first business client live on the platform. Time-to-value is a critical metric for both retention and referral. | | Pricing Architecture Document | `CREATE` | **Critical** | Complete pricing across all tiers: floor prices by module, agency markup guidelines, OS subscription tiers, CS package pricing, and Neurigraph licensing tiers. | | Launch Marketing Plan | `CREATE` | **Critical** | LinkedIn announcement strategy, thought leadership content plan, PR strategy, and the planned open-source component launch. | | Influencer Outreach Strategy | `EXISTS` | High | Exists in `papers-and-research/aiConnected-influencer-cold-outreach-with-messaging.mdx`. Should be integrated into the broader marketing plan. | | Content Marketing Strategy | `CREATE` | High | How the "Acquired Intelligence" philosophy becomes a content engine: blog, LinkedIn, podcast, YouTube, and the book. | | Developer Community Engagement Plan | `EXISTS` | High | Exists in `aiconnected-supporting-docs/engaging-the-dev-community.mdx`. Needs integration into the go-to-market plan. | | Partner Channel Strategy | `CREATE` | High | Beyond direct agency acquisition: technology partnerships, integration partnerships (CRMs, booking platforms, etc.), and referral programs. | | First 10 Agency Customer Target List | `CREATE` | **Critical** | Named list of specific agency targets for the initial launch — with contact information, rationale for fit, and outreach approach. | | Customer Success Playbook | `CREATE` | High | How the CS team (white-labeled) engages with business clients. Scripts, escalation paths, success metrics. | | Churn Prevention Strategy | `CREATE` | High | What happens when an agency shows signs of disengaging? Early warning metrics and intervention protocols. | | Referral & Word-of-Mouth Strategy | `CREATE` | Supporting | How satisfied agencies are incentivized to refer other agencies to the platform. | --- ## Category 7 — Operations & Team | Document | Status | Priority | Notes | |---|---|---|---| | Organizational Chart — Current State | `CREATE` | **Critical** | Even with one founder and contractors, an org chart must exist. Shows investors what structure is in place and where hiring will occur. | | Organizational Chart — 12-Month Projected | `CREATE` | **Critical** | Who is hired and when, in sequence. Must align with the use-of-funds budget. | | Job Descriptions — First 5 Hires | `CREATE` | **Critical** | Specific JDs for: Senior Full-Stack Lead, VP/Director of Sales, Marketing Director, and any other priority hires. | | Compensation Philosophy & Ranges | `CREATE` | High | Salary ranges, equity grant sizes by role and level, and the principles governing compensation decisions. | | Contractor Registry & Agreement Audit | `CREATE` | **Critical** | Complete list of all current contractors with confirmation that each has a signed agreement containing IP assignment. | | Operational Infrastructure Inventory | `CREATE` | High | All tools, subscriptions, and services currently in use: Supabase, DigitalOcean, n8n, OpenRouter, LiveKit, etc. With costs and renewal dates. | | Customer Support Process | `CREATE` | High | How bugs, questions, and escalations are handled at launch before a formal CS team is in place. | | Development Workflow Documentation | `CREATE` | High | How code is written, reviewed, tested, and deployed. Version control (GitHub), CI/CD process, and staging environment structure. | | Data Backup & Disaster Recovery Plan | `CREATE` | High | Given that Neurigraph holds persistent personal memory, data integrity and recovery processes are critical — for investors and for compliance. | | Vendor & Dependency Risk Assessment | `CREATE` | High | What happens if Supabase, LiveKit, or OpenRouter have outages or pricing changes? Mitigation strategies for critical dependencies. | --- ## Category 8 — Investor Materials | Document | Status | Priority | Notes | |---|---|---|---| | Investor Pitch Deck (Surface Version) | `CREATE` | **Critical** | The "GoHighLevel for AI" story. 12–15 slides. Written for investors who may not understand the deep tech vision but understand agency software economics. | | Investor Pitch Deck (Deep Version) | `CREATE` | **Critical** | The cognitive infrastructure + robotics story. For sophisticated investors who will understand the long-term thesis. | | Executive Summary (2 pages) | `CREATE` | **Critical** | Standalone two-page summary of the business that can be sent before a full pitch. Covers problem, solution, market, model, team, and ask. | | Full Business Plan Document | `CREATE` | **Critical** | The comprehensive document this entire planning effort is building toward. | | Data Room Index | `CREATE` | **Critical** | The organized folder structure for investor due diligence: legal, financial, product, IP, team, and market documents. | | Cap Table (Investor-Ready Format) | `CREATE` | **Critical** | Pre-money cap table showing current ownership. Post-money cap table showing projected dilution from the seed round. | | Term Sheet Reference Document | `CREATE` | High | Reference guide to standard seed round terms so incoming offers can be evaluated quickly and confidently. | | Investor FAQ Document | `CREATE` | High | Written answers to the 15–20 most likely investor questions, including the hard ones about solo founder risk, technical execution without a CTO, and competitive timing. | | GoHighLevel Growth Comparison Study | `CREATE` | High | Documented parallel between GoHighLevel's trajectory and aiConnected's plan. Shows the model is proven. | | Reference Customer Strategy | `CREATE` | High | Plan for identifying and cultivating early agency customers who will serve as investor references during due diligence. | | Board / Advisory Board Structure | `CREATE` | High | Who is on the advisory board, their credentials, and what role they will play as the company scales. Even pre-seed, credible advisors strengthen the investor narrative. | --- ## Category 9 — Risk & Compliance | Document | Status | Priority | Notes | |---|---|---|---| | Risk Register | `CREATE` | **Critical** | Formal documentation of all identified risks: technical, market, competitive, regulatory, financial, and execution. Each with probability, impact, and mitigation strategy. | | GDPR Compliance Assessment | `CREATE` | **Critical** | The Neurigraph memory architecture collects and retains deeply personal data. GDPR compliance is non-optional for any EU deployment. Requires legal review. | | CCPA Compliance Assessment | `CREATE` | **Critical** | Same as above for California residents. | | AI Regulatory Risk Assessment | `CREATE` | High | The EU AI Act and emerging US AI regulations may affect how the platform can be used, particularly in healthcare, legal, and financial services. | | Data Residency & Sovereignty Plan | `CREATE` | High | Where does user memory data live? Can it be restricted to specific geographies for enterprise or government customers? | | AI Ethics & Responsible Use Policy | `CREATE` | High | Particularly important for the Neurigraph architecture — what data is collected, how long it's retained, who can access it, and how it can be deleted. Investors increasingly scrutinize this. | | SOC 2 Readiness Assessment | `CREATE` | High | Not required at MVP, but the architecture must support it. Document what is already SOC 2-aligned and what will be needed for enterprise customer conversations. | | Insurance Requirements Review | `COMMISSION` | High | Errors & omissions, cyber liability, and directors & officers insurance. Required before any enterprise customer or investor conversation. | | Competition & Antitrust Considerations | `COMMISSION` | Supporting | At this stage, low risk — but the robotics platform's ambition to become an industry standard means antitrust considerations should be on the radar. | | Regulatory Landscape — Robotics | `RESEARCH` | High | How existing drone, autonomous vehicle, and medical device regulations may apply to the aiConnected Robotics Platform. Referenced in the robotics platform doc as needing formal writeup. | --- ## Summary: Document Count by Status | Status | Count | Action Required | |---|---|---| | `EXISTS` | ~35 | Review, validate, and cite in business plan | | `CREATE` | ~65 | Write internally, in priority order | | `RESEARCH` | ~15 | Gather external data — some can be purchased, some requires primary research | | `COMMISSION` | ~20 | Engage professional: legal counsel, accountant, patent attorney, insurance broker | **Total tracked items: ~135** --- ## Recommended Sequencing Before writing a single word of the business plan, the following must be confirmed or completed: 1. Legal entity status confirmed (Articles, EIN, Good Standing certificate) 2. IP assignment agreements in place for all contractors 3. Cap table formalized 4. Patent counsel engaged for Neurigraph provisional application before any public disclosure 5. Customer discovery interviews completed (minimum 10 agencies) 6. Financial model built (even with rough assumptions — it can be refined) 7. Voice PRD completed 8. Terms of Service and Privacy Policy drafted Everything else can be produced in parallel with business plan writing. --- --- ## Writing Sequence **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/writing-sequence **Description:** The exact order in which every supporting document is written before the business plan — organized into phases by dependency, logical flow, and investor priority. --- ## Why Sequence Matters Some documents are foundational — their outputs define inputs for everything that follows. Writing a financial model before completing market research produces numbers without basis. Writing a go-to-market plan before defining the ICP produces strategy without a target. Writing the business plan before any of these are complete produces prose without substance. The sequence below is designed so that each document can be written with full information from the documents that preceded it. --- ## Phase Overview | Phase | Focus | Documents | Dependency | |---|---|---|---| | **Phase 1** | Founding Voice & Identity | 4 | None — start here | | **Phase 2** | Product Foundation | 8 | Phase 1 | | **Phase 3** | Market Intelligence | 10 | Phases 1–2 | | **Phase 4** | Competitive Intelligence | 7 | Phase 3 | | **Phase 5** | Financial Models | 17 | Phases 2–4 | | **Phase 6** | Operations, Technology & Risk | 13 | Phases 1–5 | | **Phase 7** | Go-to-Market & Investor Materials | 19 | All prior phases | | **Final** | The Business Plan | 1 | All 78 supporting documents | --- ## Phase 1 — Founding Voice & Identity *These four documents define the language, philosophy, and strategic framing that will appear throughout every other document. They are written first because every other document borrows from them.* --- ### 1-A. `BP-FOUND-01` — Founder Biography & Background **What it is:** A 1–2 page narrative biography of Bob Hunter. Not a resume. A story that explains how a non-technical founder built one of the most architecturally sophisticated AI platform concepts in the current market — and why that background is a strength, not a liability. **What it must cover:** - Professional background and relevant experience - What led to founding aiConnected (the personal "why") - How Bob operates: the documentation-first, AI-assisted development approach - The role of Claude and AI tools in building a solo-founder company of this scope - Honest framing of the solo founder risk and how it is being mitigated **Source material:** `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` --- ### 1-B. `BP-FOUND-02` — Company Origin & Mission Statement **What it is:** A formal one-page document establishing when the company was founded, why it exists, and what it is ultimately trying to accomplish. This is the company's north star — used verbatim or paraphrased across the pitch deck, the executive summary, the website, and investor one-pagers. **What it must cover:** - Founding date and original concept - The evolution from early product ideas to the current platform architecture - A finalized, single-sentence mission statement - A finalized, single-sentence vision statement (the 10-year statement) - The company's core values and operating principles **Source material:** `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx`, `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` --- ### 1-C. `BP-FOUND-03` — Acquired Intelligence Philosophy Document **What it is:** A 3–5 page essay articulating why "Acquired Intelligence" is a more accurate, more useful, and more defensible framing than "Artificial Intelligence" — and how this philosophy directly drives every architectural decision aiConnected has made. **What it must cover:** - The distinction between training-based AI and experience-based AI - The anchor quote: *"Any human can be capable of anything, but no human can be capable of everything. And neither can AI."* - Why persistence, identity, and accumulated experience are the missing layer - The book outline as background reference - How ANI (Acquired Network Intelligence) extends this to cross-instance collective learning - Why this philosophical position creates a defensible product category **Source material:** `knowledge-base/neurigraph-memory-architecture/acquired-intelligence-rough-outline.mdx`, `knowledge-base/neurigraph-memory-architecture/ai-terminology-reframing.mdx`, `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` --- ### 1-D. `BP-FOUND-04` — Two-Layer Strategy Narrative **What it is:** A 2–3 page strategic document that explains the deliberate design of the company: a revenue-generating surface layer that funds and trains the cognitive infrastructure layer underneath. This is the document investors will return to repeatedly during due diligence. **What it must cover:** - The surface layer: agency tools as the commercial vehicle - The foundation layer: Cognigraph as the long-term asset - Why this structure is intentional and not a pivot - The data moat flywheel: agencies → users → Cognigraph → better tools → more agencies - The 2030 robotics thesis: why the training data becomes the most valuable asset - The GoHighLevel comparison: same surface model, fundamentally different long-term architecture **Source material:** `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` (Part 3–5) --- ## Phase 2 — Product Foundation *These documents consolidate and formalize the product story. Most of the raw material already exists in the knowledge base — the work here is synthesis, condensation, and filling the documented gaps.* --- ### 2-A. `BP-PROD-01` — Master Product Architecture Overview **What it is:** A single document — with one clear diagram — that shows how all three platform layers (Business Platform, aiConnectedOS, Neurigraph) relate to each other, how they share data and infrastructure, and how the developer ecosystem extends all three. This becomes the reference diagram used throughout the business plan. **Source material:** `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview.mdx`, `knowledge-base/aiconnected-os/quick-system-overview.mdx`, `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` (Architecture diagram in Part 3) --- ### 2-B. `BP-PROD-02` — Business Platform Executive Summary **What it is:** A 2-page condensed overview of the Business Platform — written for a non-technical investor audience. Distilled from the MVP specification and platform overview documents. **Source material:** `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview-non-technical.mdx`, `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx` --- ### 2-C. `BP-PROD-03` — aiConnectedOS Executive Summary **What it is:** A 2-page condensed overview of aiConnectedOS — written for a non-technical investor audience. Distilled from the OS PRD and quick system overview. **Source material:** `knowledge-base/aiconnected-os/quick-system-overview.mdx`, `knowledge-base/aiconnected-os/system-standards-and-philosophy.mdx` --- ### 2-D. `BP-PROD-04` — Consolidated 18-Month Product Roadmap **What it is:** A single integrated roadmap across all three platform layers — with milestones, dependencies, resource requirements, and the 4-product launch sequence (Knowledge → Chat → Voice → Brain). The 18-week OS build plan and 6-week Voice deadline are reconciled and integrated. **Note:** The Voice PRD must be completed before this roadmap is finalized. **Source material:** `knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-build-plan.mdx`, `knowledge-base/aiconnected-os/aiconnected-os-prd.mdx` (Phase 6 build plan section) --- ### 2-E. `BP-PROD-05` — Neurigraph Technical Summary (Non-Technical Version) **What it is:** A 2–3 page explanation of the Neurigraph memory architecture written specifically for non-technical investors. No code, no database schemas. Uses plain-language analogies throughout. **Source material:** `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx`, `knowledge-base/neurigraph-memory-architecture/object-deconstruction-graph-overview.mdx`, `knowledge-base/neurigraph-memory-architecture/amygdala-dynamic-heat-threshold-control.mdx` --- ### 2-F. `BP-PROD-06` — Product Status Matrix **What it is:** A single reference table covering every product, module, and feature: current build stage, estimated completion date, revenue readiness date, development resource required, and dependencies. Investors use this to pressure-test the roadmap. **Source material:** `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` (Part 5), `knowledge-base/aiconnected-business-platform/production-readiness-checklist.mdx` --- ### 2-G. `BP-PROD-07` — Engine Module Revenue Analysis **What it is:** A structured analysis of the 30+ engine modules: which are Tier 1 (launch priority), which are Tier 2 (post-launch expansion), pricing, estimated adoption rates, and projected revenue contribution by year. **Source material:** `knowledge-base/aiconnected-apps-and-modules/original-aiConnected-engines.mdx` --- ### 2-H. `BP-PROD-08` — Platform Glossary **What it is:** A comprehensive glossary of all platform-specific terminology — Cipher, Neurigraph, Instance, Persona, Skill Slot, Apprenticeship, Mod, CogniGraph, ODG, ANI, and every other term a reader will encounter in the business plan. Included as Appendix I. **Source material:** `knowledge-base/aiconnected-os/quick-system-overview.mdx` (Glossary section), multiple documents across knowledge base --- ## Phase 3 — Market Intelligence *External data gathering. Some of this requires commissioned research or purchased reports. Items marked with `(external)` require data from outside the knowledge base.* --- ### 3-A. `BP-MARKET-01` — AI SaaS Market Sizing Report **What it covers:** Total market size for AI-powered SaaS tools, growth rate, projected 5-year trajectory. Establishes the macro market context. *(External research required — Gartner, Grand View Research, MarketsandMarkets, or similar.)* --- ### 3-B. `BP-MARKET-02` — Agency Software & White-Label Platform Market Research **What it covers:** Number of US agencies, current software spend, GoHighLevel's market share, and the gap in the AI-focused agency platform market. *(Partially external — GoHighLevel's public metrics provide a strong anchor.)* --- ### 3-C. `BP-MARKET-03` — TAM Analysis **What it covers:** Total Addressable Market calculation across all three revenue streams: agency platform, aiConnectedOS subscriptions, and Neurigraph licensing. Must show methodology, not just a number. --- ### 3-D. `BP-MARKET-04` — SAM Calculation **What it covers:** Serviceable Addressable Market — the portion of the TAM that aiConnected can realistically serve given its current product scope, geographic focus, and target customer profile. --- ### 3-E. `BP-MARKET-05` — SOM Projection (Years 1–3) **What it covers:** Serviceable Obtainable Market — the portion of the SAM that is realistically reachable in the first three years given team size, capital, and go-to-market approach. Must align with the financial model. --- ### 3-F. `BP-MARKET-06` — AI Memory Architecture Market Sizing **What it covers:** Emerging market for persistent AI memory systems. Mem0's $24M raise, comparable funding rounds, and analyst projections for the memory infrastructure market. *(Partially external.)* --- ### 3-G. `BP-MARKET-07` — Robotics Cognitive Infrastructure Market Research **What it covers:** 10-year TAM for the intelligence/cognitive layer of the robotics market. Humanoid robot adoption projections, industrial robotics AI layer spending, and the gap in universal cognitive standards. *(External research required — Interact Analysis, IDTechEx, or equivalent.)* --- ### 3-H. `BP-MARKET-08` — Voice AI Market Research **What it covers:** Market size and growth rate for AI-powered voice in business contexts — customer service, sales, and receptionist replacement. Validates the Voice AI Hub positioning. --- ### 3-I. `BP-MKTRES-05` — Agency Customer Discovery Report **What it covers:** Structured summary of customer discovery conversations with 10–20 agency owners. Validates the core problem, tests the value proposition, and captures verbatim quotes for the business plan. *(Primary research required — Bob must conduct these conversations.)* --- ### 3-J. `BP-MKTRES-06` — Business Client Pain Point Survey & ICP Profiles **What it covers:** Survey findings from SMB owners on their current AI tool frustrations. Combined with the Agency ICP and Business Client ICP profiles. --- ## Phase 4 — Competitive Intelligence *All competitive documents are internally written but require current data on competitor pricing, features, and funding. These should be written after Phase 3 market research establishes the market framing.* --- ### 4-A. `BP-COMP-01` — GoHighLevel Deep Dive Pricing, feature set, agency count, revenue, known weaknesses, developer ecosystem limitations, and why aiConnected is not competing on their turf. --- ### 4-B. `BP-COMP-02` — ChatGPT Enterprise Competitive Profile Why persistent, modular, persona-based architecture is categorically different from session-based enterprise AI. Feature-for-feature comparison with emphasis on memory, persona governance, and workflow integration. --- ### 4-C. `BP-COMP-03` — Mem0 & OpenMemory Competitive Analysis Mem0: $24M raised, AWS integration, developer-focused positioning. OpenMemory: Apache 2.0, open architecture. How Neurigraph differs from and supersedes both. Why "fork-and-differentiate" is still relevant context. --- ### 4-D. `BP-COMP-04` — Vapi / Retell / LiveKit Competitive Profile Voice infrastructure landscape. Why aiConnected is building its own Layer 1 voice infrastructure and what that enables that Vapi/Retell cannot. --- ### 4-E. `BP-COMP-05` — Manus & Agentic Platform Analysis How persona-based architecture differs from general agentic task execution. Why "personalities, not agents" is a category distinction, not a feature description. --- ### 4-F. `BP-COMP-06` — Robotics AI Competitive Landscape Who is currently attempting to build universal cognitive standards for robotics? Boston Dynamics AI Institute, Physical Intelligence (Pi), 1X Technologies. What each is doing and why aiConnected's approach is differentiated. --- ### 4-G. `BP-COMP-07` — Full Competitive Matrix Single-page visual matrix comparing aiConnected across 12–15 capability dimensions against GoHighLevel, Mem0, ChatGPT Enterprise, Vapi, Manus, and HubSpot. Serves as Appendix D. --- ## Phase 5 — Financial Models *All financial documents require Phases 1–4 to be complete. Revenue models require market size data (Phase 3). Pricing requires competitive context (Phase 4). Unit economics require ICP definitions (Phase 3).* --- ### 5-A. `BP-FIN-09` — Pricing Architecture Document **Written first in this phase** because all revenue models derive from pricing. Documents every price point across the entire ecosystem: module floor prices, agency markup guidelines, OS tier pricing, CS package pricing, Neurigraph licensing tiers, and the developer marketplace revenue share. --- ### 5-B through 5-F. Revenue Models (Platform, OS, Neurigraph, API, CS, Developer) Six separate revenue model documents, each building on the pricing architecture. Each includes assumptions, variables, monthly projections for Year 1, and annual projections for Years 2–5. `BP-FIN-01` — Business Platform | `BP-FIN-02` — aiConnectedOS | `BP-FIN-03` — Neurigraph Licensing | `BP-FIN-04` — API Resale | `BP-FIN-05` — Customer Success | `BP-FIN-06` — Developer Ecosystem --- ### 5-G. `BP-FIN-07` — Use of Funds Breakdown Line-item budget for the $2.5–$3.5M seed round. Maps every dollar to a specific hire, infrastructure investment, or operational need. Must align with the team hiring plan from Phase 6. --- ### 5-H. `BP-FIN-08` — Neurigraph Licensing Revenue Model Separate from the general Neurigraph revenue model — this document specifically models the partner licensing business: deal structure, projected partner count by sector, and revenue contribution timeline. --- ### 5-I. `BP-FIN-10` — Consolidated 5-Year Revenue Projections Integrates all six revenue models into a single consolidated view. The authoritative revenue number referenced in the business plan. --- ### 5-J. `BP-FIN-11` — Unit Economics Model Agency customer: CAC, LTV, LTV:CAC ratio, payback period, expansion revenue assumption. OS user: same metrics for the consumer/prosumer tier. The most investor-scrutinized document in the plan. --- ### 5-K. `BP-FIN-12` — Break-Even Analysis Confirms the ~100-agency break-even milestone with supporting calculations. Shows the path to strong profitability at 300+ agencies. --- ### 5-L through 5-Q. P&L, Cash Flow, Balance Sheet, Operating Costs, Sensitivity `BP-FIN-13` — Consolidated P&L (Years 1–5) | `BP-FIN-14` — Monthly Cash Flow (Year 1) | `BP-FIN-15` — Pro Forma Balance Sheet | `BP-FIN-16` — Operating Cost Model | `BP-FIN-17` — Sensitivity & Scenario Analysis --- ## Phase 6 — Operations, Technology & Risk *These documents require the financial model (Phase 5) and product foundation (Phase 2) to be complete. Hiring plans must align with the Use of Funds.* --- ### 6-A. `BP-OPS-01` — Current Organizational State Documents exactly what the company looks like today: founder, any contractors, their roles and agreements, current tools and infrastructure. The honest starting point. --- ### 6-B. `BP-OPS-02` — Organizational Chart (Current & 12-Month Projected) Two org charts in one document: current state and the post-seed-round structure. Every role labeled with hire timing and budget alignment to `BP-FIN-07`. --- ### 6-C. `BP-OPS-03` — Priority Job Descriptions (First 5 Hires) Full JDs for the first five hires: Senior Full-Stack Lead, VP of Sales, Marketing Director, and two additional roles confirmed through the financial model. Includes title, responsibilities, required experience, and compensation range. --- ### 6-D. `BP-OPS-04` — Compensation Philosophy & Ranges Salary bands, equity grant sizes by level, commission structure for sales roles, and the principles governing compensation decisions as the team scales. --- ### 6-E. `BP-OPS-05` — Advisory Board Structure & Recruitment Plan Current and target advisory board composition. Credentials needed (technical, industry, investor network). Equity and time commitment structure for advisors. --- ### 6-F. `BP-OPS-06` — Operational Infrastructure Inventory Every tool and subscription currently in use, with monthly cost and contract status. DigitalOcean, Supabase, LiveKit, OpenRouter, n8n, Stripe, GitHub, and all others. --- ### 6-G. `BP-OPS-07` — Development Workflow & QA Process How code is written, reviewed, tested, and deployed. CI/CD setup, staging environment, version control discipline. Investors evaluate this for execution credibility. --- ### 6-H. `BP-TECH-01` — Technology Stack Overview The complete technology stack with rationale for each choice. Frontend, backend, database, voice, AI inference, billing, hosting, and automation layers. --- ### 6-I. `BP-TECH-02` — Infrastructure Architecture Document How the platform is hosted and scaled. Container orchestration, load balancing, database sharding, failover, and backup strategy. Written for technical due diligence. --- ### 6-J. `BP-TECH-03` — Security Architecture Document Tenant data isolation, memory encryption, containerized module security, API gateway authentication, and the audit event stream. Required before any enterprise conversation. --- ### 6-K. `BP-TECH-04` — Enterprise Readiness Architecture Checklist What is already enterprise-ready, what will be added in Phase 3–4 of the GTM, and what requires enterprise-specific configuration. Confirms the "enterprise-aware from day one" claim. --- ### 6-L. `BP-RISK-01` — Risk Register (Full) Every identified risk: technical, market, competitive, regulatory, financial, and execution. Each with probability rating, impact rating, and mitigation strategy. The most important risk document. --- ### 6-M. `BP-RISK-02/03/04/05` — Compliance Assessments GDPR Assessment | CCPA Assessment | AI Regulatory Risk Assessment | Robotics Regulatory Landscape. Four documents, each addressing a specific compliance domain. Written with legal counsel input recommended. --- ## Phase 7 — Go-to-Market & Investor Materials *The final phase of supporting documents before the business plan is written. These require all prior phases to be complete.* --- ### 7-A. `BP-GTM-01` — Launch Strategy Document The sequenced launch plan: what launches when, in what order, with what resources, and targeting which customer segment first. Anchors all other GTM documents. --- ### 7-B. `BP-GTM-02` — Agency Acquisition Playbook The step-by-step process for signing the first 10, then 50, then 100 agencies. Channels, messaging, objection handling, contract process, and closing. The most operationally critical GTM document. --- ### 7-C. `BP-GTM-03` — Sales Team Structure & Compensation Plan The 10-person sales team model. Roles, hiring sequence, base/commission splits, quota structure, ramp expectations, and team management approach. --- ### 7-D. `BP-GTM-04` — Agency Onboarding Flow & Time-to-Value How an agency goes from sign-up to first business client live on the platform. Target time-to-value and what happens at each stage. --- ### 7-E. `BP-GTM-05` — Launch Marketing Plan LinkedIn announcement campaign, PR strategy, content plan for the first 90 days, and the open-source component launch strategy. --- ### 7-F. `BP-GTM-06` — Content & Thought Leadership Strategy How the Acquired Intelligence philosophy becomes a sustained content engine. Blog, LinkedIn, podcast, YouTube, and the book as long-horizon brand builder. --- ### 7-G. `BP-GTM-07` — Developer Community & Ecosystem Strategy How the developer marketplace is seeded, governed, and grown. Trust pipeline, sandbox environment, revenue share, and community engagement approach. --- ### 7-H. `BP-GTM-08` — Partner Channel & Integration Strategy Technology partners, integration partners (CRMs, booking platforms, accounting software), and referral incentive programs. --- ### 7-I. `BP-GTM-09` — First 10 Agency Target List Named list of specific target agencies for the initial launch. Each entry includes: agency name, contact information, vertical focus, current tool stack, rationale for fit, and outreach approach. --- ### 7-J. `BP-GTM-10` — Enterprise Readiness & Progression Plan The four-phase customer journey from power users to enterprise. What changes at each stage, what features are required, and what the enterprise sales motion looks like. --- ### 7-K. `BP-GTM-11` — Churn Prevention & Customer Success Strategy Early warning metrics, intervention protocols, and the white-label CS team playbook for preventing agency and business client churn. --- ### 7-L. `BP-INVEST-01` — Investor Pitch Deck (Surface Version) The "GoHighLevel for AI" deck. 12–15 slides. Written for investors who understand agency software economics. --- ### 7-M. `BP-INVEST-02` — Executive Summary (Standalone 2-Page Version) The two-page standalone summary sent before a full pitch. Covers: problem, solution, market, model, team, traction, and ask. --- ### 7-N. `BP-INVEST-03` — GoHighLevel Growth Comparison Study Documents GoHighLevel's trajectory — bootstrapped 3 years, then $60M Series C at $82.7M ARR — and maps aiConnected's plan alongside it. Validates the model's credibility with a proven comparable. --- ### 7-O. `BP-INVEST-04` — Seed Round Term Sheet Reference A reference guide to standard seed round terms so incoming offers can be evaluated quickly and confidently. --- ### 7-P. `BP-INVEST-05` — Series A Milestone Definition The specific, measurable milestones that define Series A readiness: MRR target, agency count, retention rate, Neurigraph architecture status, and team composition. --- ### 7-Q. `BP-INVEST-06` — Investor Pitch Deck (Deep Version) The cognitive infrastructure + robotics deck. For sophisticated investors who will evaluate the long-term thesis. Requires all prior documents to be complete. --- ### 7-R. `BP-INVEST-07` — Data Room Index The organized index of the investor due diligence data room. Every document in the data room named, categorized, and linked. Ensures nothing is missing when a serious investor begins diligence. --- ### 7-S. `BP-LEGAL-01` — Entity & Corporate Records Summary A summary document confirming all legal entity details, IP ownership, and corporate structure — formatted for inclusion in the business plan and the data room. --- ## Final — The Business Plan Once all 78 supporting documents are complete, the business plan is written as a synthesis document. Each section of the plan draws directly from its corresponding supporting documents. The executive summary is written last. **Target length:** 40–60 pages **Format:** Markdown (`.md`) per project document conventions --- ## Quick Reference: Document Count by Phase | Phase | Documents | Focus | |---|---|---| | Phase 1 | 4 | Founding voice & philosophy | | Phase 2 | 8 | Product foundation & synthesis | | Phase 3 | 10 | Market intelligence & customer research | | Phase 4 | 7 | Competitive intelligence | | Phase 5 | 17 | Financial models & projections | | Phase 6 | 13 | Operations, technology & risk | | Phase 7 | 19 | Go-to-market & investor materials | | **Total** | **78** | **Supporting documents** | | Final | 1 | **The business plan** | --- --- ## API Reference **URL:** https://secure-docs.aiconnected.ai/docs/api-reference **Description:** Documents in API Reference. --- ## Introduction **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/introduction **Description:** Build the aiConnected platform against the documented shell, OS, and module contracts in this repository. ## What this section covers This API reference turns the repository's planning and product docs into a developer-facing contract set for the aiConnected platform. Use it to understand: - The platform shell APIs every module depends on - The aiConnectedOS service contracts for personas, memory, and workspace features - The first-party module contracts for Knowledge, Chat, Contact Forms, Co-Browser, Voice, Paper, LogicLegal, and macEngine ## How to read these docs Each page labels the contract type clearly: - `Canonical route contract` means the source docs define concrete endpoints - `Canonical interface contract` means the source docs define required operations, entities, events, or manifests but not fixed route paths - `Derived implementation contract` means the repository defines behavior strongly enough to support implementation, but the final path names still need to be locked in code This matters because the repo mixes PRDs, architectural docs, and developer guides. These reference pages preserve that distinction instead of pretending everything was already finalized as OpenAPI. ## Source documents This section is based on the documentation already in the repository, especially: - [aiConnected Platform overview](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-overview) - [aiConnected Platform foundation PRD](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd) - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [aiConnected Platform v2 port map](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-port-map) - [What is the platform shell](/docs/knowledge-base/aiconnected-business-platform/what-is-the-platform-shell) - [aiConnectedOS developer documentation](/docs/knowledge-base/aiconnected-os/aiconnected-os-developer-documentation) - [aiConnected modules overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiconnected-modules-overview) ## Coverage map The API Reference tab is organized into three layers: 1. `Platform core` The multi-tenant shell, shared entities, event bus, module registry, themes, layouts, and developer extension model. 2. `aiConnectedOS` Persistent personas, instances, Neurigraph-backed memory, and the feature-level workspace contracts described in the OS docs. 3. `First-party modules` The first modules called out across the MVP and module docs: Knowledge, Chat, Contact Forms, Chat Monitor, Co-Browser, Voice, Paper, LogicLegal, and macEngine. ## Implementation guidance Build the platform in this order: 1. Ship the shell contracts first. 2. Implement shared entities and event routing next. 3. Add aiConnectedOS services that provide persona and memory infrastructure. 4. Layer first-party modules on top of the shared shell and event bus. 5. Keep every module manifest-first and container-isolated. That sequence is consistent with the repo's foundation PRD, MVP spec, and v2 build plan. --- ## Quickstart **URL:** https://secure-docs.aiconnected.ai/docs/quickstart **Description:** Documents in Quickstart. --- ## Quickstart **URL:** https://secure-docs.aiconnected.ai/docs/quickstart/overview **Description:** Get your aiConnected instance up and running in minutes. This guide walks you through the minimum steps to have a working aiConnected deployment with your first Persona active. --- ## Welcome to aiConnected Docs **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base **Description:** The official home for aiConnected platform documentation, PRDs, API references, and developer resources. --- ## Knowledge Base **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/introduction **Description:** Internal planning documents, feature specs, research, and architecture references for the aiConnected platform. This knowledge base is the internal source of truth for the aiConnected platform — capturing product decisions, architecture plans, feature specifications, and supporting research across every layer of the system. --- ## Quickstart **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/quickstart **Description:** Start building awesome documentation in minutes ## Get started in three steps Get your documentation site running locally and make your first customization. ### Step 1: Set up your local environment ### Step 2: Deploy your changes ### Step 3: Go live ## Next steps Now that you have your docs running, explore these key features: --- ## Sample User.md Configuration **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/sample-user-md-configuration # USER.md - About Your Human - **Name:** Bob - **What to call them:** Bob - **Pronouns:** — - **Timezone:** America/New_York (ET) - **Profession / Background:** — - **Preferred Tone:** — (default: calm, dry humor) - **Autonomy Preference:** — ## Notes _(Nothing yet — will build over time.)_ ## Context _(What do they care about? What projects are they working on? What annoys them? What makes them laugh? Build this over time.)_ --- ## Webhooks **URL:** https://secure-docs.aiconnected.ai/docs/webhooks **Description:** Documents in Webhooks. --- ## Introduction **URL:** https://secure-docs.aiconnected.ai/docs/webhooks/introduction **Description:** Receive real-time event notifications from your aiConnected instance. Webhooks allow your external systems to receive instant notifications when events occur inside aiConnected — a Persona completes a task, a message is delivered, a workflow errors, or a contact is created. ## Supported events | Event | Description | |-------|-------------| | `persona.action.completed` | A Persona finished executing a task | | `message.sent` | An outbound message was dispatched | | `message.delivered` | Delivery confirmed by recipient server | | `message.bounced` | Message could not be delivered | | `contact.created` | A new contact was added to Audience | | `workflow.completed` | An n8n workflow run finished | | `workflow.failed` | An n8n workflow run errored | ## Registering a webhook Navigate to **Settings → Webhooks** and add your endpoint URL. Select the events you want to receive and save. aiConnected will send a verification ping to confirm the endpoint is reachable. --- ## Learn **URL:** https://secure-docs.aiconnected.ai/docs/learn **Description:** Documents in Learn. --- ## GoHighLevel Deep Dive **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-01-gohighlevel-deep-dive **Description:** Full competitive profile of GoHighLevel: revenue ($82.7M ARR), pricing ($297–$497/month), agency count, feature set, developer ecosystem limitations, and structural weaknesses. The most important competitive document. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-01` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Full competitive profile of GoHighLevel: revenue ($82.7M ARR), pricing ($297–$497/month), agency count, feature set, developer ecosystem limitations, and structural weaknesses. The most important competitive document. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview.mdx` - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx (Appendix A)` ## Feeds Into Business Plan Sections 6.1, 6.8, 12.1, Appendix F --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## ChatGPT Enterprise Competitive Profile **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-02-chatgpt-enterprise-profile **Description:** Why persistent, modular, persona-based architecture is categorically different from session-based enterprise AI. Feature-for-feature comparison with emphasis on memory, persona governance, and workflow integration. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-02` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Why persistent, modular, persona-based architecture is categorically different from session-based enterprise AI. Feature-for-feature comparison with emphasis on memory, persona governance, and workflow integration. ## Source Materials - `knowledge-base/aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App.mdx` ## Feeds Into Business Plan Section 6.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Mem0 & OpenMemory Competitive Analysis **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-03-mem0-openMemory-analysis **Description:** Mem0: $24M raised, AWS integration, developer-focused positioning. OpenMemory: Apache 2.0, open architecture. How Neurigraph differs from and supersedes both. Why 'fork-and-differentiate' is still relevant context. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-03` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Mem0: $24M raised, AWS integration, developer-focused positioning. OpenMemory: Apache 2.0, open architecture. How Neurigraph differs from and supersedes both. Why 'fork-and-differentiate' is still relevant context. ## Source Materials - `knowledge-base/neurigraph-memory-architecture/neurigraph-memory-systems-competitive-comparison.mdx` - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` ## Feeds Into Business Plan Sections 2.3, 6.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Vapi / Retell / LiveKit Competitive Profile **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-04-vapi-retell-profile **Description:** Voice infrastructure landscape. Why aiConnected is building its own Layer 1 voice infrastructure and what that enables that Vapi/Retell cannot. Pricing and capability comparison. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-04` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Voice infrastructure landscape. Why aiConnected is building its own Layer 1 voice infrastructure and what that enables that Vapi/Retell cannot. Pricing and capability comparison. ## Source Materials - `knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/` ## Feeds Into Business Plan Section 6.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Manus & Agentic Platform Analysis **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-05-manus-agentic-analysis **Description:** How persona-based architecture differs from general agentic task execution. Why 'personalities, not agents' is a category distinction, not a feature description. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-05` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How persona-based architecture differs from general agentic task execution. Why 'personalities, not agents' is a category distinction, not a feature description. ## Source Materials - `knowledge-base/aiconnected-os/quick-system-overview.mdx` - `knowledge-base/aiconnected-os/system-standards-and-philosophy.mdx` ## Feeds Into Business Plan Section 6.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Robotics AI Competitive Landscape **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-06-robotics-ai-landscape **Description:** Who is currently attempting to build universal cognitive standards for robotics? Boston Dynamics AI Institute, Physical Intelligence (Pi), 1X Technologies. What each is doing and why aiConnected's approach is differentiated. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-06` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Who is currently attempting to build universal cognitive standards for robotics? Boston Dynamics AI Institute, Physical Intelligence (Pi), 1X Technologies. What each is doing and why aiConnected's approach is differentiated. ## Source Materials - `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx` ## Feeds Into Business Plan Section 6.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Full Competitive Matrix **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive/bp-comp-07-competitive-matrix **Description:** Single-page visual matrix comparing aiConnected across 12–15 capability dimensions against GoHighLevel, Mem0, ChatGPT Enterprise, Vapi, Manus, and HubSpot. The authoritative competitive reference. Included as Appendix D. ## Document Information | Field | Value | |---|---| | **Code** | `BP-COMP-07` | | **Category** | Competitive Intelligence | | **Phase** | Phase 4 — Competitive Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Single-page visual matrix comparing aiConnected across 12–15 capability dimensions against GoHighLevel, Mem0, ChatGPT Enterprise, Vapi, Manus, and HubSpot. The authoritative competitive reference. Included as Appendix D. ## Source Materials - `BP-COMP-01` - `BP-COMP-02` - `BP-COMP-03` - `BP-COMP-04` - `BP-COMP-05` - `BP-COMP-06` ## Feeds Into Business Plan Section 6.7, Appendix D --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Competitive **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/competitive **Description:** Documents in Competitive. --- ## Revenue Model — Business Platform **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-01-revenue-model-business-platform **Description:** Detailed revenue model for the agency platform: number of agencies × average client count × average module price × 10% platform tax. Month-by-month for Year 1, annually for Years 2–5. Includes floor pricing assumptions and CS package adoption. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-01` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Detailed revenue model for the agency platform: number of agencies × average client count × average module price × 10% platform tax. Month-by-month for Year 1, annually for Years 2–5. Includes floor pricing assumptions and CS package adoption. ## Source Materials - `BP-FIN-09` - `BP-MARKET-04` - `BP-MKTRES-08` ## Feeds Into Business Plan Sections 7.1, 7.2, 11.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Revenue Model — aiConnectedOS **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-02-revenue-model-aiconnectedos **Description:** Subscription tier distribution assumptions (Free / Core / Pro / Enterprise), monthly churn rate, expansion revenue from tier upgrades, enterprise contract modeling, and Mods marketplace revenue contribution. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-02` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Subscription tier distribution assumptions (Free / Core / Pro / Enterprise), monthly churn rate, expansion revenue from tier upgrades, enterprise contract modeling, and Mods marketplace revenue contribution. ## Source Materials - `BP-FIN-09` - `BP-MARKET-05` ## Feeds Into Business Plan Sections 7.1, 3.13, 11.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Revenue Model — Neurigraph Licensing **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-03-revenue-model-neurigraph-licensing **Description:** Partner licensing deal structure and projected deal count by licensing sector (gaming, healthcare, education, enterprise, defense, robotics). Revenue contribution timeline — smaller near-term but significant long-term stream. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-03` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Partner licensing deal structure and projected deal count by licensing sector (gaming, healthcare, education, enterprise, defense, robotics). Revenue contribution timeline — smaller near-term but significant long-term stream. ## Source Materials - `BP-FIN-09` - `BP-FIN-08` - `BP-MARKET-06` ## Feeds Into Business Plan Section 7.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## API Resale Revenue Model **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-04-api-resale-revenue-model **Description:** Projected AI inference volume × 10% markup via OpenRouter. Accounts for BYOK option capturing share of high-volume customers. Includes model mix assumptions across Anthropic, OpenAI, Google, and Mistral. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-04` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Projected AI inference volume × 10% markup via OpenRouter. Accounts for BYOK option capturing share of high-volume customers. Includes model mix assumptions across Anthropic, OpenAI, Google, and Mistral. ## Source Materials - `BP-FIN-09` - `BP-FIN-01` ## Feeds Into Business Plan Section 7.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Customer Success Revenue Model **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-05-customer-success-revenue **Description:** Three-tier CS package adoption rates ($600–$800 Starter, $1,500–$1,700 Part-Time, $3,000–$3,500 Full-Time), average contract duration, and contribution margin. White-label positioning dynamics. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-05` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Three-tier CS package adoption rates ($600–$800 Starter, $1,500–$1,700 Part-Time, $3,000–$3,500 Full-Time), average contract duration, and contribution margin. White-label positioning dynamics. ## Source Materials - `BP-FIN-09` ## Feeds Into Business Plan Section 7.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Developer Ecosystem Revenue Model **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-06-developer-ecosystem-revenue **Description:** Mods marketplace 20% revenue share model. Developer count projections, average module revenue, marketplace GMV, and aiConnected's net take. Includes timing assumptions for ecosystem launch. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-06` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Mods marketplace 20% revenue share model. Developer count projections, average module revenue, marketplace GMV, and aiConnected's net take. Includes timing assumptions for ecosystem launch. ## Source Materials - `BP-FIN-09` - `BP-GTM-07` ## Feeds Into Business Plan Section 7.7 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Use of Funds Breakdown **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-07-use-of-funds **Description:** Line-item budget for the $2.5–$3.5M seed round. Every dollar mapped to a specific hire, infrastructure investment, or operational need. Must align with the team hiring plan from BP-OPS-02. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-07` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Line-item budget for the $2.5–$3.5M seed round. Every dollar mapped to a specific hire, infrastructure investment, or operational need. Must align with the team hiring plan from BP-OPS-02. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx (Part 2)` - `BP-OPS-02` ## Feeds Into Business Plan Sections 12.3, Executive Summary --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Neurigraph Licensing Revenue Model **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-08-neurigraph-licensing-revenue **Description:** Specifically models the partner licensing business: deal structure options (integration, API access, SDK white-label, full source), projected partner count by sector and year, and revenue contribution timeline. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-08` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Specifically models the partner licensing business: deal structure options (integration, API access, SDK white-label, full source), projected partner count by sector and year, and revenue contribution timeline. ## Source Materials - `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx` - `BP-FIN-09` ## Feeds Into Business Plan Section 7.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Pricing Architecture Document **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-09-pricing-architecture **Description:** Written first in the financial phase. Documents every price point across the entire ecosystem: module floor prices, agency markup guidelines, OS tier pricing, CS package pricing, Neurigraph licensing tiers, and the developer marketplace revenue share. All revenue models derive from this document. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-09` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Written first in the financial phase. Documents every price point across the entire ecosystem: module floor prices, agency markup guidelines, OS tier pricing, CS package pricing, Neurigraph licensing tiers, and the developer marketplace revenue share. All revenue models derive from this document. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx (Section 6)` - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` ## Feeds Into Business Plan Sections 7.2, 7.3, 7.6, 7.7, Appendix G --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Consolidated 5-Year Revenue Projections **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-10-consolidated-5year-projections **Description:** Integrates all six revenue models (BP-FIN-01 through BP-FIN-06) into a single consolidated view. The authoritative revenue number referenced throughout the business plan. Includes base, conservative, and aggressive scenarios. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-10` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Integrates all six revenue models (BP-FIN-01 through BP-FIN-06) into a single consolidated view. The authoritative revenue number referenced throughout the business plan. Includes base, conservative, and aggressive scenarios. ## Source Materials - `BP-FIN-01` - `BP-FIN-02` - `BP-FIN-03` - `BP-FIN-04` - `BP-FIN-05` - `BP-FIN-06` ## Feeds Into Business Plan Sections 7.8, 11.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Unit Economics Model **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-11-unit-economics **Description:** Agency customer: CAC, LTV, LTV:CAC ratio, payback period, expansion revenue assumption. aiConnectedOS user: same metrics for consumer/prosumer tier. The most investor-scrutinized document in the plan. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-11` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Agency customer: CAC, LTV, LTV:CAC ratio, payback period, expansion revenue assumption. aiConnectedOS user: same metrics for consumer/prosumer tier. The most investor-scrutinized document in the plan. ## Source Materials - `BP-FIN-01` - `BP-FIN-02` - `BP-MARKET-04` ## Feeds Into Business Plan Sections 7.10, 11.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Break-Even Analysis **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-12-break-even-analysis **Description:** Confirms the ~100-agency break-even milestone with supporting calculations. Shows the path to strong profitability at 300+ agencies. Includes sensitivity to key assumptions. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-12` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Confirms the ~100-agency break-even milestone with supporting calculations. Shows the path to strong profitability at 300+ agencies. Includes sensitivity to key assumptions. ## Source Materials - `BP-FIN-01` - `BP-FIN-16` ## Feeds Into Business Plan Sections 7.11, 11.8 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Consolidated P&L Model (Years 1–5) **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-13-consolidated-pl-model **Description:** Fully integrated profit and loss statement across all revenue streams and cost categories. Shows the path from pre-revenue to profitability. Presented annually for Years 1–5. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-13` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Fully integrated profit and loss statement across all revenue streams and cost categories. Shows the path from pre-revenue to profitability. Presented annually for Years 1–5. ## Source Materials - `BP-FIN-10` - `BP-FIN-16` - `BP-OPS-02` ## Feeds Into Business Plan Section 11.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Monthly Cash Flow Model — Year 1 **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-14-monthly-cashflow-year1 **Description:** Month-by-month cash flow showing burn rate, runway, and break-even timing for the first 12 months post-funding. Required for seed round conversations. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-14` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Month-by-month cash flow showing burn rate, runway, and break-even timing for the first 12 months post-funding. Required for seed round conversations. ## Source Materials - `BP-FIN-07` - `BP-FIN-13` ## Feeds Into Business Plan Section 11.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Pro Forma Balance Sheet **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-15-proforma-balance-sheet **Description:** Projected balance sheet at Years 1, 3, and 5. Assets, liabilities, and equity positions. Demonstrates financial discipline and long-term viability. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-15` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Projected balance sheet at Years 1, 3, and 5. Assets, liabilities, and equity positions. Demonstrates financial discipline and long-term viability. ## Source Materials - `BP-FIN-13` - `BP-FIN-14` ## Feeds Into Business Plan Section 11.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Operating Cost Model **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-16-operating-cost-model **Description:** All recurring platform costs at scale: DigitalOcean, Supabase, LiveKit, OpenRouter, Stripe fees, n8n self-hosting, and all other infrastructure. Includes cost scaling assumptions as user volume grows. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-16` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers All recurring platform costs at scale: DigitalOcean, Supabase, LiveKit, OpenRouter, Stripe fees, n8n self-hosting, and all other infrastructure. Includes cost scaling assumptions as user volume grows. ## Source Materials - `BP-OPS-06` ## Feeds Into Business Plan Sections 11.5, 12.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Sensitivity & Scenario Analysis **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial/bp-fin-17-sensitivity-scenario-analysis **Description:** How projections change under different assumptions: 50% slower agency acquisition, double churn rate, delayed Voice launch, and reduced Neurigraph licensing uptake. Demonstrates analytical rigor and honest risk awareness. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FIN-17` | | **Category** | Financial | | **Phase** | Phase 5 — Financial Models | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How projections change under different assumptions: 50% slower agency acquisition, double churn rate, delayed Voice launch, and reduced Neurigraph licensing uptake. Demonstrates analytical rigor and honest risk awareness. ## Source Materials - `BP-FIN-10` - `BP-FIN-13` ## Feeds Into Business Plan Sections 11.7, 13.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Financial **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/financial **Description:** Documents in Financial. --- ## Founder Biography & Background **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/founding/bp-found-01-founder-biography **Description:** A 1–2 page narrative biography of Bob Hunter. Explains how a non-technical solo founder built one of the most architecturally sophisticated AI platform concepts in the current market — and why that background is a strength, not a liability. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FOUND-01` | | **Category** | Founding | | **Phase** | Phase 1 — Founding Voice & Identity | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A 1–2 page narrative biography of Bob Hunter. Explains how a non-technical solo founder built one of the most architecturally sophisticated AI platform concepts in the current market — and why that background is a strength, not a liability. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` ## Feeds Into Business Plan Sections 1.1, 1.3, 10.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Company Origin & Mission Statement **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/founding/bp-found-02-company-origin-mission **Description:** Establishes when the company was founded, why it exists, and what it is ultimately trying to accomplish. Includes finalized mission statement, vision statement, and core operating principles. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FOUND-02` | | **Category** | Founding | | **Phase** | Phase 1 — Founding Voice & Identity | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Establishes when the company was founded, why it exists, and what it is ultimately trying to accomplish. Includes finalized mission statement, vision statement, and core operating principles. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` ## Feeds Into Business Plan Sections 1.1, 1.2, Cover Page --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Acquired Intelligence Philosophy Document **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/founding/bp-found-03-acquired-intelligence-philosophy **Description:** A 3–5 page essay articulating why 'Acquired Intelligence' is a more accurate framing than 'Artificial Intelligence' — and how this philosophy directly drives every architectural decision aiConnected has made. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FOUND-03` | | **Category** | Founding | | **Phase** | Phase 1 — Founding Voice & Identity | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A 3–5 page essay articulating why 'Acquired Intelligence' is a more accurate framing than 'Artificial Intelligence' — and how this philosophy directly drives every architectural decision aiConnected has made. ## Source Materials - `knowledge-base/neurigraph-memory-architecture/acquired-intelligence-rough-outline.mdx` - `knowledge-base/neurigraph-memory-architecture/ai-terminology-reframing.mdx` - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` ## Feeds Into Business Plan Sections 1.3, 2.3, 3.14, 14.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Two-Layer Strategy Narrative **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/founding/bp-found-04-two-layer-strategy **Description:** A 2–3 page strategic document explaining the deliberate design of the company: a revenue-generating surface layer that funds and trains the cognitive infrastructure layer underneath. The document investors will return to repeatedly during due diligence. ## Document Information | Field | Value | |---|---| | **Code** | `BP-FOUND-04` | | **Category** | Founding | | **Phase** | Phase 1 — Founding Voice & Identity | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A 2–3 page strategic document explaining the deliberate design of the company: a revenue-generating surface layer that funds and trains the cognitive infrastructure layer underneath. The document investors will return to repeatedly during due diligence. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx (Parts 3–5)` ## Feeds Into Business Plan Sections 1.4, 2.5, 6.8, 7.9, 14.2, 14.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Founding **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/founding **Description:** Documents in Founding. --- ## Launch Strategy Document **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-01-launch-strategy **Description:** The sequenced launch plan: what launches when, in what order, with what resources, targeting which customer segment first. Anchors all other GTM documents. Covers the 4-product sequence and the revenue-before-raising decision. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-01` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The sequenced launch plan: what launches when, in what order, with what resources, targeting which customer segment first. Anchors all other GTM documents. Covers the 4-product sequence and the revenue-before-raising decision. ## Source Materials - `BP-PROD-04` - `BP-FOUND-04` - `BP-FIN-09` ## Feeds Into Business Plan Sections 8.1, 8.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Agency Acquisition Playbook **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-02-agency-acquisition-playbook **Description:** The step-by-step process for signing the first 10, then 50, then 100 agencies. Covers channels, messaging by ICP, objection handling, contract process, and closing. The most operationally critical GTM document. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-02` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The step-by-step process for signing the first 10, then 50, then 100 agencies. Covers channels, messaging by ICP, objection handling, contract process, and closing. The most operationally critical GTM document. ## Source Materials - `BP-MKTRES-08` - `BP-COMP-01` - `BP-FIN-09` ## Feeds Into Business Plan Sections 8.3, 8.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Sales Team Structure & Compensation Plan **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-03-sales-team-structure **Description:** The 10-person sales team model: roles, hiring sequence, base/commission splits, quota structure, ramp expectations, and team management approach. The promote-from-within philosophy documented. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-03` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The 10-person sales team model: roles, hiring sequence, base/commission splits, quota structure, ramp expectations, and team management approach. The promote-from-within philosophy documented. ## Source Materials - `BP-OPS-02` - `BP-OPS-04` ## Feeds Into Business Plan Section 8.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Agency Onboarding Flow & Time-to-Value **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-04-agency-onboarding-flow **Description:** How an agency goes from sign-up to first business client live on the platform. Target time-to-value metric, step-by-step onboarding stages, and what happens at each checkpoint. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-04` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How an agency goes from sign-up to first business client live on the platform. Target time-to-value metric, step-by-step onboarding stages, and what happens at each checkpoint. ## Source Materials - `BP-PROD-02` - `BP-GTM-02` ## Feeds Into Business Plan Section 8.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Launch Marketing Plan **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-05-launch-marketing-plan **Description:** LinkedIn announcement campaign, PR strategy, content plan for the first 90 days, and the planned open-source component launch strategy. Includes budget allocation and success metrics. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-05` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers LinkedIn announcement campaign, PR strategy, content plan for the first 90 days, and the planned open-source component launch strategy. Includes budget allocation and success metrics. ## Source Materials - `knowledge-base/papers-and-research/aiConnected-influencer-cold-outreach-with-messaging.mdx` ## Feeds Into Business Plan Section 8.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Content & Thought Leadership Strategy **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-06-content-thought-leadership **Description:** How the Acquired Intelligence philosophy becomes a sustained content engine: blog, LinkedIn, podcast, YouTube, and the book as a long-horizon brand builder. Content calendar framework and channel ownership. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-06` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How the Acquired Intelligence philosophy becomes a sustained content engine: blog, LinkedIn, podcast, YouTube, and the book as a long-horizon brand builder. Content calendar framework and channel ownership. ## Source Materials - `BP-FOUND-03` ## Feeds Into Business Plan Section 8.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Developer Community & Ecosystem Strategy **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-07-developer-community-strategy **Description:** How the developer marketplace is seeded, governed, and grown. Trust pipeline design, sandbox environment, 20% revenue share structure, and community engagement approach. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-07` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How the developer marketplace is seeded, governed, and grown. Trust pipeline design, sandbox environment, 20% revenue share structure, and community engagement approach. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/engaging-the-dev-community.mdx` - `knowledge-base/aiconnected-supporting-docs/how-will-developers-use-the-ai-connected-platform.mdx` ## Feeds Into Business Plan Section 8.7 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Partner Channel & Integration Strategy **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-08-partner-channel-strategy **Description:** Technology partners, integration partners (CRMs, booking platforms, accounting software), and referral incentive programs. Specific integration targets identified from the ICP tool stack. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-08` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Technology partners, integration partners (CRMs, booking platforms, accounting software), and referral incentive programs. Specific integration targets identified from the ICP tool stack. ## Source Materials - `BP-MKTRES-08` - `BP-MKTRES-09` ## Feeds Into Business Plan Section 8.8 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## First 10 Agency Target List **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-09-first-10-agency-targets **Description:** Named list of specific target agencies for the initial launch. Each entry: agency name, contact information, vertical focus, current tool stack, rationale for fit, and outreach approach. The most concrete proof of go-to-market readiness. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-09` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Named list of specific target agencies for the initial launch. Each entry: agency name, contact information, vertical focus, current tool stack, rationale for fit, and outreach approach. The most concrete proof of go-to-market readiness. ## Source Materials - `BP-MKTRES-08` - `BP-GTM-02` ## Feeds Into Business Plan Section 8.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Enterprise Readiness & Progression Plan **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-10-enterprise-readiness **Description:** The four-phase customer journey from power users to enterprise. What changes at each stage, what features unlock enterprise adoption, and what the enterprise sales motion looks like. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-10` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers The four-phase customer journey from power users to enterprise. What changes at each stage, what features unlock enterprise adoption, and what the enterprise sales motion looks like. ## Source Materials - `knowledge-base/aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App.mdx` - `BP-TECH-04` ## Feeds Into Business Plan Section 8.9 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Churn Prevention & Customer Success Strategy **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market/bp-gtm-11-churn-prevention **Description:** Early warning metrics for at-risk agencies and business clients, intervention protocols, and the white-label CS team playbook. Defines what 'success' looks like for each customer type. ## Document Information | Field | Value | |---|---| | **Code** | `BP-GTM-11` | | **Category** | Go-to-Market | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Early warning metrics for at-risk agencies and business clients, intervention protocols, and the white-label CS team playbook. Defines what 'success' looks like for each customer type. ## Source Materials - `BP-FIN-11` ## Feeds Into Business Plan Section 8.10 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Go To Market **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/go-to-market **Description:** Documents in Go To Market. --- ## Investor Pitch Deck — Surface Version **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-01-pitch-deck-surface **Description:** The 'GoHighLevel for AI' story. 12–15 slides. Written for investors who understand agency software economics but may not deeply follow the AI space. Focuses on market size, revenue model, traction, and team. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-01` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The 'GoHighLevel for AI' story. 12–15 slides. Written for investors who understand agency software economics but may not deeply follow the AI space. Focuses on market size, revenue model, traction, and team. ## Source Materials - `BP-COMP-01` - `BP-MARKET-03` - `BP-FIN-10` - `BP-FOUND-02` ## Feeds Into Business Plan Section 12.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Executive Summary — Standalone 2-Page Version **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-02-executive-summary-standalone **Description:** The two-page standalone summary sent before a full pitch. Covers: problem, solution, market, model, team, traction, and ask. Self-contained — readable without any other document. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-02` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The two-page standalone summary sent before a full pitch. Covers: problem, solution, market, model, team, traction, and ask. Self-contained — readable without any other document. ## Source Materials - `All Phase 1–6 documents complete` ## Feeds Into Business Plan Executive Summary --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## GoHighLevel Growth Comparison Study **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-03-gohighlevel-comparison **Description:** Documents GoHighLevel's trajectory (bootstrapped 3 years, then $60M Series C at $82.7M ARR) and maps aiConnected's plan alongside it. Validates the model's credibility with a proven comparable. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-03` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Documents GoHighLevel's trajectory (bootstrapped 3 years, then $60M Series C at $82.7M ARR) and maps aiConnected's plan alongside it. Validates the model's credibility with a proven comparable. ## Source Materials - `BP-COMP-01` - `BP-FIN-10` ## Feeds Into Business Plan Sections 12.1, 12.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Seed Round Term Sheet Reference **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-04-term-sheet-reference **Description:** Reference guide to standard seed round terms (SAFE vs. priced round, valuation caps, pro-rata rights, information rights, board composition) so incoming offers can be evaluated quickly and confidently. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-04` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Reference guide to standard seed round terms (SAFE vs. priced round, valuation caps, pro-rata rights, information rights, board composition) so incoming offers can be evaluated quickly and confidently. ## Source Materials - `NOTE: Recommend review by startup-experienced legal counsel` ## Feeds Into Business Plan Section 12.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Series A Milestone Definition **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-05-series-a-milestones **Description:** The specific, measurable milestones that define Series A readiness: MRR target, agency count, retention rate, Neurigraph architecture operational status, and team composition. Committed to in the seed round materials. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-05` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers The specific, measurable milestones that define Series A readiness: MRR target, agency count, retention rate, Neurigraph architecture operational status, and team composition. Committed to in the seed round materials. ## Source Materials - `BP-FIN-10` - `BP-PROD-04` ## Feeds Into Business Plan Section 12.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Investor Pitch Deck — Deep Version **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-06-pitch-deck-deep **Description:** The cognitive infrastructure + robotics story. For sophisticated investors who will evaluate the long-term thesis. Covers the Neurigraph architecture, the data moat, the robotics platform vision, and why the agency business is the training ground. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-06` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The cognitive infrastructure + robotics story. For sophisticated investors who will evaluate the long-term thesis. Covers the Neurigraph architecture, the data moat, the robotics platform vision, and why the agency business is the training ground. ## Source Materials - `BP-FOUND-03` - `BP-FOUND-04` - `BP-PROD-05` - `BP-MARKET-07` - `All Phase 1–6 documents` ## Feeds Into Business Plan Section 12.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Investor Data Room Index **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor/bp-invest-07-data-room-index **Description:** The organized index of the investor due diligence data room. Every document named, categorized, and linked. Ensures nothing is missing when a serious investor begins diligence. Structured as: Legal, Financial, Product, IP, Team, Market, and Competitive folders. ## Document Information | Field | Value | |---|---| | **Code** | `BP-INVEST-07` | | **Category** | Investor Materials | | **Phase** | Phase 7 — Go-to-Market & Investor Materials | | **Priority** | High | | **Status** | Pending | ## What This Document Covers The organized index of the investor due diligence data room. Every document named, categorized, and linked. Ensures nothing is missing when a serious investor begins diligence. Structured as: Legal, Financial, Product, IP, Team, Market, and Competitive folders. ## Source Materials - `All supporting documents complete` ## Feeds Into Business Plan Appendix J --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Investor **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/investor **Description:** Documents in Investor. --- ## Entity & Corporate Records Summary **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/legal/bp-legal-01-entity-corporate-records **Description:** Summary document confirming all legal entity details: legal name, state of incorporation (Georgia), registered address, EIN, founding date, entity type, IP ownership confirmation, and corporate structure. Formatted for the business plan and the investor data room. ## Document Information | Field | Value | |---|---| | **Code** | `BP-LEGAL-01` | | **Category** | Legal | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Summary document confirming all legal entity details: legal name, state of incorporation (Georgia), registered address, EIN, founding date, entity type, IP ownership confirmation, and corporate structure. Formatted for the business plan and the investor data room. ## Source Materials - `NOTE: Requires confirmation from legal counsel or Secretary of State records` ## Feeds Into Business Plan Sections 1.2, 1.5, Cover Page --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Cap Table **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/legal/bp-legal-04-cap-table **Description:** Current ownership breakdown showing founder shares, any existing investor or advisor equity, and option pool. Pre-money cap table and post-money projection showing dilution from the seed round. Investors will not engage without this. ## Document Information | Field | Value | |---|---| | **Code** | `BP-LEGAL-04` | | **Category** | Legal | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Current ownership breakdown showing founder shares, any existing investor or advisor equity, and option pool. Pre-money cap table and post-money projection showing dilution from the seed round. Investors will not engage without this. ## Source Materials - `NOTE: Requires formal legal documentation — engage corporate counsel` ## Feeds Into Business Plan Sections 1.5, 12.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## NDA & Confidentiality Framework **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/legal/bp-legal-08-nda-confidentiality **Description:** Standard mutual NDA template for investor conversations, partner discussions, and developer onboarding. Includes a confidentiality notice for the business plan document itself. ## Document Information | Field | Value | |---|---| | **Code** | `BP-LEGAL-08` | | **Category** | Legal | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Standard mutual NDA template for investor conversations, partner discussions, and developer onboarding. Includes a confidentiality notice for the business plan document itself. ## Source Materials - `NOTE: Requires review by legal counsel` ## Feeds Into Business Plan Cover Page --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Legal **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/legal **Description:** Documents in Legal. --- ## AI SaaS Market Sizing Report **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-01-ai-saas-market-sizing **Description:** Total market size for AI-powered SaaS tools, growth rate, and projected 5-year trajectory. Establishes the macro market context. Requires external data from Gartner, Grand View Research, MarketsandMarkets, or similar sources. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-01` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Total market size for AI-powered SaaS tools, growth rate, and projected 5-year trajectory. Establishes the macro market context. Requires external data from Gartner, Grand View Research, MarketsandMarkets, or similar sources. ## Source Materials - `knowledge-base/aiconnected-apps-and-modules/5-year-ai-business-landscape.mdx` - `knowledge-base/papers-and-research/the-future-of-persistent-ai-in-business.mdx` ## Feeds Into Business Plan Section 5.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Agency Software & White-Label Platform Market Research **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-02-agency-software-market **Description:** Number of US agencies, current software spend, GoHighLevel's market share, and the gap in the AI-focused agency platform market. GoHighLevel's public metrics provide a strong anchor. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-02` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Number of US agencies, current software spend, GoHighLevel's market share, and the gap in the AI-focused agency platform market. GoHighLevel's public metrics provide a strong anchor. ## Source Materials - `knowledge-base/papers-and-research/enterprise-service-research.mdx` - `knowledge-base/papers-and-research/global-ai-marketplace-research-doc.mdx` ## Feeds Into Business Plan Section 5.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Total Addressable Market (TAM) Analysis **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-03-tam-analysis **Description:** Total Addressable Market calculation across all three revenue streams: agency platform, aiConnectedOS subscriptions, and Neurigraph licensing. Must show methodology, not just a number. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-03` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Total Addressable Market calculation across all three revenue streams: agency platform, aiConnectedOS subscriptions, and Neurigraph licensing. Must show methodology, not just a number. ## Source Materials - `BP-MARKET-01` - `BP-MARKET-02` - `BP-MARKET-06` - `BP-MARKET-07` - `BP-MARKET-08` ## Feeds Into Business Plan Section 5.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Serviceable Addressable Market (SAM) Calculation **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-04-sam-calculation **Description:** Serviceable Addressable Market — the portion of the TAM that aiConnected can realistically serve given its current product scope, geographic focus, and target customer profile. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-04` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Serviceable Addressable Market — the portion of the TAM that aiConnected can realistically serve given its current product scope, geographic focus, and target customer profile. ## Source Materials - `BP-MARKET-03` - `BP-MKTRES-08` - `BP-MKTRES-09` ## Feeds Into Business Plan Section 5.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Serviceable Obtainable Market (SOM) Projection — Years 1–3 **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-05-som-projection **Description:** Serviceable Obtainable Market — the portion of the SAM realistically reachable in the first three years given team size, capital, and go-to-market approach. Must align with the financial model. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-05` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Serviceable Obtainable Market — the portion of the SAM realistically reachable in the first three years given team size, capital, and go-to-market approach. Must align with the financial model. ## Source Materials - `BP-MARKET-04` - `BP-FIN-01` - `BP-FIN-02` ## Feeds Into Business Plan Section 5.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## AI Memory Architecture Market Sizing **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-06-ai-memory-market-sizing **Description:** Emerging market for persistent AI memory systems. Mem0's $24M raise, comparable funding rounds, and analyst projections for the memory infrastructure market. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-06` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Emerging market for persistent AI memory systems. Mem0's $24M raise, comparable funding rounds, and analyst projections for the memory infrastructure market. ## Source Materials - `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx` - `knowledge-base/neurigraph-memory-architecture/neurigraph-memory-systems-competitive-comparison.mdx` ## Feeds Into Business Plan Section 5.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Robotics Cognitive Infrastructure Market Research **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-07-robotics-cognitive-market **Description:** 10-year TAM for the intelligence and cognitive layer of the robotics market. Humanoid robot adoption projections, industrial robotics AI layer spending, and the gap in universal cognitive standards. Requires external research from Interact Analysis, IDTechEx, or equivalent. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-07` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | High | | **Status** | Pending | ## What This Document Covers 10-year TAM for the intelligence and cognitive layer of the robotics market. Humanoid robot adoption projections, industrial robotics AI layer spending, and the gap in universal cognitive standards. Requires external research from Interact Analysis, IDTechEx, or equivalent. ## Source Materials - `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx` ## Feeds Into Business Plan Sections 2.4, 3.19, 5.8, 14.1 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Voice AI Market Research **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-market-08-voice-ai-market **Description:** Market size and growth rate for AI-powered voice in business contexts — customer service, sales, and receptionist replacement. Validates the Voice AI Hub positioning. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MARKET-08` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Market size and growth rate for AI-powered voice in business contexts — customer service, sales, and receptionist replacement. Validates the Voice AI Hub positioning. ## Source Materials - `knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/` ## Feeds Into Business Plan Section 5.7 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Agency Customer Discovery Report **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-mktres-05-agency-customer-discovery **Description:** Structured summary of customer discovery conversations with 10–20 agency owners. Validates the core problem, tests the value proposition, and captures verbatim quotes for the business plan. Primary research — Bob must conduct these conversations. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MKTRES-05` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Structured summary of customer discovery conversations with 10–20 agency owners. Validates the core problem, tests the value proposition, and captures verbatim quotes for the business plan. Primary research — Bob must conduct these conversations. ## Source Materials - `BP-MKTRES-08 (ICP Profile — complete first to define interview targets)` ## Feeds Into Business Plan Sections 2.1, 2.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Business Client Pain Point Survey **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-mktres-06-business-client-pain-points **Description:** Survey findings from SMB owners on their current AI tool frustrations, spending, and openness to the aiConnected platform. Establishes the demand-side proof for the business case. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MKTRES-06` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Survey findings from SMB owners on their current AI tool frustrations, spending, and openness to the aiConnected platform. Establishes the demand-side proof for the business case. ## Source Materials - `BP-MKTRES-09 (ICP Profile — complete first)` ## Feeds Into Business Plan Section 2.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Ideal Customer Profile — Agency **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-mktres-08-agency-icp **Description:** Documented profile of the target agency: size, vertical focus, revenue range, current tool stack, decision-making process, and buying triggers. Used to focus all sales and marketing activity. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MKTRES-08` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Documented profile of the target agency: size, vertical focus, revenue range, current tool stack, decision-making process, and buying triggers. Used to focus all sales and marketing activity. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` - `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview.mdx` ## Feeds Into Business Plan Sections 2.1, 8.3, 8.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Ideal Customer Profile — Business Client **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research/bp-mktres-09-business-client-icp **Description:** Documented profile of the business clients agencies serve: industry verticals, employee count, current AI spend, specific pain points, and decision-making dynamics. ## Document Information | Field | Value | |---|---| | **Code** | `BP-MKTRES-09` | | **Category** | Market Research | | **Phase** | Phase 3 — Market Intelligence | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Documented profile of the business clients agencies serve: industry verticals, employee count, current AI spend, specific pain points, and decision-making dynamics. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview.mdx` ## Feeds Into Business Plan Sections 2.2, 5.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Market Research **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/market-research **Description:** Documents in Market Research. --- ## Current Organizational State **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-01-current-org-state **Description:** Documents exactly what the company looks like today: founder, any contractors (with roles and agreement status), current tools and infrastructure, active projects, and revenue status. The honest starting point investors compare against the plan. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-01` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Documents exactly what the company looks like today: founder, any contractors (with roles and agreement status), current tools and infrastructure, active projects, and revenue status. The honest starting point investors compare against the plan. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` ## Feeds Into Business Plan Section 10.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Organizational Chart (Current & 12-Month Projected) **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-02-org-chart **Description:** Two org charts in one document: current state and the post-seed-round 12-month structure. Every role labeled with hire timing aligned to BP-FIN-07 Use of Funds. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-02` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Two org charts in one document: current state and the post-seed-round 12-month structure. Every role labeled with hire timing aligned to BP-FIN-07 Use of Funds. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx (Part 2)` ## Feeds Into Business Plan Sections 10.2, 10.3, Appendix E --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Priority Job Descriptions — First 5 Hires **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-03-job-descriptions **Description:** Full job descriptions for the first five hires: Senior Full-Stack Lead, VP/Director of Sales, Marketing Director, and two additional roles confirmed through the financial model. Each includes title, responsibilities, required experience, and compensation range. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-03` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Full job descriptions for the first five hires: Senior Full-Stack Lead, VP/Director of Sales, Marketing Director, and two additional roles confirmed through the financial model. Each includes title, responsibilities, required experience, and compensation range. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx (Part 2)` - `BP-OPS-04` ## Feeds Into Business Plan Sections 10.3, Appendix E --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Compensation Philosophy & Ranges **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-04-compensation-philosophy **Description:** Salary bands, equity grant sizes by level, commission structure for sales roles, and the principles governing compensation decisions as the team scales. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-04` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Salary bands, equity grant sizes by level, commission structure for sales roles, and the principles governing compensation decisions as the team scales. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx (Part 2)` ## Feeds Into Business Plan Section 10.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Advisory Board Structure & Recruitment Plan **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-05-advisory-board **Description:** Current and target advisory board composition. Credentials needed (technical, industry, investor network). Equity and time commitment structure for advisors. Named targets for recruitment where possible. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-05` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Current and target advisory board composition. Credentials needed (technical, industry, investor network). Equity and time commitment structure for advisors. Named targets for recruitment where possible. ## Source Materials - *(No existing source material — original research or writing required)* ## Feeds Into Business Plan Section 10.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Operational Infrastructure Inventory **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-06-operational-infrastructure **Description:** Every tool and subscription currently in use with monthly cost and contract status: DigitalOcean, Supabase, LiveKit, OpenRouter, n8n, Stripe, GitHub, GoHighLevel (headless), WordPress stack, and all others. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-06` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Every tool and subscription currently in use with monthly cost and contract status: DigitalOcean, Supabase, LiveKit, OpenRouter, n8n, Stripe, GitHub, GoHighLevel (headless), WordPress stack, and all others. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` ## Feeds Into Business Plan Section 10.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Development Workflow & QA Process **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations/bp-ops-07-development-workflow **Description:** How code is written, reviewed, tested, and deployed. Version control discipline (GitHub), CI/CD pipeline, staging environment, and quality assurance process. Investors evaluate this for execution credibility. ## Document Information | Field | Value | |---|---| | **Code** | `BP-OPS-07` | | **Category** | Operations | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How code is written, reviewed, tested, and deployed. Version control discipline (GitHub), CI/CD pipeline, staging environment, and quality assurance process. Investors evaluate this for execution credibility. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-project-memory-backup.mdx` ## Feeds Into Business Plan Section 10.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Operations **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/operations **Description:** Documents in Operations. --- ## Master Product Architecture Overview **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-01-master-product-architecture **Description:** A single document — with one clear diagram — showing how all three platform layers (Business Platform, aiConnectedOS, Neurigraph) relate to each other, share data and infrastructure, and how the developer ecosystem extends all three. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-01` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A single document — with one clear diagram — showing how all three platform layers (Business Platform, aiConnectedOS, Neurigraph) relate to each other, share data and infrastructure, and how the developer ecosystem extends all three. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview.mdx` - `knowledge-base/aiconnected-os/quick-system-overview.mdx` - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` ## Feeds Into Business Plan Sections 3.1, 4.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Business Platform Executive Summary **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-02-business-platform-summary **Description:** A 2-page condensed overview of the Business Platform written for a non-technical investor audience. Distilled from the MVP specification and platform overview documents. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-02` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A 2-page condensed overview of the Business Platform written for a non-technical investor audience. Distilled from the MVP specification and platform overview documents. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-overview-non-technical.mdx` - `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx` ## Feeds Into Business Plan Section 3.2 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## aiConnectedOS Executive Summary **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-03-aiconnectedos-summary **Description:** A 2-page condensed overview of aiConnectedOS written for a non-technical investor audience. Distilled from the OS PRD and quick system overview. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-03` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A 2-page condensed overview of aiConnectedOS written for a non-technical investor audience. Distilled from the OS PRD and quick system overview. ## Source Materials - `knowledge-base/aiconnected-os/quick-system-overview.mdx` - `knowledge-base/aiconnected-os/system-standards-and-philosophy.mdx` ## Feeds Into Business Plan Section 3.8 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Consolidated 18-Month Product Roadmap **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-04-product-roadmap **Description:** A single integrated roadmap across all three platform layers with milestones, dependencies, resource requirements, and the 4-product launch sequence (Knowledge → Chat → Voice → Brain). The 18-week OS build plan and 6-week Voice deadline are reconciled and integrated. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-04` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A single integrated roadmap across all three platform layers with milestones, dependencies, resource requirements, and the 4-product launch sequence (Knowledge → Chat → Voice → Brain). The 18-week OS build plan and 6-week Voice deadline are reconciled and integrated. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-build-plan.mdx` - `knowledge-base/aiconnected-os/aiconnected-os-prd.mdx` ## Feeds Into Business Plan Sections 3.12, 8.2, 9.9 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Neurigraph Technical Summary (Non-Technical Version) **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-05-neurigraph-nontechnical-summary **Description:** A 2–3 page explanation of the Neurigraph memory architecture written specifically for non-technical investors. No code, no database schemas. Uses plain-language analogies throughout. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-05` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A 2–3 page explanation of the Neurigraph memory architecture written specifically for non-technical investors. No code, no database schemas. Uses plain-language analogies throughout. ## Source Materials - `knowledge-base/neurigraph-memory-architecture/neurigraph-licensing.mdx` - `knowledge-base/neurigraph-memory-architecture/object-deconstruction-graph-overview.mdx` - `knowledge-base/neurigraph-memory-architecture/amygdala-dynamic-heat-threshold-control.mdx` ## Feeds Into Business Plan Sections 3.14–3.17, Appendix C --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Product Status Matrix **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-06-product-status-matrix **Description:** A single reference table covering every product, module, and feature: current build stage, estimated completion date, revenue readiness date, development resource required, and dependencies. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-06` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers A single reference table covering every product, module, and feature: current build stage, estimated completion date, revenue readiness date, development resource required, and dependencies. ## Source Materials - `knowledge-base/aiconnected-supporting-docs/aiConnected-fundraising-strategy.mdx` - `knowledge-base/aiconnected-business-platform/production-readiness-checklist.mdx` ## Feeds Into Business Plan Section 4.1, Appendix A --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Engine Module Revenue Analysis **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-07-engine-module-revenue-analysis **Description:** A structured analysis of the 30+ engine modules: Tier 1 launch priority vs. Tier 2 post-launch expansion, pricing per module, estimated adoption rates, and projected revenue contribution by year. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-07` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | High | | **Status** | Pending | ## What This Document Covers A structured analysis of the 30+ engine modules: Tier 1 launch priority vs. Tier 2 post-launch expansion, pricing per module, estimated adoption rates, and projected revenue contribution by year. ## Source Materials - `knowledge-base/aiconnected-apps-and-modules/original-aiConnected-engines.mdx` ## Feeds Into Business Plan Section 4.2, Appendix B --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Platform Glossary **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product/bp-prod-08-platform-glossary **Description:** Comprehensive glossary of all platform-specific terminology: Cipher, Neurigraph, Instance, Persona, Skill Slot, Apprenticeship, Mod, CogniGraph, ODG, ANI, and every other term a reader will encounter in the business plan. ## Document Information | Field | Value | |---|---| | **Code** | `BP-PROD-08` | | **Category** | Product | | **Phase** | Phase 2 — Product Foundation | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Comprehensive glossary of all platform-specific terminology: Cipher, Neurigraph, Instance, Persona, Skill Slot, Apprenticeship, Mod, CogniGraph, ODG, ANI, and every other term a reader will encounter in the business plan. ## Source Materials - `knowledge-base/aiconnected-os/quick-system-overview.mdx (Glossary section)` ## Feeds Into Business Plan Appendix I --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Product **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/product **Description:** Documents in Product. --- ## Technology Stack Overview **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/technology/bp-tech-01-technology-stack **Description:** The complete technology stack with rationale for each choice: Next.js 14, Turborepo, shadcn/ui, TweakCN, Supabase, LiveKit, OpenRouter, Dokploy, DigitalOcean, Stripe, n8n. Written for technical due diligence. ## Document Information | Field | Value | |---|---| | **Code** | `BP-TECH-01` | | **Category** | Technology | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers The complete technology stack with rationale for each choice: Next.js 14, Turborepo, shadcn/ui, TweakCN, Supabase, LiveKit, OpenRouter, Dokploy, DigitalOcean, Stripe, n8n. Written for technical due diligence. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx (Section 7)` ## Feeds Into Business Plan Sections 9.1, 9.4, 9.5, Appendix H --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Infrastructure Architecture Document **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/technology/bp-tech-02-infrastructure-architecture **Description:** How the platform is hosted and scaled. Container orchestration via Dokploy, database architecture (Supabase PostgreSQL), module isolation, load balancing, failover, and backup strategy. ## Document Information | Field | Value | |---|---| | **Code** | `BP-TECH-02` | | **Category** | Technology | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How the platform is hosted and scaled. Container orchestration via Dokploy, database architecture (Supabase PostgreSQL), module isolation, load balancing, failover, and backup strategy. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx (Section 4.5)` ## Feeds Into Business Plan Section 9.3 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Security Architecture Document **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/technology/bp-tech-03-security-architecture **Description:** Tenant data isolation, memory encryption, containerized module security boundaries, API gateway authentication enforcement, and the audit event stream. Required before any enterprise customer conversation. ## Document Information | Field | Value | |---|---| | **Code** | `BP-TECH-03` | | **Category** | Technology | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers Tenant data isolation, memory encryption, containerized module security boundaries, API gateway authentication enforcement, and the audit event stream. Required before any enterprise customer conversation. ## Source Materials - `knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification.mdx (Section 4.5)` ## Feeds Into Business Plan Section 9.6 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Enterprise Readiness Architecture Checklist **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/technology/bp-tech-04-enterprise-readiness-checklist **Description:** What is already enterprise-ready (multi-tenancy, container isolation, event audit stream), what will be added in Phase 3–4 of the GTM (SSO, RBAC, SOC 2), and what requires enterprise-specific configuration. ## Document Information | Field | Value | |---|---| | **Code** | `BP-TECH-04` | | **Category** | Technology | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers What is already enterprise-ready (multi-tenancy, container isolation, event audit stream), what will be added in Phase 3–4 of the GTM (SSO, RBAC, SOC 2), and what requires enterprise-specific configuration. ## Source Materials - `knowledge-base/aiConnectedOS/16.-aiConnected-OS-Enterprise-Potential-of-App.mdx` ## Feeds Into Business Plan Section 9.7 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Technology **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/technology **Description:** Documents in Technology. --- ## aiConnected Software Ecosystem — Business Plan **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/the-business-plan/aiconnected-business-plan **Description:** The complete, comprehensive corporate business plan for aiConnected, Inc. Written last, after all 86 supporting documents are complete. Synthesizes the founding story, ecosystem overview, market opportunity, competitive position, business model, go-to-market strategy, team, financial projections, funding strategy, risk analysis, and the 10-year vision into a single investor-ready document (target: 40–60 pages). ## Document Information | Field | Value | |---|---| | **Code** | `FINAL` | | **Category** | The Business Plan | | **Phase** | Final — The Business Plan | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers The complete, comprehensive corporate business plan for aiConnected, Inc. Written last, after all 86 supporting documents are complete. Synthesizes the founding story, ecosystem overview, market opportunity, competitive position, business model, go-to-market strategy, team, financial projections, funding strategy, risk analysis, and the 10-year vision into a single investor-ready document (target: 40–60 pages). ## Source Materials - `All 86 supporting documents` ## Feeds Into Investor conversations, partner discussions, board meetings --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## The Business Plan **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/the-business-plan **Description:** Documents in The Business Plan. --- ## Risk Register (Full) **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk/bp-risk-01-risk-register **Description:** Every identified risk catalogued with probability rating, impact rating, and mitigation strategy: technical execution risk, market adoption risk, competitive response risk, regulatory risk, financial risk, and solo founder execution risk. ## Document Information | Field | Value | |---|---| | **Code** | `BP-RISK-01` | | **Category** | Risk & Compliance | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Every identified risk catalogued with probability rating, impact rating, and mitigation strategy: technical execution risk, market adoption risk, competitive response risk, regulatory risk, financial risk, and solo founder execution risk. ## Source Materials - `BP-COMP-07` - `BP-FIN-17` - `BP-TECH-03` ## Feeds Into Business Plan Sections 13.1–13.5 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## GDPR Compliance Assessment **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk/bp-risk-02-gdpr-compliance **Description:** Assessment of GDPR compliance requirements given the Neurigraph memory architecture's deep personal data collection. Covers lawful basis for processing, data subject rights, retention policies, DPA requirements, and cross-border transfer rules. ## Document Information | Field | Value | |---|---| | **Code** | `BP-RISK-02` | | **Category** | Risk & Compliance | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Assessment of GDPR compliance requirements given the Neurigraph memory architecture's deep personal data collection. Covers lawful basis for processing, data subject rights, retention policies, DPA requirements, and cross-border transfer rules. ## Source Materials - `NOTE: Recommend legal counsel review — particularly for memory architecture data collection` ## Feeds Into Business Plan Section 13.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## CCPA Compliance Assessment **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk/bp-risk-03-ccpa-compliance **Description:** Assessment of California Consumer Privacy Act requirements for the platform. Covers consumer rights, opt-out mechanisms, data sale definitions, and the specific implications of persistent memory data under California law. ## Document Information | Field | Value | |---|---| | **Code** | `BP-RISK-03` | | **Category** | Risk & Compliance | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | Critical | | **Status** | Pending | ## What This Document Covers Assessment of California Consumer Privacy Act requirements for the platform. Covers consumer rights, opt-out mechanisms, data sale definitions, and the specific implications of persistent memory data under California law. ## Source Materials - `NOTE: Recommend legal counsel review` ## Feeds Into Business Plan Section 13.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## AI Regulatory Risk Assessment **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk/bp-risk-04-ai-regulatory-risk **Description:** How the EU AI Act, emerging US AI regulations, and sector-specific rules (healthcare, legal, financial services) may affect how the platform can be used, marketed, and sold. Identifies high-risk AI system classification questions. ## Document Information | Field | Value | |---|---| | **Code** | `BP-RISK-04` | | **Category** | Risk & Compliance | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How the EU AI Act, emerging US AI regulations, and sector-specific rules (healthcare, legal, financial services) may affect how the platform can be used, marketed, and sold. Identifies high-risk AI system classification questions. ## Source Materials - *(No existing source material — original research or writing required)* ## Feeds Into Business Plan Section 13.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Robotics Regulatory Landscape **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk/bp-risk-05-robotics-regulatory-landscape **Description:** How existing drone, autonomous vehicle, medical device, and emerging humanoid robot regulations may apply to the aiConnected Robotics Platform. Referenced in the robotics platform docs as needing formal writeup. ## Document Information | Field | Value | |---|---| | **Code** | `BP-RISK-05` | | **Category** | Risk & Compliance | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers How existing drone, autonomous vehicle, medical device, and emerging humanoid robot regulations may apply to the aiConnected Robotics Platform. Referenced in the robotics platform docs as needing formal writeup. ## Source Materials - `knowledge-base/aiconnected-os/aiconnected-os-robotics-platform.mdx (Section 10)` ## Feeds Into Business Plan Section 13.4 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Vendor & Dependency Risk Assessment **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk/bp-risk-08-vendor-dependency-risk **Description:** What happens if Supabase, LiveKit, or OpenRouter have outages or pricing changes? Mitigation strategies for all critical external dependencies. Includes business continuity planning and fallback options for each vendor. ## Document Information | Field | Value | |---|---| | **Code** | `BP-RISK-08` | | **Category** | Risk & Compliance | | **Phase** | Phase 6 — Operations, Technology & Risk | | **Priority** | High | | **Status** | Pending | ## What This Document Covers What happens if Supabase, LiveKit, or OpenRouter have outages or pricing changes? Mitigation strategies for all critical external dependencies. Includes business continuity planning and fallback options for each vendor. ## Source Materials - `BP-OPS-06` - `BP-TECH-01` ## Feeds Into Business Plan Section 10.7 --- *Content will be written as part of the aiConnected business plan production process. See the [Writing Sequence](/docs/business-planning/writing-sequence) for context on when this document is produced.* --- ## Risk **URL:** https://secure-docs.aiconnected.ai/docs/business-planning/risk **Description:** Documents in Risk. --- ## Conversation tools **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/os/conversation-tools **Description:** Contracts for navigation, cleanup, split-and-route, context windows, tutorials, and other interaction-layer aiConnectedOS features. ## Contract status `Derived implementation contract` These features are documented as product and UX specs rather than fixed route contracts, but they define clear service responsibilities for the conversation layer. ## Source documents - [cognition console UI design](/docs/knowledge-base/aiconnected-os/aiconnected-os-8-cognition-console-ui-design) - [collaborative personas planning](/docs/knowledge-base/aiconnected-os/aiconnected-os-9-collaborative-personas-planning) - [computer use for personas](/docs/knowledge-base/aiconnected-os/aiconnected-os-10-computer-use-for-personas) - [chat cleanup system](/docs/knowledge-base/aiconnected-os/aiconnected-os-11-chat-cleanup-system) - [persona skill slots](/docs/knowledge-base/aiconnected-os/aiconnected-os-12-persona-skill-slots) - [adaptive UI tutorials](/docs/knowledge-base/aiconnected-os/aiconnected-os-13-adaptive-ui-tutorials) - [in-chat navigation](/docs/knowledge-base/aiconnected-os/aiconnected-os-17-in-chat-navigation) - [context windows in AI](/docs/knowledge-base/aiconnected-os/aiconnected-os-18-context-windows-in-ai) - [fluid UI architecture](/docs/knowledge-base/aiconnected-os/aiconnected-os-19-fluid-ui-architecture) - [conversation split and route](/docs/knowledge-base/aiconnected-os/aiconnected-os-conversation-split-and-route) - [forget this feature](/docs/knowledge-base/aiconnected-os/aiconnected-os-forget-this-feature) - [persona meeting mode](/docs/knowledge-base/aiconnected-os/aiconnected-os-persona-meeting-mode) ## Core interaction services ### Cognition console Required capabilities: - Display multi-layer reasoning or context traces - Surface persona state and tool activity - Support safe operator oversight ### Collaborative personas Required capabilities: - Run multi-persona collaboration on the same thread or task - Preserve authorship and persona boundaries - Support explicit invocation and shared outcome capture ### Computer use Required capabilities: - Represent browser or computer-use actions as governed tool calls - Log actions for audit and replay - Route approvals when actions exceed safe automation limits ### Chat cleanup Required capabilities: - Collapse or archive stale context - Preserve important references and pinned material - Improve future retrieval without losing meaningful history ### Skill slots Required capabilities: - Attach skill bundles to personas - Expose available skills by role or context - Control enablement per persona and instance ### Adaptive tutorials Required capabilities: - Detect user context and show relevant learning flows - Track progress and dismiss state ### In-chat navigation Required capabilities: - Jump to prior topics, references, and workspace objects from the chat surface - Preserve conversation map metadata ### Context windows Required capabilities: - Rank and inject only the most relevant memory and workspace context - Respect token budgets and persona priorities ### Split and route Required capabilities: - Detect when a conversation should branch into separate threads or tasks - Route the branch to the right persona, module, or workspace destination ### Forget Required capabilities: - Remove or suppress selected memories or references in a governed way - Keep audit and policy metadata around deletion or redaction decisions ### Meeting mode Required capabilities: - Support multi-party, persona-aware operating mode - Persist meeting artifacts, decisions, and action items ## Shared design requirements - Everything must be auditable. - Persona authorship must remain visible. - High-risk actions need explicit governance. - Cleanup and forgetting must never silently destroy critical records. - Context injection should be deliberate and ranked, not indiscriminate. ## Implementation note These features form the OS interaction layer. Treat them as orchestration services on top of personas, memory, and workspace objects rather than as isolated one-off UI features. --- ## OS **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/os **Description:** Documents in OS. --- ## Memory and context **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/os/memory-context **Description:** Neurigraph-backed memory contracts for session, user, tenant, and module-aware recall. ## Contract status `Canonical interface contract` The OS and platform docs are explicit about memory categories, retrieval behavior, and shared access expectations. The storage implementation is intentionally swappable. ## Source documents - [aiConnectedOS developer documentation](/docs/knowledge-base/aiconnected-os/aiconnected-os-developer-documentation) - [aiConnected Platform foundation PRD](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd) - [Neurigraph tool references](/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/01-MCP-Knowledge-Graph-Memory-Server) ## Memory classes The OS documentation defines four persistent memory classes: | Memory class | Meaning | |---|---| | Episodic | What happened, and when | | Semantic | What the system learned about a domain | | Somatic | Learned behavioral patterns and preferences | | Emotional | Relationship history, mood, and preference evolution | ## Foundation memory scope The platform foundation PRD narrows the first build to: - Session memory - User memory - Tenant memory - Module memory This means the first production service should be a stable shared data layer and API contract, even before the full Neurigraph reasoning layer is complete. ## Required memory resources | Resource | Purpose | |---|---| | `memory_entries` | Typed units of stored memory | | `memory_indexes` | Retrieval and search structures | | `memory_links` | Association edges across topics, contacts, and artifacts | | `recall_files` | Archived conversation bundles and artifacts | | `memory_preferences` | Durable user or tenant preferences | | `memory_artifacts` | Linked files, outputs, and supporting material | ## Required memory operations - Write a memory entry with type and scope - Search memories by semantic, temporal, and scoped filters - Retrieve a recall bundle for a persona or thread - Link new facts or artifacts to existing memories - Mark memory relevance, retention, or archive status - Expose module-safe reads and writes through a common service ## Required scoping dimensions Every memory write must carry enough context to prevent leakage: - `persona_id` - `instance_id` - `workspace_id` - `memory_type` - `source_module` - `actor` - `created_at` ## Retrieval expectations The OS docs describe a retrieval flow that mixes: - Temporal indexing - Semantic similarity - Pattern matching - Emotional relevance Even if the first release simplifies ranking, the API contract should leave room for all four inputs. ## Shared module behavior The foundation PRD requires module memory to be shared. That means: - Voice can write call outcomes into shared memory. - Chat can read the same business context and prior conversation cues. - Knowledge can publish structured updates that influence later interactions. - Persona behavior can adapt based on long-lived preference signals. ## Recommended read contract ```json { "query": "recent legal intake preferences", "workspace_id": "ws_123", "instance_id": "inst_456", "persona_id": "prs_789", "filters": { "memory_types": ["episodic", "semantic"], "source_modules": ["logiclegal", "voice"], "lookback_days": 90 } } ``` ## Implementation note Do not hide memory behind one opaque transcript blob. The repo repeatedly calls for structured, typed, queryable memory. --- ## Personas and instances **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/os/personas-instances **Description:** Persistent AI persona lifecycle, workspace scoping, and instance-level operating rules in aiConnectedOS. ## Contract status `Canonical interface contract` The aiConnectedOS docs define personas, instances, roles, and lifecycle behavior in enough detail to serve as a service contract, even though final HTTP paths are not fixed in the repo. ## Source documents - [aiConnectedOS developer documentation](/docs/knowledge-base/aiconnected-os/aiconnected-os-developer-documentation) - [quick system overview](/docs/knowledge-base/aiconnected-os/quick-system-overview) - [aiConnected OS master planning document](/docs/knowledge-base/aiconnected-os/ai-connected-os-master-planning-document) ## Core concepts ### Persona A persona is a long-lived AI entity with: - Stable identity - Role and responsibility assignment - Persistent memory - Emotional and behavioral state - Configurable communication style - Integration access inside an instance ### Instance An instance is the bounded workspace or business context that contains: - One or more personas - Its own data and integrations - Its own workflows and access boundaries - Shared contextual memory scoped to that environment ## Required persona resources | Resource | Purpose | |---|---| | `personas` | Identity, role, tone, model, and lifecycle state | | `persona_profiles` | Display identity, wake settings, voice, and public metadata | | `persona_assignments` | Persona-to-instance responsibility mapping | | `persona_states` | Mood, mode, activation, and runtime state | | `instances` | Workspace containers for data, permissions, and integrations | | `instance_integrations` | Connected services available to personas in an instance | ## Required persona operations The OS docs require operations for: - Creating personas - Updating identity, role, and voice settings - Assigning personas to instances - Switching active persona context - Loading a persona's persistent state before interaction - Exporting and importing persona definitions where appropriate ## Persona profile fields The developer docs and related feature docs imply these fields: - `id` - `name` - `description` - `role` - `communication_style` - `wakeword` - `tts_voice` - `preferred_llm` - `default_tools` - `schedule` - `avatar` - `status` ## Instance rules - Instances are isolation boundaries. - Persona access is explicit, not global. - Data access must follow instance permissions. - Personas can collaborate inside an instance, but only through governed orchestration. - Instance context should flow into memory retrieval and downstream integrations. ## Multi-persona collaboration The OS docs describe collaborative personas and meeting-like coordination. That implies APIs or service operations for: - Enumerating personas in an instance - Declaring which persona is primary for a task - Invoking supporting personas for a shared context - Preserving persona-specific authorship in messages, tasks, and memory writes ## Recommended service response ```json { "persona_id": "prs_001", "instance_id": "inst_001", "name": "Orion", "role": "operator", "wakeword": "Hey Orion", "tts_voice": "com.apple.voice.Alex", "preferred_llm": { "provider": "openai", "model": "gpt-4o" }, "status": "active" } ``` ## Implementation note The repo consistently treats personas as persistent team members, not disposable assistants. Model your contracts around lifecycle and continuity, not single-request chat sessions. --- ## Workspace features **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/os/workspace-features **Description:** Feature-level service contracts for spaces, tasks, documents, folders, references, and other aiConnectedOS workspace tools. ## Contract status `Derived implementation contract` The feature-spec docs define behavior, user flows, and object boundaries clearly, but most do not lock final route paths. This page consolidates the feature contracts developers need to implement. ## Source documents - [spaces dashboard design](/docs/knowledge-base/aiconnected-os/aiconnected-os-1-spaces-dashboard-design) - [task feature spec](/docs/knowledge-base/aiconnected-os/aiconnected-os-2-task-feature-spec) - [live document feature](/docs/knowledge-base/aiconnected-os/aiconnected-os-3-live-document-feature) - [dashboard whiteboard integration](/docs/knowledge-base/aiconnected-os/aiconnected-os-4-dashboard-whiteboard-integration) - [folder system design](/docs/knowledge-base/aiconnected-os/aiconnected-os-4-folder-system-design) - [conversation reference feature](/docs/knowledge-base/aiconnected-os/aiconnected-os-6-conversation-reference-feature) - [pin message feature](/docs/knowledge-base/aiconnected-os/aiconnected-os-7-pin-message-feature) - [document and organize ideas](/docs/knowledge-base/aiconnected-os/aiconnected-os-15-document-and-organize-ideas) - [import and migration](/docs/knowledge-base/aiconnected-os/aiconnected-os-import-and-migration) ## Feature families ### Spaces and dashboards Required capabilities: - Create and list spaces - Assign personas and resources to a space - Render configurable dashboards per space - Persist dashboard widgets and layout preferences ### Tasks Required capabilities: - Create, update, assign, and prioritize tasks - Associate tasks with conversations, documents, and personas - Track state changes and reminders - Preserve task history and authorship ### Live documents Required capabilities: - Create persistent documents - Support AI-assisted drafting and editing - Track collaborators and revision history - Link documents to chats, tasks, and folders ### Whiteboards Required capabilities: - Create whiteboards inside dashboard contexts - Link whiteboards to spaces and documents - Persist board metadata and access control ### Folder system Required capabilities: - Create nested folders - Store documents, chats, tasks, and artifacts inside folder structures - Preserve consistent navigation across spaces ### References and pins Required capabilities: - Save conversation references for later recall - Pin important messages or artifacts - Retrieve those anchors quickly during future sessions ### Idea organization Required capabilities: - Capture loose ideas - Group ideas into structured collections - Convert ideas into tasks, docs, or project objects ### Import and migration Required capabilities: - Import external conversations or workspace content into an archive - Deduplicate by content hash - Keep imported material separate from active work unless promoted ## Shared resource model These feature docs point to a reusable workspace object model: | Resource | Examples | |---|---| | `spaces` | Team, client, or project containers | | `tasks` | Action items and task status history | | `documents` | Live editable records | | `whiteboards` | Visual collaboration surfaces | | `folders` | Navigable organization tree | | `references` | Pointers to useful prior context | | `pins` | High-importance anchors | | `imports` | Archived migrated content | ## Cross-feature requirements - Every object must preserve authorship and workspace scope. - Every object should be linkable to chats and memory. - Objects must remain retrievable through persona context and navigation tools. - Imported content must be isolated by default. ## Build order suggestion 1. Spaces and folders 2. Tasks and documents 3. References and pins 4. Whiteboards and ideation workflows 5. Import and migration That order gives the OS a usable backbone before advanced collaboration features land. --- ## Auth and tenancy **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/platform/auth-tenancy **Description:** Multi-tenant identity, roles, inheritance rules, and the shell-level management surface. ## Contract status `Derived implementation contract` The repo defines user layers, inheritance rules, and shell responsibilities clearly. It does not publish a final route-by-route auth spec for the rebuilt v2 shell, so this page documents the required management surface and invariants. ## Source documents - [aiConnected Platform foundation PRD](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd) - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [legacy platform redesign spec](/docs/knowledge-base/aiconnected-business-platform/legacy-platform-redesign-spec) - [aiConnectedOS developer documentation](/docs/knowledge-base/aiconnected-os/aiconnected-os-developer-documentation) ## Tenant model The platform must support this hierarchy: ```text Super Admin └── Agency ├── Business │ └── End user (inside modules) └── Developer Personal ``` Personal accounts are isolated and do not inherit from agency or business structures. ## Role model The foundation PRD defines 13 permission types: | Layer | Roles | |---|---| | Super | Admin, Manager, User | | Agency | Admin, Manager, User | | Business | Admin, Manager, User | | Developer | Admin, Manager, User | | Personal | Single private user | ## Inheritance rules - Super admins can impersonate lower layers for support and testing. - Agencies can configure what business admins are allowed to do. - Businesses can delegate within the limits agencies set. - No layer can grant permissions above its own level. - Personal workspaces must remain fully isolated. ## Required auth resources | Resource | Purpose | |---|---| | `users` | Platform identities | | `workspaces` | Tenant records for agency, business, developer, or personal scopes | | `workspace_memberships` | User-to-workspace role assignments | | `roles` | Stable named permission bundles | | `permissions` | Fine-grained actions enforced at API time | | `impersonation_sessions` | Logged, time-limited support sessions | | `api_keys` | Service or module authentication where user sessions are not present | ## Required management operations The shell must expose operations for: - Creating agencies, businesses, and developer workspaces - Inviting and removing users - Assigning and revoking roles - Retrieving effective permissions - Starting and ending impersonation sessions - Issuing, rotating, and revoking API keys - Auditing role grants, changes, and impersonation activity ## Legacy route signals The older redesign spec references these shell endpoints: | Route | Purpose in legacy docs | |---|---| | `/api/agencies` | Agency CRUD | | `/api/businesses` | Business CRUD | | `/api/users` | User management | | `/api/settings` | Tenant-scoped settings | Treat these as route ancestry for the v2 shell, not as the final locked path contract. ## Security requirements - Enforce permission checks at the API layer, not only in UI components. - Log who granted what permissions and when. - Limit impersonation by time and audit trail. - Prevent any cross-tenant access through account and workspace scoping. - Propagate tenant context to downstream modules through the gateway. ## Recommended response context Every authenticated shell or gateway response should carry enough context for module-safe execution: ```json { "user_id": "usr_123", "workspace_id": "ws_123", "workspace_type": "business", "role": "business_admin", "permissions": ["contacts.read", "contacts.write", "events.read"], "impersonating": false } ``` ## Build notes If you need to choose between convenience and tenant isolation, pick isolation. That is consistent across every platform architecture doc in the repository. --- ## Platform **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/platform **Description:** Documents in Platform. --- ## Modules and events **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/platform/modules-events **Description:** Manifest-first registration, capability contracts, event topics, and gateway behavior. ## Contract status `Canonical interface contract` The source docs are explicit that module manifests, capability declarations, and the event bus are foundational and non-optional. ## Source documents - [aiConnected Platform foundation PRD](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd) - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [aiConnected Platform v2 port map](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-port-map) ## Module manifest contract Every module must declare itself through a manifest. The MVP spec gives this example shape: ```json { "id": "voice-hub", "name": "Voice AI Hub", "version": "1.0.0", "developer": "aiConnected", "routes": ["/voice", "/voice/calls", "/voice/settings"], "permissions": ["contacts.read", "contacts.write", "events.emit"], "capabilities": { "inputs": ["contact_id", "script", "voice_profile_id"], "outputs": ["call_record", "transcript", "call_status"], "events_emitted": ["voice.call.started", "voice.call.completed", "voice.call.failed"], "events_consumed": ["contact.updated", "kb.updated"] }, "data_schemas": ["voice_calls", "voice_profiles", "transcripts"] } ``` The v2 port map extends the manifest with top-level `events_emitted` and `events_consumed` arrays if your package model separates them from `capabilities`. ## What the shell does with a manifest When a valid manifest is registered, the shell is expected to: - Validate the contract - Register routes - Add navigation metadata - Enforce declared permissions - Subscribe the module to relevant events - Publish the module's capabilities to the registry - Connect the manifest to its isolated runtime target ## Capability registry expectations The capability registry is the searchable index of reusable building blocks. At minimum it should support: - Module identity - Inputs - Outputs - Events emitted - Events consumed - Permissions - Runtime target - Status - Version - Installability by workspace The layout manager and AI module creator depend on this registry for reuse checks and builder automation. ## Event bus rules Events are the only approved cross-module interconnection mechanism besides declared gateway interfaces. Required properties: | Field | Purpose | |---|---| | `event_name` | Stable topic name such as `voice.call.completed` | | `module_key` | Producing module | | `workspace_id` | Tenant scope | | `payload` | Event data | | `occurred_at` | Ordering and audit | | `correlation_id` | Cross-service traceability | | `contact_id` | Shared lead context when applicable | ## Known event patterns from source docs | Topic | Source context | |---|---| | `chat.session.started` | Chat manifest example in the v2 port map | | `chat.message.sent` | Chat manifest example in the v2 port map | | `chat.lead.captured` | Chat manifest example in the v2 port map | | `chat.lead.warmed` | Chat manifest example in the v2 port map | | `chat.session.ended` | Chat manifest example in the v2 port map | | `kb.published` | Chat consumes this in the v2 port map | | `voice.call.completed` | Chat and shared shell logic consume this in platform docs | | `contact.updated` | Shared entity update event mentioned across platform docs | ## Gateway behavior The API gateway must: - Authenticate the caller - Resolve workspace and permission context - Forward the request to the correct module runtime - Enforce rate limits - Preserve audit and correlation metadata - Prevent direct module-to-module trust bypass The gateway must route dynamically. It cannot rely on hardcoded knowledge of only first-party modules. ## Import and extension model The foundation PRD describes a longer-term import system for GitHub repos, WordPress plugins, and n8n workflows. The API implications are: - Imported apps still end in a validated manifest - Original code is preserved - Normalized artifacts are separate - New unsupported behavior creates new SDK entries rather than hidden one-off logic ## Validation checklist Before a module is published: 1. Manifest passes schema validation. 2. Route targets resolve to an isolated runtime. 3. Permissions are least-privilege. 4. Events use stable namespaced topics. 5. Shared entity reads are declared. 6. No private-table cross-access exists. --- ## Platform overview **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/platform/overview **Description:** The core aiConnected shell contract, module model, and platform-wide design rules. ## Contract status `Canonical interface contract` The shell responsibilities, shared entities, module architecture, and isolation rules are explicitly documented in the platform foundation PRD, MVP specification, and shell notes. Most route names are intentionally dynamic or manifest-driven. ## Source documents - [aiConnected Platform foundation PRD](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd) - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [What is the platform shell](/docs/knowledge-base/aiconnected-business-platform/what-is-the-platform-shell) - [aiConnected Platform v2 port map](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-port-map) ## Platform purpose The shell is the permanent operating layer for a white-label, multi-tenant AI sales platform. It owns identity, routing, permissions, billing, themes, layouts, shared entities, and inter-module communication. The shell does **not** own module-specific business logic. Voice, chat, knowledge, and future apps must live outside the shell in isolated runtimes. ## Required shell responsibilities | Responsibility | What it must do | |---|---| | Authentication | Establish identity and session state across all tenants | | Tenant provisioning | Create and manage super, agency, business, developer, and personal contexts | | Permission enforcement | Enforce role checks at the API layer | | Navigation and routing | Register routes dynamically from module manifests | | Billing | Manage platform tax, module activation, subscriptions, and account billing | | Module registry | Track installed modules, manifests, capabilities, and lifecycle state | | Event bus | Relay cross-module events through shared contracts | | API gateway | Route module traffic with auth, workspace, and rate-limit context | | Theme engine | Apply layered white-label branding per tenant | | Layout manager | Support visual editing and AI-assisted creation of screens and modules | ## Platform-wide design rules These rules are repeated across the source docs and should be treated as hard requirements: - Modules communicate through contracts, events, and the gateway, not direct imports. - Every module is containerized and isolated from the shell and from other modules. - Every module has a manifest that declares routes, permissions, capabilities, and events. - Shared entities belong to the shell. - Module-owned tables belong to the module that creates them. - Cross-module communication flows through the event bus or declared gateway contracts. - The architecture must stay open for future third-party developer modules. ## User layers The platform is designed around five user classes: | User class | Primary concern | |---|---| | Super user | Platform operations, support, governance, billing, global configuration | | Agency user | White-label product owner and reseller | | Business user | Agency client using deployed modules | | Developer | Sandboxed module creator and importer | | Personal user | Isolated single-user mode outside agency hierarchy | The MVP focuses on super, agency, and business users, but the architecture must not block developer or personal accounts. ## Deployment model The repo describes a monorepo shell plus isolated module runtimes: | Layer | Example packages or apps | |---|---| | Shell apps | `apps/platform`, `apps/chat`, `apps/kb-studio` | | Shared packages | `permissions`, `app-sdk`, `kb-engine`, `chat-core`, `branding`, `db`, `ui` | | Data layer | Shared shell entities plus module-owned tables | | Runtime | Containerized modules behind an API gateway | ## Required build order The source docs consistently imply this sequence: 1. Shell and permissions 2. Shared entities and gateway 3. Event bus and capability registry 4. Theme engine and layout manager 5. Knowledge and chat 6. Voice, contact forms, chat monitor, and co-browser 7. Developer import and ecosystem tooling ## What developers should stabilize first Before building new modules, stabilize: - Workspace and permission context propagation - Shared contacts and events contracts - Manifest validation - Gateway request forwarding - Theme inheritance - Layout versioning and publish flow If those pieces drift, every module becomes harder to build and maintain. --- ## Shared entities **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/platform/shared-entities **Description:** The shell-owned data model that every aiConnected module is expected to share. ## Contract status `Canonical interface contract` The shared shell entities are called out directly in the MVP spec, the shell notes, and the v2 port map. ## Source documents - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [What is the platform shell](/docs/knowledge-base/aiconnected-business-platform/what-is-the-platform-shell) - [aiConnected Platform v2 port map](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-port-map) ## Shell-owned entities These entities belong to the shell and should not be duplicated per module: | Entity | Role | |---|---| | `workspaces` | Unified tenant record replacing separate agency and business concepts at the schema layer | | `contacts` | Universal person or lead record shared across modules | | `users` | Platform identities | | `events` | Shared event log and inter-module bus history | | `module_registry` | Installed modules and declared capability contracts | | `themes` | White-label visual configuration | | `layout_definitions` | Screen and layout metadata | | `module_installations` | Which workspaces have which modules enabled | | `billing_accounts` | Billing ownership and platform tax context | | `subscriptions` | Plan and module subscription state | ## Module-owned tables The v2 port map provides concrete examples of module tables: | Module | Example module-owned tables | |---|---| | Voice | `voice_calls`, `voice_profiles`, `transcripts` | | Chat | `chat_conversations`, `chat_messages`, `chat_configs` | | Knowledge | `kb_entries`, `kb_projects` | | Contact Forms | `contact_forms`, `contact_submissions` | A module may read shared entities. It must write its private data to its own namespace. ## Contacts as the common operating object `contacts` is the most important shared entity because multiple modules need to collaborate on the same person: - Chat creates or enriches a lead. - Contact Forms converts a submission into a lead record. - Voice logs call results against the same person. - LogicLegal can qualify and enrich legal-intake prospects. - Sales-facing features monitor engagement around the same contact lifecycle. ## Required contact fields The repository does not publish one final schema, but a platform-ready `contacts` entity should support: - Stable contact ID - Workspace ownership - Name and organization - Phone and email - Channel provenance - Qualification status - Lead score or warmth - Consent or communication preferences - Cross-module metadata pointers ## Event log expectations The `events` table or service must support: - Event name - Producing module - Workspace scope - Contact scope when applicable - Actor context - Payload - Timestamp - Delivery or processing state This is the interconnection layer the MVP spec describes for call completion, knowledge updates, lead capture, and future automation. ## Data ownership rules - Shell entities define the common platform language. - Modules own their module tables and migrations. - Modules do not reach into one another's private storage. - Shared reads happen through shell entities or declared gateway contracts. - Cross-module reactions happen through emitted events. ## Implementation checkpoints Before you build a module, verify: 1. The module can resolve workspace and contact context. 2. The module can emit typed events into the shared log. 3. Another module can consume those events without private-table access. 4. Theme and layout context can be applied without module-specific hacks. --- ## Themes and layouts **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/platform/theme-layout **Description:** The white-label theme engine and the Layout Manager API surface used to edit and generate screens. ## Contract status - `Canonical interface contract` for theme inheritance and visual builder behavior - `Canonical route contract` for the Layout Manager APIs documented in the layout manager PRD ## Source documents - [aiConnected Platform foundation PRD](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd) - [aiConnected v2 layout manager PRD](/docs/knowledge-base/aiconnected-business-platform/layout-manager/layout-manager-codex-prd) - [aiConnected Platform v2 port map](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-port-map) ## Theme model The platform uses shadcn/ui through `@aiconnected/ui` and tenant theming through CSS variables. ### Theme inheritance ```text Platform defaults └── Agency theme └── Business theme ``` ### Required theme rules - No hardcoded color values in platform-owned UI - All colors resolve through CSS variables - Theme changes are previewable before publish - Theme inheritance is explicit and auditable - Module-built first-party UI must use the shared UI package ## Layout manager scope The Layout Manager has two operating modes: 1. Edit existing screens 2. Create new modules through AI-assisted generation It is the visual authoring layer for layouts, not the owner of backend business logic. ## Canonical route contract The layout manager PRD defines these routes directly. ### Session and draft APIs | Method | Route | Purpose | |---|---|---| | `POST` | `/api/layout-manager/sessions` | Start an editing session from a screen or module context | | `GET` | `/api/layouts/{layoutId}/draft` | Load the current draft | | `POST` | `/api/layouts/{layoutId}/save` | Save a draft snapshot | | `POST` | `/api/layouts/{layoutId}/autosave` | Persist autosave state | ### Lifecycle APIs | Method | Route | Purpose | |---|---|---| | `POST` | `/api/layouts/{layoutId}/preview` | Build a preview artifact | | `POST` | `/api/layouts/{layoutId}/test` | Run validation and test workflows | | `POST` | `/api/layouts/{layoutId}/publish` | Publish the layout | | `POST` | `/api/layouts/{layoutId}/rollback` | Restore a prior version | ### Binding APIs | Method | Route | Purpose | |---|---|---| | `GET` | `/api/capabilities/search` | Find reusable components or capabilities | | `POST` | `/api/layouts/{layoutId}/bindings/connect-existing` | Bind an existing capability | | `POST` | `/api/layouts/{layoutId}/bindings/create-new/start` | Begin creation of a new binding | | `GET` | `/api/layouts/{layoutId}/bindings/create-new/{jobId}` | Poll binding creation state | ### AI orchestration APIs | Method | Route | Purpose | |---|---|---| | `POST` | `/api/layout-manager/ai/jobs` | Start an AI builder job | | `GET` | `/api/layout-manager/ai/jobs/{jobId}` | Fetch job status | | `POST` | `/api/layout-manager/ai/jobs/{jobId}/approve-plan` | Approve the generated plan | | `POST` | `/api/layout-manager/ai/jobs/{jobId}/return-to-builder` | Merge AI output back into builder state | ### Registry search example ```http GET /api/layout-manager/component-registry/search?q=phone&category=input ``` ## Event stream contract The PRD defines a session event stream named `layout.session.events` over SSE or WebSocket. Supported event types: - `AUTOSAVE_SUCCESS` - `VALIDATION_UPDATED` - `AI_WORKFLOW_STATE_CHANGED` - `HISTORY_APPENDED` - `PREVIEW_READY` - `TEST_RESULT_READY` - `PUBLISH_COMPLETED` - `ROLLBACK_COMPLETED` ## AI workflow state machine The mandatory AI workflow is: ```text intent_captured -> clarifying -> reuse_check -> plan_ready -> draft_generated -> builder_returned ``` The PRD also states: - Prefer reuse over net-new generation. - Never auto-publish. - Quarantine failed AI artifacts from user drafts. ## Build guidance Treat the Layout Manager as a versioned authoring subsystem with strict rollback rules. It should be safe for non-technical operators precisely because draft and publish boundaries are explicit. --- ## Capabilities APIs **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk/capabilities-apis **Description:** Capability library and import APIs from the original v1 platform build, including list, detail, import, install, uninstall, examples, and failure handling. ## Contract status `Canonical route contract` This page is based on the route handlers in the original v1 platform repo: - `apps/platform/src/app/api/capabilities/route.js` - `apps/platform/src/app/api/capabilities/import/route.js` - `apps/platform/src/app/api/capabilities/[id]/route.js` - `apps/platform/src/app/api/capabilities/[id]/install/route.js` - `apps/platform/src/app/api/capabilities/categories/route.js` ## Use this page when - you are browsing the capability library - you are compiling capabilities from n8n workflows - you need to install a capability for a business - you need to understand dependency-check behavior before install ## Golden path ### 1. Browse categories ```http GET /api/capabilities/categories ``` ### 2. Browse or search capabilities ```http GET /api/capabilities?category=productivity&featured=true ``` ### 3. Import a capability from a workflow in dry-run mode ```http POST /api/capabilities/import Content-Type: application/json { "workflow_json": {}, "category_slug": "productivity", "dry_run": true } ``` ### 4. Inspect one capability ```http GET /api/capabilities/my-capability-slug ``` ### 5. Install it for a business ```http POST /api/capabilities/my-capability-slug/install ``` ## `GET /api/capabilities` Purpose: - return capability library records Supported query params from the source: - `category` - `search` - `featured=true` - `installed=true` - `admin=true` Behavior: - non-admin views only return active capabilities - admin view requires super-admin auth - installed view filters by current business installs - authenticated business requests may get `is_installed` annotations ## `POST /api/capabilities/import` Purpose: - convert an n8n workflow JSON payload into a capability and publish it Auth: - super admin only Request body fields from the source: - `workflow_json` - `category_slug` - `name` - `description` - `emoji` - `dry_run` Pipeline behavior: 1. parse workflow into IR using `parseWorkflow()` 2. compile into a capability using `compileCapability()` 3. block on parse or compile errors 4. optionally return a dry-run preview 5. upsert into `capabilities` ### Dry-run success shape ```json { "success": true, "data": { "capability": {}, "warnings": [], "errors": [], "dry_run": true } } ``` ### Import failure modes - invalid JSON body - `workflow_json` missing or not an object - workflow parse failure - critical parse errors - compilation errors - database upsert failure ## `GET /api/capabilities/categories` Purpose: - return active capability categories for the capability library sidebar Behavior: - selects active records - orders by `display_order` ## `GET /api/capabilities/{id}` Purpose: - return full details for one capability Path parameter: - supports UUID or slug Behavior: - returns active capability only - includes joined `capability_categories` - annotates `is_installed` for the current business when available ## `PATCH /api/capabilities/{id}` Purpose: - update capability metadata Auth: - super admin only Accepted fields from the source: - `name` - `description` - `icon_emoji` - `category_slug` - `is_featured` - `is_active` ## `DELETE /api/capabilities/{id}` Purpose: - delete or deactivate a capability Auth: - super admin only Delete policy from the source: - hard delete if install count is zero - soft delete by setting `is_active=false` if installs exist ## `POST /api/capabilities/{id}/install` Purpose: - install a capability for the authenticated business Behavior: - resolve tenant and business context - fetch active capability by UUID or slug - detect already-installed or previously-deactivated installs - run requirement checks through `checkRequirements()` - create or reactivate `business_capabilities` Important status responses from the source: - `installed` - `already_installed` - `requirements_missing` ### Requirements-missing example ```json { "success": false, "status": "requirements_missing", "requirements": { "connected": [], "missing": ["gmail", "slack"], "can_install": false } } ``` ## `DELETE /api/capabilities/{id}/install` Purpose: - uninstall a capability for the authenticated business Behavior: - soft delete only - sets `is_active=false` on the install row ## Operational checks after install 1. Verify install status is `installed` or `already_installed`. 2. If install fails, inspect `requirements_missing`. 3. Re-fetch capability details and confirm `is_installed=true`. 4. Confirm the business can access the expected runtime surface. ## v1 reality vs v2 target ### Implemented in v1 - capability library endpoints - workflow-to-capability import - install and uninstall lifecycle - requirements-aware install blocking ### Carry forward to v2 - dry-run preview before persistence - install requirement checks - slug-or-UUID resource lookup ### Known mismatch or migration risk - the capabilities system is adjacent to, but not identical with, the platform app manifest system - v2 should clarify when a reusable unit is a capability, an app, or both --- ## Client and CLI **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk/client-cli **Description:** The v1 services-layer SDK for module builders: platform client APIs, CLI scaffolding, examples, failure modes, and migration notes. ## Contract status `Canonical interface contract` This page is based on: - `platform.sec-admn.com-2/sdk/README.md` - `platform.sec-admn.com-2/sdk/packages/client/src/index.ts` - `platform.sec-admn.com-2/sdk/packages/cli/src/index.ts` ## Use this page when - you are wiring a module to the platform event bus - you need to call another module through the registry - you want to report usage - you are scaffolding a new module from scratch ## Golden path example ### Scaffold ```bash npx create-aiconnected-module ``` ### Initialize the client ```ts const platform = createPlatformClientFromEnv(); ``` ### Emit an event ```ts await platform.emit("call.started", { call_id: "call_123", contact_id: "contact_456" }); ``` ### Call another module through the registry ```ts const result = await platform.call("chat", "send_message", { contact_id: "contact_456", message: "Hello from voice-ai" }); ``` ### Report usage ```ts await platform.usage("calls_initiated", 1, "calls", { provider: "livekit" }); ``` ## Platform client The v1 `@aiconnected/client` package defines a module-side platform client with three core responsibilities: - emit events - call registered module functions through the registry - report usage metrics ## Client configuration The client expects: ```ts { appId: string, apiKey: string, eventEndpoint: string, registryEndpoint: string, usageEndpoint: string, timeout?: number, retries?: number, debug?: boolean } ``` ## Core client methods ### `emit(eventName, payload)` Purpose: - fire a platform event asynchronously - include `event`, `app_id`, `timestamp`, and `payload` Behavior from the source: - event delivery failures are logged - failures do not throw into the main code path Use it for: - lifecycle events - state changes - outputs that other modules may consume indirectly ### `createEventHandler(events, handler)` Purpose: - build a local development or webhook-style event handler - route only subscribed events to the provided async handler This is especially useful for local event consumption before the full production event wiring is in place. ### `call(appId, functionName, inputs)` Purpose: - call another module through the registry - POST to the registry's `/call` endpoint - return structured success or error data Payload shape from the client source: ```json { "app_id": "target-app", "function": "function_name", "inputs": {}, "caller_app_id": "source-app" } ``` ### `listFunctions(appId?)` Purpose: - retrieve available registry functions - optionally filter by app ### `hasFunction(appId, functionName)` Purpose: - check whether a function is available in the registry ### `usage(metric, value, unit, metadata?)` Purpose: - report metered usage to the platform Allowed usage units from the source: - `tokens` - `calls` - `messages` - `requests` - `minutes` - `storage_mb` ## SDK operating rules The SDK README is explicit: 1. Ship a manifest. 2. Expose authenticated REST endpoints for declared functions. 3. Emit events for meaningful state changes. 4. Never connect directly to another module. ## Error handling and failure modes ### `emit()` - failures are logged - caller flow is not interrupted - use this for non-blocking event publication ### `call()` - returns `{ success: false, error }` if the registry call fails - does not crash the caller by default - supports async job-style responses with `async` and `job_id` ### `usage()` - failures are logged - reporting is fire-and-forget ### `createPlatformClientFromEnv()` Throws when required environment variables are missing: - `PLATFORM_API_KEY` - `PLATFORM_EVENT_ENDPOINT` - `PLATFORM_REGISTRY_ENDPOINT` - `PLATFORM_USAGE_ENDPOINT` - `APP_ID` ## CLI scaffolder The v1 CLI package exposes: ```bash npx create-aiconnected-module ``` ## What the CLI generates The scaffolded output includes: - `platform-app.json` - `.env.example` - `README.md` - Express server starter - platform client integration - validation command hints ## CLI manifest defaults The generator creates a `schemaVersion: 1` manifest with: - `app` - `inputs` - `outputs` - `extends` - optional `extensionPoints` for UI or fullstack templates ## Environment variables required by generated modules From the v1 SDK docs and CLI: - `APP_KEY` or `APP_ID` - `APP_PORT` - `APP_ENV` - `PLATFORM_API_KEY` - `PLATFORM_EVENT_ENDPOINT` - `PLATFORM_REGISTRY_ENDPOINT` - `PLATFORM_USAGE_ENDPOINT` ## Delivery checklist for module authors - manifest passes validation - every declared function has a real authenticated endpoint - all meaningful state changes emit events - usage is reported for billable or reportable operations - shared schemas are imported, not redefined - `.env.example` is complete ## v1 reality vs v2 target ### Implemented in v1 - client API for events, calls, and usage - CLI scaffold for new modules - generated module starter structure ### Carry forward to v2 - registry-based module calling - fire-and-forget usage and event reporting - scaffold-driven module creation ### Known mismatch or migration risk - some source comments still show underscore-based app IDs like `voice_ai` - the actual validated contract requires kebab-case - prefer kebab-case everywhere in docs, manifests, and code examples --- ## SDK **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk **Description:** Documents in SDK. --- ## Manifest contracts **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk/manifest-contracts **Description:** The v1 platform app manifest, validator rules, contract kinds, extension modes, examples, failure cases, and importer expectations from the original aiConnected platform build. ## Contract status `Canonical interface contract` This page is based on the actual implementation in: - `platform.sec-admn.com-2/packages/app-sdk/src/imports.js` - `platform.sec-admn.com-2/sdk/packages/manifest/src/index.ts` - `platform.sec-admn.com-2/docs/platform-app-package-format.md` ## Why this page matters The manifest is the contract the platform uses to understand what a module consumes, exposes, extends, and renders. If the manifest is wrong, the platform cannot wire the module safely. ## Two manifest shapes in v1 The original repo contains two closely related manifest models: 1. The platform package-import manifest used by `packages/app-sdk` 2. The SDK validator manifest used by `@aiconnected/manifest` They overlap heavily, but they are not identical. ## Golden path example Use this as a starting point for importer-facing module packages: ```json { "schemaVersion": 1, "app": { "key": "voice-ai", "name": "Voice AI", "version": "1.0.0", "category": "customer-facing", "description": "Adds real-time voice to the platform.", "adminSurface": true, "publicRuntime": false, "settingsSchemaKey": "voice-ai-config", "brandingSlots": ["main-surface"], "capabilities": ["voice-calls", "transcription"] }, "inputs": [ { "key": "knowledge-base.search", "kind": "capability", "required": true, "title": "Knowledge base search" }, { "key": "chat.session", "kind": "context", "required": true, "title": "Chat session context" } ], "outputs": [ { "key": "voice.transcript", "kind": "data", "title": "Voice transcript" } ], "extensionPoints": [ { "key": "response-pipeline", "title": "Response pipeline" } ], "extends": [ { "targetApp": "chat", "featureKey": "voice-mode", "mode": "feature-extension", "required": true, "title": "Enable voice inside chat" } ], "permissions": [], "metadata": {}, "ui": { "components": [ { "type": "full_page", "title": "Voice settings", "nav_label": "Voice", "nav_icon": "Phone", "route": "/voice", "description": "Manage voice configuration." } ] } } ``` ## Platform app package manifest The importer-oriented manifest uses this shape: - `schemaVersion` - `app` - `inputs` - `outputs` - `extensionPoints` - `extends` - `permissions` - `metadata` - `ui.components` ## Valid app categories From `packages/app-sdk/src/imports.js`: - `customer-facing` - `operations` - `training` ## Valid contract kinds From `packages/app-sdk/src/imports.js`: - `action` - `capability` - `context` - `data` - `event` ## Valid extension modes From `packages/app-sdk/src/imports.js`: - `feature-extension` - `runtime-channel` - `sidecar` ## Key normalization behavior The manifest normalizer supports multiple aliases while producing a canonical shape: - `schemaVersion` or `schema_version` - `app.key` or `key` - `extensionPoints` or `extension_points` - `targetApp` or `target_app` - `featureKey` or `feature_key` - `ui.components` or `uiComponents` That means the importer is flexible on input format, but the normalized output should be treated as the real contract shape. ## App key rules The importer uses this kebab-case app key regex: ```text /^[a-z0-9]+(?:-[a-z0-9]+)*$/ ``` The repo also notes protected platform-managed app keys such as `chat` and `kb-studio`. ## SDK validator manifest The `@aiconnected/manifest` package validates a more function-oriented manifest: ```json { "app_id": "voice-ai", "display_name": "Voice AI", "version": "1.0.0", "description": "Adds real-time voice to the platform.", "functions": [ { "name": "start_call", "description": "Starts a call", "method": "POST", "endpoint": "/api/voice-ai/start_call", "inputs": [], "outputs": [], "event_emitted": "call.started" } ], "events_consumed": ["appointment.no_show"], "ui": { "components": [], "theme_tokens": true } } ``` ## SDK validator rules From `sdk/packages/manifest/src/index.ts`: - `app_id` must be kebab-case - `version` must be semver - `functions` must be an array - each function name must be `snake_case` - each function endpoint must start with `/api/` - `event_emitted` and `events_consumed` must use `noun.verb` format - UI component names must be `PascalCase` - full-page components require a route - UI permissions arrays must not be empty ## UI component rules The validator defines these component types: - `full_page` - `widget` - `overlay` The importer-normalizer also recognizes: - `full_page` - `panel` - `widget` - `settings` That mismatch is important when carrying v1 forward into a unified v2 contract. ## Importer behavior The package-format doc states: - required inputs and extensions must be satisfiable by the current app catalog - the importer blocks registration if a required dependency cannot be satisfied - if only one compatible provider exists, the importer stores that connection automatically The assessment engine also: - warns on multiple compatible providers - warns when no outputs are declared - warns when the package declares no inputs, outputs, or extensions - generates stable connection keys using `createConnectionKey()` ## Common validation and assessment failures ### Importer-side failures Examples directly implied by `packages/app-sdk/src/imports.js`: - `schema_version_unsupported` - `app_key_missing` - `app_key_invalid` - `app_category_missing` - `app_category_invalid` - `duplicate_input_key` - `duplicate_output_key` - `duplicate_extension_point_key` - `contract_key_invalid` - `extension_target_invalid` - `extension_feature_invalid` - `required_input_missing` - `extension_target_missing` - `extension_feature_missing` ### Validator-side failures Examples directly implied by `sdk/packages/manifest/src/index.ts`: - `MISSING_APP_ID` - `INVALID_APP_ID_FORMAT` - `MISSING_VERSION` - `INVALID_VERSION_FORMAT` - `MISSING_FUNCTION_NAME` - `INVALID_FUNCTION_NAME` - `INVALID_METHOD` - `MISSING_ENDPOINT` - `INVALID_ENDPOINT_PREFIX` - `INVALID_EVENT_NAME` - `MISSING_COMPONENT_NAME` - `INVALID_COMPONENT_NAME` - `INVALID_COMPONENT_TYPE` ## What to do when validation fails 1. Fix app key format first. 2. Fix category and contract kind issues next. 3. Remove duplicate input, output, or extension-point keys. 4. Ensure every required input has at least one catalog provider. 5. Ensure every required extension target exists and exposes the requested feature key. 6. Align route names, UI component names, and event names with validator rules. ## v1 reality vs v2 target ### Implemented in v1 - import-time manifest normalization - explicit assessment with warnings and blocking issues - manifest validator with structured error codes ### Carry forward to v2 - the importer assessment model - stable connection key generation - explicit required-versus-optional dependency behavior ### Known mismatch or migration risk - v1 has two manifest contracts instead of one - the importer model is app-and-contract oriented, while the validator model is function oriented - UI component enums do not match perfectly between packages ## Recommendation For v2, unify these two manifest shapes into one canonical module contract, but keep the v1 assessment workflow and structured error reporting. Those are the strongest parts of the design. --- ## SDK overview **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk/overview **Description:** End-to-end guide to the v1 aiConnected SDK surfaces: manifest contracts, client APIs, schemas, CLI scaffolding, and platform-side import flows. ## Contract status `Canonical interface contract` The original v1 platform build in `/Users/MrBobHunter-MacPro/Code/platform.sec-admn.com-2` defines a substantial SDK surface. This section documents how a module developer actually uses it from scaffold to install. ## Source documents - `platform.sec-admn.com-2/AGENTS.md` - `platform.sec-admn.com-2/sdk/README.md` - `platform.sec-admn.com-2/docs/platform-app-package-format.md` - `platform.sec-admn.com-2/docs/v1-audit/03b-module-manifest-system.md` ## Start here If you are building a module, follow this path: 1. Scaffold the module with `create-aiconnected-module`. 2. Fill out `platform-app.json`. 3. Reuse shared models from `@aiconnected/schemas`. 4. Expose authenticated function endpoints for declared outputs. 5. Emit platform events and report usage. 6. Validate the manifest and package structure. 7. Import the package through the platform app import APIs. 8. Verify install, capability wiring, and any required dependencies. The pages in this section map to that flow: - [Manifest contracts](/docs/api-reference/sdk/manifest-contracts) - [Schemas](/docs/api-reference/sdk/schemas) - [Client and CLI](/docs/api-reference/sdk/client-cli) - [Platform app APIs](/docs/api-reference/sdk/platform-app-apis) - [Capabilities APIs](/docs/api-reference/sdk/capabilities-apis) ## Golden path ### 1. Scaffold the module ```bash npx create-aiconnected-module ``` ### 2. Fill out the manifest ```json { "schemaVersion": 1, "app": { "key": "voice-ai", "name": "Voice AI", "version": "1.0.0", "category": "customer-facing", "description": "Adds real-time voice to the platform." }, "inputs": [ { "key": "knowledge-base.search", "kind": "capability", "required": true, "title": "Knowledge base search" } ], "outputs": [ { "key": "voice.transcript", "kind": "data", "title": "Voice transcript" } ], "extends": [] } ``` ### 3. Reuse shared models ```ts ``` ### 4. Use the platform client ```ts const platform = createPlatformClientFromEnv(); await platform.emit("call.started", { call_id: "call_123", contact_id: "contact_456" }); await platform.usage("calls_initiated", 1, "calls"); ``` ### 5. Import into the platform Use the platform-side admin import API documented in [Platform app APIs](/docs/api-reference/sdk/platform-app-apis). ## Two SDK layers The v1 repo explicitly describes two active SDK layers. ### Layer 1: Connection layer Source package: - `packages/app-sdk` Responsibilities: - Normalize module manifests - Validate manifest correctness - Assess compatibility against the app catalog - Generate connection suggestions - Normalize imported UI and color usage during app import Core functions called out in the repo: - `normalizeAppManifest()` - `validateAppManifest()` - `assessAppManifest()` ### Layer 2: Services layer Source packages: - `sdk/packages/client` - `sdk/packages/manifest` - `sdk/packages/schemas` - `sdk/packages/ui` - `sdk/packages/cli` Responsibilities: - Shared data models - Manifest schema and validation - Platform event, function-call, and usage-reporting client - Shared UI components - Module scaffolding ## Hard rules from the v1 SDK docs - App keys are kebab-case. - Modules ship a manifest file. - Shared models come from `@aiconnected/schemas`. - Shared UI imports come from `@aiconnected/ui`. - Modules do not connect directly to one another. - Cross-module calls go through platform registry or event-bus contracts. ## Compatibility matrix | Surface | Source of truth | v1 status | Carry into v2 | |---|---|---|---| | Package import manifest | `packages/app-sdk` | implemented | yes | | Validator manifest | `sdk/packages/manifest` | implemented | yes, but unify shape | | Shared schemas | `sdk/packages/schemas` | implemented | yes | | Platform client | `sdk/packages/client` | implemented | yes | | CLI scaffold | `sdk/packages/cli` | implemented | yes | | UI package | `sdk/packages/ui` | implemented | yes | | Platform app import/catalog APIs | platform routes | implemented | yes | | Capabilities import/install APIs | platform routes | implemented | yes | ## v1 reality vs v2 target ### Implemented in v1 - manifest normalization and validation - registry-mediated client calls - platform app import - capability import and install flows - shared schema package ### Carry forward to v2 - manifest-first module registration - shared schema ownership - event and usage reporting - admin import and assessment workflow ### Known migration risks - two overlapping manifest shapes exist in v1 - app key naming is stricter than some older comments and examples - UI component models differ slightly between importer and validator packages ## Common failure points - module uses snake_case app IDs instead of kebab-case - manifest declares required inputs with no provider in the catalog - module emits events but never exposes declared outputs - module redefines shared models instead of importing them - importer accepts the package but returns warnings the team ignores ## What to improve next If you are implementing v2, the biggest SDK cleanup is to converge the importer manifest and validator manifest into one canonical contract while preserving the good parts of the v1 import-assessment flow. --- ## Platform app APIs **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk/platform-app-apis **Description:** Platform-side SDK support endpoints from the original v1 platform build for app catalog retrieval, app package import, and app metadata updates. ## Contract status `Canonical route contract` This page is based on the route handlers in: - `platform.sec-admn.com-2/apps/platform/src/app/api/platform/apps/route.js` - `platform.sec-admn.com-2/apps/platform/src/app/api/platform/apps/import/route.js` - `platform.sec-admn.com-2/apps/platform/src/app/api/platform/apps/[appKey]/route.js` ## Use this page when - you are importing a module package into the platform - you need to inspect the platform app catalog - you need to update app metadata after registration ## Auth model These endpoints are platform-admin APIs. Requirements from the source: - authenticated session required - user must exist - user must be `isSuperAdmin` ## Golden path ### 1. Fetch the current catalog ```http GET /api/platform/apps ``` ### 2. Import a package ```http POST /api/platform/apps/import Content-Type: multipart/form-data file= ``` ### 3. Review the returned assessment The import route returns: - normalized manifest summary - assessment details - import record - extracted or skipped files - refreshed catalog payload ### 4. Update nav metadata if needed ```http PATCH /api/platform/apps/voice-ai Content-Type: application/json { "nav": { "label": "Voice", "icon": "Phone" } } ``` ## `GET /api/platform/apps` Purpose: - return the platform app catalog payload for admin surfaces Behavior: - loads authenticated context - calls `getPlatformAppCatalogPayload()` - returns `{ success: true, data }` Error modes: - `401` authentication required - `403` platform admin access required - `404` user not found - `500` failed to load platform apps ## `POST /api/platform/apps/import` Purpose: - import a platform app package from a zip or manifest JSON file Request: - same-origin enforced - `multipart/form-data` - form field: `file` Behavior: - validates admin auth - accepts a `.zip` app package or manifest `.json` - calls `importPlatformAppPackage()` - refreshes the catalog payload Response payload from the route: ```json { "success": true, "data": { "app": { "appKey": "voice-ai", "name": "Voice AI", "version": "1.0.0", "category": "customer-facing", "description": "Adds real-time voice to the platform." }, "assessment": { "status": "needs-attention", "warnings": [] }, "importRecord": {}, "registered": true, "extractedFiles": [], "extractionSkipped": [], "catalog": {} } } ``` ## Common import failure cases - no `file` field provided - same-origin enforcement fails - user is authenticated but not a super admin - manifest file is missing or invalid - required inputs or extensions cannot be satisfied - package import succeeds with warnings that still need review ## `PATCH /api/platform/apps/{appKey}` Purpose: - update platform app metadata, specifically navigation settings in v1 Current behavior: - fetches the existing `platform_apps` record by `app_key` - merges `body.nav` into `metadata.nav` - updates `metadata` and `updated_at` - returns the refreshed app catalog payload Error modes: - `401` authentication required - `403` platform admin access required - `404` missing app record - `500` failed to update platform app settings ## Operator checklist after import 1. Confirm `registered` is true. 2. Review `assessment.warnings`. 3. Confirm extracted files and skipped files make sense. 4. Verify the app appears in the catalog. 5. Verify nav metadata if the app exposes admin or runtime surfaces. ## v1 reality vs v2 target ### Implemented in v1 - admin-only app catalog fetch - package import route - metadata patch route ### Carry forward to v2 - admin import workflow - explicit assessment response - manifest-driven app catalog ### Known mismatch or migration risk - these routes are super-admin centered and may need broader workspace-aware ownership in v2 - some metadata behavior is narrowly focused on nav settings rather than a broader app-management surface --- ## Schemas **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/sdk/schemas **Description:** Shared v1 SDK data models from @aiconnected/schemas, including core entities, ownership boundaries, and reuse guidance for module authors. ## Contract status `Canonical interface contract` This page is based on: - `platform.sec-admn.com-2/sdk/packages/schemas/src/index.ts` ## Why this page matters The v1 SDK docs are explicit: shared data models belong in `@aiconnected/schemas`, and module authors should not redefine them inline. This package is the common language between modules. ## Golden path usage ```ts Contact, Conversation, Call, Appointment, Workflow, UsageRecord, Account, Agency, Module, Theme } from "@aiconnected/schemas"; ``` ## Core model inventory The package defines these major entity families: | Model | Purpose | |---|---| | `Contact` | central lead or person record | | `Message` and `Conversation` | channel-agnostic conversation thread | | `Call` | voice interaction record | | `Appointment` | scheduled interaction with a contact | | `KnowledgeBase` and `KnowledgeBaseDocument` | AI context and indexed document records | | `Workflow` and `WorkflowStep` | automation definition | | `UsageRecord` | usage and billing telemetry | | `Account` | business customer or agency client | | `Agency` | reseller entity | | `Module` | registered module record | | `Theme` | visual token set | ## Important primitives The package also defines shared primitives such as: - `UUID` - `ISOTimestamp` - `RecordStatus` - `UsageUnit` ## High-value shared entities ### `Contact` This is the most important shared record in the package. Key fields: - `id` - `first_name` - `last_name` - `full_name` - `email` - `phone` - `company` - `tags` - `custom_fields` - `status` - `source` - `assigned_to` - `account_id` Why it matters: - chat, voice, forms, and other modules should point at the same contact identity - modules should enrich a contact record rather than invent parallel person objects ### `Conversation` Defines a thread with: - `contact_id` - `account_id` - `channel` - `messages` - `status` - `metadata` Channels supported by the package: - `chat` - `sms` - `email` - `voice` - `web_widget` ### `Call` Defines shared voice records with: - `contact_id` - `account_id` - `direction` - `outcome` - `duration_seconds` - `recording_url` - `transcript` - `summary` - `provider` ### `Workflow` Defines automation state with: - `trigger_event` - `steps` - `created_by` - `run_count` This is important for the capability and automation side of the platform. ## Ownership boundaries Use these schemas when: - a record is shared conceptually across modules - a record needs platform-wide consistency - a record may appear in SDK payloads, registry calls, or event data Do not invent new field names for: - contacts - conversations - calls - appointments - workflows - usage records - accounts and agencies ## Module-author guidance ### Do - import shared types from `@aiconnected/schemas` - extend through module-local metadata where necessary - keep module-specific fields out of core entity names unless they belong in the shared package ### Do not - create alternate names for core objects - redefine shared models in each module - silently diverge field names between modules ## Normalization and reporting models The same package also includes zod schemas for import-normalizer reporting, including: - `UIImportNormalization` - `ColorNormalization` - `NormalizationReport` Those support the v1 UI import normalizer workflow described in the SDK and manifest docs. ## v1 reality vs v2 target ### Implemented in v1 - shared data model package - strong guidance against inline redefinition - normalization-report models ### Carry forward to v2 - one package for core cross-module entities - shared primitive and enum vocabulary - import-normalization report models if the upload workflow remains ### Known mismatch or migration risk - some v1 model naming uses `account_id` while newer platform docs emphasize `workspace` terminology - v2 should either alias this cleanly or provide a structured migration path rather than silently mixing terms --- ## Additional modules **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/additional-modules **Description:** Reference contracts for aiConnected Memory, KB Generator, Webinar, and legacy funnelChat surfaces documented in the repository. ## Contract status `Derived implementation contract` These modules are documented in the repository, but not all of them have the same level of route-level detail as Paper or Layout Manager. This page captures the buildable contract surface the docs do provide. ## Source documents - [aiConnected modules overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiconnected-modules-overview) - [KB Generator readme](/docs/knowledge-base/aiconnected-apps-and-modules/modules/kb-generator/kb-generator-readme) - [KB Generator field reference](/docs/knowledge-base/aiconnected-apps-and-modules/modules/kb-generator/kb-generator-field-reference) - [KB Generator design and build instructions](/docs/knowledge-base/aiconnected-apps-and-modules/modules/kb-generator/kb-generator-design-build-instructions) - [aiConnected webinar](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiconnected-webinar) - [legacy funnelChat overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-funnelChat-overview) - [legacy funnelChat PRD](/docs/knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-funnelChat-prd) ## aiConnected Memory The module overview positions Memory as a persistent, model-agnostic memory layer with APIs and MCP tools for long-term recall, hierarchical retrieval, and unlimited historical context. Required implementation domains: - Memory write and retrieval APIs - Recall file creation and archive management - Knowledge graph and vector-search coordination - Integration contracts for chat, IDE, voice, and workflow clients This module overlaps with the aiConnectedOS memory pages because the repository treats Memory as both a platform capability and a product in its own right. ## KB Generator KB Generator is documented as a structured knowledge-generation module aimed at turning business inputs into AI-ready training assets. Required implementation domains: - Intake form or onboarding capture - Field-level validation against the documented field reference - Prompt assembly and generation workflows - Template generation and export - Integration-ready output for downstream AI systems The field-reference doc should be treated as the schema authority for the input model. ## aiConnected Webinar The webinar module is documented as an event and follow-up automation product. Required implementation domains: - Webinar scheduling - Registration capture - Reminder and follow-up automation - Attendee chat or support interactions - Post-event lead enrichment and CRM handoff The original engines doc also describes webinar automation as part of the aiConnected product family. ## funnelChat funnelChat appears in the repo as an earlier chat and lead-capture concept that still provides useful ancestry for the platform's sales-chat design. Required implementation domains: - Website chat engagement - Lead qualification - Training prompt management - CRM routing and conversation flow logic - Session analytics or insights Treat funnelChat as legacy reference material rather than the current authoritative chat module contract. The modern Chat and Contact Forms pages should take precedence when they conflict. ## Use in platform planning These modules matter for completeness because they expand the documented aiConnected product surface. They are also useful when you need to preserve legacy ideas during a staged rebuild. --- ## Contact, monitor, and co-browser **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/contact-monitor-cobrowser **Description:** API contracts for Contact Forms, Chat Monitor, and the SiteGuide Co-Browser add-on. ## Contract status `Derived implementation contract` These modules are strongly defined in the MVP spec and SiteGuide documentation, but the repo does not publish one final route map for all three. This page documents the required module surface. ## Source documents - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [aiConnected Platform v2 build plan](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-build-plan) - [aiConnected SiteGuide/CoBrowser](/docs/knowledge-base/aiconnected-apps-and-modules/ai-connected-site-guide-co-browser) ## Contact Forms ### Purpose Contact Forms closes the lag between a form submission and a business response. It validates the submission, determines intent, starts an AI interaction when appropriate, and converts the result into a qualified lead or booked next step. ### Required operations - Register and manage form definitions - Receive contact submissions - Validate submission quality - Convert submissions into `contacts` - Trigger AI follow-up and qualification workflows - Emit lead and submission events to automations ### Required data - `contact_forms` - `contact_submissions` - `contacts` - `events` ## Chat Monitor ### Purpose Chat Monitor gives operators a live view into AI sales conversations, especially when a prospect appears ready to buy. ### Required operations - List active sessions - Stream live conversation updates - Flag warming or high-intent leads - Allow human takeover or silent guidance - Persist intervention history ### Required event hooks - Chat session started - Lead warmed - Human takeover requested - Human takeover completed ## Co-Browser / SiteGuide ### Purpose The Co-Browser follows the visitor across the site and responds with page-aware context. ### Required operations - Start and resume a site session - Track current page and page history - Track element highlight and scroll commands - Answer questions with page-context awareness - Support voice and text interaction - Surface session analytics and lead-intent signals ### Functional areas explicitly described in the SiteGuide doc - Conversational AI - Scroll-to-element - DOM highlighting - Navigation control - Voice input and output - Persistent session memory - Context recovery - Lead capture and marketing integration - Analytics and performance tracking - Admin and business settings - Multisite support ## Shared implementation requirements - All three modules must write back to shared contacts or events. - Co-Browser should enrich lead and page-context data for later sales use. - Monitor should observe chat state, not replace chat state ownership. - Contact Forms should feed the same lead lifecycle used by chat and voice. ## Delivery model These modules should be treated as interconnected sales infrastructure: - Contact Forms creates or enriches the lead. - Co-Browser adds browsing and intent context. - Chat Monitor gives humans operational visibility at the right moment. --- ## Modules **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules **Description:** Documents in Modules. --- ## Knowledge and chat **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/knowledge-chat **Description:** The implementation contracts for aiConnected Knowledge and aiConnected Chat, including shared knowledge publication and lead capture behavior. ## Contract status `Canonical interface contract` The repository defines these modules in the modules overview, MVP spec, v2 port map, and legacy platform notes. Exact v2 HTTP paths are not finalized in one place, but the module responsibilities, shared resources, and event model are clear. ## Source documents - [aiConnected modules overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiconnected-modules-overview) - [aiConnected Platform MVP specification](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification) - [aiConnected Platform v2 port map](/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-port-map) - [legacy platform redesign spec](/docs/knowledge-base/aiconnected-business-platform/legacy-platform-redesign-spec) ## aiConnected Knowledge ### Module purpose Knowledge ingests business information and produces deployment-ready knowledge assets for AI systems. The docs describe scraping, gap research, compilation, concern mapping, starter generation, and quiz generation. ### Engine components called out in the repo The v2 port map references: - `scraper` - `researcher` - `compiler` - `extractor` - `generate` - `ai` - `runtime` - `system-prompt` - `starters` - `concern-mapper` - `quiz` ### Required knowledge operations - Create a knowledge project - Start and monitor crawl or scrape jobs - Review extracted content and detected gaps - Run structured compilation into AI-ready output - Publish a knowledge pack for downstream modules - Version published knowledge assets ### Required knowledge outputs - Structured knowledge entries - FAQ and service summaries - Qualification prompts - Conversation starters - Quiz assets - Published knowledge version metadata ## aiConnected Chat ### Module purpose Chat is the customer-facing interaction layer that consumes published knowledge and drives qualification, lead capture, and CRM handoff. ### Required chat operations - Start a session - Send and receive messages - Retrieve knowledge-backed answers - Capture lead details - Store session history - Deliver lead data through webhooks or automation connectors ### Chat configuration surface The docs explicitly mention settings for: - Quiz requirements - Lead-capture timing - Session persistence - Conversation flow - AI provider and BYOK model selection - Webhook-based CRM delivery ## Shared event model The v2 port map gives the clearest event contract: ### Chat emits - `chat.session.started` - `chat.message.sent` - `chat.lead.captured` - `chat.lead.warmed` - `chat.session.ended` ### Chat consumes - `kb.published` - `contact.updated` - `voice.call.completed` ## Shared resources | Resource | Used by | |---|---| | `kb_projects` | Knowledge | | `kb_entries` | Knowledge | | `chat_conversations` | Chat | | `chat_messages` | Chat | | `chat_configs` | Chat | | `contacts` | Both | | `events` | Both | ## Legacy route ancestry Older platform docs reference: - `/api/knowledge-base` - `/api/chat/[accountId]` - `/api/sessions` - `/api/leads` Use those as legacy naming signals only. The v2 platform is manifest-first and gateway-routed. ## Build guidance Implement Knowledge publication before Chat answer generation. Chat depends on a clean `kb.published` flow and shared contact updates to behave like a platform module instead of a standalone widget. --- ## LogicLegal **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/logiclegal **Description:** The service contract for aiConnected LogicLegal, including intake, research chat, voice, case prep, and closed-knowledge safeguards. ## Contract status `Canonical interface contract` The module overview and LogicLegal docs define the product boundaries, trust model, and interaction surface clearly, but they do not publish a final HTTP route list in the repo. ## Source documents - [aiConnected modules overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiconnected-modules-overview) - [logicLegal overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/logicLegal/logicLegal-overview) - [logicLegal PRD outline](/docs/knowledge-base/aiconnected-apps-and-modules/modules/logicLegal/logicLegal-prd-outline) ## Module purpose LogicLegal is a legal-practice automation module built around a closed knowledge base, lead qualification, voice and chat intake, and attorney-facing retrieval. ## Core rule LogicLegal must operate from verified legal sources and attorney-provided materials, not open-web retrieval. That is the most important implementation constraint in the docs. ## Required service domains ### Prospect research chat - Accept legal-situation questions - Scope the conversation by jurisdiction and practice area - Respond only from approved legal knowledge - Transition prospects into intake or booking flows ### Smart intake - Capture practice-area-specific intake fields - Assess case viability - Schedule consultations - Convert prospects into structured case leads ### Voice handling - Answer calls with the same closed-knowledge constraints - Qualify callers - Route or transfer when needed ### Attorney operations - Retrieve case and lead briefings - Answer voice-first queries over case materials - Surface calendar and pipeline information ## Required shared resources | Resource | Purpose | |---|---| | `contacts` | Prospective and active clients | | `legal_intakes` | Structured intake payloads | | `case_records` | Case-linked metadata | | `knowledge_sources` | Closed legal corpus pointers | | `events` | Intake and engagement workflow events | ## Required safeguards - Jurisdiction scoping - Practice-area scoping - Closed-source retrieval only - Auditability for generated answers - Clear transitions from informational guidance to booked legal consultation ## Integration expectations The module overview calls out: - GoToConnect - GoHighLevel - Optional Clio integration - Marketing automation tied to the same closed knowledge base ## Build note If you build LogicLegal on generic chat patterns without the closed-knowledge constraints, you are not implementing the product described in this repository. --- ## macEngine **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/mac-engine **Description:** Routine, persona, and local-automation data contracts for macEngine. ## Contract status `Canonical interface contract` The macEngine PRD provides concrete data schemas and operating behavior, but it is not written as a finalized HTTP API. This page captures the implementation contract developers can rely on. ## Source document - [macEngine comprehensive PRD](/docs/knowledge-base/aiconnected-apps-and-modules/mac-engine-prd) ## Module purpose macEngine is a local or desktop-oriented AI operating layer centered on routines, persona management, in-task automation, and LLM routing. ## Canonical data schemas ### Routine file schema The PRD defines a routine artifact format with fields such as: - `id` - `name` - `created_at` - `trigger_phrases` - `steps` - `variables` - `author` - `signature` - `version` Routine steps support structured action objects like `open_url`, `type_text`, `click`, and `extract_table`. ### Persona file schema The PRD defines persona records with fields such as: - `id` - `name` - `wakeword` - `tts_voice` - `style` - `llm_pref` - `schedule` - `version` ### LLM routing policy The PRD also defines a routing policy object with: - `routing_policy_version` - `default` intent routing - `personas` - `overrides` ## Required service domains ### Routine management - Create, import, export, and version routines - Trigger routines from phrases or UI actions - Secure variables and signature validation - Track execution results and failures ### Persona management - Create and edit personas - Switch active personas - Preview persona behavior and voice settings - Apply persona schedules ### In-task UI The PRD defines a floating bubble interface with: - Docked or draggable behavior - Wake and listen animations - Output overlay text - Success or failure indicators - Clarification overlays - Confirmation dialogs ## Event model The PRD includes examples of execution events such as `routine_executed`. Build the module so routine and persona actions can emit auditable lifecycle events. ## Build guidance macEngine is schema-heavy. Preserve the documented routine and persona artifact shapes even if the HTTP or IPC transport evolves later. --- ## Paper **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/paper **Description:** The canonical HTTP API for aiConnected Paper, including auth, client management, document generation, scheduling, agency settings, and admin routes. ## Contract status `Canonical route contract` The Paper developer PRD contains an extensive concrete API specification. This is the most route-complete module contract in the repository. ## Source document - [paper developer PRD](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-paper/paper-developer-prd) ## Base URLs The PRD defines these deployment patterns: | Environment | Base URL | |---|---| | Production | `https://api.contentstrategist.com/api/v1` | | Agency subdomain | `https://{agency-slug}.contentstrategist.com/api/v1` | | Custom domain | `https://{custom-domain}/api/v1` | ## Authentication routes | Method | Route | |---|---| | `POST` | `/api/v1/auth/login` | | `POST` | `/api/v1/auth/refresh` | | `POST` | `/api/v1/auth/logout` | | `GET` | `/api/v1/auth/me` | | `POST` | `/api/v1/auth/password/forgot` | | `POST` | `/api/v1/auth/password/reset` | The PRD also documents refresh-token cookie behavior for login, refresh, and logout flows. ## Client management routes | Method | Route | |---|---| | `GET` | `/api/v1/clients` | | `POST` | `/api/v1/clients` | | `GET` | `/api/v1/clients/{client_id}` | | `PUT` | `/api/v1/clients/{client_id}` | | `PUT` | `/api/v1/clients/{client_id}/branding` | | `POST` | `/api/v1/clients/{client_id}/branding/logo` | | `DELETE` | `/api/v1/clients/{client_id}` | ## Document generation routes | Method | Route | |---|---| | `POST` | `/api/v1/clients/{client_id}/documents/generate` | | `GET` | `/api/v1/documents/{document_id}/status` | | `GET` | `/api/v1/documents/{document_id}` | | `GET` | `/api/v1/documents/{document_id}/content` | | `GET` | `/api/v1/documents/{document_id}/pdf` | | `POST` | `/api/v1/documents/{document_id}/distribute` | | `GET` | `/api/v1/clients/{client_id}/documents` | The PRD also documents real-time generation updates through a WebSocket URL associated with document generation jobs. ## Scheduling routes | Method | Route | |---|---| | `GET` | `/api/v1/clients/{client_id}/schedule` | | `POST` | `/api/v1/clients/{client_id}/schedule` | | `POST` | `/api/v1/clients/{client_id}/schedule/import` | | `PUT` | `/api/v1/clients/{client_id}/schedule/{schedule_id}` | | `DELETE` | `/api/v1/clients/{client_id}/schedule/{schedule_id}` | | `DELETE` | `/api/v1/clients/{client_id}/schedule/batch/{batch_id}` | ## Template routes | Method | Route | |---|---| | `GET` | `/api/v1/templates` | | `GET` | `/api/v1/templates/{template_code}` | ## Agency routes | Method | Route | |---|---| | `GET` | `/api/v1/agency` | | `PUT` | `/api/v1/agency` | | `PUT` | `/api/v1/agency/branding` | | `POST` | `/api/v1/agency/branding/logo` | | `PUT` | `/api/v1/agency/api-keys` | | `POST` | `/api/v1/agency/api-keys/test` | | `GET` | `/api/v1/agency/api-keys/list` | | `POST` | `/api/v1/agency/api-keys` | | `DELETE` | `/api/v1/agency/api-keys/{key_id}` | | `POST` | `/api/v1/agency/custom-domain` | | `POST` | `/api/v1/agency/custom-domain/verify` | | `GET` | `/api/v1/agency/team` | | `POST` | `/api/v1/agency/team` | | `PUT` | `/api/v1/agency/team/{user_id}` | | `DELETE` | `/api/v1/agency/team/{user_id}` | ## Admin routes | Method | Route | |---|---| | `GET` | `/api/v1/admin/agencies` | | `POST` | `/api/v1/admin/agencies` | | `GET` | `/api/v1/admin/agencies/{agency_id}` | | `PUT` | `/api/v1/admin/agencies/{agency_id}` | | `POST` | `/api/v1/admin/agencies/{agency_id}/templates` | | `GET` | `/api/v1/admin/templates` | | `POST` | `/api/v1/admin/templates` | | `PUT` | `/api/v1/admin/templates/{template_id}` | | `GET` | `/api/v1/admin/system/stats` | ## Module-specific notes - Generation can return both polling and WebSocket endpoints. - Webhook configuration is part of agency or client settings. - Document generation and distribution are asynchronous job flows. - The PRD includes detailed request and response examples for the routes above and should be treated as the implementation source of truth. --- ## Voice **URL:** https://secure-docs.aiconnected.ai/docs/api-reference/modules/voice **Description:** The aiConnected Voice service contract, including tenant setup, agent management, webhooks, and call lifecycle behavior. ## Contract status - `Canonical route contract` for setup and test endpoints documented in the voice dev environment guide - `Canonical interface contract` for the broader call pipeline, event model, and service boundaries ## Source documents - [aiConnected modules overview](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiconnected-modules-overview) - [Developer introduction](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/Developer-Introduction) - [voice pipeline architecture](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/voice-pipeline-architecture) - [dev env setup guide](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/dev-env-setup-guide) - [credentials checklist](/docs/knowledge-base/aiconnected-apps-and-modules/modules/aiConnected-voice/aiConnected-voice-credentials-checklist) ## Module purpose aiConnected Voice is a multi-tenant voice AI runtime that routes calls through GoToConnect, a WebRTC bridge, LiveKit, and AI services for STT, reasoning, TTS, and tool execution. ## Canonical setup endpoints The voice dev setup guide includes these test endpoints: | Method | Route | Purpose | |---|---|---| | `POST` | `/api/v1/tenants` | Create a tenant | | `POST` | `/api/v1/auth/api-keys` | Create an API key | | `GET` | `/api/v1/agents` | List or inspect agents | ## Canonical webhook endpoints The environment guide also references these inbound integration routes: | Route | Source | |---|---| | `/webhooks/goto` | GoToConnect | | `/webhooks/livekit` | LiveKit | The credentials checklist also requires webhook-secret validation for GoToConnect and LiveKit. ## Required voice service domains ### Tenant and agent management - Provision tenants - Configure phone and AI credentials per tenant - Create and manage voice agents - Assign knowledge and tool access to an agent ### Call lifecycle - Start inbound call handling - Track call state changes - Stream transcripts and utterances - Handle interruptions and endpointing - Execute tool calls and transfers - Close and archive the call ### Observability - Emit call lifecycle events - Track latency across STT, LLM, and TTS - Preserve trace and error context ## Event-driven architecture The developer introduction is explicit that services communicate through events, not direct calls. Examples in the repo include: - `call.connected` - state transition events published during call lifecycle updates The platform-level event bus should also receive normalized topics such as: - `voice.call.started` - `voice.call.completed` - `voice.call.failed` Those align with the platform manifest examples. ## Required data model The repo repeatedly references: - `tenants` - `agents` - `voice_calls` - `transcripts` - `call_events` - `webhook_configs` - voice profiles and credentials ## External dependencies The credentials checklist calls out integration requirements for: - GoToConnect - LiveKit - Deepgram - Claude or other LLM provider - Chatterbox - DigitalOcean Spaces - n8n webhooks ## Implementation note Treat Voice as an evented, low-latency orchestration system. The route surface is only one part of the contract. The more important behavior is real-time streaming, interruption safety, and tenant-scoped call state. --- ## 5 Year AI Business Servicing Scope **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/5-year-ai-business-landscape **Description:** aiConnected Business Platform Business Landscape In 5 Years It won’t be because of “AI hype.” It will be because: Customers will no longer tolerate delays. S... # **5 Year AI Business Servicing Scope** ### **aiConnected Business Platform** --- ### **Business Landscape In 5 Years** It won’t be because of “AI hype.” It will be because: * **Customers will no longer tolerate delays.** * **Speed, personalization, and 24/7 responsiveness** will become table stakes. * **Labor costs will rise**, but AI-native companies will operate lean, fast, and global. * **Advertising costs will spike**, but AI-native firms will track every dollar to ROI. * **Decision-making will be real-time**, not quarterly. * **Data-rich, AI-augmented businesses** will predict, adapt, and dominate. * Legacy businesses will **burn cash, lose leads, and fail to scale**—until they fold or get acquired. #### **1\. AI-First Sales Infrastructure** * 24/7 inbound qualification, outbound follow-up, and dynamic appointment booking across voice, SMS, and email. * Replaces junior reps, reduces cost per acquisition, and allows infinite scale without hiring. * Legacy gap: human-led follow-up can’t compete with speed, volume, or consistency. #### **2\. End-to-End Attribution and Profit Clarity** * AI tags every inbound lead with source, intent, cost, close rate, and LTV. * Gives business owners live dashboards that tie marketing dollars to revenue—no guesswork. * Legacy gap: SMBs still “don’t know if their marketing is working.” #### **3\. AI-Powered Cashflow Enforcement** * AI collects overdue invoices, manages subscriptions, and escalates intelligently via SMS, voice, and email. * Predicts churn and flags cashflow threats before they hit the bank. * Legacy gap: businesses die from inconsistent cash collection—even with solid sales. #### **4\. 24/7 Customer Response and Service Resolution** * AI handles 80%+ of support requests, FAQs, returns, and issues—with empathy and context. * When it hands off, it brings full history, urgency, and outcome tracking. * Legacy gap: manual support is slow, expensive, and costs the company goodwill daily. #### **5\. Smart Workforce Orchestration** * AI manages internal routing of tasks, load balancing, and prioritization across team members. * Enables teams of 5 to operate like teams of 20\. * Legacy gap: human-managed teams drown in inefficiency and missed deliverables. #### **6\. Autonomous Lead Nurture and Re-engagement** * AI detects drop-off points, reactivates cold leads, revives past customers, and runs multi-channel campaigns without human prompting. * Legacy gap: humans don’t follow up consistently or strategically—they forget, get busy, or give up too soon. #### **7\. Behavior-Driven Upselling and Expansion** * AI watches user behavior and proactively offers upgrades, add-ons, and renewals at the perfect time. * Legacy gap: most businesses leave money on the table by waiting for the customer to ask. #### **8\. AI Reputation & Review Safeguard** * AI monitors reviews across the web, filters out bad feedback for private handling, and auto-promotes positive experiences to build authority. * Legacy gap: reputation becomes the new SEO—and ignoring it is suicide. #### **9\. Zero-Delay Lead Routing & Scheduling** * New leads are contacted, qualified, and scheduled within 90 seconds of form fill or phone call—without human intervention. * Legacy gap: most leads go cold before they ever hear back. #### **10\. Automated Market Awareness & Competitive Adaptation** * AI monitors competitor pricing, offers, ad strategies, and changes—alerting the business in real time with suggested responses. * Legacy gap: legacy businesses learn months too late and lose positioning before they know it. --- # **Lead Gen & Sales** 1. AI-powered chat widget to capture and qualify leads * Best suited for: B2B services, consultants, high-ticket sellers * Reasonable monthly price: $75–$125 * Difficulty: Medium (frontend widget \+ CRM/API integration \+ fallback logic) * Comparable market solutions: Drift, Intercom, Tidio * Business urgency: High – missed leads directly reduce revenue 2. Recover abandoned carts/forms via reminders * Best suited for: eCommerce stores, service booking pages * Reasonable monthly price: $60–$90 * Difficulty: Medium (tracking abandonment, timing, multichannel delivery) * Comparable market solutions: Klaviyo, CartStack, Omnisend * Business urgency: High – converts otherwise lost revenue 3. Trigger behavior-based upsell post-sale * Best suited for: eCommerce, SaaS, online training platforms * Reasonable monthly price: $60–$100 * Difficulty: Medium (product logic \+ behavioral tracking \+ offer injection) * Comparable market solutions: ReConvert, Beeketing * Business urgency: Medium – boosts average order value 4. Automate cold outreach with email sequencing and reply tracking * Best suited for: B2B agencies, consultants, SaaS sales * Reasonable monthly price: $100–$150 * Difficulty: High (deliverability optimization \+ multi-stage logic \+ reply detection) * Comparable market solutions: Lemlist, Mailshake, Woodpecker * Business urgency: High – often primary outbound channel 5. Auto-import leads from Facebook/Google ads into CRM * Best suited for: Any business running paid ads * Reasonable monthly price: $40–$75 * Difficulty: Low * Comparable market solutions: Zapier, LeadsBridge * Business urgency: High – real-time speed-to-lead improves conversions 6. Score leads based on activity and metadata * Best suited for: SaaS, B2B sales teams * Reasonable monthly price: $60–$90 * Difficulty: Medium (logic building, data enrichment) * Comparable market solutions: HubSpot Pro, Salesforce CRM * Business urgency: Medium – enhances prioritization and follow-up efficiency 7. Create referral tracking and post-sale prompts * Best suited for: eCommerce, local businesses, gyms * Reasonable monthly price: $30–$60 * Difficulty: Medium * Comparable market solutions: ReferralCandy, Friendbuy * Business urgency: Medium – valuable for growth, not critical 8. Follow-up with leads after form fill or demo request * Best suited for: Any business using lead forms * Reasonable monthly price: $40–$75 * Difficulty: Low * Comparable market solutions: GoHighLevel, Mailchimp Automation * Business urgency: High – reduces drop-off after form submit 9. Capture leads via SMS and respond with automation * Best suited for: Home services, local businesses, event-based sellers * Reasonable monthly price: $50–$90 (SMS volume dependent) * Difficulty: Medium * Comparable market solutions: SimpleTexting, Twilio \+ Zapier * Business urgency: High – great for immediacy and mobile-first users 10. Retarget website visitors with email follow-ups * Best suited for: B2B websites, landing pages, product companies * Reasonable monthly price: $60–$100 * Difficulty: Medium (tracking pixel \+ form matching \+ content logic) * Comparable market solutions: ConvertKit, Drip * Business urgency: Medium – improves lead nurturing --- Understood. We’re building a list of **mission-critical AI automations** that fundamentally transform a business’s revenue generation, operational efficiency, scalability, or market visibility. This is not just automation — this is infrastructure. Below is the redefined direction for the list. I’ll give you the first 10 with this new framing to ensure alignment before continuing. --- # **Mission-Critical Automations (High Impact Only)** 1. AI-powered conversational lead gen with website and ad integration * Best suited for: B2B services, agencies, SaaS, high-ticket sales * Reasonable monthly price: $100–$200 * Difficulty: Medium * Comparable market solutions: Drift, Intercom (at $500+/mo for same value) * Business urgency: Critical — replaces static forms and scales lead qualification 2. AI-driven outbound calling system that qualifies leads and books appointments * Best suited for: Insurance agents, law firms, real estate, loan brokers * Reasonable monthly price: $150–$350 (depends on volume) * Difficulty: High (voice AI, scheduling sync, fallback logic) * Comparable market solutions: Slingshot, Ruby, outsourced call centers * Business urgency: Critical — replaces $5–$15/hr labor, generates revenue directly 3. SEO content engine that automatically creates, posts, and optimizes long-form blog content for ranking * Best suited for: Local businesses, SaaS, affiliate marketers, agencies * Reasonable monthly price: $100–$200 * Difficulty: High (topic planning \+ semantic optimization \+ post publishing) * Comparable market solutions: Jasper, Surfer SEO (but disjointed) * Business urgency: High — generates organic visibility, leads, and compounding traffic 4. Local review generation and suppression management system * Best suited for: Healthcare, home services, restaurants, retail * Reasonable monthly price: $90–$150 * Difficulty: Medium * Comparable market solutions: Podium, Birdeye * Business urgency: High — online reputation has direct revenue impact 5. AI-generated YouTube content & video SEO with auto-publishing and cross-posting * Best suited for: Course creators, consultants, product brands * Reasonable monthly price: $150–$250 * Difficulty: High (script → voice → video → thumbnails → tags → posting) * Comparable market solutions: None unified. Jasper, Descript, TubeBuddy (piecemeal) * Business urgency: Medium–High — builds trust and reach at scale 6. Smart CRM that triggers actions based on customer behavior (site visits, emails, missed calls) * Best suited for: Agencies, digital products, local services * Reasonable monthly price: $125–$175 * Difficulty: High (event tracking \+ conditional automation \+ notifications) * Comparable market solutions: ActiveCampaign, GoHighLevel * Business urgency: Critical — keeps leads from falling through cracks 7. Centralized customer acquisition dashboard (ads \+ SEO \+ social \+ calls) * Best suited for: Multi-channel small business operators * Reasonable monthly price: $75–$150 * Difficulty: Medium–High * Comparable market solutions: Agency dashboards, Funnel.io (complex setup) * Business urgency: High — gives visibility into performance and ROI 8. Auto-dialer for lead nurturing with voicemail drops, SMS, and AI responses * Best suited for: Sales-heavy businesses, real estate, mortgage, solar * Reasonable monthly price: $150–$250 * Difficulty: Medium * Comparable market solutions: PhoneBurner, Mojo Dialer, Twilio+Zapier * Business urgency: Critical — increases touchpoints without human effort 9. AI-powered inbound call assistant that answers, qualifies, and schedules appointments * Best suited for: Busy service businesses missing calls (plumbing, HVAC, law firms) * Reasonable monthly price: $125–$300 * Difficulty: High (natural language \+ intent detection \+ integrations) * Comparable market solutions: Ruby, Smith.ai * Business urgency: Critical — turns missed calls into customers 10. Full-funnel retargeting system that syncs leads across ads, social, SMS, and email based on behavior * Best suited for: Businesses with website \+ ads \+ lead capture * Reasonable monthly price: $125–$200 * Difficulty: High (multi-platform sync \+ conditional logic) * Comparable market solutions: High-end agency setups, GoHighLevel * Business urgency: High — maximizes ROI on lead gen spend --- Here’s the next set of **high-impact automations**, continuing from \#11 to \#20. These workflows are foundational—not auxiliary—and are priced and scoped to reflect true value for small business operators who depend on performance, scale, and ROI. --- # **Mission-Critical Automations (Continued)** 11. Inbound lead routing system with real-time scoring, notifications, and rep assignment * Best suited for: Agencies, law firms, B2B service providers * Reasonable monthly price: $100–$175 * Difficulty: Medium * Comparable market solutions: Salesforce, HubSpot workflows * Business urgency: High — ensures hot leads go to the right person instantly 12. AI voicemail-to-text with CRM syncing and smart follow-up prompts * Best suited for: Solo professionals, field service providers, clinics * Reasonable monthly price: $75–$120 * Difficulty: Medium (voice transcription \+ CRM API \+ follow-up logic) * Comparable market solutions: Google Voice, YouMail (limited automation) * Business urgency: High — turns missed opportunities into recoverable revenue 13. AI sales agent for email conversations (lead nurturing and reactivation) * Best suited for: Agencies, consultants, high-ticket services * Reasonable monthly price: $125–$200 * Difficulty: High (contextual memory \+ GPT-driven replies \+ trigger logic) * Comparable market solutions: Regie.ai, Mailshake (not truly AI-led) * Business urgency: Critical — scales follow-up beyond human bandwidth 14. Dynamic pricing automation based on inventory, demand, or time of day * Best suited for: eCommerce, seasonal services, local delivery companies * Reasonable monthly price: $100–$200 * Difficulty: High (data sync \+ conditional logic \+ real-time updates) * Comparable market solutions: Prisync, Uber-style dynamic pricing engines * Business urgency: Medium–High — increases margins and sales velocity 15. Customer reactivation system that detects churn and launches tailored win-back campaigns * Best suited for: Subscription businesses, salons, gyms, service providers * Reasonable monthly price: $90–$140 * Difficulty: Medium * Comparable market solutions: Klaviyo (requires setup), GoHighLevel * Business urgency: High — reactivation is often cheaper than new acquisition 16. Smart sales pipeline with automated stage progression and alerts * Best suited for: B2B teams, real estate agents, insurance brokers * Reasonable monthly price: $90–$150 * Difficulty: Medium * Comparable market solutions: Pipedrive, HubSpot Pro * Business urgency: High — pipeline visibility drives conversion 17. Local business SEO automation: citation management, schema, keyword monitoring * Best suited for: Clinics, restaurants, contractors, agencies * Reasonable monthly price: $125–$200 * Difficulty: High (multiple data sources \+ posting logic \+ SEO schema integration) * Comparable market solutions: BrightLocal, Whitespark * Business urgency: High — dominates local search rankings and inbound traffic 18. AI recruiting assistant for screening candidates and scheduling interviews * Best suited for: High-turnover industries, startups, growing agencies * Reasonable monthly price: $125–$200 * Difficulty: High (resume parsing \+ ranking \+ scheduling logic) * Comparable market solutions: HireVue, Paradox AI * Business urgency: Medium–High — shortens time-to-hire and improves quality 19. Proposal generation and smart contract delivery with e-sign and follow-up * Best suited for: Agencies, consultants, freelancers * Reasonable monthly price: $100–$150 * Difficulty: Medium * Comparable market solutions: PandaDoc, Better Proposals * Business urgency: Medium — accelerates deal closing, improves client onboarding 20. Automated ad budget adjustment based on ROI tracking and performance thresholds * Best suited for: SMBs spending $2k+/mo on ads * Reasonable monthly price: $125–$250 * Difficulty: High (performance tracking \+ auto-budget sync \+ multi-platform logic) * Comparable market solutions: Revealbot, Madgicx * Business urgency: High — reduces waste and increases ad profitability --- Excellent. Here is the next set of **mission-critical AI automations**, \#21–30, continuing with the same focus: solutions that either generate revenue, cut labor costs, or enable operational scale. --- # **Mission-Critical Automations (Continued)** 21. Full multi-channel review generation system with routing and escalation * Best suited for: Dental offices, home service companies, med spas, restaurants * Reasonable monthly price: $100–$150 * Difficulty: Medium (trigger automation, SMS/email delivery, filtering) * Comparable market solutions: Birdeye, Podium, NiceJob * Business urgency: High — online reputation directly drives local traffic and conversion 22. AI-powered sales voicemail drops with automated SMS follow-up * Best suited for: Real estate agents, solar reps, mortgage brokers * Reasonable monthly price: $100–$200 * Difficulty: Medium (dialer integration, audio sync, conditional SMS) * Comparable market solutions: Slybroadcast, DropCowboy \+ Zapier * Business urgency: High — increases outbound reach with zero rep effort 23. AI chatbot trained on business documents and FAQs for support and lead conversion * Best suited for: SaaS companies, law firms, agencies, course creators * Reasonable monthly price: $125–$175 * Difficulty: High (vector DB, fine-tuned model, conversational memory) * Comparable market solutions: Forethought, Intercom Fin, Custom GPT bots * Business urgency: High — reduces support load while converting more leads 24. Real-time call tracking and attribution for ad campaigns * Best suited for: Local services, phone-heavy businesses * Reasonable monthly price: $75–$125 * Difficulty: Medium * Comparable market solutions: CallRail, WhatConverts * Business urgency: High — links marketing to real revenue 25. Smart document request and collection system (for onboarding or compliance) * Best suited for: Loan brokers, legal offices, accounting firms * Reasonable monthly price: $100–$150 * Difficulty: Medium–High * Comparable market solutions: Dropbox Request, Content Snare * Business urgency: Medium–High — streamlines onboarding and speeds up client processing 26. Client onboarding sequence with triggered tasks, emails, SMS, and hand-off documentation * Best suited for: Agencies, consultants, marketing service providers * Reasonable monthly price: $100–$175 * Difficulty: Medium * Comparable market solutions: GoHighLevel, Asana \+ Zapier * Business urgency: High — eliminates delays and sets the tone for long-term retention 27. AI-based quote engine that pulls service pricing dynamically and auto-generates proposals * Best suited for: Contractors, consultants, marketing firms * Reasonable monthly price: $100–$150 * Difficulty: High (input parsing \+ calculations \+ branded output) * Comparable market solutions: Better Proposals, Quoter, PandaDoc (manual setup required) * Business urgency: Medium–High — speeds up sales cycle and adds consistency 28. Live lead monitoring system that alerts teams when a high-intent visitor is on the website * Best suited for: B2B SaaS, high-ticket services, online courses * Reasonable monthly price: $100–$150 * Difficulty: Medium * Comparable market solutions: Albacross, Leadfeeder * Business urgency: Medium–High — enables timely outreach before competitors 29. Multi-location reputation and GMB performance tracker with alerts * Best suited for: Chains, franchises, location-based businesses * Reasonable monthly price: $150–$250 * Difficulty: Medium–High * Comparable market solutions: Yext, LocalClarity * Business urgency: High — manages brand equity across regions 30. Inbound lead enrichment using Clearbit, Apollo, or similar APIs * Best suited for: B2B companies, SaaS, enterprise-focused sellers * Reasonable monthly price: $100–$200 (depending on API cost) * Difficulty: Medium * Comparable market solutions: HubSpot Pro, ZoomInfo (expensive) * Business urgency: Medium–High — enables better segmentation and targeting --- Let me know if you’d like me to keep going with 31–40 or focus on a specific domain (e.g., outbound sales, agency systems, healthcare, franchising). Got it. From here forward, every automation will be carefully selected to eliminate redundancy. No feature overlap, no near-duplicates—each one will solve a **distinct and essential problem** for scaling or optimizing a small business. Here’s the next set: **\#31–40** — all unique, high-impact, and transformative. --- # **Mission-Critical Automations (Continued)** 31. AI-powered RFP/RFQ responder that generates responses from uploaded documents and past answers * Best suited for: Government contractors, enterprise vendors, professional service firms * Reasonable monthly price: $150–$300 * Difficulty: High (document parsing \+ context reuse \+ templating) * Comparable market solutions: Responsive.io, Manual consultants * Business urgency: High — enables faster, more accurate RFP submissions at scale 32. AI call summarization with structured CRM note injection and follow-up creation * Best suited for: Sales teams, client service businesses, real estate * Reasonable monthly price: $125–$200 * Difficulty: High (call transcript → summary → structured action logic) * Comparable market solutions: Fireflies.ai, Otter.ai (limited CRM integration) * Business urgency: Medium–High — eliminates admin time, increases rep output 33. Revenue forecasting engine using historical sales, seasonal patterns, and lead pipeline activity * Best suited for: Recurring revenue businesses, seasonal operators * Reasonable monthly price: $100–$150 * Difficulty: Medium–High * Comparable market solutions: ChartMogul, ProfitWell (not real-time or customizable) * Business urgency: High — improves cash flow planning and hiring strategy 34. Real-time waitlist and capacity management system with SMS updates * Best suited for: Restaurants, clinics, salons, limited-access events * Reasonable monthly price: $75–$125 * Difficulty: Medium * Comparable market solutions: Waitwhile, Nowait * Business urgency: Medium–High — maximizes utilization, improves customer experience 35. AI agent for upselling service extensions or renewals before expiration * Best suited for: Maintenance contracts, agencies, licensing-based services * Reasonable monthly price: $100–$150 * Difficulty: High * Comparable market solutions: None unified — done manually or with account managers * Business urgency: High — drives MRR without hiring more sales staff 36. Market intel and competitor monitoring system with weekly alerts * Best suited for: Agencies, ecommerce, productized services * Reasonable monthly price: $75–$125 * Difficulty: Medium * Comparable market solutions: Crayon, Kompyte (expensive) * Business urgency: Medium — competitive awareness fuels better marketing & pricing 37. Dynamic customer segmentation based on LTV, purchase behavior, and engagement * Best suited for: Ecommerce, SaaS, digital services * Reasonable monthly price: $100–$150 * Difficulty: Medium * Comparable market solutions: Klaviyo (premium), Segment.com * Business urgency: High — personalizes experience and boosts retention 38. Multi-language AI sales assistant for businesses expanding internationally * Best suited for: Product companies, remote service firms, global SaaS * Reasonable monthly price: $150–$250 * Difficulty: High (language detection \+ response \+ fallback logic) * Comparable market solutions: None at this level of automation * Business urgency: Medium–High — enables expansion without hiring multilingual staff 39. Time-sensitive flash offer system triggered by inventory spikes, slow days, or weather * Best suited for: Retailers, restaurants, local event operators * Reasonable monthly price: $75–$120 * Difficulty: Medium (conditional triggers \+ omni-channel delivery) * Comparable market solutions: None integrated — requires multiple tools * Business urgency: Medium — converts wasted capacity into fast revenue 40. Smart internal ticketing system for task routing between departments * Best suited for: Growing companies with \>5 employees and multiple roles * Reasonable monthly price: $75–$100 * Difficulty: Medium * Comparable market solutions: Jira, Asana (not tailored to SMB internal ops) * Business urgency: Medium — improves internal efficiency and accountability --- That’s a fair callout — and exactly the kind of feedback that keeps this list sharp. You're building **infrastructure**, not convenience tools. Let’s refocus with that standard: **If this AI system disappeared, the business would suffer.** These next entries must clear that bar. Here’s the repositioned **\#41–50**, each one tied to core business survival or scalable revenue operations: --- # **Mission-Critical Automations (Recalibrated)** 41. AI-powered intake agent for law firms, clinics, and agencies that qualifies, filters, and assigns incoming leads * Best suited for: High-volume service providers with strict qualification criteria * Reasonable monthly price: $200–$400 * Difficulty: High (logic trees, calendaring, legal/medical filtering) * Comparable market solutions: Smith.ai (human), custom call centers * Business urgency: **Critical** — offloads front desk, prevents wasted time, closes faster 42. Round-robin lead distribution and escalation system for sales teams * Best suited for: Multi-rep sales teams, brokerages, franchises * Reasonable monthly price: $100–$175 * Difficulty: Medium (rules logic, rep load balancing, fallback routing) * Comparable market solutions: Salesforce, HubSpot Enterprise * Business urgency: **High** — improves conversion, eliminates lead hoarding and downtime 43. AI-driven client retention monitor that predicts churn and triggers outreach * Best suited for: SaaS, subscription services, high-ticket agencies * Reasonable monthly price: $150–$250 * Difficulty: High (trend analysis, behavioral metrics, smart actions) * Comparable market solutions: ChurnZero, Vitally * Business urgency: **Critical** — retention \= survival in recurring revenue models 44. Multi-location call overflow routing with AI triage and voice scheduling * Best suited for: Medical groups, franchise operators, distributed sales teams * Reasonable monthly price: $175–$300 * Difficulty: High (live sync \+ voice \+ location/rep availability) * Comparable market solutions: Avaya, Twilio custom builds * Business urgency: **High** — maximizes answer rate, prevents missed revenue 45. End-to-end sales funnel reporting across Google Ads, Meta, phone calls, and CRM close rate * Best suited for: Any business spending \>$2k/month on ads * Reasonable monthly price: $125–$200 * Difficulty: High (data pipeline \+ attribution logic \+ visual layer) * Comparable market solutions: Hyros ($499/mo), WhatConverts * Business urgency: **Critical** — without attribution, ad spend becomes a blind gamble 46. AI billing agent that follows up on unpaid invoices across email, SMS, and voice * Best suited for: Freelancers, B2B services, contractors, clinics * Reasonable monthly price: $125–$175 * Difficulty: Medium * Comparable market solutions: Gaviti, Collbox (limited multichannel) * Business urgency: **High** — cashflow is oxygen for small businesses 47. Automated affiliate/rewards payout system tied to referrals, purchases, or signups * Best suited for: SaaS, info products, ecommerce * Reasonable monthly price: $100–$150 * Difficulty: Medium (referral tracking \+ payout logic \+ fraud controls) * Comparable market solutions: Tapfiliate, FirstPromoter * Business urgency: **Medium–High** — drives scalable, low-cost acquisition 48. Cross-channel lead duplication detector and de-dupe engine * Best suited for: Agencies, call centers, businesses with multiple lead sources * Reasonable monthly price: $100–$150 * Difficulty: Medium–High * Comparable market solutions: Insycle, HubSpot Ops Hub * Business urgency: **High** — duplicate leads ruin tracking, waste sales time, and overcharge on ads 49. AI-powered upsell/cross-sell engine triggered by specific customer behavior or lifecycle stage * Best suited for: Ecommerce, SaaS, online courses * Reasonable monthly price: $125–$200 * Difficulty: High (event tracking \+ offer logic \+ dynamic content insertion) * Comparable market solutions: Rebuy, CartHook (for ecommerce only) * Business urgency: **High** — directly increases revenue per customer 50. Reputation crisis monitor that scrapes brand mentions and alerts the business in real time * Best suited for: Agencies, local chains, influencers, consumer brands * Reasonable monthly price: $100–$175 * Difficulty: High (web scraping, NLP sentiment, alerting pipeline) * Comparable market solutions: Brand24, Mention * Business urgency: **High** — protects brand trust, enables instant response to damaging reviews or PR --- You're absolutely right to hold the bar this high. The last batch drifted back toward “nice-to-have” instead of **"critical business infrastructure."** That’s on me. Thank you for calling it out. Let me reset the lens: This list isn’t about “automation.” It’s about **survival, scale, and systemization**—the things that separate a freelancer from a company, or a $100k/year business from a $10M/year operation. Your users aren’t hobbyists. They’re building engines. So here’s how I’m recalibrating every next entry: * If it doesn’t **directly generate revenue, capture leads, close deals, retain customers, or scale operations**—it’s cut. * If losing it doesn’t cause **immediate pain, loss, or bottlenecks**—it’s gone. * Each one must solve a **core constraint** that keeps a business small. Let’s correct course with a clean slate. Here's **51–55**, this time properly aligned. --- # **Non-Negotiable Automations (True Infrastructure Level)** 51. AI sales closer that follows up with warm leads via voice, email, and SMS until a deal is closed or contact is lost * Best suited for: High-ticket services, insurance, law firms, real estate * Reasonable monthly price: $200–$500 * Difficulty: High (multi-channel sync \+ contextual memory \+ escalation logic) * Comparable market solutions: None at this level — replaces a junior sales rep * Business urgency: **Critical** — converts interest into cash, 24/7 52. Full-scale AI appointment setter that handles inbound and outbound scheduling via SMS, phone, and web chat * Best suited for: Clinics, contractors, consultants, sales teams * Reasonable monthly price: $175–$300 * Difficulty: High * Comparable market solutions: Ruby Receptionists ($300+/mo), human assistants * Business urgency: **Critical** — removes admin bottleneck, fills calendar, protects revenue flow 53. 24/7 AI inbound phone receptionist that handles customer calls, intake, and urgent issue escalation * Best suited for: Service businesses, legal, medical, home repair * Reasonable monthly price: $250–$400 * Difficulty: High (voice AI \+ fallback to live reps \+ real-time sync) * Comparable market solutions: Slingshot, Answering services * Business urgency: **Critical** — missed calls \= missed money. AI answers everything. 54. Lead qualification AI that filters all new contacts, scores them, and routes them to the appropriate sales pipeline * Best suited for: Any business generating inbound leads at scale * Reasonable monthly price: $125–$200 * Difficulty: Medium–High * Comparable market solutions: HubSpot Lead Scoring, Drift * Business urgency: **Critical** — prevents bad leads from wasting time, surfaces buyers instantly 55. Instant quote and close system for service businesses—user fills form, AI builds quote, negotiates, and closes * Best suited for: Agencies, contractors, consultants * Reasonable monthly price: $200–$350 * Difficulty: High (pricing logic \+ conversation flow \+ CRM sync \+ e-sign) * Comparable market solutions: None fully unified. PandaDoc \+ human rep still required * Business urgency: **Critical** — removes sales lag, wins fast-moving buyers, eliminates human bottlenecks --- Noted—and you're absolutely right again. Redundancy creeps in when I focus too much on channels (voice vs. SMS vs. email) and not enough on the **functional outcome** and **strategic uniqueness** of the system itself. You don’t need 10 ways to follow up—you need 10 ways to solve **different critical constraints** in the business. From this point forward, I will enforce the following internal checklist for each automation: ✅ Solves a **unique business constraint** ✅ Would be **painful or expensive to operate without** ✅ **Doesn’t duplicate the function** of a previously listed system ✅ Reflects the kind of **operational leap** that AI uniquely enables ✅ Fits into the larger ecosystem of a business that’s trying to **scale** Let’s now do **56–60**, with every line item surgically differentiated. --- # **Infrastructure-Level Automations (No Redundancy, No Fluff)** 56. AI onboarding concierge that walks new customers through activation, account setup, and success path via chat/email/voice * Best suited for: SaaS, service retainers, platforms with a setup curve * Reasonable monthly price: $175–$250 * Difficulty: High (multi-step flow, conditional logic, onboarding state detection) * Comparable market solutions: Intercom (manual), human CS teams * Business urgency: **Critical** — without onboarding, churn is inevitable. This protects LTV and retention 57. Revenue rescue engine that identifies deals stuck in pipeline \>X days and re-engages via AI, then notifies sales * Best suited for: B2B sales orgs, agencies, high-ticket services * Reasonable monthly price: $150–$200 * Difficulty: Medium (pipeline integration, smart thresholds, multi-channel outreach) * Comparable market solutions: Salesforce \+ manual rep follow-ups * Business urgency: **High** — saves deals that would otherwise die silently 58. AI-powered team load balancer that monitors calendars, task queues, and client demand to auto-assign work * Best suited for: Agencies, law firms, field service teams, internal ops teams * Reasonable monthly price: $200–$400 * Difficulty: High (live calendar sync, rule-based logic, fairness models) * Comparable market solutions: ServiceTitan (specific to home services), custom ops tools * Business urgency: **Critical** — ensures teams aren’t over/underutilized; preserves delivery timelines 59. Cross-platform upsell trigger engine that monitors user behavior (time, spend, usage) and auto-launches custom upgrade offers * Best suited for: SaaS, ecommerce, services with tiered packages * Reasonable monthly price: $125–$175 * Difficulty: Medium–High * Comparable market solutions: Chargebee, Rebuy (limited scope) * Business urgency: **High** — lifts ARPU without sales effort; scalable compounding revenue 60. Contract lifecycle automation system with renewal alerts, renegotiation prompts, and smart escalation * Best suited for: Retainer agencies, legal service providers, IT firms, B2B SaaS * Reasonable monthly price: $150–$250 * Difficulty: Medium (document tracking \+ timeline logic \+ notifications) * Comparable market solutions: ContractWorks, Ironclad (enterprise pricing) * Business urgency: **Critical** — forgotten contracts \= lost revenue, missed renewals, legal risk --- --- ## aiConnected SiteGuide/CoBrowser **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/ai-connected-site-guide-co-browser # siteGuide **User:** Oxford Pierpont **Created:** 7/28/2025 19:26 **Updated:** 8/10/2025 15:02 **Exported:** 8/10/2025 15:04 **Link:** [https://chatgpt.com/c/688806fb-9d10-8332-88f4-0d15f5785a85](https://chatgpt.com/c/688806fb-9d10-8332-88f4-0d15f5785a85) ## Here’s the updated and expanded **feature list** for **SiteGuide with Co-Browsing by aiConnected**, including persistent session memory and email-linked session recovery: --- ## 🧠 **Core Intelligence Features** * **Natural Language Understanding (NLU)** Conversational AI that comprehends plain language, technical terms, and layered inquiries. * **Live Context Awareness** Interprets the current page’s content and adjusts responses accordingly. * **Multi-Intent Recognition** Processes compound requests and multi-step inquiries within a single input. * **Smart, Search-Backed Responses** Pulls relevant content from page text, FAQs, documents, or external APIs. --- ## 🧭 **Co-Browsing Capabilities** * **Smart Navigation & Scroll Control** Automatically scrolls to relevant sections as the user asks questions. * **Real-Time Element Highlighting** Visually emphasizes content sections during conversation for clarity and engagement. * **Persistent Page-to-Page Memory** Session memory continues uninterrupted as the user browses across different pages. * **Voice-Controlled Navigation (Mobile Compatible)** Allows users to control the experience completely hands-free via natural speech. * **Dynamic Journey Mapping** Displays a visual trail of content accessed via the AI assistant. --- ## 🧬 **Persistent Session Intelligence** * **Multi-Day Session Memory** Remembers previous interactions even if the user leaves and returns days later on the same device. * **Email-Linked Session Recall** Users can tell the AI their email address to recover previous sessions on any device or browser. * **Cross-Device Continuity** Resume where you left off from desktop to mobile (or vice versa) using secure email verification. * **Long-Term Context Retention** Supports session recovery and memory for weeks or even months for returning users. --- ## 💬 **User Interaction & Lead Capture** * **Passive Lead Collection During Chat** Gathers user info and contact details naturally during the flow of conversation. * **Auto-Populated Contact Forms** Fills in website forms from user-provided information during the chat session. * **Pre-Submission Smart Routing** Offers targeted help or staff escalation before the user finishes a question or submits a form. * **Segmented Inquiry Detection** Categorizes questions (e.g. billing vs. tech support) and routes them appropriately. --- ## 🛠️ **Platform Integration** * **One-Click WordPress Plugin Deployment** Easy install, even across multi-site environments. * **Powered by n8n Workflows** Every interaction runs through fully customizable n8n automations on your backend. * **WebSocket Infrastructure for Real-Time Sync** Real-time messaging persists through page loads, site navigation, and browser refreshes. * **CRM \+ Calendar Sync** Connects directly to client CRMs and calendars to schedule meetings, save lead data, or create tasks. --- ## 🎨 **Customization & Brand Control** * **Custom Persona & Language** Fully customizable tone of voice, vocabulary, and branded phrasing. * **Text \+ Voice Interface** Users can type or speak freely and seamlessly switch between modes. * **Style Controls** Modify the bubble appearance, position, assistant avatar, and animation behaviors. * **Page-Specific Intelligence** Tailor assistant behavior and prompt logic to different URLs or sections of the site. --- ## 📊 **Analytics & Optimization** * **Real-Time Dashboard** View user engagement stats, conversion triggers, and performance data. * **Conversation Review \+ Playback** Replay anonymized chat logs to refine training and detect missed opportunities. * **A/B Testing for Copy & UX** Test different versions of the assistant’s greeting, responses, and behavior logic. * **Lead Attribution Tracking** Tracks where leads came from and what pages or phrases triggered their inquiry. --- ## 🔐 **Security & Control** * **Bot Detection Before Submission** Identifies spam bots or automated tools before they reach the contact form. * **End-to-End Session Security** Sessions linked by email are encrypted and validated securely. * **Privacy-First Design** Complies with GDPR, CCPA, and other major privacy standards out of the box. * **Live Agent Override** Human team members can jump in and take over at any point via internal alerts. --- ### 🔌 1\. **Infrastructure & Hosting** These support the real-time interaction layer and persistent communication: * **WebSocket Server** (Real-time bidirectional comms) * Self-hosted (DigitalOcean / VPS): \~$12–$40/month per instance * Managed (e.g. Pusher, Ably, or Socket.io cloud): \~$49–$199/month depending on connection limits and message volume * **Static Hosting for Plugin Assets** (JS/CSS bundles) * Typically negligible if included in WordPress plugin or bundled with your site * \~$0 if served via WordPress CDN or your own cloud * **SSL & Domain Setup** * Included in most cloud providers or \~$5–10/month if separately hosted ✅ *Total: $12–$199/month depending on scale and hosting choices* --- ### 🧠 2\. **AI-Powered Page Analysis & Navigation Control** While AI itself can be handled separately, co-browsing requires: * **DOM Parsing & Element Mapping Scripts** * One-time development cost to extract meaningful content blocks * Ongoing cost: minimal unless AI is being used to interpret every page on load * **Client-Side Highlighting/Scrolling Logic** * One-time front-end development expense * Open-source libraries like `scroll-into-view`, `IntersectionObserver`, and `MutationObserver` are free ✅ *Total: Dev time (1–2 weeks for MVP), no major recurring costs here* --- ### 🗂️ 3\. **Session Persistence Across Visits** * **LocalStorage/IndexedDB (on device)** * Free, used for same-device memory * Stores session token or context ID * **Server-Side Session Storage (cross-device)** * Redis, PostgreSQL, or Firestore: * \~$5–$30/month for storing session IDs, page history, and user metadata * **Email-Linked Context Recovery** * Secure database lookup \+ encryption * Very lightweight unless storing extensive conversation history ✅ *Total: \~$5–$30/month depending on the database and volume* --- ### 🔧 4\. **Maintenance & Scaling** * **Error Monitoring / Logging (optional but recommended)** * Sentry or LogRocket: Free tier or \~$20/month * **Traffic Scaling (if co-browsing is used by thousands simultaneously)** * May require load balancing or multiple instances of WebSocket servers * Scale cost \= \~$20/month per 10k concurrent users (estimate, varies heavily) ✅ *Total: Optional, but plan for scalability depending on expected traffic* --- ### 💡 **Estimated Monthly Overhead for Co-Browsing Features** | Tier | Approx. Monthly Cost | | :---- | :---- | | **Solo/Small Client Site** | **$20–$50** | | **Medium Agency Setup** | **$80–$200** | | **Enterprise (Multi-Client)** | **$250–$500+** | --- **Product Name:** **SiteGuide with Co-Browsing** Powered by aiConnected --- **What It Is:** SiteGuide is an AI-powered website assistant that doesn't just answer questions — it actively *navigates* the website with the user in real time. Think of it as a digital concierge that not only knows everything about the site, but can also guide users through it step-by-step, like a knowledgeable human assistant would in a physical store. This is far beyond traditional live chat or chatbots. SiteGuide is *interactive, intelligent,* and *navigational.* --- **What Problem It Solves:** Most websites are built like brochures — static, passive, and hard to navigate if you're in a hurry or don’t know where to look. Visitors often: * Get lost or frustrated * Abandon their inquiry before reaching the right page * Never complete the form or call Even with live chat, most users don’t engage unless they *already* know what they need. **SiteGuide fixes this** by proactively helping the user explore the site, learn what’s available, and get answers without ever needing to search, scroll endlessly, or wait on hold. --- **What It Does (Key Functionality):** 1. **Conversational Interface** Users interact with the AI using natural language — typing or speaking — just like they would with ChatGPT or Siri. 2. **Real-Time Website Control** When a user asks something like, “What are your pricing options?” or “Where’s the warranty info?” the AI will not only answer, but automatically scroll to the relevant section, highlight it, and guide the user’s attention there. 3. **Cross-Page Memory** The assistant keeps track of what’s been discussed even as the user navigates between different pages. 4. **Session Persistence** If a user leaves the site and comes back days or weeks later — they can pick up right where they left off. Even if they’re on a different device, they can tell the AI their email address and resume their previous session. 5. **Lead Capture** As the conversation unfolds, SiteGuide naturally collects user information — name, email, questions — without feeling intrusive. It fills out forms for them, routes inquiries to the right department, and can even book appointments via integration with calendars and CRMs. 6. **Voice \+ Mobile Support** On mobile, users can speak to the assistant hands-free, making this ideal for on-the-go interactions. --- **How It Works (Technical Stack):** * **Frontend (Website Plugin):** A lightweight WordPress plugin injects SiteGuide onto any website. This script manages the UI, handles user interactions, and displays the assistant in a friendly chat-style interface. * **Backend (DigitalOcean Server):** The real-time communication and logic are powered by a custom backend running on DigitalOcean. It includes a WebSocket server for persistent two-way communication. * **Automation Engine (n8n):** All interactions are processed through n8n workflows — a no-code automation engine — allowing SiteGuide to be deeply customized for each business (e.g., different responses for different pages or industries). * **Data Layer (Supabase):** Session memory, user history, and conversation context are stored securely in Supabase — a modern backend-as-a-service platform that offers real-time sync, authentication, and PostgreSQL storage. --- **Why It’s a Game-Changer:** * **For Users:** SiteGuide transforms the browsing experience into something *active and human*. It lowers friction, increases satisfaction, and helps users reach what they want faster. * **For Businesses:** It dramatically improves lead generation, conversion rates, and support efficiency — without requiring live staff. Every interaction is logged, measured, and repeatable. It's like giving every website visitor their own intelligent assistant. * **For Agencies & SaaS:** This is deployable at scale. One plugin. One backend. Thousands of clients — each with their own AI, workflow logic, and memory. --- **Revenue Model:** Because this system runs on existing infrastructure (DigitalOcean \+ Supabase), the overhead is minimal. The service can be priced as: * A monthly subscription per site (e.g., $19–$97/month) * Or bundled into higher-tier web service packages * With optional per-session or per-lead pricing for larger enterprise clients Margins are high because recurring infrastructure costs are already covered under a single shared backend. --- **In Summary:** SiteGuide is redefining what websites can do. It turns passive pages into intelligent, conversational experiences — blending AI, real-time navigation, and lead generation into one unified solution. It’s scalable. It’s cost-effective. And it’s already aligned with emerging expectations of how users want to interact with digital content: hands-free, fast, and conversational. --- ### 🚀 **Zero-Friction Deployment: Instant Compatibility with WordPress** Unlike most co-browsing or AI assistants that require heavy integration, custom API connections, or JavaScript SDKs, **SiteGuide is installable via a simple WordPress plugin**. This means: * **No developer required** Any business owner or agency can deploy the AI assistant in seconds, without writing a single line of code. * **Instant Compatibility with 43% of the Internet** WordPress powers over **43% of all websites worldwide**. By starting here, SiteGuide gains access to a massive install base without needing enterprise partnerships or long sales cycles. * **Built-in Plugin Infrastructure** With updates, support, and configuration handled inside the WordPress admin dashboard, it behaves like any other premium plugin — seamless, familiar, and manageable. --- ### 🔄 **Scalable to Any Website** Once the WordPress plugin is mature, **the same frontend script can be packaged for:** * **Shopify** As a storefront assistant with product navigation, cart reminders, and support built in. * **Webflow, Wix, Squarespace** For solopreneurs, artists, and small businesses that need frictionless onboarding. * **Custom-built platforms** By offering a drop-in JavaScript snippet (like Google Analytics or Drift), SiteGuide can run on *any* website — even enterprise portals and SPAs. --- Here’s the **full feature set for SiteGuide with Co‑Browsing**, organized as a numbered list with a **clear title**, **concise description**, and a **priority level** (High, Medium, or Low) based on its strategic impact and technical feasibility. --- ### 🔥 CORE FEATURES 1. **Conversational AI Interface** Natural language interaction through voice or text, enabling users to ask questions or give commands. **Priority:** High 2. **Smart Scroll & Element Highlighting** AI scrolls the page and highlights relevant content in real time, drawing user attention to specific sections. **Priority:** High 3. **Cross-Page Memory** Maintains the user's conversation and context as they navigate across different pages of the site. **Priority:** High 4. **Persistent Sessions** Saves user sessions to allow them to return days or weeks later and resume where they left off — even across devices using email-linked recovery. **Priority:** High 5. **Real-Time Lead Capture** Seamlessly collects user data during natural conversation without relying on traditional form submissions. **Priority:** High 6. **Voice-Controlled Navigation** Users can navigate the site hands-free using spoken commands, ideal for mobile users. **Priority:** Medium 7. **Page-Aware Behavior** AI customizes its tone, responses, and actions based on the type of page the user is on (e.g., pricing, blog, checkout). **Priority:** Medium --- ### 🧭 CO-BROWSING & INTERACTION LAYERS 8. **Visual Pointer Overlay** The AI uses subtle visual cues (arrows, pulses, highlights) to direct the user’s attention during guidance. **Priority:** Medium 9. **Dynamic Journey Map** Shows a breadcrumb or visual timeline of where the user has been guided, allowing quick backtracking. **Priority:** Low 10. **Agent Shadow Mode** Lets a team member silently view what the user is doing in real time and optionally take over if needed. **Priority:** Medium 11. **AI-to-Human Handoff with Context Transfer** Enables seamless escalation to a human rep, with the full chat log and navigation trail handed over. **Priority:** Medium 12. **Live Element Tracking (Scroll Sync \+ DOM Awareness)** Ensures the AI always understands the structure of the current page and tracks active user sections. **Priority:** High --- ### 🔧 PLATFORM INTEGRATION & AUTOMATION 13. **Instant WordPress Plugin Deployment** Easily install SiteGuide on any WordPress site with a plugin — no code required. **Priority:** High 14. **Supabase-Powered Session Storage** Securely stores and retrieves session data using Supabase for long-term, cross-device memory. **Priority:** High 15. **n8n Workflow Engine Integration** Every user interaction flows through n8n automations, allowing full customization per site. **Priority:** High Here is the detailed and thorough Product Requirements Document (PRD) for **SiteGuide with Co‑Browsing by aiConnected**: --- # 🧾 SiteGuide with Co-Browsing **Product Requirements Document (PRD)** **Version:** 1.0 **Date:** 2025-07-30 **Prepared for:** Development and engineering teams **Prepared by:** aiConnected (Bob) --- ## 📌 1\. Overview ### 1.1 Product Summary **SiteGuide** is an AI-powered web assistant embedded into websites to help users navigate, understand, and interact with site content through conversation. Unlike typical chatbots, SiteGuide **controls the actual webpage** in real-time, scrolling to relevant sections, highlighting key content, and maintaining persistent memory across pages and visits. ### 1.2 Core Objective The goal is to create a **self-guided, AI-native co-browsing assistant** that can be deployed via WordPress plugin (and eventually other platforms), enabling real-time navigation, user interaction, lead capture, and intelligent follow-up — **with no live agent required.** --- ## 🎯 2\. Core Features & Functional Requirements --- ### 2.1 Conversational AI Interface **Description:** The assistant accepts natural language input via voice or text and responds with helpful, context-aware answers. **UI Requirements:** * Chat bubble visible on all site pages (unless suppressed by admin settings) * Opens into a floating modal with: * Input field (text) * Microphone icon (voice) * AI response pane with smooth message rendering * Widget must be draggable and mobile-responsive **Functional Requirements:** * Understands conversational questions (e.g., “What’s your refund policy?”) * Can reference current page context and DOM content * Pulls FAQs, structured page data, and metadata for its responses * Allows seamless switching between text and voice **Back-End Requirements:** * AI inference handled via OpenAI, Claude, or other LLM APIs * Memory and conversation history stored in Supabase, tied to session token * Voice processed via Web Speech API (MVP) or ElevenLabs (enhanced) --- ### 2.2 Scroll-to-Element & Highlighting **Description:** Based on user queries or internal logic, the assistant scrolls to a target section of the page and visually highlights it. **Functional Requirements:** * DOM is scanned on page load using custom JavaScript to identify common blocks: headers, pricing tables, FAQs, images, etc. * Scroll to the relevant element using `scrollIntoView({ behavior: 'smooth' })` * Apply temporary CSS highlighting animation (glow, border pulse) **Highlight Options:** * `box-shadow` pulse on container div * Border color shift for attention * Optional pointer icon (Phase 2\) **Target Identification Logic:** * Use semantic tags, `data-siteguide` attributes, or nearest heading anchors * Classify content using tag weight (e.g., `

` over `

`) * Use XPath or document structure for fallback targeting --- ### 2.3 Persistent Session Memory **Description:** All user interactions are saved to a session and retrievable by device or user email. **Anonymous Memory System:** * Session ID generated and stored in `localStorage` * All interactions logged in Supabase under session ID * Memory includes chat, page visits, scrolls, highlights, lead data **Email-Linked Sessions:** * At any point, user can say “Remember me” or give their email * Session ID linked to email in Supabase * On future visits (even new devices), user can say “Pick up where I left off” or input email to resume **Requirements:** * Session TTL: 6 months minimum * All context is loaded and rehydrated into frontend memory store on reconnect * Expired sessions are archived but queryable for analytics --- ### 2.4 Multi-Page Context Tracking **Description:** AI maintains full memory of what the user has viewed and asked across different site pages. **Mechanism:** * Each page change updates `currentPage` and `referrerPage` in memory * Supabase logs: * `session_id` * `page_url` * `timestamp` * `scroll_position` * `interaction_type` **Expected Behavior:** * If a user asks about a feature they saw earlier, AI should be able to say: “You were looking at the pricing page earlier. Would you like me to take you back?” --- ### 2.5 Lead Capture via Conversation **Description:** As the user interacts, SiteGuide collects lead information naturally — without explicit form fields. **Collection Points:** * Name (inferred from question: “Hi, I’m Sam — I had a question about pricing.”) * Email (“You can send it to [sam@email.com](mailto:sam@email.com)”) * Phone number (if mentioned) * Type of inquiry (detected from conversation content) **Behavior:** * Data auto-injected into any active form on the page (via JavaScript) * Optionally pushed to Supabase or n8n workflow for CRM sync **n8n Integration:** * Webhook or Supabase trigger sends lead to connected CRM (e.g., HubSpot, Salesforce) * Triggers follow-up automation --- ### 2.6 WordPress Plugin Delivery **Description:** SiteGuide is deployed via plugin on WordPress sites. **Features:** * Easy upload and install via .zip or WordPress marketplace * Plugin admin panel includes: * AI assistant name and welcome message * Toggle for voice mode * Page exclusion rules * Widget placement settings (bottom right, left, inline, etc.) * Plugin auto-loads core script across public pages **Script Behavior:** * Asynchronous script load * Degrades gracefully if disabled * Securely connects to WebSocket backend --- ### 2.7 Voice Interaction (MVP \+ Enhanced) **MVP:** Web Speech API for TTS and STT **Enhanced:** ElevenLabs for more realistic voice output **Voice Input Requirements:** * Microphone toggle on widget * Press-to-talk or voice wakeup (MVP: manual only) **Voice Output Requirements:** * AI responds using browser TTS or ElevenLabs API * Should reflect tone: cheerful, helpful, confident, etc. **Accessibility:** * Voice must fallback to text if browser lacks microphone * All spoken output must also appear as text --- ### 2.8 WebSocket Real-Time Sync **Description:** Persistent 2-way connection between frontend and AI backend. **Uses:** * AI sends scroll or highlight commands in real-time * AI receives live context updates (user location, clicks, etc.) **Requirements:** * Socket ID assigned per session * Keep-alive with heartbeat every 15s * Reconnect with exponential backoff **Libraries:** * Socket.io or WS (Node.js) * Host on DigitalOcean server with TLS and rate limits --- ### 2.9 Supabase Storage Architecture **Tables Required:** * `users` → \{ id, email, created_at \} * `sessions` → \{ id, user_id, anon_id, created_at, last_active \} * `interactions` → \{ id, session_id, message, intent, timestamp \} * `page_visits` → \{ id, session_id, page_url, timestamp \} * `leads` → \{ id, session_id, name, email, phone, tags \} **Security:** * Use Supabase RLS (Row-Level Security) * Read/write access only for authenticated backend --- ### 2.10 Admin/Agent Shadow Mode (Phase 2\) **Description:** Admin can observe active sessions in real-time. **Features:** * View active session list in dashboard * Click into a session to view current page and scroll state * Option to “ghost” the user without intervention **Requirements:** * Streaming scroll position via WebSocket * Read-only DOM mirror (sanitized) --- ## 🧪 3\. Success Criteria (MVP) | Feature | Success Metric | | :---- | :---- | | AI answers contextually | \>85% questions answered using current page content | | Scroll/highlight accuracy | \>90% successful targeting of correct element | | Persistent memory | Session resumes accurately across pages/devices | | Lead capture efficacy | \>50% completion rate in natural convo flow | | Plugin install time | \<3 minutes average install by non-technical user | | Voice accuracy (STT) | \>90% correct speech transcription | | WebSocket latency | \<200ms round trip for scroll/highlight commands | --- ## ✅ What’s Already Excellent ### 🧠 Conceptual Clarity * The product's purpose, goals, and unique value are clearly defined. * Differentiation from competitors is well understood and implementation-focused. ### 🔧 Functional Coverage * Features are broken down with detailed behavioral expectations. * Technical systems like WebSockets, Supabase schema, and session logic are outlined clearly. * Integration points (n8n, WordPress, Supabase) are mapped. ### 🧪 Success Metrics * Each feature includes a measurable performance benchmark, which guides QA and iteration. --- ## 🔍 What’s Still Needed for Development Readiness ### 1\. **UX/UI Specifications (Missing)** **What’s needed:** * Full wireframes or UI mockups for: * Assistant interface (desktop and mobile) * Plugin admin panel in WordPress * Lead capture confirmation state * Interaction design (e.g., animations for scroll/highlight, voice input UI behavior) **Why it matters:** Developers need to know not just *what* to build, but *how it should look and feel* to the user. --- ### 2\. **LLM Prompt Engineering Guidance** **What’s needed:** * Prompt templates for: * Initial greeting * Memory-aware follow-ups * Scroll-to commands (“scroll to the pricing section”) * Highlight decisions * Fallback behavior if no matching element is found **Why it matters:** The effectiveness of the assistant relies heavily on prompt quality. Without these templates, behavior could be inconsistent or underwhelming. --- ### 3\. **Data Flow Diagrams / Sequence Charts** **What’s needed:** * Sequence diagram for: * Session creation → interaction → session recall * Scroll/highlight command flow (AI → backend → frontend) * Lead capture and dispatch via n8n **Why it matters:** It ensures all teams — frontend, backend, automation — are aligned on when and how each component fires. --- ### 4\. **Testing Plan & Edge Cases** **What’s needed:** * What to test and how: * Session recovery across incognito vs. logged-in devices * How the AI handles failed scroll/highlight attempts * How voice behaves on unsupported browsers * Race conditions with fast page switching **Why it matters:** QA teams (or devs themselves) need precise criteria for catching edge-case bugs or performance failures. --- ### 5\. **CI/CD \+ Deployment Environment Plan** **What’s needed:** * Where the plugin JS is hosted (CDN?) * Deployment structure for the WebSocket server (Docker? PM2? Horizontal scaling?) * Versioning and rollback strategy * Staging vs. production environment separation **Why it matters:** Without this, DevOps becomes a bottleneck. Plugin updates, backend logic fixes, and real-time systems must be safely deployable. --- ## 🟢 Final Verdict As a developer, I’d say: * **This PRD is 80–85% complete** It gives me the **why, what, and how** of the system. * To **ship with confidence**, I’d need: * Visual mockups / UX spec * Prompt templates for LLM behavior * Architecture and event flow diagrams * Deployment and testing details --- # 🧾 **PRD Outline for SiteGuide with Co-Browsing** *(Developer-Grade, Zero-Assumption Version)* --- ## 1\. 📌 Introduction ### 1.1 What is SiteGuide? Explain the concept in plain language: what it is, what it does, and why it matters. ### 1.2 Core Value Proposition Who it's for (e.g., business websites), what problem it solves (user navigation & lead conversion), and why it's better than traditional live chat or bots. ### 1.3 Deployment Targets Start with WordPress, then expand to other platforms. --- ## 2\. 🎯 Product Goals ### 2.1 Primary Goals * Real-time AI-powered navigation * Automatic scrolling and content highlighting * Persistent sessions across visits/devices * Lead capture during conversation ### 2.2 Success Criteria Define measurable outcomes (e.g., time to install, scroll accuracy, session recall success rate, lead conversion rate). --- ## 3\. 🧠 Feature Overview ### 3.1 Summary Table Each feature with: * Title * Description * Inputs/outputs * Priority --- ## 4\. 🧱 System Architecture ### 4.1 High-Level Diagram Visual: frontend, backend, Supabase, n8n, WebSocket server, LLM provider ### 4.2 Data Flow Maps * Page load → chat open → AI response → scroll * Session creation → memory store → recall via email * Lead captured → send to CRM --- ## 5\. ⚙️ Technical Components (Modular Breakdown) ### 5.1 Frontend Widget * Chat UI (text & voice) * Scroll and highlight logic * DOM observer * Local session storage * Voice interaction handler ### 5.2 WordPress Plugin * Script injector * Admin panel (branding, placement, toggles) * Page-level control ### 5.3 WebSocket Server * Persistent connection * Message routing (scroll/highlight/data) * Authentication (anon or email-linked) ### 5.4 Supabase * Data schema * Row-level security * Realtime listeners ### 5.5 LLM Logic Layer * Prompt templates * Context embedding * Response validation * Fallback handling ### 5.6 n8n Integration * Lead push (to CRM, email, etc.) * Session activity logging * Trigger-based automations --- ## 6\. 🛠️ Feature Specifications (One-by-One) Each feature will have: * **Title** * **User Story** * **Functional Requirements** * **Edge Cases** * **Frontend Behavior** * **Backend Logic** * **Storage Requirements** * **Success Criteria** * **Dependencies** Features to cover: * AI conversation engine * Scroll-to-element * DOM highlighting * Visual pointer * Voice commands (STT and TTS) * Email-linked session recall * Multi-page session memory * Lead capture * Plugin install experience * Mobile and desktop behavior * Real-time communication via WebSocket * Offline/degraded mode behavior --- ## 7\. 🧪 Testing & QA Plan ### 7.1 Unit Tests What components must be individually verified ### 7.2 Integration Tests How systems should behave across modules (e.g., frontend \+ Supabase \+ AI) ### 7.3 Manual QA Flows Click-through test scripts for testers (e.g., "Ask a question → scroll → return tomorrow") --- ## 8\. 🎨 UI/UX Specifications ### 8.1 Widget Wireframes Floating chat bubble, open chat, voice input state ### 8.2 Highlight/Pointer Animations CSS specs for glow, border, pulse, transitions ### 8.3 Plugin Admin Panel Mockups Toggle behaviors, customization inputs, field validation --- ## 9\. 🔐 Security & Privacy ### 9.1 Data Collection Rules Anonymous tracking, opt-in consent for email memory, GDPR/CCPA compliance ### 9.2 Storage Security Encryption in transit, Supabase RLS enforcement, API key handling --- ## 10\. 🚀 Deployment Plan ### 10.1 Environments Local, staging, production setup ### 10.2 Hosting DigitalOcean for WebSocket \+ logic servers Supabase for DB and API Plugin distributed via WordPress ### 10.3 CI/CD How updates are deployed, plugin versioning, rollback procedures --- ## 11\. 🧰 Developer Resources ### 11.1 LLM Prompt Library Standard prompt patterns for AI behavior ### 11.2 DOM Element Targeting Guide Classes and data attributes to annotate key content (for scroll/highlight accuracy) ### 11.3 WebSocket Message Formats Send/receive payload specs for scroll, highlight, resume session, etc. ### 11.4 Supabase Schema & ERD Detailed database schema, relationships, and example queries ### 11.5 API Documentation Internal REST/WebSocket/n8n endpoint specs --- ## 12\. 📎 Appendices * Glossary of Terms * Fallback UI Modes * Integration FAQs * Support Ticket Handling (post-launch) --- Here’s the revised outline for the **SiteGuide with Co-Browsing** Product Requirements Document (PRD), optimized for clarity, technical precision, and zero ambiguity. --- # SiteGuide with Co-Browsing **Product Requirements Document (PRD)** **Prepared for:** aiConnected **Prepared by:** OpenAI Assistant **Audience:** Frontend Developers, Backend Engineers, Full-Stack Developers, QA Engineers, Product Managers **Version:** 1.0 --- ## 1\. Introduction ### 1.1 Product Summary ### 1.2 Problem Statement ### 1.3 Target Users ### 1.4 Use Cases and Scenarios ### 1.5 Goals and Non-Goals ## 2\. Product Objectives and Success Criteria ### 2.1 Primary Objectives ### 2.2 Key Performance Indicators (KPIs) ### 2.3 Constraints and Assumptions ## 3\. System Overview ### 3.1 Component Architecture ### 3.2 Data Flow and Lifecycle ### 3.3 High-Level Diagrams ### 3.4 Technologies and Tools Used ## 4\. Functional Feature List A summary table of all major features including: * Feature Name * Description * Dependencies * Priority (Must, Should, Could) ## 5\. Module Specifications Each feature/module will have a full specification including: * Purpose * Trigger/Event * Inputs * Outputs * Behavior and Flow * UI Requirements * State Management * Error Handling * API Integration (if any) * Storage or Persistence * Edge Cases ### 5.1 Conversational Interface ### 5.2 Scroll-to-Element Functionality ### 5.3 DOM Highlighting ### 5.4 Visual Pointer (Optional) ### 5.5 Voice Input and Output ### 5.6 Session Persistence and Memory ### 5.7 Email-Linked Session Recovery ### 5.8 Cross-Page Memory Management ### 5.9 Lead Capture via Conversation ### 5.10 WordPress Plugin Delivery ### 5.11 WebSocket Connection Management ### 5.12 Supabase Integration for Storage ### 5.13 n8n Integration for Automation ### 5.14 Mobile-Responsive and Accessibility Behavior ### 5.15 Admin Shadow Mode (Optional, Phase 2\) ## 6\. Data and Schema Definitions ### 6.1 Supabase Table Schema ### 6.2 Entity Relationship Diagrams (ERD) ### 6.3 WebSocket Message Structure ### 6.4 LocalStorage and SessionStorage Structure ### 6.5 n8n Webhook Structures ## 7\. UI/UX Specifications ### 7.1 Chat Widget Behavior and States ### 7.2 Voice Interface UX ### 7.3 Scroll and Highlight Animations ### 7.4 Assistant Avatar and Branding Options ### 7.5 Admin Panel UI (for WordPress Plugin) ## 8\. Prompt Design and AI Behavior ### 8.1 Base Prompt Templates ### 8.2 Dynamic Prompt Variables ### 8.3 Page-Aware Prompt Adjustments ### 8.4 Fallback and Safety Logic ### 8.5 User Input Classification and Routing ## 9\. Testing and Quality Assurance ### 9.1 Unit Test Requirements ### 9.2 Integration Test Plans ### 9.3 Manual QA Checklist ### 9.4 Regression Testing ### 9.5 Voice/Accessibility Testing ## 10\. Deployment Plan ### 10.1 Hosting Requirements ### 10.2 Deployment Pipelines ### 10.3 Plugin Distribution and Versioning ### 10.4 CI/CD Strategy ### 10.5 Error Logging and Monitoring ## 11\. Security and Compliance ### 11.1 Session Security ### 11.2 Supabase Row-Level Security ### 11.3 GDPR/CCPA Compliance ### 11.4 Voice and Data Consent ### 11.5 API Rate Limiting and Abuse Handling ## 12\. Developer Tools and Support Materials ### 12.1 API and WebSocket Documentation ### 12.2 DOM Targeting Strategy and Examples ### 12.3 Local Dev Setup Guide ### 12.4 Sample Prompt Library ### 12.5 Troubleshooting and Debugging Guide ## 13\. Appendix ### 13.1 Glossary of Terms ### 13.2 Browser Support Matrix ### 13.3 Phase 2 and Phase 3 Feature Planning ### 13.4 References and External Docs --- # 1\. Introduction ### 1.1 Product Summary **SiteGuide** is an embeddable AI-powered web assistant that guides users through a website in real-time using natural language interaction. It behaves like a human guide or concierge, helping visitors locate relevant content by directly controlling the page—scrolling, highlighting, and referencing specific sections of the website visually and conversationally. SiteGuide differs from traditional chatbots by offering a co-browsing experience. The assistant not only answers questions, but physically manipulates the site as the user watches. This includes scrolling the page to specific areas, highlighting content, and capturing leads—all through a natural, conversation-driven experience. The assistant can also speak and listen using built-in voice recognition and text-to-speech, making it fully voice-enabled and mobile-friendly. Session memory is persistent: users can return to the site days or even months later and resume their previous interaction, either automatically (via device) or by identifying themselves (e.g., email address). The MVP is delivered as a WordPress plugin and later as a platform-agnostic JavaScript library for use on any website. --- ### 1.2 Problem Statement Most websites today are passive and rely on users to find their way around. Even those that implement live chat or automated bots are still heavily dependent on: * User familiarity with site layout * Traditional form submissions * Human intervention for sales or support * Session loss upon reloads, navigation, or future visits Visitors often bounce from websites due to friction, confusion, or fatigue, especially on mobile where navigation can be clunky. Businesses lose potential customers every day not because their content is missing—but because users can’t *find it fast enough*. SiteGuide solves this by transforming the site into an interactive, guided experience—reducing drop-offs, increasing conversions, and making self-navigation effortless. --- ### 1.3 Target Users **1\. Website Owners and Agencies** * Small-to-medium businesses using WordPress or custom websites * Marketing teams looking to improve engagement and lead capture * Agencies who want to deploy a smart assistant across multiple client sites **2\. End-Users (Site Visitors)** * First-time visitors seeking fast answers * Mobile users who prefer voice or hands-free interaction * Users evaluating services (e.g., legal, health, B2B, education, etc.) --- ### 1.4 Use Cases and Scenarios 1. **New Visitor Asking a Common Question** “Where is your pricing?” → SiteGuide scrolls to the pricing section, highlights it, and explains key points. 2. **Returning Visitor** “Pick up where we left off.” → SiteGuide recalls the previous session, brings the user to the last page viewed, and reminds them of the conversation. 3. **Mobile User with Voice Only** “Can I schedule a consultation?” → SiteGuide responds audibly and begins the appointment booking process. 4. **Lead Generation Without a Form** As the user asks questions, SiteGuide captures name, email, and interest, then syncs it with the business CRM in the background. --- ### 1.5 Goals and Non-Goals **Goals** * Create a real-time, AI-driven assistant that can: * Answer questions contextually * Control website scroll and highlight functions * Persist user sessions over time and across devices * Work on mobile and desktop with voice input * Seamlessly capture leads during conversation * Deliver via a lightweight WordPress plugin with zero developer setup required **Non-Goals** * SiteGuide is not a human-agent chat platform (e.g., Intercom or Zendesk) * It does not offer live screen-sharing or video calling * It is not designed to provide support for complex account management or troubleshooting workflows * It does not require, and should not rely on, external APIs for static site structure parsing --- # 2\. Product Objectives and Success Criteria ### 2.1 Primary Objectives The following objectives define what SiteGuide must accomplish by the end of the MVP phase: 1. **Enable real-time AI-guided browsing** Users must be able to ask a question or express a need in natural language, and the assistant must: * Understand the request * Determine the relevant content on the page * Automatically scroll to and highlight that content 2. **Capture lead information conversationally** Without requiring a formal form submission, SiteGuide must detect and extract contact details (name, email, phone, intent) during natural dialogue and send them to the backend or CRM. 3. **Support persistent session memory across time and devices** The assistant must remember what a user saw, asked, or did in past visits, and allow: * Session recall via the same device (local ID) * Session recovery via email (cross-device) 4. **Voice interaction for hands-free control** On mobile and desktop browsers that support it, the assistant must allow: * Voice input (speech-to-text) * Voice output (text-to-speech) * Seamless toggling between voice and text modes 5. **Deploy via WordPress plugin with no developer setup** The entire co-browsing system must function as a plug-and-play solution. WordPress site owners must be able to: * Install the plugin * Customize its behavior through a visual admin interface * Activate it without writing or modifying any code 6. **Real-time AI control via WebSocket** Actions like scrolling and highlighting must be triggered instantly via two-way communication between the LLM backend and the browser widget. WebSocket architecture must support: * Persistent connections * Low latency (\<200ms) * Session reconnection across page reloads 7. **Front-end behavior must be fast and intuitive** The assistant must not delay page load, interfere with page behavior, or create user frustration due to visual lag, misfires, or conflicting styles. --- ### 2.2 Key Performance Indicators (KPIs) These KPIs define whether SiteGuide is functionally and commercially successful: | Objective | KPI | Target | | :---- | :---- | :---- | | Scroll-to-section accuracy | % of scrolls that land on correct target | ≥ 90% | | Lead capture rate | % of engaged sessions resulting in email or name capture | ≥ 50% | | Session recall success | % of returning users whose session was successfully resumed | ≥ 80% | | Voice recognition accuracy | % of correctly interpreted voice commands | ≥ 90% | | Widget load time | Time from page load to widget ready | \< 1.5s | | Real-time latency | Time from AI decision to scroll/highlight action | \< 200ms | | Plugin install time | Time from plugin install to first working assistant interaction | \< 3 minutes | --- ### 2.3 Constraints and Assumptions **Known Constraints:** * SiteGuide must not rely on modifying the structure of client websites (i.e., works with unknown HTML structures) * LLM processing and WebSocket backend are hosted centrally and must serve many sites * Not all devices or browsers will support voice interaction * The assistant must work across both SPA (Single Page Application) and MPA (Multi-Page Application) WordPress themes **Assumptions:** * Most users will not have JavaScript or cookies disabled * Most deployments will be on modern WordPress websites using themes that follow semantic HTML practices * Businesses using SiteGuide will prefer ease of use over customization * Internet connectivity is stable enough to support persistent WebSocket communication --- # 3\. System Overview ### 3.1 Component Architecture SiteGuide is composed of the following architectural layers: #### 1\. Client-Side Widget (Frontend) A JavaScript-based assistant that is injected into a website via WordPress plugin (and later via universal embed). It handles: * The user interface (chat, voice, and assistant behavior) * Page manipulation (scrolling, highlighting, pointer rendering) * Real-time communication with the backend over WebSocket * DOM scanning and target matching * Local memory and session management #### 2\. WordPress Plugin A self-contained plugin that: * Installs and injects the SiteGuide widget on all site pages * Provides a GUI admin panel for customization (e.g., assistant name, color, visibility rules) * Connects to the aiConnected backend via provided credentials #### 3\. WebSocket Server A persistent connection layer that: * Bridges real-time communication between the assistant frontend and the AI backend * Receives structured commands from the LLM (e.g., “scroll to pricing”) and emits them to the appropriate client * Maintains socket sessions per user/site * Supports multi-tenant infrastructure (each WordPress site \= a tenant) #### 4\. LLM Processing Layer Responsible for: * Interpreting user inputs (voice or text) * Generating context-aware responses based on website structure, session memory, and intent * Producing structured action instructions (e.g., `{action: "scroll", target: "faq_section"}`) Can be powered by OpenAI, Claude, or custom models. #### 5\. Supabase (Database and Session Memory) Supabase handles: * Session persistence (page visits, conversation logs, memory embeddings) * Lead storage (name, email, message intent) * Cross-device session recall via user ID/email * Real-time row-based syncing (optional) #### 6\. n8n Workflow Automation n8n powers backend automation tasks including: * Sending captured leads to CRM or email * Triggering internal alerts or follow-ups * Logging analytic events (e.g., session started, session resumed, form auto-filled) --- ### 3.2 Data Flow and Lifecycle #### Basic Lifecycle: Anonymous User 1. Page loads → widget initializes → anonymous session ID created 2. User opens assistant → begins chat or voice interaction 3. Input sent to AI via WebSocket → AI interprets \+ responds 4. AI sends structured action (e.g., scroll, highlight) to frontend 5. Actions are executed and logged (conversation, actions, DOM references) 6. User may provide an email → anonymous session is upgraded to persistent session #### Returning User (Same Device) 1. Widget checks for `siteguide_session_id` in localStorage 2. If found, fetches memory from Supabase 3. Assistant greets the user and optionally offers to resume last session #### Returning User (Different Device) 1. User says “Pick up where I left off” or provides email 2. Widget makes authenticated query to Supabase to fetch session history 3. Memory is restored and interaction resumes --- ### 3.3 High-Level Diagrams A future version of this document will include full visual diagrams: * Component Communication Flow (Frontend → WebSocket → AI → Supabase) * Session Lifecycle Diagram * DOM Interaction Flow (scroll → highlight → pointer → confirmation) * Multi-tenant architecture for scaling across many websites *(Let me know if you want me to generate those as vector diagrams or Mermaid syntax.)* --- ### 3.4 Technologies and Tools Used | Component | Technology | | :---- | :---- | | Assistant Frontend | Vanilla JS (or React), Tailwind CSS (optional), Web Speech API | | Voice Processing | Web Speech API (MVP), ElevenLabs (enhanced) | | DOM Interaction | IntersectionObserver, MutationObserver, scrollIntoView | | Backend AI Logic | OpenAI, Anthropic, or LLM via API | | Real-Time Communication | Node.js \+ socket.io or ws | | Memory \+ Storage | Supabase (PostgreSQL \+ RLS) | | Plugin Platform | WordPress (PHP 7+, Gutenberg compatible) | | Automation | n8n (self-hosted or cloud-hosted) | | Hosting | DigitalOcean VPS (WebSocket), Vercel/Cloudflare (static JS), Supabase (backend) | --- # 4\. Functional Feature List Each feature listed below will be fully defined in Section 5\. This list provides a high-level overview of what the assistant must do, how critical each item is to the MVP, and what other systems or components it depends on. | \# | Feature Name | Description | Dependencies | Priority | | :---- | :---- | :---- | :---- | :---- | | 1 | Conversational AI Interface | Accepts user input via chat or voice, sends it to the AI, and renders natural responses | LLM API, WebSocket | Must | | 2 | Scroll-to-Element Functionality | Automatically scrolls to the most relevant section on the page based on AI instruction | DOM scanning, WebSocket | Must | | 3 | DOM Element Highlighting | Visually highlights target content using animation and styling | DOM targeting engine | Must | | 4 | Voice Input and Output | Users can speak to the assistant and hear its responses aloud | Web Speech API, ElevenLabs | Should | | 5 | Persistent Session Memory | Tracks user behavior and history locally and in Supabase | Supabase | Must | | 6 | Email-Linked Session Recovery | Allows users to recover past sessions across devices using their email address | Supabase, UI logic | Must | | 7 | Cross-Page Session Continuity | Remembers the conversation and behavior across internal site page navigations | Local memory \+ Supabase | Must | | 8 | Lead Capture via Conversation | Collects name, email, and intent as part of natural chat flow | n8n, Supabase | Must | | 9 | WordPress Plugin | Allows easy installation, customization, and activation on any WordPress site | WordPress, Admin UI | Must | | 10 | Real-Time AI Command Execution | Receives AI commands like “scroll to pricing” over WebSocket and executes them | WebSocket, AI backend | Must | | 11 | Widget Customization Panel | Admin UI in WordPress to customize assistant name, icon, color, voice mode, page exclusions | Plugin Admin Panel | Must | | 12 | Page-Aware Prompting | AI adjusts tone and behavior based on page context (e.g., homepage vs. FAQ) | URL resolver, prompt system | Should | | 13 | Visual Pointer Overlay (Optional) | Optional arrow or pulse pointer that visually emphasizes what the AI is referencing | CSS renderer, DOM mapping | Could | | 14 | Session Rehydration on Load | Automatically restores memory and scroll state on new visit or page load | Supabase, session engine | Must | | 15 | n8n Workflow Automation | Routes leads, stores logs, sends notifications or analytics data | n8n Webhooks or Supabase Triggers | Must | | 16 | Fallback and Error Handling | Handles failure cases (e.g., no scroll target found, AI timeout) gracefully | Error monitoring system | Must | | 17 | Mobile and Accessibility Compliance | Assistant is responsive, accessible via keyboard, and screen-reader friendly | Frontend \+ voice UI | Should | | 18 | WebSocket Connection Management | Reconnects on refresh, detects dropped sockets, resumes session context | WebSocket handler | Must | | 19 | Shadow Mode (Phase 2\) | Allows admin to observe live sessions (read-only DOM stream) | WebSocket, viewer UI | Could | | 20 | Analytics and Event Logging | Logs usage events such as opens, closes, scrolls, highlights, and lead captures | Supabase, n8n, optional dashboard | Should | --- ### Feature Priority Key * **Must**: Required for MVP launch. These features must be stable, tested, and integrated. * **Should**: Not strictly required for MVP but highly recommended for product completeness or reliability. * **Could**: Nice-to-have or experimental features that can be deferred or gated behind admin controls. --- # 5.1 Conversational AI Interface ### Purpose The Conversational AI Interface is the user-facing module that accepts user input (typed or spoken), forwards it to the AI backend, and renders the AI’s response within a styled chat widget. It serves as the primary interface for interacting with the assistant, and is responsible for triggering all downstream co-browsing actions, such as scrolling, highlighting, and lead capture. --- ### User Story * As a visitor to a website, I want to ask questions in a natural way so that the AI can help me find the content I need without searching manually. * As a mobile user, I want to speak to the assistant and receive spoken answers hands-free. * As a returning visitor, I want the assistant to remember me and pick up where I left off. --- ### Functional Requirements #### Input Handling * Accepts natural language input from user via text field. * Optional microphone button allows speech-to-text input via Web Speech API. * Detects when input is empty, too short, or outside expected behavior (e.g., non-verbal sounds). * Detects user requests that are: * Content-related (e.g., “Where is the pricing?”) * Procedural (e.g., “I want to schedule a consultation.”) * Memory-linked (e.g., “Pick up where I left off.”) * Identifying (e.g., “My email is [john@acme.com](mailto:john@acme.com).”) #### AI Query Execution * Formats user input into a structured JSON payload: ```json { "session_id": "abc123", "message": "Where is the pricing?", "page_url": "https://clientsite.com/pricing", "timestamp": 1692721934, "device_info": {...}, "memory_context": {...} } ``` * Sends query to backend AI processor via WebSocket or REST (depending on architecture decision). * Waits for AI response or fails after a defined timeout (e.g., 10 seconds). * On error, shows fallback response: “I didn’t quite catch that. Can you try rephrasing it?” #### Output Rendering * Renders AI response in chat bubble using typing animation (e.g., one character per 10ms). * Response may include: * Plain text * Action instructions (e.g., `scrollTo`, `highlight`, `captureLead`) * Follow-up question prompts (e.g., “Would you like me to show you that section?”) #### Session Interaction * Saves every message (user and AI) to session memory in Supabase, with timestamp and message type. * Tracks which messages triggered downstream actions. * Supports follow-up chaining: AI can ask for clarification or present follow-up options. #### Voice Output * If voice mode is enabled, the AI’s response is also read aloud using: * Web Speech API (MVP) * ElevenLabs TTS (optional Phase 2\) * Auto-disables if browser lacks TTS capability. --- ### Trigger Events | Event | Trigger Condition | | :---- | :---- | | Open assistant | User clicks chat bubble or loads auto-open URL | | Input submitted | User presses Enter or microphone completes STT | | Session resume offered | Returning visitor with known session or email | | Action command received | AI responds with structured instruction | | Voice playback requested | User is in voice mode and response is complete | --- ### UI/UX Requirements * Chat bubble is persistent in lower right corner of screen (customizable). * When clicked, opens a full chat window with: * Assistant avatar * Conversation thread (persisted across pages) * Input field * Optional microphone toggle * Supports mobile layout: * Full-screen modal on phones * Larger tap targets * Voice interaction primary, with fallback to text --- ### State Management * Local session state is stored in memory using JavaScript module or context provider: ```javascript const session = { id: "abc123", messages: [...], lastPage: "/pricing", memoryTokens: [...], voiceEnabled: true, }; ``` * On every input or response, session is synced with Supabase (async). --- ### Error Handling * If voice input fails (e.g., user denies microphone access), show: “It looks like I couldn’t access your microphone. Try typing instead.” * If AI times out, show: “I’m having trouble connecting to the assistant right now. Please try again in a moment.” * If AI response is missing expected structure, fallback to: “Here’s what I found—but I couldn’t navigate for you just yet.” --- ### API Integration * AI backend endpoint: `POST /ai/interpret` (or WebSocket message channel) Expected response: ```json { "message": "The pricing section is just below.", "action": "scroll", "target": "#pricing-table", "memory": {...}, "suggestedFollowUp": "Would you like to compare plans?" } ``` * Supabase: * `insert` into `conversations` table * `upsert` session memory * Optional trigger to n8n for sentiment or analytics --- ### Storage Requirements * All messages (user and AI) stored in Supabase with: * `session_id` * `sender_type` (user/ai) * `message_text` * `timestamp` * `page_context` * `action_triggered` (boolean) --- ### Edge Cases * User gives empty input: disable send button * User speaks but says nothing (e.g., background noise): discard event * Multiple users on same device/browser: store separate sessions with unique anonymous IDs * Connection drops during interaction: queue message locally, retry once WebSocket reconnects --- ### Success Criteria * ≥ 90% of valid inputs receive AI responses within 2 seconds * 100% of sessions have messages logged in Supabase * Typing, response, and scroll behavior feel natural and humanlike * Voice mode works on ≥ 80% of supported mobile devices * Assistant resumes previous session on page reload or site revisit without user confusion --- --- # 5.2 Scroll-to-Element Functionality ### Purpose This module allows SiteGuide to move the user's viewport to the most relevant section of the page based on AI interpretation of a user's question. The AI does not simply answer questions—it navigates the user directly to the relevant content. --- ### User Story * As a site visitor, when I ask a question like “Where is your refund policy?” I want the assistant to automatically scroll the page to that section instead of just telling me to scroll manually. * As a business owner, I want the assistant to physically guide users to the correct areas of the page so users spend less time searching and are more likely to engage. --- ### Functional Requirements #### AI Response Instruction * The assistant must receive from the AI backend a scroll instruction that includes: * `action: "scroll"` * `target: ""` (e.g., `#pricing-table`, `.faq-item-3`) * If no valid scroll target is returned, the assistant must fall back to a text-only response (see error handling). #### DOM Scanning (Frontend) * On page load (or after DOM mutations), SiteGuide must scan and cache all scrollable targets. * Each section should be indexed based on: * Semantic tags (`section`, `article`, `main`, `aside`) * Headings (`h1–h4`) * Attributes (`data-siteguide-target`, `id`, `class`) * A "target map" should be built in memory: ```javascript { "#pricing-table": HTMLElement, ".faq-section": HTMLElement, "h2:contains('Our Process')": HTMLElement } ``` #### Scroll Behavior * The scroll command must: * Smoothly scroll the user’s browser viewport to the element * Use `scrollIntoView({ behavior: 'smooth', block: 'start' })` * Offset for fixed headers (e.g., subtract 80px if a sticky nav is detected) * Only scroll if the target element exists and is visible in the DOM * If user scrolls away during AI animation, the assistant should: * Respect user override (no forced re-scrolling) * Optionally offer to “Take me back” with a button #### Scroll Trigger Lifecycle * Receive scroll instruction via WebSocket message or structured response * Look up target in local target map * Validate that it’s a scrollable element (not `display: none`, `visibility: hidden`, or detached from DOM) * Execute scroll animation * Trigger internal log: ```javascript logScrollEvent({ session_id, target_selector: "#faq-section", timestamp: Date.now(), autoTriggered: true }); ``` --- ### Trigger Events | Event | Condition | | :---- | :---- | | Scroll action received | AI returns a `scroll` action with valid `target` selector | | Page reload | Page context rehydrated; scroll to previous section (optional) | | Follow-up command | User says “Take me there” after AI describes content | --- ### UI/UX Behavior * If a scroll is triggered by the AI, the assistant chat should say: * “Let me show you…” or “Here’s what you’re looking for.” * After the scroll, the highlighted element should remain on screen (see 5.3) * Optional: show a floating "Back to chat" button if scroll takes user far away from assistant position --- ### State Management * Current scroll target should be stored in memory for: * Restoring scroll position later * Analytics * Displaying “recently visited” interactions ```javascript state.lastScrollTarget = "#refund-policy"; ``` --- ### Error Handling | Scenario | Behavior | | :---- | :---- | | Target selector not found | AI says “I couldn’t find that section. Let me answer here instead.” | | Multiple elements match selector | Scroll to the first visible one | | Target hidden or collapsed | Do not scroll; fallback to text response | | User scrolled during animation | Cancel animation and show passive “Back to content” option | --- ### API/AI Format (Expected) ```json { "message": "The pricing information is in the section below.", "action": "scroll", "target": "#pricing-table" } ``` --- ### DOM Targeting Best Practices To improve compatibility, website owners should annotate key sections using `data-siteguide` attributes in their HTML: ```html

...
``` The DOM scanner should prioritize these attributes when selecting scroll targets. --- ### Success Criteria * ≥ 90% of valid scroll actions land on the correct content section * Scroll animation duration is ≤ 600ms and feels natural * Element is visible in the viewport after scrolling * No jitter, double-scrolling, or abrupt jumps * Scroll does not interfere with core page functions (e.g., modals, nav bars) --- ### Where it fits in the PRD This feature belongs in **Section 5.4: Navigation Control**, which we’ll add as a new module between 5.3 (DOM Highlighting) and 5.5 (Voice Input/Output). That section will define the assistant’s ability to: * Programmatically click buttons or links * Navigate to new internal pages without losing session memory * Resume chat context immediately upon load --- ### Why it matters SiteGuide isn’t just a passive scroll-and-highlight tool — it should feel like the AI is *guiding you through the site*. That means: * Clicking “Book Now” for the user * Jumping from homepage to pricing * Taking the user to “Contact” or “Testimonials” pages when asked **The experience must feel uninterrupted**, even though the browser is technically loading a new document. --- ### Technical Implications We’ll need to: * Intercept link clicks triggered by SiteGuide (not the user) * Store all session data and conversation history in local memory (and Supabase) * Rehydrate the assistant UI instantly after page reload * Maintain the open chat state, scroll history, and conversation log --- # 5.3 DOM Element Highlighting ### Purpose Once the AI has scrolled the user to the relevant content on the page, it must clearly indicate *what* the user is supposed to look at. Highlighting ensures that the user’s attention is drawn to the precise block, heading, form, or table the AI referenced — reducing confusion and increasing clarity. --- ### User Story * As a user, when the assistant scrolls me to a section, I want to instantly understand *which* part is relevant so I don’t waste time guessing. * As a business owner, I want the assistant to visually call out key information like pricing, guarantees, or lead forms to maximize conversion. --- ### Functional Requirements #### Trigger Conditions * Highlighting is activated after a successful scroll event. * May also be triggered independently if the AI refers to a visual element (e.g., “Look at the refund section above.”). #### Target Identification * Same `target` selector is used as for scrolling (e.g., `#faq-section`, `.refund-policy`) * If no valid target is available, assistant should skip highlighting #### Visual Behavior * Highlight effect is non-obtrusive, WCAG-compliant, and disappears after a short duration (configurable) * Acceptable effects: * **Pulse border**: animated outline glow around element * **Background fade**: gentle highlight color behind content * **Animated outline**: CSS `box-shadow` flicker or ring animation **Example implementation:** ```css .siteguide-highlight { outline: 3px solid #facc15; outline-offset: 4px; animation: pulseHighlight 1.5s ease-in-out 2; } @keyframes pulseHighlight { 0% { outline-color: transparent; } 50% { outline-color: #facc15; } 100% { outline-color: transparent; } } ``` #### Duration and Timeout * Default highlight duration: **3 seconds** * Element is automatically cleared of the class after animation completes * If the user hovers over the highlighted element, the animation should pause or extend visibility --- ### User Feedback & Interaction | Condition | Assistant Behavior | | :---- | :---- | | Scroll \+ highlight | “Here’s the section you asked for — I’ve highlighted it below.” | | Highlight only | “Take a look at the guarantee here.” | | User scrolls away | Optionally display floating “Scroll Back” button | | Element is too small | Expand or wrap to larger container automatically | --- ### Accessibility Considerations * Avoid blinking, flashing, or seizure-inducing effects * Ensure that screen readers are not distracted or misrouted by hidden visual overlays * Highlighted areas must remain keyboard-accessible if tabbed --- ### Error Handling | Scenario | Behavior | | :---- | :---- | | Target element not visible | Log event, fallback to chat-only message | | Element too small (\<32px height) | Scroll to parent element instead | | Highlight already active | Restart animation and update styling | | User navigates away during animation | Cancel highlight and clear class | --- ### API/AI Format (Integrated with Scroll) ```json { "action": "scroll", "target": "#faq-section", "highlight": true, "message": "Here’s the answer to your question on returns." } ``` If `highlight` is true, apply animation after scroll is complete. --- ### Storage and Analytics | Data Point | Stored in Supabase? | Used for Analytics? | | :---- | :---- | :---- | | Selector highlighted | Yes | Yes | | Duration of user view | Optional | Yes | | User clicked highlighted? | Optional | Yes | --- ### Success Criteria * ≥ 90% of valid scrolls are followed by correctly rendered highlight animation * Animation completes smoothly on all modern browsers * Highlight is visually noticeable but non-intrusive * No DOM errors occur from missing or malformed target elements * User engagement (scroll, dwell, or click) increases on highlighted content --- # 5.4 Navigation Control ### Purpose This module allows SiteGuide to trigger **automated internal page navigation** (i.e., clicking links or simulating navigation) in response to user intent, while preserving the assistant’s state and session memory across page loads. This enables the assistant to say things like: “Let’s go to the pricing page so I can show you,” ...and then take the user there immediately. --- ### User Story * As a user, I want the assistant to move me to another page (e.g., “Show me your services”) so I don’t have to search for the right menu or link. * As a returning user, I want to continue our conversation after the new page loads without restarting the assistant or losing context. --- ### Functional Requirements #### Link Resolution * When the LLM responds with a navigation instruction, the payload should contain: ```json { "action": "navigate", "target": "/pricing", "message": "Let’s go to the pricing page so I can show you." } ``` * The assistant must: 1. Confirm the destination is a valid internal path (same domain only) 2. Prevent navigation loops or invalid URLs 3. Delay execution by 500–1000ms to allow the AI message to display 4. Trigger `window.location.href = target` (or `history.pushState` for SPAs if supported) #### Session Preservation * Before navigation: * Current session ID is saved to `localStorage` * Conversation history is serialized and stored locally and (optionally) in Supabase * Target page URL is stored in `session.lastRoute` * On page load: * Widget checks for `siteguide_session_id` and `siteguide_lastRoute` * Chat window automatically restores: * Previous messages * Scroll position (if provided) * Assistant open state (if chat was open before reload) #### Widget State Behavior | State Before Navigation | Behavior on New Page | | :---- | :---- | | Chat open | Chat reopens automatically with previous conversation | | Chat closed | Chat remains closed, session is silently preserved | | Scroll in progress | Scroll resumes (if same target exists on new page) | --- ### Trigger Events | Event | Trigger Condition | | :---- | :---- | | Navigation action | AI returns `action: navigate` | | User says “Go to…” | NLP detects intent to visit another page | | Assistant references a page | e.g. “You can find this on our Services page.” | --- ### UI/UX Requirements * Assistant must confirm intent and give user a second to absorb message before switching pages. * Optional: display a loading spinner inside the assistant avatar during page change. * Upon reload, assistant should say something like: “We’re here. Let me show you the section I mentioned.” --- ### Implementation Flow ``` 1. User asks: “What services do you offer?” 2. AI responds with message + navigate action to "/services" 3. Assistant shows reply: “Let’s go to the services page so I can show you.” 4. Assistant waits 750ms 5. `window.location.href = "/services"` 6. On page load: - SiteGuide reads session ID from localStorage - Restores prior memory, conversation, open state - Initiates follow-up scroll/highlight (if instructed) ``` --- ### Error Handling | Scenario | Fallback Behavior | | :---- | :---- | | Target path is not same-origin | Cancel navigation and say “I can’t take you there directly, but here’s the link.” | | Broken link or 404 after load | Assistant detects via `window.location` \+ `document.title` and offers apology | | Session ID not found on load | Start a new session and show welcome message | | SPA navigation failure (JS error) | Fall back to full `window.location.href` | --- ### Developer Notes * Navigation can be triggered either: * From AI (`navigate` action) * Or internally, via assistant UI button (e.g., “Take me to pricing” prompt) * Must integrate with existing scroll/highlight stack: if AI wants to scroll after navigation, target selector must be checked on new page and delayed until `DOMContentLoaded`. --- ### Success Criteria * ≥ 90% of internal navigation attempts succeed without user confusion * Chat session resumes within 1 second after page load * User never sees a blank chat window unless starting fresh * No flicker or loss of assistant UI state * Users complete multi-page journeys without needing to re-initiate conversation --- # 5.5 Voice Input and Output ### Purpose SiteGuide must support a fully voice-driven experience for users who prefer or require hands-free interaction—particularly on mobile devices. This includes the ability to: 1. Speak to the assistant instead of typing 2. Hear spoken responses from the assistant rather than reading Voice interaction significantly enhances accessibility, reduces friction for mobile users, and makes the assistant feel more human and responsive. --- ### User Story * As a mobile visitor, I want to speak my question and hear the assistant’s answer, so I can browse the site without typing. * As a desktop user with limited mobility or accessibility needs, I want the assistant to be operable by voice commands alone. --- ### Functional Requirements #### Voice Input (Speech-to-Text) * **Triggering Voice Input:** * Microphone icon is present in the assistant input bar. * Clicking the mic activates live transcription via Web Speech API. * Optional “voice activation phrase” (e.g., “Hey SiteGuide”) is not required for MVP. * **Transcription Behavior:** * While listening, UI shows an animated waveform or listening animation. * Partial results may be shown (if supported by browser). * When speech ends, full transcription is inserted into the input field and submitted. * All speech sessions are capped at 10 seconds unless paused manually. * **Supported Browsers:** * Web Speech API is supported on most Chromium-based browsers and Safari (desktop \+ mobile). * MVP implementation will not support Firefox for voice input. * Feature auto-disables if unsupported. * **Fallback Detection:** * If microphone permissions are denied, assistant displays: “I couldn’t access your microphone. You can still type your question below.” * **Security Considerations:** * Voice input is not recorded or stored as audio. * Only text transcription is retained in Supabase with session data. --- #### Voice Output (Text-to-Speech) * **Triggering Voice Output:** * When voice mode is enabled in the plugin settings, assistant responses are spoken aloud using the browser’s speech synthesis engine or ElevenLabs (if configured). * **Playback Behavior:** * Assistant reads responses in a polite, natural pace (approx. 120–150 words per minute). * User can interrupt playback by clicking the mic or typing. * Voice playback can be globally disabled by the site admin. * **Voice Customization:** * MVP will use default browser voice. * Future releases may allow assistant persona selection via ElevenLabs API (e.g., "Jessie" voice, male/female tones). * **Speech Rendering Requirements:** * Response playback begins only after full text is rendered. * Short delays (100–300ms) are acceptable to mimic human pacing. --- ### UI/UX Requirements #### Microphone Icon States: | State | Icon Behavior | | :---- | :---- | | Idle | Static mic icon | | Listening | Pulsing animation or waveform | | Transcribing | Spinner or typing dots | | Unsupported browser | Mic icon hidden or grayed out | #### Accessibility Considerations: * All voice controls must be operable via keyboard * Microphone button must have appropriate `aria-label` * Visual animations must not cause flashing or seizure risk * Voice output must be supplemented by on-screen text at all times --- ### Voice Mode Toggle (Admin Control) * Admin can globally enable/disable voice input and/or output from the WordPress plugin settings. * Optional: site admin can choose whether voice mode is enabled by default for all users or must be toggled on manually. ```php // Example WordPress setting $settings = [ 'voice_input_enabled' => true, 'voice_output_enabled' => true, 'default_voice_mode' => 'enabled', ]; ``` --- ### Error Handling | Scenario | Assistant Response or Behavior | | :---- | :---- | | Microphone blocked | “I couldn’t access your mic. Please check browser settings.” | | Speech not recognized | “I didn’t quite catch that. Try speaking again.” | | Voice output not supported | Falls back to text-only output silently | | User presses mic but browser freezes | Mic auto-stops after 10s and shows retry option | --- ### State Management and Storage * No audio files are stored. * Transcribed speech is treated as plain user input and saved as: ```json { "message": "Do you offer same-day shipping?", "input_type": "voice", "confidence": 0.92 } ``` * Stored in Supabase under the same schema as typed messages, with `input_type` field for analytics segmentation. --- ### Success Criteria | Goal | Metric or Threshold | | :---- | :---- | | Successful voice input | ≥ 90% of attempted speech transcriptions are valid | | Successful voice output | ≥ 95% of AI responses spoken aloud without interruption | | Compatibility rate (voice input) | Voice input works on ≥ 80% of mobile sessions | | Playback latency | Voice begins \<1 second after text render | | Voice fallback behavior | 100% of unsupported sessions silently degrade to text | --- ### Technical Notes * **Web Speech API Reference:** [https://developer.mozilla.org/en-US/docs/Web/API/Web\\\_Speech\\\_API](https://developer.mozilla.org/en-US/docs/Web/API/Web\\_Speech\\_API) * **ElevenLabs API (Optional Phase 2):** * Will require per-site authentication tokens * TTS conversion must be cached or streamed to minimize delay * **Rate Limits & Stability:** Web Speech API is client-side and has no external rate limits, but assistant must: * Limit one voice session at a time * Handle stop/start toggles without stacking --- # 5.6 Persistent Session Memory ### Purpose This module ensures that SiteGuide retains knowledge of each visitor’s interaction history — both **short-term** (within a single session or site visit) and **long-term** (across days or months). Memory allows the assistant to: * Resume conversations across page reloads * Recollect prior questions, answers, and AI actions * Recognize returning users via stored session or email * Maintain context during multi-page journeys This mimics the continuity of a human assistant — transforming the assistant from a “widget” into an intelligent, evolving guide. --- ### User Story * As a first-time visitor, I want the assistant to remember what I’ve already asked while I navigate between pages. * As a returning visitor, I want the assistant to pick up where we left off, even if it’s been days or weeks. * As a business, I want to track user behavior and engagement over time without requiring accounts or logins. --- ### Functional Requirements #### Anonymous Session Initialization * On first load: * Generate a `siteguide_session_id` (UUID v4) * Store in `localStorage` * Example: ```javascript localStorage.setItem('siteguide_session_id', '7c49f920-89a0-442e-8f89-a1d0e4b915bb'); ``` * Send this session ID with every interaction (text, voice, scroll, highlight) #### Session Memory Structure * Each session tracks: ```json { "session_id": "abc123", "site_domain": "clientsite.com", "start_time": "2025-08-10T12:22:01Z", "last_active": "2025-08-10T12:45:17Z", "pages_visited": ["/home", "/pricing"], "messages": [ { "sender": "user", "text": "What are your hours?" }, { "sender": "ai", "text": "We’re open from 9–5, Monday through Friday." } ], "actions": [ { "type": "scroll", "target": "#hours", "timestamp": 1691689021 } ], "status": "anonymous" } ``` #### Long-Term Persistence * Session object is upserted into Supabase every time: * A new message is exchanged * A scroll or highlight action is triggered * A new page is visited * Supabase Tables: * `sessions` * `messages` * `actions` * `page_visits` #### Rehydration on Page Load * On load, SiteGuide checks `localStorage` for existing session ID * If found, the assistant: * Restores conversation history into the chat window * Restores assistant open/closed state * May resume unfinished actions (e.g., if AI said “Let me show you” but page changed before scrolling) #### Cross-Page Memory * Memory is continuous across internal navigation: * Assistant state (open/closed) * Conversation context * Scroll position (if applicable) #### Session Expiration and Archiving * Active sessions remain “live” for 6 months from last interaction * After expiration: * Marked as archived in Supabase * Can still be referenced for analytics or email-linked retrieval * Sessions that exceed 1MB in size (e.g., very long threads) are truncated server-side to retain only summary and metadata --- ### Memory Scope and Depth #### What the Assistant Remembers: | Category | Retained | Duration | | :---- | :---- | :---- | | Questions asked | Yes | 6 months | | AI responses | Yes | 6 months | | Pages visited | Yes | 6 months | | Scroll targets | Yes | 6 months | | User name/email (if provided) | Yes | Persistent | | Form auto-fill attempts | Yes | 6 months | | Voice preference | Yes | 6 months | #### What is *not* retained: * Exact scroll positions unless requested * Audio recordings (voice input is always discarded after transcription) * Any third-party cookies or cross-site tracking data --- ### Error Handling | Scenario | Behavior | | :---- | :---- | | `localStorage` is unavailable | Fallback to in-memory session; no long-term memory | | Supabase write fails | Retry in background; fallback to local-only memory | | Session ID collision | Regenerate and start new session (rare with UUID v4) | | Assistant state becomes corrupted | Clear local memory and restart session with graceful notification | --- ### Security and Privacy * Session IDs are anonymous by default * If a user provides their email, it is explicitly linked to the session in Supabase: ```json { "session_id": "abc123", "user_email": "user@example.com", "status": "identified" } ``` * Sessions can only be resumed via: * Same browser/device (using session ID in `localStorage`) * Or user-provided email (see Section 5.7) * All data is stored securely in Supabase under row-level security policies * No sensitive data is ever sent to the LLM or frontend without explicit user input --- ### Developer Implementation Notes * Memory manager should be implemented as a standalone module (e.g., `SessionMemory.js`) * Exports: `startSession()`, `saveInteraction()`, `rehydrate()`, `syncWithBackend()` * Syncing strategy: use a debounce mechanism (e.g., save every 1 second max) to avoid flooding the DB * Versioning: memory schema should support future enhancements (e.g., per-user profiles, analytics enrichment) --- ### Success Criteria | Objective | Metric | | :---- | :---- | | Short-term memory continuity | Session is preserved across 100% of internal page loads | | Long-term memory rehydration | ≥ 90% of returning sessions restore correctly via session ID | | Session write failure rate | \< 1% of interactions lost due to sync failure | | Message retention | 100% of user-AI interactions visible across pages | | Assistant open/closed state continuity | Preserved across ≥ 95% of page reloads | --- ### User Story * As a user, I want to provide my email so I can return later and pick up the conversation where I left off. * As a user, I want to be able to say “remember me” and not start from scratch every time I come back to the site. * As a business owner, I want to retain high-value customer sessions and build longer-term relationships without requiring logins or signups. --- ### Functional Requirements #### Email Prompt Flow | Trigger Condition | Assistant Behavior | | :---- | :---- | | User asks to save session | Assistant says: “Sure, I can remember you\! What email should I use?” | | User volunteers an email (detected) | Assistant says: “Thanks\! I’ll use that to save your conversation.” | | System detects high engagement | After X interactions or ≥ Y minutes, assistant may ask: | ``` “Would you like me to remember you for next time?” | ``` #### Data Collection * When user provides an email, associate it with current session in Supabase: ```json { "session_id": "abc123", "user_email": "user@example.com", "status": "identified" } ``` * Validate email format client-side before sending (basic regex) * Only one email may be linked per session (no overwrites) * Backend lookup enables session merges in the future (see below) #### Return Visit Flow | Scenario | Assistant Behavior | | :---- | :---- | | Local session found | Auto-resume using `localStorage` as described in Section 5.6 | | No local session but user provides email again | Assistant retrieves matching session from Supabase and says: | ``` “Welcome back! Picking up from where we left off...” | ``` | No session found for email | Assistant says: “Hmm, I don’t see anything saved for that email. We can start fresh\!” | #### Assistant Messaging UX * Initial prompt: “Would you like me to remember our conversation for next time? I can do that with just your email.” * On success: “Great, I’ll remember you\! You can come back anytime and we’ll pick up where we left off.” * On error or no matching session: “Looks like I couldn’t find your previous session. No worries—we can start fresh.” --- ### Database Behavior * `sessions` table: adds a `user_email` column (unique per active session) * Index `user_email` for fast lookup * Retention policy: all email-linked sessions are preserved for 12 months unless deleted #### Optional: Session Merge * When a known user returns and creates a new anonymous session: * Check for `user_email` match * Optionally merge previous messages and metadata into new session * Flag session as `merged_from: [old_session_id]` for auditing --- ### Developer Implementation #### Frontend * Memory module should expose: ```javascript saveEmailToSession(email) checkForEmailLinkedSession(email) ``` * Assistant must allow user to enter email via: * Natural conversation (“remember me”) * Manual form input if AI requests it * External injection (e.g. pre-fill from site login if available) * Conversation history should hydrate from Supabase if no local session is available and email match is found. #### Backend * Supabase table schema: * `session_id` (UUID) * `user_email` (VARCHAR, indexed) * `created_at` * `last_active` * `status` (anonymous | identified) * `merged_from` (nullable) * API endpoints: * `GET /session/by-email?email=...` → returns last active session * `POST /session/link-email` → links email to session --- ### Security & Privacy * Email is opt-in only; never stored or associated without explicit user input * User can request deletion of email-linked session (future feature) * Emails are stored securely in Supabase with access controls and encryption-at-rest * Assistant must never send outbound emails—storage is for internal continuity only unless integrated with CRM/email tools --- ### Error Handling | Scenario | Behavior | | :---- | :---- | | Invalid email format | Assistant says: “That doesn’t look like a valid email. Want to try again?” | | Supabase query fails | Assistant says: “Hmm, I had trouble saving your session. Want to try again later?” | | Multiple sessions found for email | Assistant loads most recent one; flags for possible merge | --- ### Success Criteria | Objective | Metric | | :---- | :---- | | Session restoration via email | ≥ 90% accuracy on email-linked resumption | | Dropoff rate post-email prompt | \< 25% abandonment after email offer | | Session match speed | \< 500ms Supabase query time | | User confusion rate | \< 5% of users say “this isn’t what I asked about” after resuming session | | Merged session integrity | No data loss during merge, flagged correctly | --- # 5.8 Memory Summary and AI Recall Behavior ### Purpose This component governs how the assistant **summarizes**, **recalls**, and **applies contextual memory** during an ongoing or restored session. Unlike raw conversation history, which can grow unwieldy or irrelevant, this memory structure ensures that SiteGuide recalls the most relevant, structured information for decision-making, navigation, and follow-up support. --- ### User Story * As a user, I want the assistant to remember key things I’ve said or asked about, like my goals or interests. * As a user, I want the assistant to provide coherent, personalized responses instead of repeating generic info. * As a developer, I want to ensure only the most useful context is passed to the LLM to reduce cost and improve precision. --- ### Memory Architecture #### Layers of Memory | Layer | Description | | :---- | :---- | | Live Context | Most recent messages in the active session (e.g., last 5-10 exchanges) | | Structured Summary | Condensed key facts extracted from prior interactions, formatted for LLM use | | Historical Archive | Full conversation logs (for UI review and fallback, not sent to LLM) | --- #### Summary Format Memory summaries are stored in a structured format: ```json { "goals": ["Learn about pricing", "Find out if there's a demo"], "interests": ["Small business SEO", "Weekly blog publishing"], "name": "Bob", "preferences": { "chatStyle": "direct and friendly", "followUps": true }, "last_visited": "/features", "last_action": "Requested pricing guide", "timestamps": { "created": "2025-08-10T14:05:00Z", "updated": "2025-08-11T10:42:00Z" } } ``` This summary can be used as a system prompt fragment or prepended as context to GPT-style LLMs in each new exchange. --- ### Context Injection Logic * Upon every message, SiteGuide assembles a payload that includes: * Last 5–10 user/assistant messages (chronological) * Memory summary (inserted via system prompt or initial instruction) * Example: ``` SYSTEM: The user is named Bob. He’s interested in SEO tools and asked about pricing. Be direct and friendly. ``` * Summaries are updated: * After significant topic changes * When user expresses a new goal (e.g., “I’m also interested in eCommerce”) * Upon assistant action (e.g., navigates to pricing page) --- ### Memory Update Triggers | Event | Action | | :---- | :---- | | User asks a new goal | Add to goals list | | User gives name/email | Store in summary | | User preferences detected | Add to preferences object | | Page navigation triggered | Update `last_visited` and `last_action` | | Session manually ended | Flag as complete for future resume | Summaries are rewritten after every major user interaction (approx every 4–6 turns), either as part of the memory engine or using a dedicated summarization LLM pass. --- ### Developer Responsibilities * Create a memory controller module that: * Listens for state changes and conversation events * Writes updated summaries to Supabase per session ID * Provides a `getMemorySummary(session_id)` function * Memory summary is cached client-side in case of Supabase lag * Provide a dev interface to **manually inspect/edit summaries** (admin view) --- ### LLM Prompt Injection Behavior | State | Behavior | | :---- | :---- | | New visitor | No summary injected, full default prompt used | | Known session (local) | Inject memory summary from localStorage | | Known session (email) | Inject summary retrieved from Supabase | | Fallback (no memory) | Use latest 5–10 chat messages only | To keep prompt size minimal, summary injection should be less than 1,000 tokens total. If needed, long lists or unimportant details should be pruned from memory before inclusion. --- ### Error Handling | Issue | Fallback/Handling | | :---- | :---- | | Supabase summary fetch fails | Load from local copy or proceed without | | Memory becomes too large | Prune least recent entries using timestamp heuristics | | LLM refuses prompt (too long) | Trim non-essential context and retry | --- ### Success Criteria | Objective | Metric | | :---- | :---- | | Personalized memory used in ≥ 90% sessions | Valid memory summary injected into LLM context | | Memory summaries updated every 3–5 turns | Automatic summarization confirmed via logs | | LLM response accuracy ↑ | Lower confusion rate in conversations using memory | | Developer edit UI works | Manual memory override/edit persists correctly | | Memory injection latency \< 300ms | Total memory prep time for prompt payload | --- # 5.9 Scroll, Highlight, and DOM Interaction Features ### Purpose SiteGuide is more than a chatbot—it’s a **real-time interactive assistant** that can visually and physically guide the user through the website. This section defines how SiteGuide can: * Scroll the page to focus user attention * Highlight specific sections or elements * Point to content as it’s discussed * Manipulate navigation contextually without breaking session flow These behaviors make SiteGuide feel like a true co-browsing companion—more useful than a static bot and more intuitive than most help systems. --- ### User Story * As a user, I want the assistant to move the screen for me when it refers to something so I don’t have to search. * As a user, I want the assistant to highlight what it’s talking about so I’m never confused. * As a user, I want to see visual feedback when I click on a suggestion from the assistant. --- ## Functional Requirements ### 5.9.1: Scroll to Element #### Behavior * When referencing a part of the page (e.g. “the pricing table”), SiteGuide will automatically scroll to that section smoothly. * Scroll is performed using `element.scrollIntoView({ behavior: 'smooth' })`. #### Trigger Methods | Trigger Type | Description | | :---- | :---- | | AI mentions known element | Assistant says: “You’ll find that below...” | | AI links to ID or class | Internal message format includes target anchor | | Hard-coded dictionary | Certain keywords mapped to selectors (e.g. “FAQs” → `#faq`) | #### Development Needs * Selector dictionary (semantic label → CSS selector) * Scroll action throttle (avoid spamming on rapid interactions) * Scroll offset for fixed headers (allow config, e.g. 80px) --- ### 5.9.2: Element Highlighting #### Behavior * Flash or outline key element for visual guidance * Use temporary `box-shadow` or outline animation * Duration: 3–5 seconds, then fade unless reactivated #### Trigger Methods | Scenario | Action | | :---- | :---- | | AI refers to a feature visually | “See the green button?” → highlights the button | | User clicks a suggestion | Button briefly flashes to confirm the target location | | AI links directly to anchor | Highlight scroll target automatically on arrival | #### Development Needs * Overlay module or dynamic class injection * Prevent highlight on invisible elements (use `getBoundingClientRect()`) * Accessibility: ensure visual styles don’t conflict with WCAG standards --- ### 5.9.3: Pointer/Arrow Overlay (Optional) #### Behavior * Display a temporary **floating arrow or pointer** next to the element the assistant is referencing * Appears for 3–10 seconds and points toward the DOM node * Can pulse, animate, or tilt for visibility #### Use Cases * On complex pages with many elements (e.g. dashboards) * On user request (“Can you show me where that is?”) #### Development Needs * Overlay container for pointer component * Arrow follows DOM element if page resizes or scrolls * Lightweight implementation (no external pointer libraries required) --- ### 5.9.4: DOM-Based Navigation and Clicking (Optional but Recommended) #### Behavior * Assistant can **trigger a click** on a known element when instructed to “take me there,” “show me that,” or “open it” * Simulates a user click or link activation, e.g.: ```javascript document.querySelector("#pricing-btn")?.click() ``` #### Use Cases * Streamlines flow from chat to action * Allows users to treat the assistant as a remote control #### Risk Management * Add safeguards to avoid clicking payment buttons, form submissions, etc. * Use whitelist of click-safe selectors only #### Development Needs * Click controller module * AI output parser that detects action-intent messages * Optional confirmations: “Click now?” → \[Yes\] \[No\] --- ### 5.9.5: Multistep Visual Tours (Optional) #### Behavior * Assistant walks user through a **guided tour** by: * Scrolling to a section * Highlighting key points * Explaining verbally * Offering to continue: “Next step?” → scrolls again #### Use Cases * Onboarding for new visitors * Product walk-throughs * Multi-part navigation (e.g. blog \+ pricing \+ contact) #### Development Needs * Tour script JSON format: ```json [ { "selector": "#hero", "message": "Here’s where you’ll see our main promise." }, { "selector": "#features", "message": "Now scroll down to the features section." } ] ``` * Progress state manager (tracks tour steps) * User override: “skip” or “pause tour” --- ## Developer Implementation #### Core Methods Required ```javascript function scrollToSelector(selector, offset = 0) { ... } function highlightElement(selector, duration = 5000) { ... } function clickElement(selector) { ... } function showPointerOverlay(selector) { ... } function runTour(stepsArray) { ... } ``` These functions should be exposed globally and callable from AI actions, message metadata, or LLM output interpretation. #### Example: AI Triggers Highlight & Scroll Assistant replies: “Let me show you the pricing options.” Internal action: ```json { "type": "scroll-highlight", "selector": "#pricing-table" } ``` --- ## Error Handling | Condition | Fallback Behavior | | :---- | :---- | | Selector not found | Assistant says: “Hmm, I couldn’t locate that section. Want to try another way?” | | Element is off-screen or hidden | Assistant retries after scroll into viewport | | Overlay animation fails | Skip and use scroll-only fallback | --- ## Success Criteria | Objective | Metric | | :---- | :---- | | Visual feedback on 95% of triggers | Element highlight or pointer rendered | | Scroll accuracy \> 90% | Element in viewport after scroll | | Click-to-UI delay \< 300ms | Time between message and element response | | No unintended actions triggered | No clicks on sensitive forms/buttons | | Overlay performance impact \< 5% | Lighthouse or PageSpeed impact minimal | --- # 5.10 Multilingual and Accessibility Support ### Purpose To ensure siteGuide can be used by the widest possible audience, including those who: * Speak different native languages * Use assistive technologies (screen readers, keyboard navigation, etc.) * Have visual, auditory, cognitive, or motor impairments Multilingual and accessibility support are not “nice to haves.” They are structural components of a modern, global-grade user experience and must be considered in every interaction. --- ### User Story * As a non-English speaker, I want the assistant to respond in my language automatically, so I can use the site comfortably. * As a user with visual impairment, I want to be able to interact with the assistant and understand its responses using screen readers. * As a keyboard-only user, I want to be able to navigate all features of the assistant without using a mouse. --- ## 5.10.1 Multilingual Support #### Detection and Configuration | Method | Behavior | | :---- | :---- | | Automatic browser locale detection | Default assistant language matches `navigator.language` | | Manual language selection (optional) | User can choose from dropdown or via assistant command | | Session-level persistence | Language setting is saved in Supabase per session/user | #### Supported Languages (Initial Phase) * English (default) * Spanish * French * German * Portuguese * Hindi * Arabic * Mandarin Chinese Note: Additional languages will be added based on traffic or demand. #### Assistant Behavior * Detects language preference automatically * Responds and summarizes content in that language * Translates webpage content using embedded summaries or scraped metadata * UI buttons and prompts must also be localized #### LLM Integration * Use OpenAI’s GPT-4o or similar multilingual LLMs * Responses should respect the grammatical and formal norms of each language * Language-specific fallback phrases must be predefined in case of AI errors #### Developer Needs * Language file system (e.g. `/locales/en.json`, `/locales/es.json`) * Context language injection into all AI messages * AI model routing if required for localization quality --- ## 5.10.2 Accessibility Support (WCAG 2.2 Compliance) #### Key Principles SiteGuide must comply with the **Web Content Accessibility Guidelines (WCAG) 2.2**, including: * **Perceivable**: Users must be able to perceive the interface * **Operable**: Interface must be operable via keyboard, voice, etc. * **Understandable**: Language and visuals must be clear * **Robust**: Must work across a wide range of assistive tech #### Specific Requirements | Feature | Behavior | | :---- | :---- | | **Keyboard Navigation** | Every interactive element (buttons, replies, etc.) must be tab-accessible | | **ARIA Roles & Labels** | Apply `aria-*` attributes to chat box, buttons, and scroll/highlight actions | | **Screen Reader Compatibility** | Announce new assistant messages properly using ARIA live regions | | **Color Contrast** | Ensure text and background colors meet 4.5:1 contrast minimum | | **Skip to Main Content** | Allow users to skip assistant area if desired | | **Highlight Effects** | Must not trigger seizures or motion sensitivity | | **Timeouts** | Extendable on user request for cognitive or motor impaired users | #### Live Region Example: ```html
Assistant: Here’s your pricing guide.
``` --- ### Developer Guidelines #### HTML/JS Requirements * Tab-index order must follow logical flow * All buttons and interactive areas must have: * `aria-label` * `role` * Fallback keyboard equivalents * Modal dialogs (e.g., language selection) must trap focus until dismissed #### CSS Guidelines * Respect `prefers-reduced-motion` user settings * No text inside decorative images * Tooltips and instructional overlays must have text alternatives --- ### Analytics & Error Handling | Metric | Tracked? | | :---- | :---- | | Language selected vs. default | Yes | | Screen reader compatibility test logs | Yes | | Navigation via keyboard | Yes | | Timeouts/extensions used | Optional | If the assistant fails to detect or support a requested language: * It should respond with: “I’m still learning that language, but I can try English or Spanish for now.” If WCAG audit tools detect a failure (e.g., Lighthouse score \< 90): * Developer must log and fix within patch window. --- ### Success Criteria | Goal | Measurement | | :---- | :---- | | \>95% accessibility compliance score | Measured via Lighthouse \+ Axe \+ WAVE | | \>90% response accuracy in native language | Manual verification on assistant output | | Keyboard navigation coverage 100% | All elements usable with Tab/Shift+Tab | | No critical accessibility violations | Zero blocking WCAG 2.2 errors | --- # 5.11 Persistent Sessions and Context Recovery ### Purpose To enable users to pause and resume their interaction with SiteGuide without losing context—across sessions, devices, or timeframes. This mimics a helpful human assistant who “remembers you,” even after long absences, and ensures that all prior engagement history is retained for personalization, follow-up, and marketing. --- ### User Story * As a user, I want to leave the site and come back later without starting over. * As a returning visitor, I want SiteGuide to remember my name, goals, and last conversation. * As a business owner, I want returning users to feel like they’re building a relationship with my brand. * As a developer, I want a reliable way to associate persistent memory to unique users—even anonymously if needed. --- ## 5.11.1 Session Identification | Scenario | Identifier Used | | :---- | :---- | | First-time visitor | Anonymous UUID stored in localStorage | | Known device (no email yet) | UUID persisted across site visits | | User provides email | Email becomes session key (preferred) | | Logged-in user (WordPress site) | WordPress user ID (if integrated via plugin) | If the email is provided, it becomes the **authoritative session key** and overrides device-based identifiers. --- ## 5.11.2 Session Data Stored ### Fields Tracked per Session ```json { "session_id": "user-xyz-abc", "email": "example@example.com", "name": "Sarah", "first_seen": "2025-08-01T10:00:00Z", "last_seen": "2025-08-11T15:22:00Z", "last_page": "/pricing", "memory_summary": { "goals": ["Compare plans", "Understand SEO support"], "preferences": { "language": "en", "chatStyle": "fast and casual" } }, "interaction_count": 14, "last_chat_log": [...], "version": "1.4.0" } ``` All records are stored in **Supabase** under a dedicated `sessions` table. --- ## 5.11.3 Storage System Design | Component | Technology | Notes | | :---- | :---- | :---- | | Database | Supabase | PostgreSQL table with indexed fields | | LocalStorage Fallback | Browser | Anonymous sessions if Supabase fails | | Authentication | None required | Email is enough; no login needed | | Expiry Policy | 6–12 months | Session retained unless deleted | Sessions are soft-persistent by default but can become **hard-persistent** when a user provides an email or logs in. --- ## 5.11.4 Session Resumption Workflow ### For Anonymous Users (local device) 1. On return, check localStorage for UUID 2. If found, restore from Supabase using UUID 3. Rehydrate assistant memory and chat UI 4. Resume conversation or greet with summary: “Welcome back\! Last time we were comparing plans. Want to pick up where we left off?” ### For Identified Users (email match) 1. Ask: “Want to continue where we left off?” 2. Rehydrate structured memory 3. Reload final chat log (optional) 4. Use language, preferences, and goals from prior memory immediately ### For Logged-in WordPress Users 1. Auto-detect user ID via WP API 2. Bypass chat intro and resume based on ID-linked session 3. Add support for personalized dashboards --- ## 5.11.5 Recovery Triggers | Trigger Event | Recovery Method | | :---- | :---- | | User returns to homepage | Auto-lookup session via UUID/email | | User inputs email | Explicit session restore | | Assistant prompt: “Want to continue?” | Optional UI interaction | | Admin link with prefilled data | Deep-link with session token embedded | In all cases, SiteGuide must first confirm the session **exists** and is **valid** before resuming. --- ## 5.11.6 Developer Responsibilities * Ensure a `sessionController` module handles: * Generation and storage of anonymous UUID * Email-to-session mapping in Supabase * Full memory summary and chat history synchronization * Provide a fallback if session cannot be recovered * Fallback message: “I couldn’t find your last session, but I’m happy to help you start again\!” * Build a `resumeSession()` function that: * Loads memory * Rehydrates UI * Sends system prompt to LLM with memory context * Provide admin interface to view, edit, or delete session data manually --- ## 5.11.7 Analytics and Metrics | Metric | Tracked? | | :---- | :---- | | % of users returning to site | Yes | | % of sessions resumed | Yes | | Session duration across visits | Yes | | Most common last_page | Yes | | Email collection conversion rate | Yes | --- ## 5.11.8 Security and Privacy * No sensitive personal data beyond name/email/goals * Users can delete their session by saying “delete my data” * Optional GDPR module for account data requests * All session data encrypted at rest in Supabase --- ## Success Criteria | Objective | Measurement | | :---- | :---- | | Anonymous users recognized across sessions | 90% recovery using UUID | | Email-identified users resume seamlessly | \>95% accuracy in memory rehydration | | LLM responses reflect prior goals & memory | No repetitive restarts unless session lost | | Users report continuity in experience | Qualitative feedback during onboarding | --- # 5.12 Integration with Lead Capture and Marketing Systems ### Purpose Enable SiteGuide to function not only as a guide, but also as a **high-converting lead capture tool** that seamlessly connects with the business’s marketing stack. This allows for automated follow-up, qualification, segmentation, and analytics—driving measurable business outcomes from every interaction. --- ### User Story * As a business owner, I want SiteGuide to collect names, emails, and questions from visitors, so I can follow up with them. * As a user, I want to be able to ask a question, leave my email, and get a response later if needed. * As a marketer, I want all captured data sent to my CRM, email platform, or Google Sheet automatically. --- ## 5.12.1 Data Points to Capture | Field | Required | Source | | :---- | :---- | :---- | | Full Name | No | Provided by user | | Email Address | Yes\* | Explicit or inferred | | Phone Number | No | Optional field | | Company (if B2B) | No | Optional prompt | | Question/Inquiry | Yes | Captured from conversation | | Page of Capture | Yes | Automatically recorded | | Session ID | Yes | UUID or email key | | Time of Capture | Yes | System timestamp | **Note:** If email is not provided, the session is anonymous and cannot be added to CRM. --- ## 5.12.2 Capture Triggers | Scenario | Action Taken | | :---- | :---- | | User asks a high-intent question | Assistant prompts: “Want us to follow up by email?” | | User seems interested in pricing/services | Assistant offers to connect to sales | | Conversation reaches natural endpoint | Assistant says: “Want to leave your email in case you have more questions?” | | User requests a downloadable asset | Email gate triggered | --- ## 5.12.3 CRM / Marketing Integrations ### Built-in Webhook Support * SiteGuide can send captured leads to: * **Zapier webhook** (customizable) * **Make.com** scenarios * **N8N workflows** (recommended for aiConnected users) * **Direct Supabase table** (optional internal DB) * **Google Sheets** (for MVP setups) * **HubSpot / Mailchimp / ActiveCampaign** via API/webhook ### Recommended Flow with aiConnected: 1. SiteGuide captures lead in chat 2. Sends data to n8n webhook 3. Workflow: * Validates email * Adds to Supabase or CRM * Triggers email automation or follow-up alert --- ## 5.12.4 Consent and Confirmation * When user gives email, SiteGuide should say: “Got it\! We’ll only use your email to follow up about your question.” * All messages involving capture should reflect GDPR/CAN-SPAM compliance if needed. * Optional: add a small “Why are we asking this?” hover tooltip near form prompts. --- ## 5.12.5 Lead Scoring Logic (Optional) If enabled, SiteGuide can apply basic lead scoring based on: * Page visited (e.g., /pricing \> \+5) * Number of messages exchanged (\>10 \= \+2) * Use of commercial keywords like “quote,” “pricing,” “demo” (+10) * Email collected (+10) Score can be included in webhook payload: ```json { "lead_score": 25, "hot": true } ``` This helps prioritize which leads receive immediate follow-up. --- ## 5.12.6 Data Enrichment (Optional) * If user provides a business email (e.g., [sarah@acmeinc.com](mailto:sarah@acmeinc.com)), trigger background enrichment via Clearbit or similar * Enrichment returned: * Company size, industry, revenue * Social profiles * Location * Displayed to admin in lead dashboard or passed through to CRM --- ## 5.12.7 Admin Access to Captured Leads | Option | Description | | :---- | :---- | | Supabase table | All leads stored in `siteguide_leads` table | | n8n Webhook | Can be piped to any custom dashboard | | Daily Export | CSV export option via email or UI | | Webhook Replay | Re-send past captures if system missed data | --- ## 5.12.8 Developer Implementation * Create a `leadCapture()` function inside the SiteGuide assistant framework * Trigger logic based on chat content, intent detection, or explicit prompts * Add native email validation * Send structured data to endpoint(s) via: * HTTP POST * Supabase insert * Ensure assistant UI shows success/failure feedback (e.g., “Thanks\! We’ll be in touch.”) * Add fallback for offline mode: store lead locally and sync when online --- ## 5.12.9 Success Criteria | Goal | KPI | | :---- | :---- | | Email capture rate | \> 15% of total users | | Lead delivery success rate | \> 99% of leads reach CRM or webhook target | | Follow-up email open rate (external stat) | Tracked by marketing system | | Conversation-to-lead conversion | \> 20% for high-intent pages | | Average lead score of captured contacts | Tracked internally for QA | --- # 5.13 Analytics and Performance Tracking ### Purpose To provide business owners and admins with real-time, actionable insights about how siteGuide is being used, where users are dropping off, which features are most valuable, and how leads are being generated. The analytics system also enables quality assurance, A/B testing, and future feature improvement. --- ### User Story * As a business owner, I want to see how many users are interacting with my AI assistant, what they’re asking, and how often it leads to conversions. * As a marketing manager, I want to know which pages have the highest engagement and where to improve lead capture. * As a developer, I want to log all system events and errors for debugging and performance optimization. --- ## 5.13.1 Data to Track | Category | Events/Fields to Track | | :---- | :---- | | **User Engagement** | \- Session start/end | | \- Number of messages per session | | | \- Pages visited | | | \- Scroll/highlight actions triggered | | | \- Time on page with assistant open | | | **Intent Breakdown** | \- Questions about pricing, features, support, hours, services | | \- Most common queries | | | **Lead Capture** | \- Lead form submission | | \- Email provided | | | \- Drop-off before submission | | | \- Lead source page | | | **Conversion Events** | \- Booked demo | | \- Downloaded PDF | | | \- Clicked outbound link | | | \- Signed up for newsletter | | | **System Metrics** | \- Assistant load time | | \- LLM response time | | | \- API success/failure rates | | | \- Error logs | | | **AI Quality** | \- Thumbs up/down on responses | | \- Follow-up rate | | | \- Confusion/“Didn’t help” flag rate | | --- ## 5.13.2 Tracking Infrastructure ### Database Tables (Supabase) * `sessions`: Stores session IDs, start/end time, user ID (if known), and page source * `messages`: Logs all assistant/user exchanges with timestamp, category, language * `events`: Logs scrolls, highlights, clicks, lead capture, and other user actions * `leads`: See Section 5.12 – includes source, intent tag, timestamps, score * `errors`: Tracks all system exceptions, API timeouts, and integration failures ### Real-Time Analytics Pipeline * Optional: Mirror events to PostHog, Plausible, or Segment for enhanced dashboards * Create a Supabase view or materialized table for: * Daily active users * Lead conversion rate * Average response time * Top 10 queries --- ## 5.13.3 Developer Implementation Plan 1. **Tracking Library** * Create `analytics.ts` utility with functions like `trackEvent()`, `logMessage()`, `recordError()` * Include session UUID in every call * Automatically log `startSession()` on assistant open 2. **Frontend Hook** * Use a centralized analytics handler (e.g., React Context or Vue plugin) * Trigger on assistant events like: * Message sent * Message received * Page scrolled * Element clicked * Input field shown * Lead form submitted 3. **Supabase Write** * Use Supabase client to write rows to relevant tables in real time * Implement rate-limiting/batching if needed * Use row-level security tied to domain/project 4. **External API Forwarding (Optional)** * If client uses Segment, allow event forwarding * Setup event mirror with filters to external destinations (PostHog, GA4, etc.) --- ## 5.13.4 Built-In Dashboard Features An internal dashboard should be available to each business showing: | Dashboard Section | Details | | :---- | :---- | | **Summary Stats** | \- Total sessions | | \- Messages per session | | | \- Avg session duration | | | \- Leads captured | | | **Query Analysis** | \- Word cloud | | \- Top 10 assistant questions | | | \- Breakdown by page | | | **Performance** | \- AI response time | | \- LLM error rates | | | \- Assistant load time | | | **Leads Funnel** | \- Email capture rate | | \- Drop-off rate | | | \- Conversion events triggered | | | **Engagement Heatmap** | \- Scroll/highlight frequency by page | | **QA Metrics** | \- Thumbs up/down on answers | | \- Flagged messages | | | \- Manual review log | | This dashboard can be built inside Supabase’s built-in UI or using a frontend dashboard integrated via API. --- ## 5.13.5 Notifications and Alerts (Optional) | Type | Triggered When | Method | | :---- | :---- | :---- | | High engagement | \>100 sessions in a day | Email to admin | | Lead spike | \>10 leads in \<1hr | Email or webhook | | Error spike | \>5 API errors in 10 minutes | Slack/Discord | | Negative feedback | \>5 thumbs-downs in a day | Internal flag | --- ## 5.13.6 Privacy & Compliance * IP addresses and page data must be anonymized or excluded if required by GDPR/CCPA * Session UUID must not be directly linked to identity unless email is provided * Include notice in privacy policy that “This site uses an AI assistant which may track usage and anonymized questions to improve quality.” --- ## 5.13.7 Success Criteria | Metric | Target Value | | :---- | :---- | | Daily active sessions | \>10 per 1,000 visitors | | Session-to-lead conversion rate | \>15% | | LLM response time | \<2 seconds (average) | | Assistant load time | \<1.5 seconds (95th percentile) | | Error-free sessions (API uptime) | 99.9% | | Dashboard availability | 100% via Supabase or external | | Thumbs-up to thumbs-down ratio | \>4:1 | --- # 5.14 Admin Interface and Business Settings Panel ### Purpose To give non-technical users full control over their siteGuide assistant without needing to edit code or manage infrastructure. The admin panel allows users to customize prompts, manage branding, configure lead forms, review analytics, export leads, and set AI behavior boundaries. --- ### User Story * As a business owner, I want an intuitive dashboard where I can set up and personalize my assistant, review leads, and see performance metrics without writing a single line of code. * As a marketing manager, I want to adjust branding and tone, tweak lead form fields, and monitor assistant usage across pages and campaigns. * As a support staff member, I want to export the leads and session logs for follow-up or CRM import. --- ## 5.14.1 Access and Authentication | Feature | Behavior | | :---- | :---- | | **Login/Signup** | OAuth with Google or email/password with magic link fallback | | **Roles** | Admin (full access), Manager (no billing), Viewer (read-only) | | **Access Control** | Based on domain verification and email whitelist | | **Multi-Tenant Support** | Each account is isolated by project key; Supabase handles row-level security | --- ## 5.14.2 Dashboard Modules Each module below is accessible via a left-hand sidebar, organized by function: ### 1\. **Home Overview** * Total sessions this week/month * Lead capture summary * Click-through events (e.g., “Contact Us” clicked) * Uptime and assistant performance graph ### 2\. **Branding and Appearance** * Business name and logo upload * Accent color / assistant bubble color picker * Assistant name and avatar image upload * Chat icon position (bottom left, bottom right) * Widget width and height (responsive preview) * Voice option (text-only or voice \+ text) ### 3\. **Content and Behavior Settings** * Welcome message (editable prompt with variable injection: \{business_name\}, \{visitor_first_name\}) * Assistant tone (e.g., Formal, Friendly, Playful, Concise) * Navigation prompt structure (choose between informative or persuasive styles) * Blacklisted keywords or topics * Preferred default scroll behavior (smooth, instant, offset) ### 4\. **Lead Form Configuration** * Toggle lead form on/off * Add/remove form fields (email, phone, name, custom questions) * Required vs optional field configuration * GDPR/CCPA compliance notice toggle * Lead follow-up webhook or email notification settings ### 5\. **FAQ and Suggestion Seeds** * Seed up to 10 FAQs that the assistant will offer as clickable suggestions * Upload FAQ as CSV or write manually * Label each with display title and assistant response * Sync with on-site FAQ section (optional scraper or selector) ### 6\. **Pages & Paths** * Set different behaviors per URL path (e.g., `/pricing`, `/contact`) * Custom welcome messages per page * Optionally disable siteGuide on certain pages (e.g., `/checkout`) * Assign priority paths to increase attention (e.g., homepage gets full animations) ### 7\. **Analytics** * Real-time traffic with assistant engagement overlay * Scroll events per section * Highlight usage * Conversion funnel: visit → interaction → scroll → form shown → form submitted ### 8\. **Leads** * Sortable, filterable lead table (by date, intent, page, field) * Export as CSV, JSON, or sync via webhook to CRM * View full chat log associated with each lead * Manual lead score override ### 9\. **Voice Settings** * Choose AI voice style (e.g., calm, confident, cheerful, professional) * Upload fallback text for key actions (optional) * Enable/disable voice on mobile ### 10\. **Privacy and Security** * Add cookie consent banner trigger * Request user consent before activating voice or tracking * Purge data by session ID or email * Enable/disable persistent memory storage per region * Enable/disable IP logging --- ## 5.14.3 Settings Architecture and Storage (Technical) | Setting Type | Stored In Supabase Table | Notes | | :---- | :---- | :---- | | Branding & UI | `site_settings` | Logo URL, colors, position, size | | Behavior Config | `assistant_behavior` | Welcome message, tone, fallback responses | | Lead Form Config | `lead_fields` | Field label, type, required flag | | FAQ & Seed Data | `assistant_faqs` | Text, click triggers, path association | | Page-Specific Behavior | `page_settings` | Path URL, overrides, active status | | Analytics Logs | `events`, `sessions`, `leads` | Stored in real-time | | Voice Options | `voice_settings` | TTS engine selection, pitch/speed preferences | | Security Preferences | `compliance_settings` | Consent config, privacy flags | All settings are scoped to the customer’s project key and domain, with row-level security to prevent cross-access. --- ## 5.14.4 UI/UX Principles * Mobile-first responsive design * Side navigation with collapsible modules * Toast-based notifications on save, error, or success * Inline previews for branding updates * Tooltip help text for advanced options * Setup checklist wizard on first login --- ## 5.14.5 Success Criteria | Objective | Metric | | :---- | :---- | | Easy setup | 90%+ of users complete onboarding in \<15min | | Lead visibility | 100% of leads logged and visible in panel | | Customization adoption | \>75% of users modify branding or messaging | | Data security | Zero cross-tenant data leakage | | Dashboard responsiveness | Loads in \<2s on 4G connection | | Export reliability | 100% download success for CSV exports | --- # 6\. Deployment, Hosting, and Technical Stack --- ## 6.1 Deployment Strategy Overview siteGuide is a JavaScript-based co-browsing assistant that integrates into any WordPress (and eventually any CMS or custom HTML) website via a single script tag. The backend services for memory, persistent sessions, lead storage, and admin controls are hosted on a cloud stack combining DigitalOcean, Supabase, and open-source runtime tools. Deployment is structured to minimize client setup complexity while maintaining scalability across thousands of accounts. --- ## 6.2 Frontend Integration (Client Websites) ### Script Loader Each client receives a unique ` ``` ### Script Features * Loads widget and assistant UI dynamically * Pulls branding, welcome prompts, and voice settings from Supabase via the site ID * Tracks user interactions, scroll targets, highlights, and form submissions * Establishes socket or polling connection to maintain co-browsing state ### Installation Platforms * **WordPress:** Plugin wrapper that auto-injects the script in `` * **Shopify:** Theme snippet and admin console helper app (Phase 2\) * **Custom Sites:** Copy-paste embed code --- ## 6.3 Hosting Infrastructure | Component | Platform | Purpose | | :---- | :---- | :---- | | Frontend Embed Script | DigitalOcean CDN | Fast delivery of siteGuide widget across all sites | | Widget UI & Assets | DO App Platform | HTML/CSS/JS for assistant, voice overlay, chat interface | | Backend API | DO App Platform | Handles session tracking, actions, lead collection | | Database | Supabase (Postgres) | Stores user sessions, memory data, leads, preferences | | Auth/Access Control | Supabase | Role-based access to Admin Panel | | Admin Panel | DO App Platform (Next.js) | Business-facing control dashboard | | Persistent Vector Store | Supabase Edge Functions | Lightweight embeddings for ongoing memory recall | | AI Model Runtime | Local LLM or hosted endpoint (Phase 2\) | Low-latency response generation | | Analytics | Supabase \+ Logflare | Event tracking and funnel analysis | --- ## 6.4 Technical Stack Overview ### Frontend (Client-Facing) * **Language:** JavaScript (ES6+) * **Framework:** Vanilla JS \+ Stimulus/AlpineJS (lightweight control) * **Voice:** Web Speech API or ElevenLabs (if enabled) * **UI Styling:** TailwindCSS, CSS custom properties injected per site * **Browser Storage:** `localStorage`, `sessionStorage`, and optional IndexedDB ### Backend (Server-Facing) * **Runtime:** Node.js (API and sync calls) * **Database:** Supabase (PostgreSQL \+ RLS) * **Authentication:** Supabase Auth with JWT * **Realtime:** Supabase Channels (WebSockets for memory refresh, voice sync) * **Serverless Logic:** Supabase Edge Functions (Python/Node handlers) ### Admin Panel * **Frontend:** Next.js with Tailwind and ShadCN components * **State Mgmt:** React Context \+ SWR * **API Calls:** Supabase JS SDK * **Deployment:** DO App Platform CI/CD --- ## 6.5 Project Environment Structure ``` /siteguide-core /src /embed # JS loaded into client site /assistant # Chat assistant logic /scrolling # Scroll and highlight handlers /voice # Voice controls + speech handling /navigation # Path prediction and page changes /forms # Lead form UI & validation /admin-panel /pages # Next.js Admin Routes /components # Configurable dashboards /utils # API + local state helpers /api /functions # Supabase Edge or DO API functions ``` --- ## 6.6 Continuous Deployment Workflow | Action | Toolchain | | :---- | :---- | | Code pushed to main branch | GitHub | | Build triggered | DO App Platform CI | | Admin panel deployed | Static Next.js output auto-pushed | | Embed script redeployed | Bundled & uploaded to DigitalOcean CDN | | Supabase migrations | Auto-run via CLI (SQL schema \+ RLS enforcement) | | Error logging | Sentry (widget) \+ Logflare (backend) | --- ## 6.7 Environment Configuration | Key Setting | Environment Variable | Notes | | :---- | :---- | :---- | | Supabase Project URL | `SUPABASE_URL` | Required for all API calls | | Supabase Anon Key | `SUPABASE_ANON_KEY` | Read access for front-end | | Admin Auth Secret | `ADMIN_JWT_SECRET` | For role-based Admin Panel | | CDN Base URL | `CDN_BASE_URL` | Script delivery \+ assets | | SiteGuide Instance ID | `SITE_ID` | Passed via script tag per client | | Voice API Key (Optional) | `ELEVENLABS_API_KEY` or TTS Provider | Only needed for premium voice | --- ## 6.8 Success Criteria | Metric | Threshold | | :---- | :---- | | Time to deploy on new client site | \< 2 minutes via script or plugin | | Script load time (embed \+ UI) | \< 800ms over 4G | | Admin Panel load time | \< 1.5s first contentful paint | | Supabase API response latency | \< 250ms average | | Real-time co-browsing sync events | 99.5% delivered within 500ms | | Deployment errors per release | Zero regressions in script loader | --- # 7\. Data, Privacy, and Security This section outlines how siteGuide manages user data, protects personal information, and ensures full compliance with privacy laws such as GDPR, CCPA, and other international standards. Given that siteGuide operates on public-facing websites and can collect lead data, interaction data, and usage history, strict security and transparency standards are required at every layer. --- ## 7.1 Data Types Collected siteGuide collects and stores a mix of behavioral, contextual, and optionally, personally identifiable information (PII). These are categorized into three tiers: ### Tier 1: Anonymous Session Data (Always Collected) * Site ID * Session UUID (auto-generated, anonymized) * Pages visited (URL paths) * Time spent per page * Clicked buttons, scrolled sections * AI assistant prompts and responses * Device type, browser, and location (city/country only) ### Tier 2: Behavioral Memory Data (Optional, if enabled) * Previous session interactions (persisted via Supabase) * Scroll targets and FAQ clicked history * Assistant confidence scores or misfires * Tracked goals (e.g., clicked “book now” or submitted a form) ### Tier 3: Personally Identifiable Information (Optional, Explicit) * Name (via lead capture) * Email address (for follow-ups or persistent sessions) * Phone number (if captured in form fields) * Business name, industry (if provided) --- ## 7.2 Consent & User Control ### Anonymous Mode (Default) * All tracking is non-personal unless the user engages the assistant and chooses to leave information. * No cookies are required for basic session tracking. ### Explicit Consent for PII * Users are only asked for PII when initiating a lead submission or selecting “resume session via email”. * All PII entry points are accompanied by: * A consent checkbox (e.g., “I agree to receive follow-up emails from this business.”) * Link to the privacy policy * PII is stored only after consent is given and includes a timestamped consent log. ### Session Persistence Disclosure * The first time a user revisits a site with active memory, the assistant displays: * “Welcome back\! I remember your last visit. Would you like me to resume where we left off?” * Options: Yes / No, start fresh * If “Yes” is selected, session UUID is reused. If “No,” a new session is generated. --- ## 7.3 Data Storage and Retention ### Primary Storage: Supabase PostgreSQL * Role-based access enforced via RLS (Row Level Security) * Business owners can only view data for their own site ID * All leads and PII stored with AES-256 encryption at rest ### Session History / Memory Storage * Persisted sessions stored in structured JSON blobs * Indexed by session ID and optionally by email hash * Sessions auto-purge after 90 days of inactivity unless marked as "active lead" ### Vector Memory Embeddings (Optional Feature) * If enabled, past interactions are stored in vector format for memory recall * Stored in Supabase Edge Functions or local Pinecone-compatible store * Only assistant prompts/responses are embedded — no raw PII --- ## 7.4 Data Transmission and Encryption | Transmission Context | Encryption Protocol | | :---- | :---- | | Embed script from CDN | HTTPS (TLS 1.2 or higher) | | Supabase API calls (client) | HTTPS | | Realtime updates (WebSockets) | WSS with token auth | | Voice recording / playback | HTTPS streaming (TTS only) | | Admin dashboard login | Supabase Auth \+ JWT | All data-in-transit uses modern TLS protocols. Authentication tokens are scoped per role and expire after 12 hours. --- ## 7.5 Data Access and Permissions | Role | Access Scope | | :---- | :---- | | Anonymous visitor | No access to stored data beyond own session | | Business Owner | Only data from sessions on their own site ID | | Admin (internal) | Full access for support and debugging only | ### Admin Panel Restrictions * No raw PII can be exported unless explicitly authorized * All export/download buttons must include a GDPR notice * Audit logs must be stored for all admin data access --- ## 7.6 Legal Compliance ### GDPR * Consent-based data capture * Right to access, update, or delete data supported via email or admin interface * Data Protection Officer contact listed in privacy policy ### CCPA * Opt-out banner for California visitors * “Do Not Sell My Info” link embedded in assistant’s settings menu ### International Data Protection * Supabase supports global hosting, fallback plan includes EU-region storage if required * Client-specific data location setting can be added in Phase 2 --- ## 7.7 User Rights & Removal * **Delete my data** request form available in assistant settings and on host site privacy policy * Users can enter their email address and receive a confirmation link to delete stored data * Admin tools include “Forget Session” and “Forget User” functions to fully wipe records * All deletions are hard-deleted, not just flagged --- ## 7.8 Breach Mitigation and Logging * Daily audit logs of all data accesses and exports * Error and anomaly detection on spike in PII access * Internal alerts (Slack/email) for: * Failed auth attempts * Abnormal access patterns * Large export operations In case of breach: * Affected businesses are notified within 72 hours * Users are notified by the host business (not aiConnected) * Full forensics retained and logged --- ## 7.9 Success Criteria | Metric | Target | | :---- | :---- | | User PII stored without consent | 0 incidents | | Average time to fulfill deletion request | \< 48 hours | | % of sessions tracked anonymously | ≥ 90% unless lead is captured | | Admin exports logged and auditable | 100% | | Compliance review status | GDPR \+ CCPA certified policies | --- # 8\. Admin Tools and Business Dashboard This section details the full feature set of the administrative dashboard provided to business owners who install siteGuide. It defines how users (businesses) can configure, monitor, and optimize their assistant, view session replays, manage leads, and adjust behavior to better match their conversion goals. The admin panel is hosted by aiConnected and accessed via secure login at `dashboard.aiConnected.ai`. --- ## 8.1 Authentication and Access ### Login * Secure login via Supabase Auth (email \+ password or OAuth) * Optional 2FA via email or authenticator app (Phase 2\) * Each business user account is linked to one or more websites via a unique `site_id` ### User Roles * **Owner:** Full access to all data and settings for a given site * **Editor:** Can modify assistant behavior and branding * **Viewer:** Read-only access to leads, transcripts, and analytics --- ## 8.2 Site Onboarding and Setup Upon first login, the user is taken through a 4-step assistant setup process: 1. **Site Details** * Site name * Industry category * Public URL 2. **Assistant Configuration** * Select use-case focus: Lead Generation, FAQ Help, Navigation, or All * Upload up to 5 key pages (for initial semantic parsing) 3. **Branding** * Upload logo (used in chat bubble) * Pick assistant color scheme * Set assistant greeting (e.g., “Hi\! Need help finding anything?”) 4. **Embed Script** * One-line JS snippet provided (customized with `site_id`) * Includes step-by-step WordPress instructions * Includes check for script installation (active/inactive status) All assistant settings are editable later in the dashboard. --- ## 8.3 Real-Time Interaction Feed Business users can view a live feed of interactions on their site. ### Features * Scrollable timeline of sessions, labeled by: * Session UUID * Entry page (e.g., `/pricing`) * Time of visit * Assistant topic (e.g., “Asked about refund policy”) * Toggle to view chat transcript per session * “Highlight in replay” option for scroll & click actions ### Filters * By date range * By action type (clicked button, submitted form, etc.) * By page (e.g., all sessions on `/contact`) --- ## 8.4 Lead Management siteGuide automatically saves leads captured by the assistant. ### View Leads * Table view with: * Name, email, phone, timestamp * Assistant summary (e.g., “Interested in monthly subscription plan”) * Lead source (page and session ID) * Click to view full transcript of interaction ### Actions * Export to CSV * Push to CRM (Zapier or webhook) * Mark as contacted * Delete or redact lead ### Smart Tags * Auto-generated tags (e.g., “Pricing Inquiry,” “Booking Request”) * Searchable and filterable by tag * Option to assign custom tags --- ## 8.5 Assistant Customization Within the dashboard, users can fine-tune the assistant’s: ### Greeting * Change default greeting based on page context * Set greeting delay (e.g., greet after 15s on site) ### Lead Prompt Behavior * Set “When should the assistant offer to collect contact info?” * After 2+ questions * After goal reached (e.g., visited booking page) * After 60+ seconds of activity ### Tone of Voice * Options: Friendly, Professional, Casual, High-Energy * Future: Custom fine-tuning per business (e.g., import brand tone document) ### Language Support * Choose one default language * Option to auto-detect browser language (Phase 2\) --- ## 8.6 Analytics and Performance Tracking ### Key Metrics * Total sessions * Avg. session duration * Leads generated * Lead conversion rate (% of total sessions) * Most clicked elements (based on scroll & highlight) ### Conversion Goals * Define conversion goals (e.g., clicked “Book Now” or submitted form) * View goal completions over time * AI will learn which phrases and paths lead to conversion and adjust behavior ### Funnel View * Visualization of how users navigated via the assistant * Drop-off points highlighted * Common click paths mapped --- ## 8.7 Session History and Replay Each session is stored with: * Page paths visited * AI actions (scrolls, highlights, clicks) * Full assistant transcript * Lead form status * Dwell time and exit page Business users can replay sessions in real-time or scrub through a timeline to analyze drop-offs and assistant accuracy. --- ## 8.8 Privacy Controls * “Forget this user” option per session (deletes memory and transcript) * Toggle assistant memory on/off per site * Set default session expiry duration (e.g., forget after 30 days) --- ## 8.9 Success Criteria | Functionality | Success Definition | | :---- | :---- | | Assistant installed | \>95% of registered users complete embed | | Leads captured | ≥15% of sessions yield lead or booking | | Business user login frequency | 2+ logins per week | | Customization usage | \>50% of users change at least 2 default settings | | Export/download compliance | 100% consent and access logs recorded | --- # 9\. Multisite Support and Scalability This section outlines how siteGuide will support businesses with multiple websites, teams, or assistant configurations, while ensuring robust infrastructure performance and clear segmentation of data. This is especially important for agencies, franchises, and enterprise clients managing multiple domains or regional sites. --- ## 9.1 Multisite Support ### Overview Each business user account can create and manage multiple “Sites.” A **Site** represents a single domain or subdomain with its own assistant configuration, memory, and analytics. ### Use Case Examples * A marketing agency installs siteGuide on 50 client websites. * A franchise business operates 10 local domains with distinct offerings. * An enterprise has different language sites (e.g., `us.example.com`, `de.example.com`). ### Site Independence * Each site has: * Its own `site_id` * Separate assistant memory * Unique branding, prompts, lead fields, and settings * Separate analytics dashboard ### Switching Sites * Admin users can switch between sites in the dashboard via a dropdown. * Each session and assistant instance reports to the correct site via `site_id` embedded in the JS snippet. --- ## 9.2 Multi-User Team Management (Future) **Not required at launch**, but the architecture must support future team permissions per site: | Role | Permissions | | :---- | :---- | | Owner | Full access across all sites under their account | | Site Admin | Full access to one site | | Assistant Editor | Modify assistant prompts only | | Lead Viewer | View leads and transcripts only | Admin panel UX must be built with this future expansion in mind, using componentized RBAC (role-based access control) logic. --- ## 9.3 Namespace Isolation Each `site_id` creates a namespace for: * Supabase tables (e.g., `leads_site_abc123`) * Vector memory storage * AI context injection (no bleed between sites) * Session cookies (stored as `siteguide_{site_id}_session`) Isolation is critical to prevent: * Cross-site data leakage * Confused memory injection * Duplicate analytics across different domains --- ## 9.4 Performance Scaling Strategy siteGuide must remain performant even when installed on thousands of websites with concurrent usage. The architecture supports this by offloading responsibilities: ### On-Page Load * Assistant assets (JS, CSS, UI logic) are served via CDN * Only lightweight UI bundle is loaded on client * Memory and reasoning are cloud-based (via aiConnected APIs) ### Interaction Workflow * Frontend sends prompts → aiConnected API handles reasoning * aiConnected returns next action (chat reply, scroll, highlight, etc.) * Local browser executes the action; no blocking behavior ### Storage * Supabase handles: * Session metadata * Leads and transcripts * Interaction logs * Vector memory stored separately per site for AI retrieval ### Load Management * All API endpoints and memory functions are stateless * Persistent memory is stored externally, only loaded when needed * No live WebSocket unless co-browsing view is active (very rare) --- ## 9.5 Deployment Strategy for Large Clients For enterprise or agency-level installations: * Provide white-label version of the dashboard * Allow API access to pull leads into external CRM * Custom subdomains per client (`clientname.aiConnected.ai`) * Dedicated memory instance per enterprise tenant Optional: Offer service-level guarantees for uptime, replay storage, and assistant memory limits via SLAs. --- ## 9.6 Success Criteria | Goal | Success Metric | | :---- | :---- | | Cross-site stability | Zero data leakage between sites | | Time to add new site | Under 5 minutes with full configuration | | Site switching usage | 70% of agency/franchise users manage 2+ sites | | Performance degradation threshold | No slowdown up to 10,000 simultaneous sessions | --- # 10\. Data Retention, Privacy, and Security This section defines how siteGuide handles all user and business data with strict regard for security, privacy compliance (e.g., GDPR, CCPA), and retention policies. It ensures that siteGuide can be confidently deployed on high-trust websites — including healthcare, finance, legal, and education — without risk of data compromise or misuse. --- ## 10.1 Data Categories The platform interacts with the following categories of data: ### 1\. Visitor Data (End User) * Session ID (UUID) * Page visits * Clicks, scrolls, highlight paths * Chat transcript with the assistant * Lead capture data (e.g., name, email, phone) ### 2\. Business Data (Site Owner) * Assistant configuration * Uploaded brand assets (logo, colors) * Custom prompts and overrides * Lead management records ### 3\. System Metadata * Time stamps * API logs (request/response) * Browser/user agent * Memory vector keys (hashed) No sensitive credit card or health data is ever collected by default. --- ## 10.2 Data Retention Rules ### For Visitor Sessions: * Active memory: 30 days by default * Transcript: 90 days stored (configurable per business) * Full replays (scroll/click): 30–60 days (configurable, auto-expiry) * Leads: Stored indefinitely unless deleted by user or business ### For Business Accounts: * Configurations and assistant settings are stored until account closure * Deletion of a site permanently removes assistant memory and leads for that site Businesses may configure auto-expiry rules per data category. --- ## 10.3 Privacy Tools for Website Visitors siteGuide complies with privacy regulations by offering the following end-user protections: ### GDPR/CCPA Banner Integration * Auto-detects cookie banner tools (e.g., Cookiebot, Termly) * Delays assistant activation until consent is granted ### Data Access & Deletion * In-chat message: “Forget my data” triggers memory and transcript wipe * Link in the siteGuide assistant footer: “Privacy Settings” * Supabase triggers delete logs and scrubs all indexed vectors for session ID ### Opt-Out Mechanisms * Memory-free mode (temporary session, no persistence) * Ability for businesses to turn off memory or auto-delete after each session --- ## 10.4 Encryption Standards ### In Transit * All API communication encrypted via HTTPS/TLS 1.3 * All websocket or push-based updates encrypted via secure channels ### At Rest * Supabase database encrypted with AES-256 * Vector memory storage encrypted at disk level * Passwords stored using bcrypt (Supabase default) --- ## 10.5 Security Architecture ### Access Controls * Role-based access system per site and user * Tokens for assistant instances scoped to `site_id` * No cross-site access possible ### API Protection * Rate-limited public endpoints * Token auth (JWT) with auto-refresh * All read/write operations scoped to authorized `site_id` ### Admin Monitoring * Admin audit logs for every assistant update or lead export * IP logging for dashboard activity * Alerts for unusual data export volumes ### Hosting Security (DigitalOcean) * Hosted behind firewall * Backups run daily with encrypted snapshots * Auto-scaling infrastructure with DDOS mitigation via CDN --- ## 10.6 Compliance and Certifications | Standard | Compliance Status | | :---- | :---- | | GDPR | Fully compliant | | CCPA | Fully compliant | | HIPAA | Not covered (future add-on) | | SOC 2 | Planned via DigitalOcean infra roadmap | | WCAG | AA-level accessible assistant UI | --- ## 10.7 Success Criteria | Objective | Measurable Indicator | | :---- | :---- | | User privacy control | 100% compliance with deletion and opt-out requests | | Security incidents | Zero breaches or unpatched vulnerabilities | | Encryption coverage | 100% of stored PII encrypted at rest and in transit | | Business adoption in sensitive fields | At least 10% of users from regulated industries | --- # 11\. Optional Enhancements and Future Features This section outlines advanced capabilities that are not part of the core MVP for siteGuide but represent high-value additions for future iterations. These features aim to deepen personalization, streamline integrations, and expand the assistant’s utility across more complex customer journeys. --- ## 11.1 Persistent Cross-Device Memory (User-Level Identity) ### Overview Currently, session memory is stored per browser via session cookies and optionally resumed via email input. Future updates will enable: * Memory that persists across different devices (mobile, desktop, tablet) * Seamless recall of past conversations regardless of browser or IP ### Implementation * Add user account creation for site visitors (email \+ OTP, no password) * Upon login, assistant retrieves full memory tied to that user across all sessions * Memory entries will now use `user_id` in addition to `session_id` ### Benefit * Enables deeper personalization (e.g., “Welcome back, here’s where we left off.”) * Ideal for e-commerce (cart recovery), SaaS onboarding, and service industries --- ## 11.2 CRM/Inbox Memory Training ### Overview SiteGuide could eventually use historical data (e.g., past customer emails, CRM conversations, FAQs) to train the assistant’s tone, knowledge, and objection handling. ### Implementation * Allow business to connect Gmail, HubSpot, Salesforce, or import CSVs * N8N workflow processes text content → cleans → indexes into memory * System adds tagged knowledge as non-user memory into vector database ### Use Cases * Customer support pretraining * Personalized onboarding flows * Sales conversation reference material --- ## 11.3 Sentiment-Aware Conversation Routing ### Overview The assistant can monitor sentiment during a live conversation and take specific actions based on tone or urgency. ### Examples * Angry tone → escalate to human * Hesitation or doubt → offer clarification or schedule a callback * Excitement → accelerate toward conversion (e.g., direct booking link) ### Implementation * Sentiment detection via OpenAI or local model * Assign confidence scores to emotional state * Trigger conditional responses in chat flow --- ## 11.4 Event-Based Assistant Behavior ### Overview Let the assistant react to specific user behaviors, such as: * Inactivity for 15 seconds → assistant re-engages * Scrolls to bottom of page → assistant offers help * Copies coupon code → assistant logs intent * Leaves a form half-filled → assistant offers to resume later ### Implementation * Small JS listener library bundled with siteGuide script * Events forwarded to assistant via n8n node or native web socket * Assistant modifies behavior contextually --- ## 11.5 Custom Action Buttons ### Overview Businesses can configure reusable call-to-action buttons that appear contextually in the chat (e.g., “Download Brochure,” “Book a Demo,” “Request a Quote”). ### Features * Buttons tied to tracked actions (downloads, form opens, calendar launches) * Trigger scripts, open URLs, or emit custom DOM events * Responses can vary based on page URL or user attributes --- ## 11.6 Multilingual Support ### Overview Enable automatic detection of the user’s preferred language (via browser locale or explicit choice) and localize: * Assistant UI * Voice output (with accent control) * Chat responses with translated memory ### Tech * Translation memory index per language * Optional integration with DeepL or OpenAI multilingual model * Supabase row-level localization support --- ## 11.7 AI-Powered Dynamic Product Tours ### Overview Assistant visually guides the user through onboarding or product education by: * Moving across multiple pages * Highlighting specific UI elements * Narrating what each feature does * Waiting for user input before advancing ### Use Cases * SaaS onboarding * Guided demos for apps * Product walkthroughs for e-commerce --- ## 11.8 Advanced Lead Routing Rules ### Overview Lead data from conversations can be conditionally routed to different destinations: * Sales rep assignment based on region * Different CRM pipelines for product categories * Instant Slack alerts for “hot” leads only ### Configuration * Rules defined in dashboard (If/Then UI) * N8N integrations execute delivery --- ## 11.9 Success Criteria for Future Feature Rollouts | Feature | Success Indicator | | :---- | :---- | | Cross-device memory | 30% increase in user return-to-chat rates | | CRM memory training | 25% reduction in live agent transfers | | Sentiment routing | 40% faster lead escalation | | Event triggers | 10% increase in lead engagement rates | --- # 12\. Roadmap and Development Milestones This section defines the phased development plan for siteGuide, breaking the project into achievable milestones with clear deliverables. It ensures alignment between technical teams, product leads, and business stakeholders by mapping each stage of the platform’s rollout — from initial prototype to full feature maturity. --- ## 12.1 Phase 0: Internal Proof of Concept (Weeks 1–2) **Objective:** Prove feasibility of real-time DOM interaction, voice control, and persistent session memory using minimal stack. **Deliverables:** * Embeddable JS snippet that attaches AI to a test website * Working co-browsing overlay (mouse follow \+ highlight) * Basic chat window with GPT-powered responses * DOM element targeting for text highlight and scrolling * Session memory stored in localStorage and Supabase * Voice input test (Web Speech API) and text-to-speech (ElevenLabs or fallback) **Success Criteria:** * Assistant can read and highlight a paragraph on command * Page reload does not lose the session transcript * Voice interaction succeeds in \>90% of test cases --- ## 12.2 Phase 1: MVP Beta (Weeks 3–6) **Objective:** Deliver a fully functional co-browsing assistant with persistent memory, working chat interface, and voice interaction on any WordPress site. **Key Features:** * AI overlay with chat UI and draggable co-browsing assistant * DOM scanning and tag-based element detection * Smooth scrolling and mouse-follow animation * Persistent session memory (local and Supabase) * Voice input/output (toggleable) * Email-based session resumption * Page-to-page memory continuity **Technical Setup:** * Supabase instance for storage, auth, and vector memory * Next.js management dashboard for site owners * Embedded JS loader script (deferred, async-ready) * n8n orchestration for memory, triggers, lead routing **Success Criteria:** * Installable via 1-line script on any WordPress site * Leads successfully captured and stored * Memory persists across navigation and logout/login * Works with \>80% of tested themes and site builders --- ## 12.3 Phase 2: Public Launch (Weeks 7–10) **Objective:** Launch siteGuide as a production-ready AI assistant with basic customization options and onboarding workflow. **New Features:** * Assistant appearance configuration (avatar, colors, tone) * Memory viewer for business owners * Lead export tools * Activity log (visits, transcripts, heatmaps) * Usage-based billing integration **Platform Stability Goals:** * 99.9% uptime for API and Supabase * Secure authentication and encryption standards * No memory loss or duplication bugs **Success Criteria:** * 100 active businesses onboarded within first 30 days * \<1% session loss rate * CSAT \>90% for assistant UX across test users --- ## 12.4 Phase 3: Expansion (Weeks 11–14) **Objective:** Begin adding optional modules and partner integrations for advanced use cases. **Expansion Modules:** * CRM/email inbox memory training * Cross-device persistent identity * Event-based engagement triggers * Custom action buttons * Full language localization * Zapier/Make.com integration **Developer Support:** * SDK or plug-in points for external developers * API access for programmatic lead retrieval **Success Criteria:** * CRM integration used by at least 25% of active customers * Average lead volume per business increases \>30% over beta * Third-party developer contributions submitted --- ## 12.5 Maintenance & Support Cycle (Ongoing) **Responsibilities:** * Weekly check-in on Supabase logs and memory usage * Monthly security audit of token/auth layers * Proactive UI updates for browser compatibility * Quarterly feature reviews based on customer feedback **Ongoing Metrics to Monitor:** * Assistant open rate per visitor * Drop-off points in conversations * Percentage of leads converted from assistant --- ## ✅ Missing or Underdeveloped Areas ### 1\. **Security & Compliance Guidelines** **What’s missing:** A clear, dedicated section on how to handle: * User data encryption (at rest and in transit) * Cross-site scripting (XSS) and injection protections in the chat overlay * Secure handling of memory/session data * Supabase row-level security policies * Optional GDPR/CCPA compliance for data deletion or user export **Why it matters:** Investors, enterprise clients, and CTOs will expect clarity around data security — especially since siteGuide stores identifiable memory and possibly voice data. --- ### 2\. **Analytics & Insight Framework** **What’s missing:** A description of what will be tracked, where it will be stored, and how businesses will view it: * Heatmaps (page areas most highlighted or requested) * Assistant usage stats (open rates, most clicked responses, voice usage) * Lead funnel performance (drop-offs, completions) * Session replay or text playback options **Why it matters:** Data reporting is a huge competitive differentiator, and analytics are essential to prove ROI for small business clients. --- ### 3\. **Unit Tests & QA Expectations** **What’s missing:** A brief QA/testing protocol section specifying: * What should be tested (UI components, memory persistence, DOM targeting) * Acceptable test coverage threshold * Bug classification and triage priorities (e.g., memory loss \= P0, misaligned scroll \= P2) * How often regression testing occurs (especially for DOM updates on client sites) **Why it matters:** Even junior developers benefit from seeing what “done” means in code quality and test resilience. --- ### 4\. **Browser & Device Compatibility Matrix** **What’s missing:** Explicit list of: * Minimum browser versions (Chrome, Safari, Firefox, Edge) * Supported devices (desktop, iPad/tablet, mobile) * Voice input/output compatibility (e.g., Safari on iOS may block mic access) **Why it matters:** This prevents confusion and support tickets when customers say "the assistant isn’t talking to me on my iPhone.” --- ### 5\. **Disaster Recovery & Failover Handling** **What’s missing:** Scenarios and protocols for: * Supabase outage * GPT model failure or API timeout * Frontend script failure due to site conflicts * Session loss or memory desync **Why it matters:** Even if just briefly noted, having recovery mechanisms planned builds trust in the system’s resilience. --- ### 6\. **In-Chat Context Menu / Tooltips** **What’s missing:** A UI addition that lets users: * Click a highlighted term for more info * View why a certain element was selected * Hover over past memory or assistant replies to expand context **Why it matters:** Improves user transparency and makes the AI feel more explainable — especially important for trust and legal/sensitive use cases. --- ### 7\. **Developer Environment Setup Instructions** **What’s missing:** The current PRD assumes the dev will figure out how to start. You should include: * GitHub repo structure * Initial command-line setup * Environment variable list (`.env.example`) * Recommended deployment environment (e.g., DigitalOcean droplet \+ Supabase project \+ Vercel frontend) **Why it matters:** Reduces ramp-up time and ensures developer onboarding is smooth — especially helpful if you later outsource pieces of the work. --- ### 8\. **Glossary of Terms** **What’s missing:** A simple glossary defining: * Co-browsing * Session memory * Highlighting * DOM targeting * Rehydration * Vector memory * Supabase (if junior developers are unfamiliar) **Why it matters:** Removes ambiguity, aligns the team’s mental model, and prevents incorrect assumptions during buildout. --- ## ✅ Must-Have Components Already Covered The PRD **already does** an excellent job defining: * AI overlay and chat interface * DOM targeting and element highlighting * Smooth co-browsing via scrolling and auto-focusing * Voice input and output with fallback behavior * Persistent session memory using Supabase/localStorage * Page-to-page continuity and assistant UI hydration * Email-linked session resumption * Developer roadmap, milestone plan, and fallback behavior These are the **core capabilities**. Nothing essential to the app's core promise has been omitted in design. --- ## ❗Remaining Gaps That Could Block or Break the Build These are *the last few real blockers* that, if not addressed, could cause the app to fail in live use or break user expectations. --- ### 1\. **Universal Page Context Restoration** **Problem:** After clicking to a new page, the assistant must **instantly restore** the exact scroll position, memory log, and highlight state. **Gap:** The PRD touches on this concept but doesn’t define a technical spec for: * Re-scanning DOM after page load * Reapplying the last command (e.g., re-highlighting paragraph 3\) * Rehydrating open conversation state in the UI **Why it matters:** If the AI clicks "Learn More" and the user lands on a new page with a blank assistant and lost memory, the illusion is broken. **Solution:** Define a **reinitialization protocol**: * Snapshot last action (DOM selector, command, scroll pos) * Reapply it after `window.onload` * Restore chat UI with `sessionId` --- ### 2\. **DOM Targeting Consistency** **Problem:** Live websites often use dynamic classes or DOM mutations (e.g., from page builders, sliders, or animations). Relying on `querySelector` alone is brittle. **Gap:** There is no fallback or adaptive targeting strategy if selectors fail. **Why it matters:** AI might “click” something that doesn’t exist anymore or highlight the wrong element — causing user confusion or failure to complete an action. **Solution:** * Use multiple DOM targeting strategies: static selectors \+ text match fallback \+ XPath * Store not just the selector, but the **text content \+ position index** for fuzzy recovery * Gracefully degrade with a message like: “It looks like this section changed — let me find the new version for you” --- ### 3\. **Race Conditions in DOM Rendering** **Problem:** If the AI tries to scroll/highlight/click before the DOM is fully hydrated (e.g., on SPA sites or heavy WordPress themes), the action will silently fail. **Gap:** There’s no defined method for detecting **DOM readiness** before performing assistive actions. **Why it matters:** Some client sites will appear “broken” because the AI moves too quickly after navigation or user commands. **Solution:** * Use `MutationObserver` or wait for specific element load before interaction * Add retry logic for element-based actions (e.g., scroll \+ highlight up to 3x with delay) --- ### 4\. **WordPress Script Isolation** **Problem:** Many WordPress sites inject tons of JS (e.g., Elementor, WPBakery, Divi) that can conflict with your script or override events. **Gap:** The PRD doesn’t define how to **sandbox** or isolate the AI’s scripts from common WordPress clashes. **Why it matters:** You may see bugs that are hard to debug because other plugins intercept clicks, hijack styles, or reset DOM state. **Solution:** * Wrap assistant inside a **Shadow DOM** * Use CSS prefixing for isolation * Avoid assuming control over `window`, `document`, or global classes --- ### 5\. **Fail-Safe UI Behavior** **Problem:** If the AI crashes or stalls, there’s no guidance yet on how to gracefully recover or notify the user. **Gap:** No status indicators or fallback UI states are defined. **Why it matters:** An invisible or frozen assistant \= a completely broken experience. Especially problematic in customer-facing websites. **Solution:** * Add a UI status pill: “Thinking...”, “Listening...”, “Connection lost” * Retry on GPT/Supabase failure * Trigger a self-heal or refresh prompt if assistant is unresponsive for 10+ seconds --- ### 6\. **Cross-Origin Memory Behavior** **Problem:** You mention expanding to “all websites” — but the PRD assumes you’ll always have access to embed a script on the target site. **Gap:** There’s no plan for supporting co-browsing across sites you **don’t own**, or across multiple domains (e.g., subdomains, client portals). **Why it matters:** This becomes relevant once you expand beyond single-site deployments and want full session memory across apps (e.g., WordPress \+ Shopify). **Solution:** * For now, scope to same-origin sessions * Flag this as a future milestone: **Cross-Origin Shared Session Layer**, possibly using OAuth \+ client-side session tokens --- ## ✅ Summary: Build is Feasible with Minor Additions You are **95% complete** in design. The remaining \~5% are critical execution details that, if skipped, would cause: * Inconsistency across real-world websites * Broken assistant behavior after navigation * Perceived bugs due to race conditions or DOM conflict * Loss of user trust due to UI crashes or incorrect highlights Once you address the above 6 points explicitly in the PRD (even briefly), a developer — junior or senior — should be able to build the **exact experience you envision**, end-to-end. Would you like me to revise the PRD to include these, or just generate the add-on sections to paste in? --- --- ## aiConnected Apps And Modules **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules **Description:** Documents in aiConnected Apps And Modules. --- ## macEngine Comprehensive Product Requirements Document (PRD) **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/mac-engine-prd **Description:** Absolutely. Here’s a thorough, section by section revision of your PRD, with full integration of every feature, workflow, and expectation discussed—including... Absolutely. Here’s a **thorough, section-by-section revision** of your PRD, with full integration of every feature, workflow, and expectation discussed—including all gaps identified above. I’ll write this as if you’re handing it directly to a senior macOS/AI engineer who needs **zero ambiguity** and **no unanswered questions** about what to build. --- # **macEngine Comprehensive Product Requirements Document (PRD)** --- ## **1\. Introduction** ### **1.1 Product Vision** macEngine is a locally installed, voice-first, AI-powered “operating layer” for macOS that gives users hands-free, intelligent control over their Mac. Unlike chatbots, macEngine listens for user requests, understands screen context, and **performs real actions**—opening apps, navigating UIs, typing, scheduling, searching, and executing complex workflows. It provides a near-invisible, always-on “J.A.R.V.I.S.” experience—learning and adapting to user routines, switching personalities on the fly, and connecting to a broader mesh of aiConnected engines for extended reach. ### **1.2 Business Goals** * Launch a $149.97/mo utility that is indispensable after a 7-day trial. * Require no ongoing AI inference cost to us (users supply LLM API keys). * Achieve NPS \> 60 and \<1% monthly churn. * Establish a secure, modular core for macEngine Pro and aiConnected engines. --- ## **2\. User Personas & Use Cases** ### **2.1 Students** * “What’s my next exam?” → Reads school portal, finds and speaks answer. * “Open all materials for my next assignment in tabs.” → Browser, login, tabs. ### **2.2 Executives** * “Join my next meeting and take notes.” → Opens Zoom, records, summarizes. * “Book a flight for Monday, 8AM to NYC.” → Opens browser, fills in booking. ### **2.3 Developers** * “Pull latest, run all tests, DM me the failures.” → Terminal, Git, Slack. ### **2.4 Creatives** * “Summarize these five PDFs, build a Notion outline.” → Reads, parses, posts. --- ## **3\. System Architecture** ### **3.1 High-Level Subsystems** * **Voice Interface:** Wake-word, STT, TTS, multiple personalities/voices. * **Command Interpreter:** NLU, intent extraction, clarification, context history. * **LLM Dispatcher:** Local (llama.cpp) vs. cloud LLM (OpenAI, Anthropic, Gemini) logic. * **Screen Interpreter:** OCR, vision models, widget detection, semantic screen map. * **Executor:** Accessibility API, AppleScript, robust and recoverable UI automation. * **Routine Engine:** User-trained, replayable, shareable multi-step workflows. * **Personality Manager:** Swappable personas, voice & response config, auto-switch logic. * **Configuration & Security:** Keychain, preferences, permissions, API key flow, privacy. * **Subscription Management:** Daily license validation (internet required); graceful fallback. --- ### **3.2 Detailed Module Descriptions** #### **3.2.1 Voice Interface** * **Wake-word Detection:** * Local/offline (“Hey macEngine”, custom name support). * Porcupine or Apple VoiceTrigger; \<50 MB RAM. * Wake-word profiles stored per-persona. * **Speech-to-Text:** * whisper.cpp or Apple Speech (configurable fallback). * Streams mic, segmenting per wake/utterance. * **Text-to-Speech:** * Native Apple voices (default), ElevenLabs option (API key). * Latency \<300ms. * Voice selection by persona, instant switch on “Switch to \[persona\] mode.” #### **3.2.2 Command Interpreter** * **Intent Extraction:** * Local rules for top-20 built-in intents; LLM fallback for complex queries. * Slot filling (parse date/time/app, user, command context). * Clarification dialog if intent confidence \<0.65 (“Did you mean...?”) * **Context History:** * Retains previous N (configurable) interactions for context-sensitive commands. * Example: “Email them the last summary” → resolves “them” from history. #### **3.2.3 LLM Dispatcher** * **Local Model:** * llama.cpp, 7B minimum (CPU-optimized, user can upgrade models). * Handles system commands, routines, local search, privacy-first flows. * **Cloud Model:** * OpenAI, Anthropic, Gemini, etc.—user provides API keys during onboarding. * Handles coding, research, content gen, ambiguous or “long context” tasks. * **Routing Logic:** * Decision tree: direct system actions to local, open-ended requests to cloud. * Users can override per-persona or per-command. * Failover: if cloud API fails, fallback to local or return user-facing error. * **API Key Management:** * Entered via onboarding, stored in Keychain, editable in preferences. * Keys never leave user’s machine; no cloud proxying by macEngine. #### **3.2.4 Screen Interpreter** * **Capture:** * CGDisplay snapshot at 1 fps when active, on-demand if needed. * Suspend in idle state to preserve privacy and CPU. * **OCR:** * Apple Vision for fast/built-in, Tesseract fallback for complex cases. ```text * Output: \[{bbox, text, element type, confidence}\] ``` * **Vision Models:** * Local CLIP or small vision transformer to classify UI controls. * Semantic labeling: “Button: Submit”, “Table: Assignments”, etc. #### **3.2.5 Executor** * **Actions:** * click(x,y), doubleClick, typeText(str), pressHotkey, scroll, openURL, runShell, openApp, closeApp. * **Implementation:** * Accessibility API preferred; AppleScript for system and “legacy” app control. * Visual confirmation after every action (element highlight or screen OCR match). * **Safety/Confirmation:** * All destructive ops (delete, overwrite, move, close unsaved) require explicit voice confirmation (“Are you sure? Say yes to continue.”) * **Error Recovery:** * If UI element not found (fuzzy match ±20 px); prompt user for manual correction (“Point to what you want me to click.”). #### **3.2.6 Routine Engine** * **Training Workflow:** * Triggered by user (“Let me show you how”) or failed automation attempt. * Step-by-step “watch me do it”: 1. User performs each UI action (click, type, scroll, etc.). 2. macEngine records: * Action * Screen snapshot, element anchors (bbox, text, widget type) * Delay/interval * Optional voice explanation per step (for Routine clarity/sharing) * When finished: user names Routine, assigns trigger phrases. * **Replay Workflow:** * User invokes Routine by phrase or voice (“Run my grade check Routine.”). * macEngine replays steps using current screen context and anchor matching. * Supports variables (e.g., “next exam,” “all assignments,” etc.). * **Editing/Export:** * Routines are managed in a Routine Library in preferences UI. * Users can edit, rename, delete, export/import (for sharing or backup). * All routines stored locally, exportable as signed JSON (.mre); pro version supports routine sharing/marketplace. * **Adaptation:** * If screen layout changes, macEngine prompts user for correction and “remembers” update. #### **3.2.7 Personality Manager** * **Personas:** * At onboarding, user names macEngine and selects from pre-set voices/personalities (e.g., Professional/Orion, Casual/Elara, Creative/Nova), or imports their own (Pro). * Personality \= wake-word, TTS voice, response style (concise, verbose, witty, etc.), LLM preference, context schedule (work hours \= Professional; night \= Casual). * **Voice/Persona Switching:** * User can switch at any time via voice (“Switch to Creative mode.”) * Persona change is immediate: affects voice, tone, LLM, and (if scheduled) context-aware auto-switch. * All persona configs are stored and synced locally (future: sync via aiConnected cloud). * **Multiple/Custom Personas:** * Users may define and save custom personas, mapping them to their own voices or style templates. #### **3.2.8 Configuration, Security, and Subscription Management** * **Key Storage:** * All secrets (API keys, routine vars) stored in macOS Keychain (never plain-text on disk). * **Permissions:** * On install/first launch, guided overlay for enabling Mic, Screen Recording, Accessibility, and (optionally) Full Disk Access. * macEngine does not run until permissions are granted; checks status at launch. * **Subscription Management:** * On first launch, user is prompted for account creation (or trial activation). * macEngine performs a daily internet check (via secure HTTPS) to validate subscription; if offline, continues for 3-day grace period. * If validation fails, user is notified with clear UX (“Your subscription needs to be renewed—please reconnect to the internet.”) * All subscription logic is transparent and documented. * **Privacy:** * No user data is uploaded, shared, or analyzed outside of user device. * Any error reports or telemetry are strictly opt-in, anonymized, and user-controlled. --- ## **4\. Functional Requirements** ### **4.1 Voice Interaction** * Reliable, responsive wake-word detection with custom names (per-persona). * Accurate STT with latency \<1s, including fallback if mic or model errors. * Clear, context-appropriate TTS (switches with persona). * All commands available via hotkey (for accessibility). ### **4.2 Task Automation** * Support for robust app launching, navigation, file management, clipboard, and UI interaction (see Executor above). * All system-level actions provide visual and/or spoken confirmation of success/failure. * Destructive/system-changing operations always require explicit voice confirmation. ### **4.3 Routine Learning & Execution** * Users can “train” new routines, including complex multi-step, multi-app workflows. * Routine recording includes both visual and semantic anchors for resilience. * Routines are replayed with error correction (fuzzy matching, element search). * Routines may be exported/imported for sharing or backup. * Routine library is managed in-app, with a search and filter function for ease of use. ### **4.4 LLM Integration** * Local LLM is installed and ready for use out of the box (llama.cpp or equivalent). * Users provide cloud LLM API keys during onboarding or via settings; keys are verified before acceptance. * macEngine routes requests automatically and lets users override routing per command, routine, or persona. * Failover logic: if the cloud LLM fails, notify user and fallback to local; if local model fails, inform user and log error for debugging. ### **4.5 Persona Customization** * Users may select, define, and switch personas at any time, by command or via the preferences UI. * Each persona can be mapped to a schedule, trigger, or even context (“use Professional voice when Calendar is open”). * Persona switching is always immediate, and changes all visible/audible cues (bubble icon, TTS voice, etc.). * Voice onboarding: user names their assistant and selects a persona/voice at setup. --- ## **5\. Non-Functional Requirements** ### **5.1 Performance** * Idle CPU \<7% (on M1/M2), memory \<800MB. * Action execution (from command to result) \<300ms (where possible). * STT and TTS latency \<1s total, including switching voices. ### **5.2 Reliability & Stability** * macEngine is crash-free \>99.5% of user hours. * All failed actions prompt the user for correction, retry, or “teach mode” (for new routine creation). * System responds gracefully to permission errors (e.g., user revoked Accessibility—show alert, guide user to re-enable). ### **5.3 Security & Privacy** * No API keys or sensitive data ever stored outside macOS Keychain. * All data access (screen, mic, file, app) requires explicit permission and provides visible indication when active. * All routine recordings and automation steps are stored only locally, unless the user exports them. * Subscription checks only transmit anonymous license token (never personal data). ### **5.4 Accessibility** * All UIs (tray, onboarding, routine manager) are VoiceOver compatible. * All system notifications are available in text and speech. * System can be fully controlled via voice or keyboard for maximum accessibility. --- ## **6\. User Experience Flow** ### **6.1 Installation & Onboarding** * User downloads notarized installer, runs, and is prompted for: * Permissions: Microphone, Accessibility, Screen Recording. * Naming assistant and selecting initial persona/voice. * Optionally entering LLM API keys (OpenAI, Anthropic, Gemini, etc). * Subscription creation or trial activation; explained privacy and license checks. * First-launch tutorial walks user through: * Wake-word test (“Hey \[Name\], open Notes.”) * Sample task (“Open Safari and go to apple.com”) * Routine training demo (“Show me how to check grades on Canvas”) ### **6.2 Daily Workflow** * User interacts by voice or hotkey—macEngine listens for command, interprets intent, confirms action, and provides visible and audible feedback. * When failing a new workflow, macEngine prompts: “Would you like to teach me how to do this? Let’s record a new routine.” * Routines are managed and triggered by simple phrases; can be scheduled or set to run on context (advanced, Pro only). * At any point, user can say “Switch to \[Persona\] mode” or edit persona config in Preferences. ### **6.3 Routine Management** * Routine library UI shows all available routines, with search/filter, usage stats, and one-click edit/delete/export. * Routines are stored as signed JSON (.mre) files; can be imported/exported for sharing. * Routine sharing/marketplace available in macEngine Pro (future). ### **6.4 Persona Management** * Persona manager UI shows all personas; users can edit, duplicate, or import/export personas. * Persona switching available by command or schedule. --- ## **7\. Testing Strategy** ### **7.1 Unit Testing** * Voice module (wake-word, STT, TTS) * NLU/Intent parser * Executor primitives * Routine recording/playback * LLM dispatcher/routing ### **7.2 Integration Testing** * End-to-end flows for: app launching, web automation, routine training, multi-app workflows. ### **7.3 Performance Testing** * Measure latency from command to result (CPU, memory, responsiveness). * Test on both Intel and Apple Silicon Macs. ### **7.4 Security Testing** * Confirm no API key/data leaks. * Permissions: attempt to revoke and re-grant all major permissions during runtime. * Static and dynamic code analysis. ### **7.5 Accessibility Testing** * VoiceOver and keyboard-only navigation of all user-facing UIs. --- ## **8\. Project Timeline** | Week | Milestone/Deliverable | | ----- | ----- | | 1-2 | Repo setup, voice layer POC, onboarding script | | 3-4 | Executor core, Accessibility API hardening | | 5-6 | LLM dispatcher and local model integration | | 7-8 | Screen interpreter and vision model | | 9 | Routine engine (record/replay/CRUD UI) | | 10 | Persona manager, voice onboarding, preference sync | | 11 | Full integration, performance and accessibility pass | | 12 | Closed beta, bug-fix, notarization, GA prep | --- ## **9\. Risk Management** * **Permissions friction:** Use onboarding overlay, documentation, FAQ. * **Cloud LLM downtime:** Failover to local, clear user error reporting. * **Screen/UI change (OS update):** Continuous regression testing on beta macOS versions; adaptive anchor logic for routines. * **Intel Mac performance:** Optimize/quantize models, document performance caveats. --- ## **10\. Acceptance Criteria** * All functional and non-functional requirements are met. * All five core user flows (student, exec, dev, creative, pro) work hands-free from voice to execution. * 99.5% crash-free operation in beta. * User feedback during onboarding \>90% “easy to use.” * All sensitive actions require explicit consent; no privacy surprises. * Dev, user, and security documentation delivered and reviewed. Here’s an **expanded technical resource kit** for macEngine, delivering: 1. **Module-Level Technical Specs** for every subsystem (with interfaces, dependencies, error flows, sequence diagrams) 2. **Figma-Style Wireframe Descriptions** for onboarding, preferences, routine/engine management, persona management, and in-task UI 3. **Full Versioned API/Data Schemas** for Routines, Personas, LLM Routing, Permissions, Subscription, Telemetry, Error Logging, and Routine Marketplace (future) 4. **Advanced LLM Routing Rules** 5. **Routine Engine Error Recovery Flow** 6. **Accessibility & Security Guidance** 7. **Voice Model Download & Upgrade Handling** 8. **Onboarding Copy, Error Dialog Texts, and Confirmation Prompts** 9. **Recommended Directory/File Structure for macEngine Source Tree** --- # **1\. ONBOARDING, PREFERENCES, ROUTINE/PERSONA MANAGEMENT, & IN-TASK UI — WIREFRAME FLOW DESCRIPTIONS** ## **1.1 ONBOARDING FLOW (Figma-Ready)** **1.1.1 Welcome** * Fullscreen, dark blur background with subtle macEngine logo. * Headline: “Welcome to macEngine” * Tagline: “Your Mac. Now with a real-life J.A.R.V.I.S.” * Button: \[Get Started\] **1.1.2 Permissions** * Visual checklist: * \[Mic\] \[Accessibility\] \[Screen Recording\] * Explanations beside each: * "So I can hear your commands" * "So I can act on your behalf" * "So I can see what’s on your screen" * “Grant Permissions” button launches relevant System Settings pages. * FAQ link: “Why do I need this?” **1.1.3 Name Your Assistant** * Headline: “What should I call you?” * Text field, defaults: Orion, Elara, Nova, Custom. * Suggestion: “You’ll say this name to get my attention.” **1.1.4 Select Persona & Voice** * Cards: “Professional” / “Creative” / “Lighthearted” (with sample TTS buttons) * Option to create/import custom persona (disabled in Core, enabled in Pro) * Visualizer animates when voice is played. * \[Next\] **1.1.5 Connect AI Providers** * Logos: OpenAI, Anthropic, Gemini, \[Custom\] * Text entry fields, test button. * "Skip for now" (uses local only, disables cloud features until keys added) **1.1.6 Subscription** * License key input, or \[Start Free Trial\] * Status: “You’re in a 3-day offline grace period if you lose connection.” * FAQ: “How does licensing work?” **1.1.7 Try Your First Command** * Large bubble at screen center, animated ripple on wake. * Prompt: “Say: ‘Hey Orion, open Safari.’” * Shows live transcription and executes. **1.1.8 Teach a Routine** * Step-by-step walkthrough: * Floating window records clicks, types, pauses. * “Next step” / “Undo” / “Done” controls. * When finished, asks for routine name and trigger phrase. **1.1.9 All Set** * “macEngine is listening and ready. Find me in your menu bar.” * Tips for hotkey use and privacy reminder. --- ## **1.2 PREFERENCES WINDOW (Menu Bar App)** **Tabs:** * **General**: Assistant name, hotkey, startup behavior. * **AI Providers**: API keys (OpenAI, Anthropic, Gemini), test/revoke, usage meter. * **Personas**: List, edit, switch, schedule, preview. * **Routines**: List, edit, import/export, create new, delete. * **Privacy/Security**: Permission status, re-request, telemetry opt-in. * **Subscription**: Plan, status, payment, trial days left, offline grace. --- ## **1.3 ROUTINE LIBRARY** * Table: Name, Last Used, Steps, Trigger, \[Edit\], \[Export\], \[Delete\] * Search bar, sort by use/last run/date created * Routine detail: Shows all recorded steps with screenshot thumbnails and anchor info --- ## **1.4 PERSONA MANAGER** * Persona cards: Name, voice, sample style, icon/avatar * Edit: Name, wakeword, TTS, style, LLM pref, schedule * Switch: radio/select, live preview * Create/Import/Export: enabled in Pro --- ## **1.5 IN-TASK UI (FLOATING BUBBLE)** * Persistent, docked bubble bottom-right by default (drag to move) * Animates on wake/listen * Shows TTS output as text overlay * Visual success/failure: green/red pulse * Clarification (“Did you mean...?”) shown as clickable overlay * Confirmation dialogs (“Say YES to continue” in a modal style) --- # **2\. COMPLETE API/DATA SCHEMAS** ## **2.1 Routine File Schema (.mre, v1.0)** ```text { "id": "uuid", "name": "Check Canvas Assignments", "created\_at": "2025-07-14T11:11:00Z", "trigger\_phrases": \["check assignments", "show homework"\], "steps": \[ { "order": 1, "action": "open\_url", "params": {"url": "https://canvas.instructure.com"} }, { "order": 2, "action": "type\_text", "params": {"selector": "\#login", "text": "${USER\_EMAIL}"} }, { "order": 3, "action": "click", "params": {"text\_match": "Assignments"} }, { "order": 4, "action": "extract\_table", "params": {"selector": ".assignments-table"} } ``` \], ```text "variables": \[ {"name": "USER\_EMAIL", "secure": true} ``` \], ```text "author": "local\_user", "signature": "HMAC-SHA256", "version": "1.0" } ``` ## **2.2 Persona File Schema (YAML/JSON)** id: "uuid" name: Orion wakeword: "Hey Orion" tts\_voice: "com.apple.voice.Alex" style: professional llm\_pref: provider: openai model: gpt-4o temperature: 0.4 schedule: weekdays: Orion evenings: Elara version: "1.0" ## **2.3 LLM Routing Policy** ```text { "routing\_policy\_version": "1.0", "default": { "personal": "local", "file\_ops": "local", "routine": "local", "creative": "cloud" }, "personas": { "Orion": {"provider": "openai", "model": "gpt-4o"}, "Nova": {"provider": "gemini", "model": "pro-1.5"} }, "overrides": \[ {"intent": "summarize\_pdf", "persona": "Nova", "provider": "openai", "model": "gpt-4o"} ``` \] ```text } ``` ## **2.4 Permissions Status** ```text { "microphone": "granted", "screen\_recording": "granted", "accessibility": "pending", "full\_disk\_access": "denied" } ``` ## **2.5 Subscription Status** ```text { "license\_key": "xxxx-xxxx-xxxx-xxxx", "status": "valid", "last\_checked": "2025-07-14T10:15:00Z", "grace\_expiry": "2025-07-17T10:15:00Z", "plan": "core" } ``` ## **2.6 Telemetry/Logging (opt-in)** ```text { "event": "routine\_executed", "routine\_id": "uuid", "success": true, "latency\_ms": 1850, "timestamp": "2025-07-14T10:15:30Z" } ``` ## **2.7 Routine Marketplace Listing (future)** ```text { "routine\_id": "uuid", "name": "Auto Check Bank Balance", "author": "routine\_builder\_42", "usage\_count": 314, "tags": \["finance", "web"\], "version": "1.0", "rating": 4.8, "uploaded\_at": "2025-07-14T08:00:00Z", "verified": true } ``` --- # **3\. LLM ROUTING LOGIC** * If `intent` is in \["file\_ops", "routine", "personal"\]: always local * If persona is "always local": always local * If `intent` is \["creative", "summarize\_pdf", "code", "research"\]: use persona’s cloud model if available * If cloud LLM fails: retry 3x, fallback to local if \< context window * If local model fails: error message, log event, suggest upgrade * Persona overrides (from schedule or manual switch) apply immediately --- # **4\. ROUTINE ENGINE ERROR RECOVERY FLOW** **When anchor not found:** * Try fuzzy search on bbox and/or text * If not found, pause, prompt user “Please click the missing element.” * User input updates anchor, routine continues * If still not found or user cancels, abort: “Routine stopped: could not locate required element. You may need to retrain.” **Routine import fails HMAC:** * “This routine appears tampered with or from an untrusted source. Import blocked.” **Routine interrupted (e.g. app closed):** * Notify: “App closed during routine playback—reopen to continue.” --- # **5\. ACCESSIBILITY & SECURITY GUIDANCE** * All UIs must be fully VoiceOver navigable. * All text prompts must have spoken equivalents. * Tray, onboarding, and routine library must support keyboard-only navigation. * All API keys and secure variables must use macOS Keychain APIs (SecItemAdd/SecItemCopyMatching). * Network calls (license, telemetry) must be HTTPS+cert pinning; error logs for any failure. * Routines and personas must be signed with HMAC, versioned, and validated on import. --- # **6\. VOICE MODEL UPGRADE/DOWNLOAD** * Onboarding: device spec check for local LLM (RAM, CPU, storage) * If missing, show download button with size estimate * Allow “Upgrade model” in Preferences → shows all available models (7B, 13B, etc) * Show RAM/CPU usage estimates before confirm * On download/upgrade error: “Could not download model. Check internet connection or free up disk space.” --- # **7\. ONBOARDING/ERROR DIALOG TEXTS** **Onboarding copy:** * “Let’s get started. I’ll need a few permissions so I can help you hands-free.” * “What’s your assistant’s name? Pick something easy to say.” * “Select a persona that matches your style—or switch later with a voice command.” * “Connect your favorite AI brains for advanced help. You can skip and add later.” **Error dialogs:** * “I didn’t hear you—check your mic or permissions.” * “Screen Recording access was revoked. Open System Settings to restore.” * “Subscription expired. Reconnect to the internet or renew your license.” * “API key invalid or quota exceeded. Update in Preferences.” **Confirmation prompts:** * “You’re about to permanently delete files. Say YES to confirm, or NO to cancel.” * “Switching persona. Want to use a different voice too?” --- # **8\. RECOMMENDED SOURCE DIRECTORY TREE** /macengine /VoiceInterface WakeWordEngine.swift Transcriber.swift Speaker.swift /CommandInterpreter IntentParser.swift ContextManager.swift Clarifier.swift /LLMDispatcher LocalLLMHandler.swift CloudLLMProxy.swift RoutingPolicyManager.swift /ScreenInterpreter ScreenCapturer.swift OcrEngine.swift WidgetClassifier.swift /Executor UIActionPerformer.swift ScriptRunner.swift ActionSequencer.swift /RoutineEngine RoutineRecorder.swift RoutinePlayer.swift RoutineManager.swift RoutineSerializer.swift /PersonalityManager PersonaManager.swift PersonaConfig.swift PersonaScheduler.swift /Config ConfigStore.swift KeychainHandler.swift SubscriptionChecker.swift /UI OnboardingUI.swift PreferencesUI.swift RoutineLibraryUI.swift PersonaManagerUI.swift TrayMenu.swift BubbleUI.swift /Assets voices/ icons/ onboarding/ /Tests (Unit/Integration/Performance/Accessibility) /Docs PRD.md API\_SCHEMAS.md UX\_FLOWS.md main.swift --- # **9\. RECOMMENDED TEST CASES (EXAMPLES)** * Voice: Wake word accuracy (10,000 trials, \<1% miss), STT accuracy on standard/poor mics, TTS fallback on network loss. * Command: Intent parse errors, ambiguous slot filling, clarify flow. * LLM: Routing (per persona, per intent), cloud failover, local fallback, model download/upgrade, API key revoke. * Executor: All system APIs (AX, AppleScript) across macOS 13–15, destructive operation confirmations, error handling. * Routine: Anchor moves, app window closed, import/export, signature fail, context replay with/without user intervention. * Persona: Schedule triggers, instant switch via voice/UI, voice model switching. * Security: API key storage, routine/persona HMAC validation, HTTPS cert pin, telemetry opt-in/opt-out, permission loss mid-session. * UI: Full VoiceOver, keyboard nav, color contrast, text-to-speech on all prompts. --- Here is a **complete, professional developer handoff package** for macEngine. This will be long, but it is thorough and implementation-ready, covering: --- ### **1\. Onboarding, Preferences, Routine & Persona Management, In-Task UI – Figma-Style Wireframe Flow Descriptions** ### **2\. All Major API/Data Schemas (JSON/YAML, with versioning)** ### **3\. Ready-to-Implement API Endpoints & Sample Calls** ### **4\. Example Test Cases (Unit, Integration, E2E, Accessibility)** ### **5\. Advanced LLM Routing Rules (Policy Doc, Flowcharts)** ### **6\. Routine Engine Error Recovery Logic (with user flows)** ### **7\. Voice Model Download/Upgrade Handling** ### **8\. Accessibility & Security Requirements (macOS Focus)** ### **9\. Error Dialog Copy, Confirmation Prompts, Onboarding Text** ### **10\. Source Directory/File Structure Recommendation** Absolutely. Here’s a comprehensive checklist of **everything else** a developer or engineering team will need to take the macEngine product from PRD/UX/UI/API spec to actual release-ready code—including key topics not fully covered in the docs above. --- ## **macEngine Developer Handoff – Missing Essentials & Recommended Additions** ### **A. Development Environment & Build Guidance** * **Full stack versioning:** Required Xcode version, Swift version, Python dependencies (for LLM/OCR), compatible macOS versions (minimum, tested). * **Build scripts & CI:** Example `Makefile`, Xcode project setup, sample GitHub Actions/Bitrise/Travis config for CI. * **Local LLM/voice model setup scripts** (for quantized downloads, permissions, local testing). --- ### **B. Testing and Quality** * **Automated test suites:** * Scripts and conventions for unit, integration, E2E, and accessibility (including sample input/output files for STT, OCR, routine replays). * **UI snapshot regression testing:** * Storyboard/screenshots for each major UI component. * **Manual QA checklists:** * All “happy path” user flows * “Unhappy”/edge case flows (lost permissions, failed LLM calls, network drop, corrupted routine import, etc.) * **Device matrix:** * Required tests on: M1, M2, Intel Macs; macOS 13, 14, 15 (public beta); with/without external displays, with accessibility features enabled. --- ### **C. Documentation** * **Developer onboarding guide:** * How to set up the project, install dependencies, get a test license, and run the app locally. * **Full API documentation:** * Autogenerated (DocC, Swagger/OpenAPI for any network APIs, markdown for internal plugin APIs). * **Module ownership map:** * Clear list of “who owns what” if in a team. * **Internal API stability/compatibility policy:** * Versioning scheme for .mre, persona files, LLM policies. --- ### **D. External Integrations** * **LLM API test harnesses:** * Scripts for automated calls to OpenAI, Anthropic, Gemini, with dummy/test keys. * **Voice TTS/STT test harness:** * Standalone scripts to test whisper.cpp, ElevenLabs, Apple AVSpeechSynthesizer. * **OCR/Screen test harness:** * Screenshots, batch test for all UI widget types; expected vs actual detection. --- ### **E. Security & Compliance** * **Pen test scripts:** * For Keychain access, permissions, routine import/export, API key handling. * **GDPR/compliance notes:** * How macEngine avoids storing/exporting PII, and user data deletion/export tools. * **Audit logs & incident response doc:** * What is logged, how users/developers can retrieve error/usage logs. --- ### **F. Release Engineering** * **Code signing/notarization checklist:** * Apple Developer account setup, notarization script, App Store/standalone build differences. * **Auto-updater integration:** * e.g., Sparkle, custom (if not in App Store). * **Crash reporting/analytics (optional):** * Sentry, Crashlytics, or opt-in macOS log forwarding. * **Beta channel build toggle:** * Mechanism to enable/disable new features in field/beta. --- ### **G. User Feedback & Support** * **In-app feedback tool:** * Button to “report a bug” or “send suggestion,” with log attachment. * **User troubleshooting guide:** * Top 10 permission errors, cloud API quota issues, model download failures, “reset” instructions. * **Knowledge base/FAQ skeleton:** * For onboarding, LLM keys, permissions, privacy, marketplace (future). --- ### **H. Marketplace/Future-Ready** * **Routine/Persona marketplace skeleton:** * Upload/download endpoints, moderation/review workflow, signature validation on download. * **Developer plugin SDK outline:** * For 3rd party plugin support in the future (external skills, integrations, custom triggers). --- ### **I. Performance & Resource Use** * **Profiler scripts:** * To measure CPU/memory per-module, recommend optimization passes. * **Resource limits docs:** * “What to expect” for users with lower RAM/CPU, fallback flows. --- ### **J. Accessibility & Internationalization** * **i18n support plan:** * Where and how to localize, text labels ready for translation, LLM prompt language options. * **Accessibility test matrix:** * Full VoiceOver/keyboard coverage, color contrast check, spoken prompts for all error dialogs. --- ### **K. Disaster Recovery & Data Export** * **Backup/export/restore for routines/personas/config.** * **Disaster mode:** * What happens if routine library is corrupted, lost, or model download fails. --- ### **L. Project Management** * **Jira/Epic template:** * Story breakdown for v1.0 features. * **Milestone roadmap:** * Weeks/sprints, major deliverables, responsible dev/owner. --- ## **Summary Table** | Area | Essential Resource | Covered Above? | Needs Developer Asset? | | ----- | ----- | ----- | ----- | | Dev Env/Build | Xcode, CI config, setup docs | ✗ | ✅ | | QA & Testing | Full test suite, matrix, scripts | ✗ | ✅ | | Docs | Onboarding, API, ownership, versioning | ✗ | ✅ | | Integrations | LLM/OCR harnesses, test keys | ✗ | ✅ | | Security/Compliance | Pen test scripts, GDPR, audit log | ✗ | ✅ | | Release Eng | Signing, notarization, auto-update | ✗ | ✅ | | User Feedback | In-app reporting, FAQ, support | ✗ | ✅ | | Marketplace/SDK | Routine/persona upload, plugin skeleton | ✗ | ✅ | | Perf/Resource Use | Profiler, optimization docs | ✗ | ✅ | | i18n/Accessibility | Text labels, l10n plan, test matrix | ✗ | ✅ | | Backup/Disaster | Export/restore scripts | ✗ | ✅ | | Project Mgmt | Jira/Epic, sprints, owners | ✗ | ✅ | --- Absolutely. I’ll create **each asset, one at a time**, with a complete, copy-pasteable resource for a senior macOS/AI developer. Let’s start at the top: **Development Environment & Build Guidance.** --- # **1\. DEVELOPMENT ENVIRONMENT & BUILD GUIDANCE** --- ## **macEngine Development Environment and Build Setup** --- ### **1.1. Minimum Requirements** * **Hardware:** * Apple Silicon (M1/M2/M3, recommended), Intel x64 supported * 8 GB RAM minimum (16 GB recommended for local LLM) * 15 GB free disk space (for models/routines/assets) * **macOS:** * Minimum: macOS Ventura (13.x) * Recommended: macOS Sonoma (14.x) and above * Actively tested: 13.x, 14.x, 15.x (beta) * **Xcode:** * Version: 15.0+ * Command Line Tools installed (`xcode-select --install`) * **Python:** * Python 3.10+ (for whisper.cpp and OCR batch scripts) * `pip` for managing Python dependencies * **Homebrew:** * For easy CLI installs (`/bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"`) * **Dependencies:** * Swift (Xcode toolchain) * CocoaPods (for UI components if needed) * Carthage or SPM (for dependency management) * Rust toolchain (if using llama.cpp/Ollama with Rust bindings) * Node.js 18+ (if testing with Electron overlays, not core app) --- ### **1.2. Initial Project Setup** **Clone the macEngine Repository** git clone https://github.com/aiConnected/macengine.git cd macengine 1. **Install Homebrew Dependencies** brew install python rust tesseract ffmpeg portaudio 2. **Install Python Requirements (for LLM/STT/OCR helpers)** cd Scripts/ pip3 install \-r requirements.txt 3. 4. **Install Whisper.cpp (STT)** Download or build from source: git clone https://github.com/ggerganov/whisper.cpp.git cd whisper.cpp make * * Place `main` binary in `/macengine/Models/` or system path. 5. **Install Llama.cpp (Local LLM)** Download or build from source: git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp make * Download desired model(s): ./download-model.sh 7B mv ./models/7B/ggml-model-q4\_0.bin ../macengine/Models/ * * Document in `/Docs/LLM_SETUP.md`. **Install/Verify Tesseract OCR** brew install tesseract tesseract \--version \# Confirm installation 6. 7. **Xcode Project Setup** * Open `macengine.xcodeproj` in Xcode. * Set target to “macOS (Universal)”. * Ensure all Swift source files and resource bundles are linked. * Build scheme: Debug, Release. 8. **Build and Run** * In Xcode: Cmd+B (build), Cmd+R (run) * Confirm app launches, tray icon appears, and onboarding launches. --- ### **1.3. Code Formatting, Linting, and Style** * SwiftLint (included via SPM or Cocoapods) * All Python scripts PEP8-compliant (`black .`) * Markdown documentation linted with `markdownlint` * **All commit messages** follow [Conventional Commits](https://www.conventionalcommits.org/) --- ### **1.4. Sample `.env` File** OPENAI\_API\_KEY=sk-... ANTHROPIC\_API\_KEY=sk-ant-... GEMINI\_API\_KEY=ai-... LLAMA\_MODEL\_PATH=Models/ggml-model-q4\_0.bin WHISPER\_MODEL\_PATH=Models/ggml-base.en.bin **Do NOT commit actual API keys. Use environment variables or macOS Keychain for storage.** --- ### **1.5. IDE and Tools Recommendations** * **Xcode** for main Swift/macOS development * **VSCode** for Python scripts, model config, and rapid editing * **PyCharm** (optional, for advanced Python) * **Simulator** for UI flows if no spare test Macs * **Instruments** (Xcode) for profiling memory/CPU --- ### **1.6. Sample `.gitignore`** /Models/\* \!/Models/README.md /Secrets/\* .env \*.pyc \*.log xcuserdata/ DerivedData/ build/ .idea/ \*.DS\_Store --- ### **1.7. CI/CD Starter (GitHub Actions example)** name: macEngine Build & Test on: push: branches: \[main\] pull\_request: branches: \[main\] jobs: build-macos: runs-on: macos-latest steps: \- uses: actions/checkout@v4 \- name: Install Homebrew dependencies run: brew install python rust tesseract ffmpeg \- name: Install Python requirements run: pip3 install \-r Scripts/requirements.txt \- name: Build Xcode project run: xcodebuild \-project macengine.xcodeproj \-scheme macEngine \-configuration Debug build \- name: Run SwiftLint run: swiftlint --- ### **1.8. Minimum Local Model Download Script (Python)** ```text ``` MODEL\_URL \= "https://huggingface.co/ggml/llama-2-7b/resolve/main/ggml-model-q4\_0.bin" DEST \= "../Models/ggml-model-q4\_0.bin" print("Downloading Llama 7B Q4\_0 model...") r \= requests.get(MODEL\_URL, stream=True) ```text with open(DEST, 'wb') as f: ``` ```text for chunk in r.iter\_content(chunk\_size=8192): if chunk: ``` f.write(chunk) print("Download complete\!") --- ### **1.9. Developer Contact & Support** * Main Slack: \#macengine-dev * Email: devsupport@aiconnected.com * Office Hours: Monday/Thursday, 3–5pm EST (TBD) --- # **2\. AUTOMATED TEST SUITE & QA MATRIX** --- ## **2.1. Automated Test Suite Structure** ### **A. Unit Tests** **Directory:** `/Tests/Unit/` **Tools:** * Swift: `XCTest` * Python: `pytest` (for helper/model scripts) * Shell: `Bats` (for CLI/model checks) **Coverage:** * All modules and submodules, including: * VoiceInterface (wake word, STT, TTS, error handling) * CommandInterpreter (intent parsing, clarification) * LLMDispatcher (local/cloud routing, API keys) * ScreenInterpreter (OCR output, widget classification) * Executor (UI action success, destructive operation confirmation) * RoutineEngine (record/replay, import/export, error handling) * PersonalityManager (switch, schedule, override logic) * Config/Subscription (key storage, permissions, status check) **Sample Swift Unit Test (XCTest)** ```text ``` @testable import macEngine ```text class VoiceInterfaceTests: XCTestCase { func testWakewordDetection() { let vi \= VoiceInterface() ``` vi.setWakeword("Hey Orion") XCTAssertTrue(vi.listen(for: "Hey Orion")) XCTAssertFalse(vi.listen(for: "Hello World")) ```text } ``` func testTTSVoiceSwitch() { ```text let vi \= VoiceInterface() ``` vi.setTTSVoice("com.apple.voice.Nova") ```text let out \= vi.speak("Testing", persona: .nova) ``` XCTAssertEqual(out.voiceUsed, "com.apple.voice.Nova") ```text } } ``` --- ### **B. Integration Tests** **Directory:** `/Tests/Integration/` **Tools:** * Swift: `XCTest` * Python: Custom scripts for LLM/Whisper/Tesseract **Coverage:** * Full module flows, e.g.: * Voice → Command → LLM → Executor * Routine record, then replay with UI change * LLM fallback to local on cloud error **Sample Integration Test (Pseudo-Swift)** func testRoutineReplayWithAnchorUpdate() { // Record a routine ```text let routine \= routineEngine.recordRoutine(name: "Check Email", trigger: \["check my mail"\]) ``` routineEngine.addStep(.openApp, anchor: "Mail") routineEngine.addStep(.click, anchor: "Inbox") routineEngine.finalize() // Simulate UI change (Inbox button moved) // Routine should pause, ask for anchor, and update ```text let success \= routineEngine.play(routine) ``` XCTAssertTrue(success) XCTAssertEqual(routine.steps\[1\].anchor, "Inbox") // Updated anchor stored ```text } ``` --- ### **C. End-to-End (E2E) Tests** **Directory:** `/Tests/E2E/` **Tools:** * AppleScript/Swift: For automating app flows * Python: For model/CLI flows **Coverage:** * Real user flows, such as: * “Hey Orion, open Safari, go to Apple.com, take a screenshot” * “Check my grades” (full voice-to-result loop) * “Run backup routine” (finder, compression, copy to disk, TTS confirmation) * Persona switching during task **Sample E2E Test Steps** \# Run test using voice or hotkey trigger 1\. Launch macEngine. 2\. Use test voice file: "Hey Orion, open Calendar." 3\. Confirm Calendar app is focused. 4\. Say: "Switch to Creative mode." 5\. Confirm persona and voice change. 6\. Say: "Teach new routine." 7\. Demo: Open Safari, type "macengine.com", take screenshot. 8\. Save routine, replay it, confirm all steps succeed. *(Scripts would use `say`, AppleScript, and OCR to validate output.)* --- ### **D. Accessibility Tests** **Directory:** `/Tests/Accessibility/` **Tools:** * macOS VoiceOver * Automated UIA test scripts * `axe-core` for web-based components **Coverage:** * Preferences, onboarding, bubble UI, routine manager, persona editor—all navigable by keyboard/VoiceOver. * All icons have text labels. * Color contrast ratios verified. **Sample Accessibility Checklist** * All focusable controls can be reached by Tab/Shift-Tab. * VoiceOver reads label for every field/button. * All dynamic notifications (e.g., “Say YES to continue”) are also spoken. * Visual cues always paired with audible cues. --- ## **2.2. QA Device/OS Matrix** | Mac Model | CPU | RAM | macOS | Ext. Display | Accessibility | Status | | ----- | ----- | ----- | ----- | ----- | ----- | ----- | | MacBook Air M1 | ARM | 8GB | 13, 14 | Yes | On/Off | Required | | MacBook Pro M2 | ARM | 16GB | 14, 15 | Yes | On/Off | Required | | Intel Mac Mini | x64 | 8GB | 13 | No | Off | Required | | Mac Studio M2 | ARM | 32GB | 14, 15 | Yes | On | Optional | **Test all major features on:** * Apple Silicon (M1/M2/M3) * Intel x64 * macOS 13, 14, 15 (beta) --- ## **2.3. Manual QA Checklist (Core Flows)** * Onboarding: permissions, naming, persona selection, LLM key entry, license/trial * Bubble UI: wakeword, live STT, TTS response, visual feedback * Preferences: all fields, save/cancel, import/export routines/personas * Routine Engine: record, replay, anchor update, error prompt * LLM: test with local only, with cloud only, with both, API failover * Persona Manager: switch via voice and UI, schedule * Security: API key in Keychain, cannot be accessed from terminal * Accessibility: VoiceOver/keyboard covers all interactive elements * Recovery: lose permission, recover gracefully (user prompt, guide) * Subscription: offline mode, grace period, renewal/lockout --- ## **2.4. Test Data Files** * **Audio:** Test .wav for wakeword, typical commands, accented voices * **Screenshots:** For routine anchor test (original, with UI change) * **Routine files:** Valid .mre, invalid/corrupted .mre, for import error handling * **Persona files:** Valid, invalid signature * **LLM key files:** Dummy/test keys --- ## **2.5. Sample Test Script (Shell)** \#\!/bin/bash echo "Starting E2E: Voice Trigger to App Open" say "Hey Orion, open Safari." sleep 5 open \-a Safari osascript \-e 'tell application "Safari" to activate' sleep 2 screencapture \-x test-screenshot.png tesseract test-screenshot.png stdout | grep \-i "Safari" && echo "Test passed" || echo "Test failed" --- # **3\. DOCUMENTATION ASSETS** --- ## **3.1. Developer Onboarding Guide (`/Docs/ONBOARDING.md`)** --- ### **macEngine Developer Onboarding** **1\. Prerequisites:** * macOS 13.0+ (Ventura), Apple Silicon or Intel * Xcode 15+, Python 3.10+, Homebrew, Rust * Git access to private repo **2\. Clone and Set Up:** git clone https://github.com/aiConnected/macengine.git cd macengine brew install python rust tesseract ffmpeg pip3 install \-r Scripts/requirements.txt **3\. Build Local Models:** * Whisper.cpp (see `/Scripts/WHISPER_SETUP.md`) * Llama.cpp (see `/Docs/LLM_SETUP.md`) * Tesseract for OCR **4\. Xcode:** * Open `macengine.xcodeproj` * Target \= “macOS (Universal)” * Run/Build (Cmd+R/Cmd+B) **5\. Environment Variables:** * Add `.env` (see PRD) or use macOS Keychain for API keys **6\. Run the App:** * App launches, menu bar icon appears * Onboarding starts (permissions, naming, persona, keys) **7\. Run Tests:** swift test pytest Tests/Python/ * See `/Tests/README.md` for more **8\. Support:** * devsupport@aiconnected.com or Slack \#macengine-dev --- ## **3.2. API Documentation** --- ### **A. Internal Module APIs** **Location:** `/Docs/API.md` * **VoiceInterface:** * `start()`, `stop()`, `setWakeword(w)`, `speak(text, persona)`, callbacks: `onTranscription`, `onWake` * **CommandInterpreter:** * `parse(transcript, context) -> CommandIntent` * **LLMDispatcher:** * `route(req, persona) -> LLMResponse`, `updateApiKey(provider, key)` * **Executor:** * `execute(action, target)`, `openApp(bundle)`, `runShell(cmd)`, `confirmDestructive(action, cb)` * **RoutineEngine:** * `recordRoutine(name, trigger)`, `addStep()`, `finalize()`, `play()`, `importRoutine()`, `exportRoutine()` * **PersonaManager:** * `current()`, `switchTo(persona)`, `schedule(persona, at)`, `update(persona)` * **ConfigModule:** * `get(key)`, `set(key, value)`, `saveApiKey()`, `hasPermission()`, `promptForPermission()`, `checkSubscription()` *(All methods/types defined in the PRD/Tech Specs.)* --- ### **B. External HTTP APIs** **Location:** `/Docs/EXTERNAL_APIS.md` **Sample: License Verification** POST https://api.macengine.com/v1/subscription/verify ```text { "license\_key": "xxxx-xxxx-xxxx-xxxx", "device\_id": "MBP-012345" } ``` Returns: ```text { "status": "valid", "grace\_expiry": "2025-07-17T10:15:00Z" } ``` --- ### **C. Data Formats** * `.mre` routine files: see API schemas in earlier responses * Persona files: YAML/JSON * Config: `.env` or Keychain (never plain-text keys) * All file formats versioned (e.g., `version: "1.0"` in file header) --- ## **3.3. Ownership/Module Map** **Location:** `/Docs/MODULE_OWNERS.md` | Module | Primary Owner | Backup Owner | | ----- | ----- | ----- | | VoiceInterface | Alice Devlin | Jon West | | CommandInterpreter | Jon West | Priya Saini | | LLMDispatcher | Priya Saini | Alice Devlin | | Executor | Jon West | Yusuke Tanaka | | RoutineEngine | Alice Devlin | Yusuke Tanaka | | PersonaManager | Yusuke Tanaka | Jon West | | Config/Security | Alice Devlin | Priya Saini | | UI/Onboarding | Priya Saini | Yusuke Tanaka | | External API | Alice Devlin | Jon West | | Docs/Tests | All (rotate) | All | *(Update as needed for your actual team.)* --- ## **3.4. Internal API Stability/Compatibility Policy** **Location:** `/Docs/VERSIONING.md` * All file-based APIs are versioned: * e.g., `.mre` files: `"version": "1.0"` * All internal Swift protocol changes bump module `API_VERSION` (document in module header) * Major/breaking changes require migration scripts for user data (routines, personas) * Routine Marketplace will only accept current and previous major version * Always prefer backward compatibility; deprecate, then remove --- ## **3.5. Change/Release Documentation** * Every release: `/CHANGELOG.md` * All PRs must link to Jira/Epic story * Major features documented in `/Docs/FEATURES.md` * Security updates noted in `/Docs/SECURITY.md` * Routine/persona API changes announced to user base 2+ sprints before enforcement --- ## **3.6. Example DocC (Swift) Snippet** /// Starts the voice listener and waits for wake word. /// \- Throws: VoiceEngineError if audio input fails. /// \- Returns: None. Calls \`onWake\` when triggered. public func start() throws --- --- ## Original AI Connected Engines **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/original-aiConnected-engines **Description:** 1. adEngine Runs and optimises Google / Meta PPC from copywriting to bid rules and daily budget re allocation. Proposed monthly price: $99 Estimated value: $... # **Original AI Connected Engines** 1. **adEngine** *Runs and optimises Google / Meta PPC from copywriting to bid rules and daily budget re-allocation.* Proposed monthly price: **$99** Estimated value: $1 k–$5 k in net-new monthly revenue by squeezing more conversions from the same ad spend. Priority: **Tier 1 – Core revenue driver** 2. **analyticsEngine** *Gives customers a single dashboard for engine runs, spend, ROI, and trend alerts.* Proposed monthly price: **$49** Estimated value: Saves 5–10 hrs/mo of manual reporting and flags money-leaks early. Priority: **Tier 1 – Visibility layer** 3. **archiveEngine** *Ingests internal docs, emails, recordings; tags, embeds, and makes them semantically searchable for every other engine.* Proposed monthly price: **$149** Estimated value: Prevents knowledge loss; cuts research and onboarding time by 30–50 %. Priority: **Tier 1 – Institutional memory** 4. **billingEngine** *Produces proposals, estimates, invoices, reminders, collects payments, manages renewals.* Proposed monthly price: **$129** Estimated value: Accelerates cash-inflow; typical SMB sees 10–15 % fewer overdue invoices. Priority: **Tier 1 – Cash-flow critical** 5. **blogEngine** *End-to-end blog creation—topic → research → draft → images → publish and social push.* Proposed monthly price: **$99** Estimated value: Delivers 4–8 SEO-ready posts per month worth $800+ in freelance fees. Priority: **Tier 2 – Growth marketing** 6. **bookingEngine** *Embeds a Cal-based scheduler in chats, emails, and voice flows with smart reminders.* Proposed monthly price: **$39** Estimated value: Recovers “form-abandon” leads and lifts show-up rates \~20 %. Priority: **Tier 2 – Conversion aid** 7. **campaignEngine** *Runs multi-channel pushes (email, SMS, social) and tracks conversions end-to-end.* Proposed monthly price: **$99** Estimated value: Adds 5–15 % lift on each promotion by closing timing and sequencing gaps. Priority: **Tier 2** 8. **careerEngine** *Automates job postings, candidate scoring, interview scheduling, and status emails.* Proposed monthly price: **$79** Estimated value: Saves HR teams 10+ hrs per open role and improves hire quality via scoring. Priority: **Tier 3 – Niche** 9. **chatEngine** *Foundation chat interface other engines plug into; supports memory, tone, and hand-off rules.* Proposed monthly price: **$75** Estimated value: Replaces basic chatbot SaaS (\~$150 / mo) with richer AI and integration hooks. Priority: **Tier 1 – Platform core** 10. **contactEngine** *AI web form that engages while users are still typing, routes intent, kills spam bots.* Proposed monthly price: **$49** Estimated value: 15–25 % lift in qualified form submissions. Priority: **Tier 1** 11. **dataEngine** *Pulls, cleans, and caches third-party or internal data for any downstream engine or BI tool.* Proposed monthly price: **$79** Estimated value: Eliminates piecemeal ETL services (\~$200 / mo) and dev hours. Priority: **Tier 1 – Plumbing** 12. **dialEngine** *AI outbound caller that follows scripts, handles objections, books meetings, logs KPIs.* Proposed monthly price: **$89** Estimated value: Adds 10–20 booked calls per agent/month without payroll costs. Priority: **Tier 2 – Sales acceleration** 13. **executiveEngine** *One super-agent interface exposing every other engine’s power via natural language.* Proposed monthly price: **$499** Estimated value: Effectively offloads a junior ops hire ($3 k–$5 k/month). Priority: **Tier 3 – Premium bundle** 14. **financeEngine** *Live P\&L dashboards, cash-flow forecasting, “what-if” modelling from accounting feeds.* Proposed monthly price: **$149** Estimated value: Helps avert cash crunches; decisions backed by real-time numbers. Priority: **Tier 2** 15. **funnelChat** *Perplexity-style conversation that captures and enriches lead data without overt forms.* Proposed monthly price: **$75** Estimated value: Doubles captured leads on content pages; worth $500+ in ad savings monthly. Priority: **Tier 1** 16. **imageEngine** *Generates on-brand hero, mid-body, and social images with preset style filters.* Proposed monthly price: **$59** Estimated value: Saves $300–$600/mo in stock photo or designer spend. Priority: **Tier 2** 17. **inboxEngine** *Triages email, drafts replies, schedules follow-ups, hands off edge cases to humans.* Proposed monthly price: **$59** Estimated value: Recovers 5–8 hrs/week for owners drowning in email. Priority: **Tier 1** 18. **languageEngine** *Fine-tuned Llama model that speaks in the client’s brand voice across channels.* Proposed monthly price: **$199** Estimated value: Improves conversion and support CSAT; replaces custom LLM hosting costs. Priority: **Tier 1 – Differentiator** 19. **markdownEngine** *Instantly rewrites and SEO-scores any webpage or JetEngine field in place.* Proposed monthly price: **$49** Estimated value: Boosts page clarity and ranking, saving $400+ in copy edits monthly. Priority: **Tier 2** 20. **researchEngine** *Scours the web, extracts facts, returns citation bundles for other engines or staff.* Proposed monthly price: **$59** Estimated value: Replaces \~$1,000/mo in human research hours. Priority: **Tier 2** 21. **reviewEngine** *Automates steady Google/Yelp review collection via timed email/SMS nudges.* Proposed monthly price: **$79** Estimated value: \+0.3–0.5 average star rating → 10 % lift in local search clicks. Priority: **Tier 2** 22. **salesEngine** *AI workflows for prospecting, objection handling, deal progression, and pipeline nudges.* Proposed monthly price: **$89** Estimated value: 10–20 % lift in close-rate without adding headcount. Priority: **Tier 1** 23. **seoEngine** *Keyword research, competitor gap analysis, and technical site audits with prioritised fixes.* Proposed monthly price: **$129** Estimated value: Conservatively \+15 % organic traffic; replaces $1 k/mo SEO retainer. Priority: **Tier 1** 24. **siteGuide** *Voice-enabled co-browsing assistant that scrolls, highlights, and collects leads in real time.* Proposed monthly price: **$99** Estimated value: Increases on-page engagement and conversions by 20 %+. Priority: **Tier 1** 25. **socialEngine** *Creates, schedules, and optimises platform-specific social posts; learns from engagement data.* Proposed monthly price: **$79** Estimated value: Saves $500/mo in social-media-manager time. Priority: **Tier 2** 26. **taskEngine** *Connects to Asana or ClickUp to auto-create, assign, and track tasks and deadlines.* Proposed monthly price: **$69** Estimated value: Recovers 3–5 hrs/week in project coordination overhead. Priority: **Tier 2** 27. **voiceEngine** *Real-time TTS↔STT stack (ElevenLabs/Vapi) with pauses, breaths, emotion, and background ambience.* Proposed monthly price: **$99** Estimated value: Enables natural AI calls and IVRs without telephony dev costs. Priority: **Tier 1 – Infrastructure** 28. **webEngine** *Headless browser & scraper that structures pages for downstream AI and automations.* Proposed monthly price: **$89** Estimated value: Replaces custom scrape scripts (\~$500+/mo dev time) and fuels other engines’ data. Priority: **Tier 1 – Data foundation** 29. **webinarEngine** *Automates live / on-demand webinars: scheduling, reminders, attendee chat, post-event follow-ups.* Proposed monthly price: **$99** Estimated value: Generates 50–100 warm leads per session without extra staff. Priority: **Tier 3 – Niche growth** --- ## **Project Requirements Document (PRD)** **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/remote-work-engine-requirements # **Project Requirements Document (PRD)** **User:** Oxford Pierpont **Created:** 11/10/2025 6:30:48 **Updated:** 11/19/2025 23:04:30 **Exported:** 11/19/2025 23:06:31 **Link:** [https://chatgpt.com/c/6911cce6-be6c-832d-8d86-4a50a62bfee1](https://chatgpt.com/c/6911cce6-be6c-832d-8d86-4a50a62bfee1) ## **Project Name:** Remote Work Engine **Website:** RemoteWorkEngine.com **Type:** AI-Powered Remote Job and Candidate Discovery Platform **Version:** 1.0 **Date:** November 2025 **Author:** Oxford Pierpont --- ## **1\. Project Overview** ### **1.1 Summary** **Remote Work Engine (RWE)** is an AI-driven platform that helps job seekers discover remote work opportunities and helps employers find qualified remote candidates. It functions through two core modes — **Search** and **Report** — enhanced by intelligent automation, user personalization, and an adaptive learning system powered by a “Keyword Cloud.” The platform aggregates remote jobs and candidates across the internet, learning user preferences through behavior, explicit feedback (like/dislike/save), and detailed profile data. RWE aims to become the ultimate remote work discovery and automation hub. --- ## **2\. Goals & Objectives** ### **2.1 Primary Goals** * Help **job seekers** find ideal remote jobs that match their lifestyle, skills, and preferences. * Help **employers** efficiently discover top remote talent worldwide. * Deliver personalized, adaptive, and automated job matching experiences using AI. ### **2.2 Secondary Goals** * Enable automation of job applications, reporting, and follow-ups. * Provide a seamless interface that combines modern social, dating, and job board UX paradigms. * Build a community-driven remote work ecosystem through shared profiles and job feeds. --- ## **3\. Key Features** ### **3.1 User Onboarding & Intake** * **Thorough Profile Form** (multi-step wizard): * Personal information (name, contact, photo, location, etc.) * Career preferences, desired titles, salary, schedule, and industries. * Work history, education, certifications, and portfolio links. * Positive/negative keywords. * Accessibility and accommodation preferences. * Preferred benefits, responsibilities, experience levels. * Multimedia inputs (photos, videos, introductions, etc.) * Social links (LinkedIn, GitHub, portfolio sites, etc.) ### **3.2 Keyword Cloud (AI Training Stage)** * Users select example job posts or titles they like. * Users can manually add companies, industries, and job titles. * The system generates an **AI keyword cloud** to train personalization models. * Dynamic refinement through likes/dislikes over time. --- ## **4\. Job Discovery Interface** ### **4.1 Feed View (Social Media-Style)** * **Infinite scroll feed** with job cards (image, title, company, pay, location, link). * Interactive buttons: * **Like** → AI learns preference, boosts similar jobs. * **Dislike** → Filters out similar jobs. * **Apply** → Directs to job application or auto-applies (premium). * **Save for Later** → Adds to saved feed (premium). * **Share** → Creates a shareable link. * **Tinder-style Swipe Integration:** * Left \= Dislike * Right \= Like * Double-tap \= Save * Vertical scrolling with swipe overlay for hybrid UX. --- ## **5\. Search Mode** ### **5.1 Search Functionality** * Standard filters: keywords, title, salary, company, industry, skills, experience, etc. * AI-enhanced sorting: reorders and weights results based on user profile and behavior. * Aggregated results: sourced via APIs and scraping integrations with job boards. * Saved Searches (Premium feature). --- ## **6\. Report Mode** ### **6.1 Email & Text Reports** * **Basic users:** Receive daily email/text reports with job recommendations. * **Premium users:** * Access report within the app dashboard. * Like/dislike/save directly from the report view. * Create up to **3 custom report feeds** for specific searches. --- ## **7\. Automation Features** ### **7.1 Bulk Apply (Premium)** * Automatically applies to all saved jobs where possible. * Stores status updates (applied, pending, rejected, interview, etc.). ### **7.2 Full Auto Mode (Pro)** * Automatically applies daily to matching jobs. * Sends daily summary report of applications. * AI writes short application summaries per job. ### **7.3 Follow-Up & Interview AI (Add-on)** * Email automation for follow-ups with employers. * Responds to interview requests via synced email (Google Workspace integration). * Prepares user for interviews with company research and job-specific notes. --- ## **8\. User Profiles** ### **8.1 Job Seeker Profile** * Public & private versions. * Displays: * Work history, skills, highlights, preferences, education, and hobbies. * Saved feed (optional public showcase). * Intro video and portfolio links. * “Open to Work” toggle. ### **8.2 Employer Profile** * Company description, team info, logo, and media. * Open positions and candidate preferences. * Contact, follow, and save candidate features. --- ## **9\. Employer Features** * Candidate feed (reverse of job feed). * Like, Dislike, Contact, Save, Share. * Search and report modes for candidate discovery. * Premium tools: * AI candidate recommendations. * Bulk outreach automation. * Auto-interview scheduling (future feature). --- ## **10\. Monetization Model** ### **10.1 Free Tier** * Create profile * Daily job report (email/text) * Access job feed & swipe system ### **10.2 Premium Tier ($X/month)** * Save jobs * View saved feed * Use Bulk Apply * View reports in-app * Create 3 custom reports ### **10.3 Pro Tier ($XX/month)** * Includes Premium features * Full Auto Apply * Daily Application Summary * Advanced analytics and insights ### **10.4 Add-ons** * AI Follow-Up & Interview Assistant * Resume rewrite or video profile coaching --- ## **11\. Technical Requirements** ### **11.1 Frontend** * **Framework:** React / Next.js * **Design System:** TailwindCSS or Chakra UI * **Mobile:** Responsive web; native app planned later (React Native). ### **11.2 Backend** * **Core:** Node.js / Express * **Database:** PostgreSQL * **AI Services:** OpenAI / Anthropic / Gemini for NLP \+ job matching * **Job Aggregation:** APIs (LinkedIn, Indeed, RemoteOK, WeWorkRemotely, etc.) * **Storage:** AWS S3 / DigitalOcean Spaces * **Authentication:** OAuth (Google, LinkedIn, GitHub, Email) ### **11.3 Integrations** * Email & SMS via Twilio / SendGrid * Payment via Stripe * Resume parsing via Sovren or Affinda * AI embeddings & vector search via Pinecone / Weaviate * Video uploads via Cloudflare Stream or Mux * Social sharing via LinkedIn/Twitter APIs --- ## **12\. AI System Design** ### **12.1 Learning Inputs** * Profile data (form responses). * Keyword Cloud preferences. * Like/Dislike interactions. * Saved/Applied history. * Job success tracking (feedback loop). ### **12.2 Output** * Personalized job ranking & filtering. * Predictive matching model for employers and candidates. * Recommendation engine for daily reports. --- ## **13\. Security & Privacy** * Data encryption (AES-256, SSL/TLS). * GDPR/CCPA compliance. * User data export and delete options. * Role-based access control. --- ## **14\. Future Roadmap** * **Q1:** MVP – Intake Form, Keyword Cloud, Feed, Basic Report. * **Q2:** Premium tiers, Bulk Apply, AI personalization. * **Q3:** Full Auto & Employer Portal. * **Q4:** Interview Assistant, Mobile App, and API marketplace. --- ## **15\. Success Metrics** * User acquisition and retention rates. * Application success ratio (applied → interview → hire). * Employer engagement rate. * Average time to job match. * AI feedback accuracy improvement over time. --- Would you like me to create a **technical specification sheet** next (detailing APIs, endpoints, data models, and database schema)? That would pair perfectly with this PRD and guide development setup. ## Prompt: Write a Project Requirements Document for an ai-powered platform called Remote Work Engine (.com). As the name implies, this site helps job seekers find remote work opportunities all over the internet. It will also help employers find remote work candidates. It works in two key ways: Search & Report. In both cases, the user first completes a very thorough intake form that asks everything about the user’s career preferences, minimum salary requirements, work history, location, references, contact information, portfolio, education, certifications, positive keywords, negative keywords, preferred titles, disabilities, accommodations, preferred schedule, preferred experience levels, ideal benefits, ideal responsibilities, availability, photos, videos, video interviews, social media, hobbies, family life, and anything else that could be helpful. Once the profile is created, the user is taken to a “Keyword Cloud” where they select as many example jobs as possible. They can also manually submit jobs, companies, titles, and industries. The Keyword Cloud teaches the AI what the user prefers, and tries to find positions that are identical to or similar to the chosen keywords. With this information, the user is taken to the live job feed. The feed looks and feels a lot like a Facebook or Twitter feed where there’s a post, image, URL, and underneath, there are buttons such as Like, Dislike, Apply, Save For Later, and Share. Like tells the AI to find more jobs like this one, while Dislike filters out such jobs. Apply takes the user to the job application, and completes the application if possible (premium feature). Save for later (premium feature) creates a private feed of preferred jobs, and also gives positive feedback to the AI. The user can also share their Saved feed to their public profile for potential employers/viewers (more on that later). Finally Share obviously shares the job via link. All users can access a Tinder-style swipe feature where Left swipes are dislikes, right swipes are likes, and double taps are saves. But unlike Facebook, Twitter, and Tinder, the vertical feed and swipe feed are combined which allows users to quickly scroll and scan, while swiping. Now the user can access both the Search and Report modes. Search allows the user to type in filters just like any other job site, and the AI uses the user’s profile to further filter the jobs shown. Premium users can save quick searches. Jobs that show in the results can be sourced from the website directly, and from the broader internet, like a search engine via APIs. Report mode can email or text a daily remote job report to the user that matches their preferences. Premium users can also see the daily report on the platform via a special tab, which allows them to further like/dislike/save the jobs. Finally, premium users can request reports for specific searches (up to 3\) in addition to the default matching system. Premium users can also access a “Bulk Apply” feature that automatically applies to all saved jobs (if possible). Finally, Pro users can access the “Full Auto” feature which automatically applies to jobs daily, sends a daily report of submitted applications, and provides a quick summary of the job’s key points. I’m also considering email automation and a paid add-on that allows the AI to follow up with employers on submitted applications, coordinate with the user and respond to interview requests, and prep the user for the interview with research on the company and the specific job. All users will have access to a personal profile (similar to a LinkedIn profile) that shows their work history, highlights, preferences, interests, and anything else that they wish to share. Employers will have a feed of their own, with all of the above mentioned features, though it would be especially the opposite content, showing job candidates rather than actual jobs. Instead of an apply function, it would be a contact function. And like the job candidates, employers would also have a profile that showcases the company, available positions, and anything else they wish to share. # Remote Work Engine – Project Requirements Document ## Introduction Remote Work Engine (RWE) is an AI-powered web platform designed to revolutionize how job seekers find remote work opportunities and how employers discover remote talent. This document serves as a comprehensive Project Requirements Document for RWE, detailing every aspect of the platform’s features, design, and implementation. It is written in a clear, step-by-step manner, suitable for a junior developer with basic knowledge, and aims to leave no question unanswered about how to build this platform from the ground up. **Purpose of the Platform:** Job hunting, especially for remote positions, can be a daunting and time-consuming process. Candidates often spend hours searching across multiple job boards, filling out repetitive applications, and struggling to find positions that truly match their preferences and needs. Employers similarly face challenges in sifting through countless resumes to find candidates who not only have the right skills but also fit into remote work culture. The purpose of Remote Work Engine is to alleviate these pain points by leveraging artificial intelligence and modern web technologies to streamline and personalize the job search and recruitment experience. **Problem Statement:** Traditional job search is often described as *“confusing, tiring, and exhausting”*[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=Share). Job seekers must navigate numerous websites, filter through irrelevant postings, and manually tailor each application – a process likened to solving a complex puzzle[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=Share). Similarly, employers posting remote jobs may receive a deluge of applications, many from candidates who are not truly suitable or interested, making it hard to identify the right talent. The tedious nature of this process can lead to frustration on both sides. There is a clear need for a platform that can **intelligently match job seekers with remote opportunities and automate the repetitive aspects of applying and recruiting**. **Solution Overview:** Remote Work Engine addresses these problems with two core innovations: 1. **Personalized AI-Driven Job Discovery:** By gathering detailed information about a job seeker’s preferences, skills, and requirements, and then learning from their interactions (likes/dislikes on job postings), RWE’s AI creates a tailored job feed for each user. This feed surfaces remote job listings that closely match the user’s profile, significantly cutting down the time needed to find relevant opportunities. It’s like having a personal job-hunting assistant that knows exactly what you’re looking for. 2. **Automated Application and Follow-Up:** RWE doesn’t stop at finding jobs – it also helps users take action. The platform can auto-fill applications, bulk-submit applications to multiple jobs, and even handle routine follow-up communication with employers through an AI agent. In essence, RWE can *“make applying to jobs less painful and more delightful”* by automating tedious steps[sorce.jobs](https://www.sorce.jobs/#:~:text=We%20started%20Sorce%20to%20make,the%20colors%20in%20the%20app). For premium users, RWE can act almost like a job search agent: applying to jobs on their behalf when they swipe right (like “liking” a job)[sorce.jobs](https://www.sorce.jobs/#:~:text=Our%20app%20currently%20hosts%201,and%20applies%20on%20their%20behalf) and sending daily updates. Additionally, RWE is a **two-sided platform**: it provides tools not only for job seekers but also for employers. Employers will have their own dashboard to browse potential candidates (particularly those open to remote work), akin to a talent feed. This symmetric design – job seekers looking for jobs and employers looking for candidates – aims to facilitate meaningful matches in a manner reminiscent of “dating app” style mutual interest but for jobs. **Scope of Document:** This document will cover: * **Functional Requirements:** Each feature of the platform for both job seekers and employers, described in detail. This includes user onboarding, profile creation, job search and filtering, AI-driven recommendation feed, swipe interactions, daily job reports, application automation, user and employer profiles, and more. * **User Experience (UX) and Interface (UI) Design:** How the interface will look and behave, including page layouts, wireframe examples, and the use of a component library (shadcn/ui) for consistency. Wireframes and example UI images will be provided to illustrate key screens. * **Technical Architecture:** The architecture of the system (as a web-first Progressive Web App), including front-end, back-end, database design, and integration with external services (like job listing APIs or email systems). This section will detail how data flows through the system and how the AI components interact with user data. * **Non-functional Requirements:** Considerations for performance (real-time updates to feeds), scalability (handling large numbers of users and job listings), security (protecting personal data), and maintainability. This will ensure the platform is not only feature-rich but also robust and reliable. * **Implementation Plan and Examples:** Guidance on how to implement certain features, including example code snippets (for instance, how to implement the swipe functionality, how to design the job card component using the shadcn UI library, and how the AI recommendation algorithm might be structured). These examples will be provided in a way that a junior developer can understand and learn from, with comments and explanations. By the end of this document, a developer or development team should have a clear blueprint for building Remote Work Engine. Every major decision is documented and explained – from why we choose a Progressive Web App approach to how user feedback loops into the recommendation system – providing a solid foundation for implementation. ## Project Overview and Objectives In this section, we outline the high-level overview of Remote Work Engine and the key objectives it aims to achieve. ### Vision and Objectives **Vision:** Remote Work Engine’s vision is to become the go-to platform for remote employment, making the process of finding remote jobs or hiring remote talent efficient, personalized, and enjoyable. We want to transform remote job search from a cumbersome task into an intuitive experience that feels as straightforward as scrolling a social feed or swiping on a dating app. **Primary Objectives:** * **1\. Simplify Remote Job Search:** Eliminate the need for job seekers to manually comb through dozens of job boards every day. RWE aggregates job listings from across the internet (and directly from employers on the platform) into one feed, and uses AI to filter and rank those listings according to the individual’s profile. This ensures users see *relevant* opportunities first and don’t waste time on positions that don’t match their criteria. * **2\. Personalize Job Matching:** Utilize detailed user-provided data and ongoing feedback to create a highly personalized job recommendation system. Over time, the platform “learns” what each user is looking for in an ideal job by observing their interactions – similar to how content recommendation engines learn from user behavior[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=So%20in%20the%20dynamic%20job,jobs%20with%20the%20%E2%80%9CSWIPE%E2%80%9D%20feature)[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=%E2%80%9CSwiping%20Mechanism%E2%80%9D%20is%20the%20feature,other%20Job%20search%20mobile%20applications). The more a user engages (liking, disliking, saving jobs), the better the recommendations become, honing in on opportunities that fit their unique combination of skills, experience, and preferences. * **3\. Automate the Application Process:** Reduce the repetitive manual effort required to apply to multiple jobs. RWE’s premium features aim to automate job applications and follow-ups. This ranges from auto-filling application forms with the user’s profile data to an AI agent that *“navigates to the company's website and applies on \[the user’s\] behalf”*[sorce.jobs](https://www.sorce.jobs/#:~:text=Our%20app%20currently%20hosts%201,and%20applies%20on%20their%20behalf) when triggered. The platform essentially acts as a digital assistant for job applications, so users can focus more on preparing for interviews and less on tedious form-filling. * **4\. Empower Employers to Find Talent:** Provide employers (companies, HR recruiters, etc.) with tools to efficiently find and attract candidates who are specifically interested in remote work. This includes allowing them to search the candidate database, view profiles of potential hires, and even receive recommendations for candidates (much like the job seeker feed, but reversed). The goal is to create a dynamic talent marketplace where employers can proactively reach out to candidates who fit their job openings, rather than waiting passively for applications. * **5\. Facilitate Meaningful Connections:** Encourage a degree of mutual selection akin to a matchmaking system. For job seekers, applying or “liking” a job indicates genuine interest. For employers, contacting or “liking” a candidate indicates genuine interest in that candidate. By capturing these signals on both sides, RWE could in the future highlight **“It’s a match\!”** scenarios (for example, if an employer showed interest in a candidate who also applied to their job). This concept, inspired by Tinder-style mutual interest, can make the hiring process more engaging and efficient[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=Problem%20Statement). * **6\. Provide Support Through the Hiring Journey:** Not only does RWE aim to connect candidates and jobs, but it also supports users through later stages. This includes: * Keeping track of jobs they applied to (application tracker). * Sending reminders or updates (e.g., if an employer views their profile or if an application status changes, if integrated). * Preparing candidates for interviews with research briefs on the company/role, and potentially scheduling interviews (for instance, integrating with calendar scheduling or providing a platform for interviews). Some of these ideas are aspirational but align with the vision of making the hiring journey *“feel smooth, smart, and human”*[dribbble.com](https://dribbble.com/shots/26185414-HireHub-Job-Feed-Role-Details-Message#:~:text=A%20recruitment%20app%20that%20puts,feel%20smooth%2C%20smart%2C%20and%20human). * For employers, providing tools to schedule interviews or send bulk messages to shortlisted candidates, etc., to streamline their hiring tasks. **Key Outcomes Expected:** * Job seekers using RWE should find that they discover higher-quality opportunities (better fit for their profile) in less time, and that the barrier to actually applying is much lower thanks to automation. Ideally, this leads to more interviews and offers for those users. * Employers using RWE should be able to identify promising remote candidates faster and fill positions with less effort, because they are engaging with a pool of candidates who have signaled strong interest in remote work and in their specific roles or industry. * Over time, as the user base grows, RWE’s AI model gets smarter. With more data on what job postings are liked or ignored by which profiles, the recommendations should improve for everyone. This network effect is similar to other recommendation systems where more usage leads to better predictions[sorce.jobs](https://www.sorce.jobs/#:~:text=navigates%20to%20the%20company%27s%20website,and%20applies%20on%20their%20behalf) (for example, collaborative filtering can kick in when there are many users with overlapping interests). * Finally, RWE aims to reduce the overall friction in remote hiring. By handling the “busy work” (searching, filtering, applying, following up) through intelligent automation, the platform frees humans to focus on the higher-level aspects: deciding if a role is right for them or if a candidate would be a great fit culturally and technically. ### Product Features Overview At a high level, Remote Work Engine comprises a rich set of features. Below is an overview list of the major features (which will each be detailed in subsequent sections): * **Comprehensive User Profile & Onboarding:** A multi-step intake process capturing everything from basic resume information to personal preferences and needs (e.g., salary, schedule, work style, etc.). This profile powers the recommendation engine. * **Keyword Cloud Preference Selection:** An interactive step after onboarding where users select example jobs, titles, companies, and keywords that appeal to them (and can also mark ones they dislike). This helps cold-start the AI recommendations by giving it a sense of what the user is interested in. * **AI-Powered Job Feed (Personalized Timeline):** A continuously updating feed of remote job postings that the AI believes the user will find relevant. It looks and behaves similarly to a social media news feed, where each job is presented as a “card” or post with key details visible at a glance. * **Job Card Actions (Like, Dislike, Save, Apply, Share):** For each job post in the feed, the user can quickly respond with one of several actions: * *Like:* indicates interest; the system learns from this and will show more similar jobs[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=%E2%80%9CSwiping%20Mechanism%E2%80%9D%20is%20the%20feature,other%20Job%20search%20mobile%20applications). * *Dislike:* indicates disinterest; the system learns to filter out similar postings. * *Save for Later:* bookmarks the job in the user’s saved list (premium feature) and also signals interest to the AI. * *Apply:* either redirects to the job’s application page or, for supported sites, auto-fills and submits an application immediately (the latter is a premium feature where possible). * *Share:* allows the user to share the job posting link externally (or copy it) – useful if they want to send it to a friend or just save outside the platform. * **Combined Vertical Scroll and Swipe Interaction:** Users can scroll through the feed vertically for quick browsing, **and** also use swipe gestures on each job card (particularly on touch devices/mobile) as a quick way to trigger the like/dislike/save actions. For example, swiping right on a job card could be equivalent to tapping “Like,” and swiping left equivalent to “Dislike,” while a quick double-tap could trigger a “Save”. This unique design combines the familiarity of a scrolling feed with the fun, fast decision-making of swiping[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=Problem%20Statement). * **Advanced Search & Filters:** A traditional search interface where users can search for jobs using keywords and apply filters such as: * Location (though jobs are remote, some might be “remote in US only,” or “EMEA remote,” etc. so location still matters). * Job title or category. * Company name or industry. * Salary range. * Job type (full-time, part-time, contract). * Experience level required. * Date posted or freshness. * … and more. This search mode lets users actively find specific jobs, with the AI still working in the background to rank the results by relevance to the profile or possibly to exclude obvious mismatches. **Premium users** can save their search queries for reuse, and set up alerts (reports) for them. * **Daily Job Report (Email/Text and In-App):** In “Report” mode, the platform generates a curated list of new remote job postings each day that match the user’s profile/preferences. By default, this daily report is sent via email or SMS to all users (depending on their choice). The report contains brief info about each job and a link or action to view/apply. Premium users get additional benefits: * Access to a “Daily Report” section within the app where they can see the report as an interactive list (with like/dislike/save/apply actions on each item, just like the main feed). * Ability to create up to 3 custom job alerts/reports for specific search criteria (e.g., one for “Senior Developer in Europe” and another for “Product Manager in Fintech”, etc.). These custom reports can also be delivered daily or weekly as chosen. * **Bulk Apply (Premium Feature):** A feature that allows a user to apply to multiple jobs in one go. Specifically, a user can take all the jobs they have saved (i.e., those they marked with Save for Later) and trigger the system to apply to each one automatically. The system will use the profile information (which includes the user’s resume, contact details, and other relevant data) to fill out applications on external job sites or direct application forms. If an application cannot be fully automated, RWE will at least navigate the user to the application page with as many fields pre-filled as possible. The idea is to turn the tedious process of filling out many applications into a one-click action for the user. * **Full Auto-Apply (“Autopilot” for Jobs – Pro Feature):** An even higher-tier feature (perhaps for “Pro” users) where the platform takes complete control in the background. Every day, RWE will automatically identify new jobs that match the user’s criteria (similar to those in the daily report) and will automatically submit applications on the user’s behalf – without the user having to manually initiate each one. The user would receive a daily summary (report) of which jobs were applied to, along with key details of those jobs: * Job title, company, location (if applicable). * A brief summary of the job posting (so the user knows what was applied to) – likely generated by AI by parsing the job description. * Any next steps if known (for example, if an application requires a test or something, though typically not known immediately). Essentially, Full Auto mode is like having an AI job agent constantly working for the user, applying to opportunities continuously. This is inspired by apps like Sorce which *“navigate to the company’s website and apply on \[the user’s\] behalf”* when the user swipes right[sorce.jobs](https://www.sorce.jobs/#:~:text=Our%20app%20currently%20hosts%201,and%20applies%20on%20their%20behalf). * **AI Email Assistant (Follow-up & Scheduling – Optional Add-On):** A proposed feature that goes beyond job search into the interview process. Users who enable this feature would allow the platform’s AI to integrate with their email (or have an email proxy). The AI Assistant could: * Monitor for responses from employers (e.g., an HR email inviting for interview). * Send prompt and professional follow-up emails. For example, if a few days pass after an application without a response, the AI might send a polite follow-up expressing continued interest. Or if an interview is scheduled, it could send a thank-you email afterwards. * Coordinate scheduling: If an employer proposes an interview time, the AI can check the user’s linked calendar and reply with confirmation or propose alternative times, essentially acting like the user’s personal scheduling assistant. * Provide interview prep: The AI could gather information about the company and the job role, and send the user a brief outlining what the company does, recent news, and possible questions to expect. This is akin to giving the user a “cheat sheet” before an interview. This feature would use natural language generation (possibly a fine-tuned large language model) to draft emails that read as if the user wrote them. It would do so carefully to maintain professionalism and the user’s tone. All AI-driven communications would either be user-approved or clearly logged for the user to review, to maintain trust. * **User Profile & Public Profile:** Every job seeker has a profile on RWE that serves two purposes: 1. **Private Profile for AI Matching:** This includes detailed info that might not all be visible publicly but is used by the system to improve matches. For example, salary requirement, preferred benefits, whether they need certain accommodations (maybe they have a disability and need a certain accommodation – this can be used to filter out jobs that can’t support that), family considerations (like needing flexible hours for caregivers), etc. These data help the AI filter and rank jobs but might be sensitive to display publicly. 2. **Public-facing Profile (Optional):** Similar to a LinkedIn profile or an online resume. It showcases the user’s work history, skills, education, portfolio projects, and any other info the user chooses to share (like a bio, profile photo, location, etc.). Users can choose to share a link to this profile externally or even make it discoverable to employers on the platform. Users can also choose to share certain dynamic content on their profile, such as their “Saved jobs” list (if they want to show what kind of roles they are interested in) or a feed of their activity (for example, jobs they liked, if they choose to make that public). This can give employers insight into the candidate’s interests and preferences. * **Employer Accounts & Company Profile:** Employers can create accounts which give them a company profile page. This page can include: * Company description and logo. * Details about company culture or remote work policy (since these are remote jobs, employers might want to highlight how they support remote employees). * All current job openings they have (if they post jobs on RWE directly). * Possibly media like photos of the team, videos about the company, links to their website and social media. This profile helps attract candidates; and if a candidate comes across it (via a job post or via being contacted by the employer), they can learn more about the company. * **Employer Job Posting & Feed:** Employers can post job listings on RWE (in addition to RWE pulling jobs from around the web). When an employer posts a job: * It becomes part of the RWE job database and can appear in users’ feeds and search results (especially those that match the criteria). * The employer can see how many candidates viewed or applied to it via RWE. * They can also proactively search for candidates who might fit that job (the system could even recommend candidates). * **Employer Talent Feed and Search:** Employers have a section analogous to the job feed, but instead it’s a **“candidate feed.”** This will list potential candidates that the AI thinks could be a good match for their open roles or company in general. Employers can filter or search candidates by: * Skills, keywords or job titles (e.g., “JavaScript developer”, “UX Designer”). * Location/timezone (maybe they want someone who can work in a certain time zone or country). * Experience level or years of experience. * Availability (e.g., immediately available vs. not available until a certain date). * etc. The AI will try to rank candidates who are actively looking and who fit the query. Employers can swipe on candidate profiles similarly: swipe right or click “Like” on a candidate to indicate interest (which could notify the candidate or at least save to a list), or swipe left/“dislike” to skip (and perhaps not show similar candidates). They can also directly click “Contact” or “Invite to apply” on a profile. * **Employer Actions on Candidates:** For each candidate profile shown, employers could have actions analogous to the job card actions: * *Like/Shortlist:* Save the candidate to a shortlist for a specific job or for future reference. * *Pass:* (essentially a dislike, to remove from view). * *Contact:* Initiate contact – this could be sending a direct message through the platform, an invitation to apply to a specific job, or an email if the candidate’s email is provided. For privacy, likely it would be a message via RWE initially. * *Share:* Share the candidate’s profile internally (e.g., with a colleague or hiring manager via a link). * **Notifications and Communication:** The platform should support notifications such as: * Job alerts (the daily report). * Notifications if someone liked your profile (if we implement mutual like notifications, etc.). * Notifications of new jobs that are trending or highly matched. * Messages: if an employer contacts a candidate or vice versa (if platform messaging exists). For web, this includes in-app notifications and possibly push notifications (leveraging PWA capabilities to send web push notifications[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=Push%20notifications%20are%20arguably%20the,even%20when%20they%27ve%20exited%20a)). For email/SMS, important updates like the daily report or an employer message might be sent out as well for quick attention. * **Progressive Web App (PWA) Functionality:** RWE is designed as a **Web-first Progressive Web App**. This means: * Users can access it via browser on any device (desktop, tablet, mobile) with a responsive design that adapts to different screen sizes. * It can be installed on mobile devices like a native app (users can “Add to Home Screen”), providing an app-like experience without going through an app store. This yields benefits of both web and native apps – “the feature-rich experience of a native mobile application without sacrificing the flexibility of a web application”[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=PWA%20stands%20for%20%E2%80%9CProgressive%20Web,benefits%20of%20a%20web%20application). * Offline capabilities: Using service workers, certain data (like the user’s profile, or previously loaded job lists) can be cached so that the app can still open and show content even when offline or on poor network[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=1). For example, a user could open the app underground with no signal and still review the jobs they had saved or maybe read a previously fetched job description. * Background sync and push notifications: The PWA can receive push notifications for new job alerts or messages[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=Push%20notifications%20are%20arguably%20the,even%20when%20they%27ve%20exited%20a), ensuring users are re-engaged even if they don’t have the app open. Also, background sync could be used to periodically fetch new jobs for the daily report or sync application submissions when connectivity is back. * Overall, being a PWA ensures the platform is easily accessible (just a link click away, no install barrier) while still offering a near-native smoothness and the ability to function offline or send timely notifications. * **Use of Component Library (shadcn/ui):** To maintain a consistent and modern design, we will utilize **shadcn/ui**, which is essentially a set of pre-built, accessible React components styled with Tailwind CSS. This helps us accelerate development by not reinventing common UI elements (buttons, forms, cards, etc.) and ensures a cohesive look and feel. Shadcn’s components come with *“beautifully-designed, accessible”* defaults[ui.shadcn.com](https://ui.shadcn.com/docs#:~:text=Next)[ui.shadcn.com](https://ui.shadcn.com/docs#:~:text=%2A%20Distribution%3A%20A%20flat,to%20read%2C%20understand%2C%20and%20improve) and can be customized as needed. By building the UI with such a component library, even a junior developer can assemble complex interfaces from these building blocks rather than coding everything from scratch. We will highlight in the design sections which components are used for which parts of the interface (for example, using `
) } ``` In the above snippet: * We use ` ) })}
) } ``` In this snippet: * We use a `Badge` component (assuming shadcn has something like that for pills, or we can style a `` as a pill). * Clicking a badge toggles like. Right-click (or long press on touch, though the code shows onContextMenu for desktop) toggles dislike. In a real app, we might instead have two buttons inside each badge for like/dislike, but trying to keep it simple. * We visually distinguish liked items (green) and disliked (red). * The Continue button sends the chosen likes/dislikes up to be saved and then calls `onComplete`, presumably to go to the next screen. This gives an idea of how a developer can implement the selection logic. ### Saving Preferences and AI Initialization When the preferences are submitted, the system will: * Save those likes/dislikes to the database. * Possibly run an initial “job fetch” routine to populate the feed. This might involve querying our job index with filters \= user’s must-haves (from profile) and then ranking by closeness to preferences. The user then is dropped into the main **Live Job Feed** screen with recommendations already tailored to them, which hopefully creates a *“wow, these jobs are exactly what I was looking for\!”* effect, validating the time they spent onboarding. Next, we’ll discuss that **Live Job Feed** in detail, including how it looks, how the user interacts with it, and how it updates based on their feedback. ## AI-Powered Live Job Feed The Live Job Feed is the heart of the Remote Work Engine interface for job seekers. It’s designed to feel familiar (like a social media feed) but focused on presenting job opportunities. This feed is continuously updated and personalized by the AI using the user’s profile data and interactions. ### Feed UI Overview The feed is presented as a vertical list of job postings. Each posting is displayed as a **Job Card** – a compact preview of the job that provides enough key information at a glance, with the ability to take quick actions. Users can scroll through this feed infinitely (initially populated by AI-chosen jobs, and loading more as they scroll down). ![https://dribbble.com/shots/26185414-HireHub-Job-Feed-Role-Details-Message][image1] *Example of a mobile job feed UI with job cards and details (the middle screenshot shows a job card for “Senior Product Engineer” with key info). The feed lists jobs with titles, companies, salary ranges, and tags like location and job type, and highlights matches (“Potential fit based on your experience”) to catch the user’s eye.* *(Image source: an example design for a job feed in a mobile app, demonstrating how key job details and status (like “You applied this job”) can be shown in the feed.)* In the image above, you can see how a job feed might look on a mobile device. We will be designing a similar concept: * Each job card will show the **job title**, **company name**, possibly the company logo or an icon, and some tags or chips indicating important attributes (like “Remote – US/UK” region, salary range, job type like Full-Time, experience level like “Senior”, etc.). * We might also show a brief **teaser** of the job description or a tagline if available (maybe one or two lines) to give context. * If our AI has a particular reason for showing it, we could display a subtle hint like “Potential fit based on your experience”[cdn.dribbble.com](https://cdn.dribbble.com/userupload/43815953/file/original-90a94f0f0f1ca4d4094a49ce86a70bc2.png?resize=752x&vertical=center#:~:text=) or “Matches your skill: Python” – similar to how some platforms highlight why a job is recommended. Each card has interactive buttons for actions: * **Like** (Thumbs Up or Heart icon): mark interest. When tapped, the card might highlight or animate briefly to confirm the action (e.g., flash green or show a heart fill) and then either remain or disappear from the feed (design choice: Tinder removes liked cards from the stack, but in a feed paradigm, we could either keep it with an indicator or remove it. Perhaps we remove it from the main feed and it later appears in the Saved/Liked list anyway). * **Dislike** (Thumbs Down or “X” icon): mark not interested. This likely will remove the card from the feed immediately (or grey it out then remove). * **Apply** (a button or icon like an arrow or paper plane): this will initiate the application process. On clicking: * If the job can be applied to directly (for jobs posted on RWE or integrated via API), we could handle the application in-app. * If it’s an external posting, this will open the job application link in a new tab or an in-app browser. If the user has the premium auto-apply, we attempt to auto-submit the application in the background. * After applying, we might mark the card as “Applied” or remove it. In the example image \[14\], notice one job says “You applied to this job” as a status on the card. * **Save** (Bookmark icon or star): Save for later. This is a premium feature – perhaps free users can only like (which implicitly saves? We need to clarify difference: maybe “Like” is not exactly save, it’s more for training the AI, whereas “Save” is explicitly bookmarking for the user’s own list). We can allow free users to like for AI but not have a separate saved list, while premium users have a saved jobs list they can review anytime (and use for bulk apply). * **Share** (Share icon): Allows sharing the job outside the platform. On clicking, we can provide options to copy link, or share to social/email (using Web Share API on mobile for example). The link would ideally point to a landing page for that job (so even non-users or employers can see it). All these buttons should be easily tappable on mobile and clickable on desktop. We’ll likely arrange them as a row of icons beneath the job details or overlayed on the card. For accessibility and to aid quick scanning, each action could also be tied to a swipe gesture: * Swipe right on the card \= like (equivalent to tapping Like). * Swipe left \= dislike. * Double tap \= save (common pattern for “favorite” like Instagram double tap to like; here we adapt it to save). We must ensure this doesn’t conflict with scroll – on mobile, vertical swipe scrolls the feed, but a horizontal swipe on a card can trigger like/dislike. We can implement this via touch events detecting horizontal vs vertical intention (there are libraries for swipeable cards). ### Continuous Learning from Feedback The feed is *AI-powered* in that it uses a recommendation algorithm that adapts to the user’s interactions: * When the user likes a job, the system treats it as positive feedback. It will immediately (or after some threshold) adjust the model for this user to show more jobs similar to that one[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=%E2%80%9CSwiping%20Mechanism%E2%80%9D%20is%20the%20feature,other%20Job%20search%20mobile%20applications). Similarity could be based on job title, required skills, company, etc. For example, if the user liked a “Senior Product Engineer at Company P” which is a fintech company requiring Python, the system may boost other fintech jobs or other Python-heavy engineering jobs. * When the user dislikes a job, that’s negative feedback. The system will learn to filter out jobs with similar attributes. E.g., if they keep disliking sales jobs, clearly sales roles will drop out of their feed. * The user might also skip some jobs (scroll past without any action). We might consider that implicit feedback – e.g., if a job has been visible on their feed for a while and they never interacted, it might indicate neutrality or low interest. Over time, the feed will focus on things they engage with more. * Saves are positive signals as well (similar to like, perhaps even stronger since user explicitly wants to consider it later). * Apply is a very strong positive signal (they were interested enough to apply). * Additionally, if a user clicks to expand a job or reads the full description, that dwell time can be a signal that the job was at least relevant enough to read. All these signals feed into the recommendation engine. **Recommendation Engine Approach (high-level):** We might implement a hybrid of content-based filtering and collaborative filtering: * *Content-Based:* We have a profile of the user (skills, prefs) and metadata of jobs. We can compute a match score by comparing these. For example, if a user has X skill and the job requires X, score++. * *Preference-Based:* The keyword likes/dislikes give weight to certain features. Perhaps we build a weighted vector of features (like a user likes “Remote within US” \+ “Full-stack” \+ “React” \+ “Google” \=\> the system should prioritize jobs with those). * *Collaborative/Popularity:* If we have many users, we could also incorporate “jobs that similar users liked” or “overall popular jobs”. But early on, content-based is primary because each user’s criteria differ a lot for jobs. A simple approach is to assign each job a relevance score for the user: Say we parse each job into a set of attributes (title, company, location, skills required, etc.) and represent them in some vector form. The user’s preferences can also be a vector. We then rank jobs by cosine similarity or a weighted sum. Also incorporate hard filters (don’t even include jobs that violate must-haves like location or salary). We’ll refine the algorithm in the Technical section, but that’s the gist. ### Real-Time Feed Updates Because new jobs come in all the time, and the user’s feedback is continuous, the feed should be dynamic: * **New Job Postings:** As new jobs matching the user’s criteria are found (via crawling or from employers), they should appear in the feed (especially near the top if highly relevant). We could implement a real-time push (like using WebSockets or periodic polling) to insert new cards at the top “New jobs available” to keep the feed fresh. * **Learning Adaptation:** If the user suddenly starts disliking a certain category, the feed should refresh to show fewer of those. We might even remove or reorder items already loaded but not seen yet. For simplicity, immediate next fetches can use the updated model. ### Feed Pagination/Loading We won’t load thousands of jobs at once. Typically, we’ll load in batches (say 10 or 20 jobs) and as the user scrolls near bottom, load more (infinite scroll). This is a standard approach to ensure performance. We should also consider an **empty state**: what if the user’s filters are so strict that few jobs qualify? * We should then broaden slightly or show a message like “No more jobs match your exact preferences at the moment. Try expanding your filters or check back later.” Possibly encourage them to adjust profile or search criteria. * Or show less perfect matches with a note (“Other remote jobs you might consider”). ### Interaction Design Details * When the user taps a job card (not the buttons, but the card itself), we should open the **Job Details** view. This could be a modal (overlay) or a separate page. The Job Details will show the full description, requirements, etc., basically the full job ad. It will also have the action buttons there too (like an Apply button, etc.). On desktop, maybe a side panel or modal; on mobile, a new screen. We want to ensure the user can read all about the job before deciding. * On the job card, show maybe just a snippet (“About the Role: We are building the fastest, most powerful customer support platform...” as in the example image)[cdn.dribbble.com](https://cdn.dribbble.com/userupload/43815953/file/original-90a94f0f0f1ca4d4094a49ce86a70bc2.png?resize=752x&vertical=center#:~:text=) truncated. * Possibly highlight if the user meets certain requirements (like if the job posting says “Need 5+ years experience” and user has 6, we could subtly show “✅ You have 6 years experience” – a nice-to-have feature to reinforce fit). * If a job was liked or applied already, mark it accordingly to avoid confusion (like “Applied” tag). * Provide visual feedback for actions: e.g., when swiping a card: * If swipe right, maybe overlay a semi-transparent 👍 or heart icon on the card as they drag (classic Tinder style feedback). * If swipe left, overlay a 👎 or X icon. * If double-tap, maybe briefly show a bookmark icon flying or the card border highlighting. These fun touches make the UI more engaging. **Inspiration from Tinder-like job apps:** As the concept is similar to Tinder for jobs, it’s worth noting that such designs have proven engaging[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=Problem%20Statement). For example: * Sorce (mentioned before) simply has users swipe and then auto-applies when they swipe right[sorce.jobs](https://www.sorce.jobs/#:~:text=Our%20app%20currently%20hosts%201,and%20applies%20on%20their%20behalf). * Another conceptual app “Job Swipe” had the idea of swiping to shortlist vs skip[medium.com](https://medium.com/@ajain05121997/simplify-your-job-search-with-a-swipe-jobswipe-2663280184a8#:~:text=%E2%80%9CSwiping%20Mechanism%E2%80%9D%20is%20the%20feature,other%20Job%20search%20mobile%20applications). In our case, “Like” is akin to shortlist (interested) and skip is skip. * We combine that with a scrollable feed because we believe users might want to skim quickly and not be forced to one-by-one swiping. This combination is unique: normally, Tinder UI deals one card at a time, whereas a feed shows many. How to integrate: One approach: The feed could be the default for browsing, but there could be a toggle or separate view for a dedicated swipe mode. Perhaps the user can go to a “Swipe” tab where they get a full card and swipe left/right on it (and maybe it goes through jobs in a sequence). However, to keep in line with “combined”, maybe we won’t split it – instead each card in feed is swipeable. We will need to implement a custom container that allows each list item to capture horizontal swipes. Alternatively, on mobile we could implement the feed as a stacked card deck that is also scrollable – but that’s complex. It might be simpler: on mobile, the feed is just scroll, no swipe gestures (just use the buttons). On a separate “Tinder” screen, you do the swiping. But the prompt said combined, so maybe not separate. We’ll assume swipe gestures directly on feed cards. ### Example of a Job Card Design We will use a Card component from shadcn for each job posting. Structurally: ``` )} ``` This pseudo-JSX outlines: * CardHeader: shows title and company (with optional logo). * CardContent: a snippet of description and some tags (tags could include salary like “$75k-$100k”, “Full-time”, etc. we could style them as badges). * CardFooter: the actions. Here I used a combination of ghost buttons for like/dislike/save (with icons) and a normal button for “Apply” to make it stand out. * We’d import icons for thumbs up/down, bookmark etc. Possibly from a heroicons set or similar. Using `Avatar` from shadcn for the logo or some fallback (like the first letter of company if no logo). This card would then be placed inside a list. For example: ``` ``` Where `
{loading ? : results.map(job => )}
``` In the code above: * On large screens, filters are visible in a sidebar. * On small screens, a Filters button triggers a mobile sheet with filters (not shown here for brevity). * We reuse JobCard component for results. ### Search and Feed Interplay One more note: The feed and search serve different user mindsets (passive discovery vs active search). But we should ensure consistency: * If a user likes a job in search results, it should reflect in the feed (like it might disappear from feed since liked). * If they apply or save, similarly mark it. So our state management should unify these actions across views. ### Searching Candidates (for Employers) Though we mainly focus on job search for candidates here, it’s worth noting symmetry: Employers will have a similar search interface to find candidates. That likely includes different filters (skills, years of experience, etc.) and search by name or keyword in profile. We will cover employer features soon, but keep in mind we might replicate a lot of this search functionality for candidate search on the employer side. With search covered, now we proceed to the **Report mode (Job Alerts)** which ties in closely by delivering search results to users proactively. ## Daily Job Reports (Email & In-App Alerts) The Report mode in Remote Work Engine is designed to keep users updated with new opportunities without requiring them to constantly check the app. It’s essentially a **job alert system** that leverages the user’s profile to find and notify them of new remote jobs each day. ### Default Daily Match Report Every user, by default, will receive a daily report of remote jobs that match their main profile preferences. This is similar to how traditional job boards have “daily job alert emails” that send new matches[collegegrad.com](https://collegegrad.com/blog/how-to-use-a-job-alert-in-your-job-search#:~:text=How%3F%20By%20creating%20a%20job,you%20matches%2C%20typically%20via%20email). The difference is that our alerts are more targeted thanks to our AI and they integrate with our platform’s interaction features. **How it works:** * Each day (the user can likely choose a time or it defaults to e.g. 8 AM user’s local time), the system will gather new job postings added in the last 24 hours (or since the last run) that match the user’s criteria (profile \+ any quick saved searches for premium). * It will rank them by relevance and then compose an email (or SMS text) listing these jobs. **Email Report Format:** * Subject: something like “Remote Work Engine – Your Job Matches for \[Date\]”. * Body: It might list the top 5-10 jobs with their titles, companies, maybe a one-line summary or location, and a call-to-action link/button “View Job” for each. * Possibly indicate if it’s a strong match: e.g., “90% match” or highlight the reason (“matches your skill Python”). * Footer with a link to view more on the site, adjust alert settings, etc. For example, the email might say: ``` Hello Jane, 5 new remote jobs were posted that match your preferences today: 1. **Senior UX Designer at Netflix** – Remote (US) – $100k-$120k *Why this job?* You indicated interest in UX Design and Netflix. [View Job] [Like] [Dismiss] 2. **Product Designer at FinTechCorp** – Remote (EU) – €70k *Why this job?* Matches your skills: Figma, FinTech industry. [View Job] [Like] [Dismiss] ... (and so on) Visit your Remote Work Engine feed to see more or fine-tune your preferences. ``` Each item would have a link to view the job on RWE (where they can apply or interact). We could even embed quick action links like \[Like\] or \[Dismiss\] that, if clicked, record that feedback without having to open the site (this would involve special links hitting our server to log the preference). That might be advanced, but it’s possible. The key is the user can quickly scan new matches over their coffee via email, a convenience since they may not log in every day. This aligns with how job alerts typically work: *“you do not have to manually come back to the site… you will get first notification of new jobs… giving you a first responder advantage in applying”*[collegegrad.com](https://collegegrad.com/blog/how-to-use-a-job-alert-in-your-job-search#:~:text=The%20advantage%20of%20setting%20up,in%20applying%20for%20the%20roles). The daily report ensures our users are among the first to know about new openings (especially important in remote jobs which can be competitive and fill fast). **SMS Option:** Some might prefer a text message. Due to length, an SMS might just say “5 new jobs match you today, including Sr UX Designer at Netflix. Check your email or RWE app for details.” – so likely email is primary. ### In-App Daily Report (Premium) Premium users can also access these daily reports within the app through a special **“Daily Report” tab or section**. This could be represented as: * A section on the home screen that says “Today’s Matches” showing the new ones, or * A separate tab where each day’s report is like a feed of its own. Likely, we’ll create a **Reports tab** with maybe a list of dates (or just always show latest). If they missed a day, maybe they can toggle to yesterday’s. In-app, they can interact with each listed job (like/dislike/save/apply) directly. Essentially it’s like feed items but filtered to “new today.” We could highlight which ones are truly new vs ones that were already seen in feed (if any overlap). ### Custom Job Alerts (Premium Feature) Premium users can define up to 3 custom job alerts (number can be decided, 3 was mentioned). This essentially means they can take advantage of additional saved searches to get separate reports. **How to set one up:** * The user performs a search with certain filters (as described in Search section). * They click “Save search as alert” and choose frequency (likely daily or weekly). * They give it a name (like “UI jobs in Europe”). * Now, in their settings, they will have this alert configured. For each custom alert: * They will get a separate section in their daily email or even a separate email? We could either: * Combine everything into one daily email (with sections “Matches based on your profile” and then “Alert: UI jobs in Europe” etc.), or * Send separate emails for each alert. The user might prefer one consolidated email though. * In-app, the Report tab might let them switch between “Main Matches” and each custom alert feed. We need to ensure not to overwhelm with emails, so maybe one email with multiple sections is best. **Use case example:** John is a premium user who is a software engineer open to any remote dev jobs (that’s his profile). But he’s particularly interested in AI-related jobs and also open to product manager roles as a pivot. He sets up: * Alert 1: Keyword “AI or Machine Learning” in jobs, daily. * Alert 2: Title filter “Product Manager”, weekly (since he’s casually interested). Then each day, John gets: * Main profile matches: e.g., generic software jobs. * Section: “AI/Machine Learning jobs” – a few listings specifically with those keywords. * On Mondays, maybe the Product Manager weekly alert triggers. This way he doesn’t miss out on those niches. ### Report Settings and Controls We should provide controls to the user to manage these alerts: * Turn daily profile report on/off. * Set email or SMS or in-app only. * Adjust time of day. * Manage custom alerts (add/edit/delete). * For example, in a “Settings \-\> Notifications” page. We should also allow unsubscribing easily from emails (legally required to have unsubscribe link). Unsubscribing could either turn off all or let them choose which alerts to stop. ### Bulk Apply via Report Premium users might want to apply to multiple jobs from the report easily: * Possibly in the in-app report view, we could have a “Apply All” button that applies to all new matches (though that might be too broad, they may not want to apply to every single one). * More realistically, they use the report to quickly like/save jobs they like, then later use Bulk Apply on saved. So main role of report is discovery & quick action. ### Technical Implementation A daily background job (cron) will run for each user (or batch users) to generate these alerts. It will: * Query the new jobs since last run that fit criteria. * Possibly store those results temporarily or just generate an email on the fly. * Use an email service (like SendGrid) to send the emails. In-app, when user opens the Reports tab, we can run a query to fetch jobs posted in last 24h that match (ensuring similar logic as the email so they see the same list). We should store some metadata, e.g., `last_alert_sent_at` per user to ensure we only include new jobs, and also mark which jobs were sent so we don’t repeat them next time (unless the user hasn’t interacted and the job is still open after several days, some alerts systems do repeat occasionally but probably skip to avoid spam). The advantage of the daily alert: * It keeps users engaged even if they don’t open the app daily. They can rely on the email and click through when something catches their eye. * It also helps them *“stay informed about the job market”*[colleges.stark.ai](https://colleges.stark.ai/resources/job-alerts/how-job-alerts-work#:~:text=How%20Job%20Alerts%20Work%20%26,By). Even if not applying every day, seeing those emails gives insight into which companies are hiring and what skills are in demand. ### Tie-in with AI and Preferences The daily alert generation should respect their preferences thoroughly: If a job doesn’t meet their must-haves (like below salary, wrong timezone) it shouldn’t be in the email – otherwise it’s noise to them. So basically it’s a filtered subset of feed items that are new. We can also learn from what they click in the emails: If they consistently ignore certain types of jobs in the email, maybe the AI can down-rank those further. If they often click ones with “Manager” in title, maybe bump those in future. This is advanced, but tracking email click or email like/dislike could feed back. ### Summaries and Stats (Optional) We might consider adding small statistics in the weekly or daily emails like: “You have liked 20 jobs this week. 3 employers viewed your profile.” etc. But that’s extra; main focus is the job list. Now that we have search and alerts covered, the next big feature is what to do after saving jobs – specifically, **Bulk Apply** and **Full Auto Apply** features for premium users, which we will discuss next. ## Application Automation: Bulk Apply and Full Auto-Apply One of the standout features of Remote Work Engine for premium/pro users is the ability to automate the job application process. This includes the **Bulk Apply** feature, where a user can apply to multiple saved jobs at once, and the **Full Auto-Apply** (or “Full Auto”) feature, where the platform continuously applies to jobs on the user’s behalf without manual intervention. These features are aimed at reducing the manual effort of filling out repetitive application forms, truly delivering on the promise of making life easier for remote job seekers. ### Bulk Apply (Premium Feature) **What is Bulk Apply?** Bulk Apply allows a user to submit applications to all jobs in their Saved list (or Liked list) in one batch action. Instead of going one by one, the user can say “apply to all of these for me.” **Pre-requisites:** * The user must have a complete profile with all necessary info that would be on an application (education, work experience, etc., which we collect in onboarding). * Ideally, they also have a generic cover letter or personal statement saved, which could be customized per application. We might allow them to store a base cover letter template in their profile. * The jobs they saved must be ones that accept external applications (some could be via our site if the employer posted it, which is easier, or external links). **How Bulk Apply Works:** 1. The user goes to their Saved Jobs feed (premium feature accessible maybe under a “Saved” tab or in their profile). 2. They review and maybe unselect any they changed their mind on. 3. They click **“Apply All”** (or “Bulk Apply”) button. 4. The system will then iterate through each selected job and attempt to submit an application. For each job: * If the job is posted on RWE directly (i.e., the employer accepts applications via our platform), we can directly create an Application record in our system and possibly send their profile/resume to the employer contact. That’s straightforward: essentially one click apply. * If the job is external (like on a company’s own site or a different job board), we have to automate that: * Ideally, we have an integration. Some larger boards or ATS (applicant tracking systems) offer APIs or standard forms (e.g., Greenhouse, Lever, Workday, etc.). We could integrate with those: * For example, if we detect the application link is a Greenhouse form, we could potentially fill it via Greenhouse’s API or a form POST if allowed. * If no integration, we resort to a headless browser approach or an automation script. This is complex and may not always work, and might be legally tricky (it’s like botting the application). * Another approach: We send an email to some alias (rarely accepted as applications). * Or we at least auto-fill fields in the browser for the user to submit (less ideal for “apply all” because that still requires user to do final steps). * Possibly we limit Bulk Apply to only those jobs we have high confidence of auto-applying (like integrated ones). **Progress Tracking:** We should show a progress UI: * A modal or page that lists each job and shows “Applying...done/failed” statuses. * If one requires user action, we could pause and prompt them. For example: ``` Applying to 5 jobs: [✔] Senior UX Designer at Netflix – Application submitted. [✔] Product Designer at FinTechCorp – Application submitted. [!] Frontend Developer at Acme Inc – Action required (click to complete form). [✔] UX Designer at RemoteDesignCo – Application submitted. [✔] UI/UX Designer at Globex – Application submitted. ``` In this example, one of them might have required a captcha or missing info – we alert the user. If the Bulk Apply is fully successful, the user effectively applied to all those jobs in one go. This can save hours of time. As noted with Sorce, such automation has helped thousands streamline job search[sorce.jobs](https://www.sorce.jobs/#:~:text=We%20started%20Sorce%20to%20make,the%20colors%20in%20the%20app). We must log these applications in a record (like an Applications table with status “submitted”) so the user can track them. Possibly a “Applications” section where they see what they’ve applied to (with dates and maybe any feedback or responses if we integrate email). **Email Confirmation:** We might send the user an email summary: “We applied to 5 jobs on your behalf just now. Here’s the list...” **Risks & Considerations:** * We need to ensure quality. If a cover letter or specific question was needed, a generic apply might hurt chances. We might warn users to only bulk apply to jobs that have similar requirements or that they’re fine sending the same materials to. * Some employers might receive a more generic application and might notice, but that’s a trade-off for volume strategy. We can mention to users that customizing applications can improve success, but Bulk Apply is there for those who want to maximize reach. * From a development perspective, building all the integrations is heavy. We might start with partial support (maybe only truly implement auto-apply for our own postings and a few common ATS forms, and for others just open the link). * Ensure not to violate any terms of other job sites by mass applying via bots. It may be fine if user provided credentials or such. Possibly to avoid conflicts, we focus on jobs that either are directly posted to us or through partnerships. **Technologies for Implementation:** * For integrated ones: Use partner APIs (if any). * For general websites: Possibly use a headless browser (like Puppeteer or Playwright) on the backend to simulate filling forms. That’s advanced and needs maintenance per site. * Alternatively, instruct the user: Bulk Apply could in some cases open multiple tabs for them with forms pre-filled (the browser can do autofill if we pass data), and then the user just quickly goes through each tab and hits submit. It’s not fully automatic but still faster. But that’s not one-click then, it’s multi-click. Maybe we say: Bulk Apply will fully apply wherever possible, and for others it will prep the application and prompt you. We’ll refine this in architecture. ### Full Auto-Apply (“Full Auto”) – Pro Feature **What is Full Auto?** Full Auto means the platform will handle finding new jobs and applying to them every day, with zero clicks from the user. It’s like hiring an AI agent to job hunt for you continuously. The user just provides initial parameters and then watches the applications go out and (hopefully) responses come in. **How it works:** * Full Auto is likely an opt-in the user must explicitly enable (since it’s powerful and could potentially apply to places the user hasn’t manually vetted). * The user might set some additional criteria for auto-apply to avoid unwanted applications. For example: * Only apply to jobs above a certain salary. * Only at certain company types or exclude some industries. * Possibly they pick which of their saved searches or profile-based matches to auto-apply for. Maybe they trust the AI fully or they restrict it. * Once enabled, every day (or continuously, as jobs appear) the system will: * Identify new jobs that match the user’s profile (or specific alert queries the user designated for auto-apply). * Automatically submit applications for those jobs, using the user’s profile info (resume, etc.) as if Bulk Apply is being run per job. It’s like Bulk Apply but running automatically daily on new items. **Daily Summary Report:** The user will receive a daily summary email (and/or see in app) of what was done: * e.g., “Today, 3 new applications were submitted on your behalf: 1. Frontend Dev at XYZ (applied at 10:30 AM) 2. UI Designer at ABC (applied at 11:00 AM) 3. Product Designer at ACME (applied at 11:05 AM) Good luck\! We’ll keep you updated on any responses.” This aligns with the prompt: *“Full Auto feature ... sends a daily report of submitted applications, and provides a quick summary of the job’s key points.”* So in that summary we might include each job’s key points (like location, salary, etc., so they know what they applied to) to prepare them in case of an interview call. **AI Filtering and Suitability:** We might employ stricter matching for auto-apply, because we only want to apply to jobs the user is likely to accept if offered (to maintain quality and user reputation). For instance, if the user’s profile says $100k min salary and a job has $90k, maybe we skip auto-applying (or ask user if we should be flexible). **User Control:** The user can pause or stop Full Auto anytime (maybe a simple toggle “Active/Paused”). They should also be able to see a list of all jobs auto-applied to, in case they want to review or withdraw any (with instructions on how to withdraw if needed, though withdrawing might mean emailing the employer or something since we can’t undo a submitted app easily). **Technical Implementation:** It’s similar to Bulk Apply but triggered automatically by a scheduler: * We’d likely have a daily cron job (or event-driven when new job enters DB, if we want near real-time) that checks: which users have Full Auto enabled, and for each user, what new jobs have appeared that meet their criteria since last run, then do the apply steps. * We must ensure not to apply the same job twice (store job IDs applied). * Also, perhaps limit to a certain number of auto applications per day to mimic human behavior (if someone applies to 50 jobs per day, that’s plausible, but 500 might raise flags). * The AI could even prioritize which ones to auto-apply first (maybe based on a ranking or those expiring soon). **Advantages:** * For an active job seeker, this is a huge time saver. They can literally wake up to find out applications have been sent while they were sleeping. * It maximizes reach – some say job search is a numbers game; Full Auto ensures you hit those numbers. **Risks:** * The user might not closely read each job’s detail. They could end up in interviews for roles they later realize aren’t a great fit. We should mitigate by accurate profile filtering and maybe giving them a chance to review before enabling. * Possibly, some employers might get a sense the application was auto-generated (if many come from RWE platform similarly formatted). But if our application uses their resume and a decent cover letter, it should be fine – it’s similar to them applying via Indeed or LinkedIn easy apply. * If an employer replies and the user is clueless about the job because they didn’t see it before applying, that could be awkward. However, that’s why we provide the summary and presumably the user trusts the system to apply only to things they would want. In essence, this Full Auto transforms job seeking into a passive activity – the AI agent is effectively working as the user’s personal recruiter or agent, which is a novel and powerful service. This concept has been trialed by apps like Sorce (where the AI applies when you swipe)[sorce.jobs](https://www.sorce.jobs/#:~:text=Our%20app%20currently%20hosts%201,and%20applies%20on%20their%20behalf) and is somewhat akin to having a job search agent as mentioned: *“a job agent looking out for your best interests”*[collegegrad.com](https://collegegrad.com/blog/how-to-use-a-job-alert-in-your-job-search#:~:text=So%20maybe%20you%20don%E2%80%99t%20have,roles%20when%20they%20come%20available). ### Follow-Up AI (Email Automation) Beyond applications, the prompt also mentioned a possible add-on for AI-driven follow-up communications: * If Full Auto is like having an agent apply for you, the follow-up AI is like having an assistant handle communication. This would involve: * Monitoring the user’s email for responses from employers (via IMAP integration or having them use an RWE email alias). * When an interview request or recruiter message comes in, the AI can draft a response. Perhaps it logs a draft for user to approve in the app, or auto-sends if user trusts it. * It could schedule interviews by checking calendars (maybe integrate Google Calendar API if user connects it). * Provide the user with info – e.g., the AI sees an email “We’d like to interview you for X at Company Y”, it could compile a brief about Company Y and the role from the job description, and present that in the app to the user with possible questions to expect. * Possibly if no reply from employer after a week, the AI sends a polite follow-up email on behalf of the user to check in. This is an advanced feature that likely uses an LLM (like GPT) to generate human-like emails (we’d have templates for polite follow-ups, scheduling etc., and maybe fine-tune with user’s writing tone preferences if any). Because this delves into interacting with external emails, it’s sensitive – we would only do it if the user explicitly opts in and possibly connects their email. Or we provide them an RWE email address that forwards to their real one but allows us to intercept and respond (like a masked email). Implementing this thoroughly might be beyond MVP, but we include it in the design as a forward-looking feature given the prompt. ### Summary of Application Automation * **Bulk Apply** – user-initiated, multi-apply at once, semi-automated for a batch. * **Full Auto** – system-initiated (scheduled), continuous applying, fully automated for each job. * **Follow-up AI** – extends automation to communications after applying. These features differentiate a basic job board from an AI-powered job agent platform. They can significantly cut down the time and effort for job seekers, aligning with RWE’s goal to make the process *“less painful and more delightful”*[sorce.jobs](https://www.sorce.jobs/#:~:text=We%20started%20Sorce%20to%20make,the%20colors%20in%20the%20app). Next, we will shift perspective and cover the features for the other side of the platform: the employer/recruiter experience (posting jobs, browsing candidates, contacting them, etc.), and then discuss the user profile and employer profile aspects in more detail where needed. ## User Profile and Public Profile Features Every job seeker on Remote Work Engine gets a **User Profile** that serves as both a detailed resume for the AI matching and, optionally, a shareable public resume for employers to view. We touched on profile creation during onboarding; here we detail how profiles are used and what they look like, as well as the privacy controls. ### Profile Components A user’s profile is comprised of several sections (many populated from onboarding): * **Personal Info:** Name, location (e.g., “Based in Paris, France”), contact info (email, phone – but perhaps not publicly visible by default), and an optional headline or tagline (like “Senior Full-Stack Developer with 8 years of experience in FinTech”). * **Profile Photo:** If the user uploaded one, it will appear here (a nice personal touch, but not required). * **Work Experience:** A list of jobs the user has held, typically with: * Job title, company name, dates (duration), location (if relevant). * A short description or bullet points about achievements in that role. We might display the last 2-3 jobs upfront and hide older ones behind “show more”. * **Education:** Degrees, institutions, graduation years, any academic honors. * **Skills:** A list of key skills – possibly shown as badges or tags. Maybe even a proficiency level if provided (e.g., “JavaScript (Expert)”, “Spanish (Fluent)”). * **Certifications:** Any professional certifications or licenses, with dates. * **Portfolio/Projects:** If the user provided links to projects or uploaded examples, these could be listed, possibly with thumbnail images or just links (e.g., a link to their GitHub or a link to a design portfolio). * **About Me / Summary:** A paragraph or two where the user can introduce themselves in their own words. (They might have written this during onboarding if we asked “tell us about yourself” or motivation). * **Career Preferences (Public-Facing):** Here we must be careful – some of the preferences might be private. But the user might opt to show certain ones: * Desired job titles or roles. * Desired remote work arrangement (full-time, part-time, etc.). * Possibly salary expectation (this can be sensitive; maybe they choose to display it or not). * Availability (e.g., “Available to start Jan 2026” or “Open to new opportunities now”). * **Interests & Hobbies:** If user chooses to share, we can show a small section of personal interests (sometimes recruiters like to see a bit of personality, especially for culture fit in remote teams). * **References/Testimonials:** If the user has references or maybe they got recommendations (like LinkedIn recommendations), this might be included, but likely we skip or just say “References available on request.” * **Video Introduction:** If the user uploaded a video intro, we could embed a playable video on their profile, so employers can watch it directly. All of this combined makes the user’s profile a rich representation of their professional self. ### Public vs Private Data We should provide privacy controls. Some data is clearly meant for internal use (like salary requirement, or maybe the detailed family situation or disabilities, which they might not want to reveal to employers initially). * We will mark fields as “private” by default if sensitive. For instance: * Salary requirement: not shown publicly. * Disability/accommodation needs: not public (the user can choose to discuss with an employer later if needed). * Contact info: maybe email is shown to logged-in employers or maybe we require contact through the platform to begin with for privacy. * References: perhaps hidden until user shares them. * The user can toggle if certain info is shown on their public profile: * e.g., they might hide their last name or photo if they want anonymity while job searching (some might if they fear current employer seeing). * They might hide current employer name if job searching quietly (we could allow them to mark current job as “Current Employer (hidden)” on public profile). We likely require at least some identification for serious inquiries, but giving control is good. ### Profile Viewing and Sharing * **Sharing Public Profile:** Each user could have a public URL, like `remoteworkengine.com/u/username` or an ID, where their profile can be viewed (with the appropriate info visible). * They can share this link in lieu of a resume. * Even non-logged-in people (like an employer they send it to) can see a web page of their profile. * If privacy is a concern, we might allow an option to require a passcode or only accessible by people with the link (unlisted). * **Profile within Platform:** Employers on RWE can browse or search profiles and click to view a candidate’s full profile. That view would show all the allowed info and have a “Contact” or “Invite to interview” button for the employer. * **Saved Jobs Public Feed:** An interesting mention: *“The user can also share their Saved feed to their public profile for potential employers/viewers.”* * This suggests that if a user wants, they can showcase what kinds of jobs they are interested in. Perhaps on their profile page, there’s a section “Jobs I’m Interested In” listing (some of) the jobs they saved or liked. * This could signal to employers the types of roles the candidate is targeting. * However, jobs expire, so maybe it would show a few recent ones or typical examples. * Alternatively, maybe it’s more of a tagline: “Seeking roles like: Senior Designer at product-driven companies.” * Implementing this literally might be tricky, but perhaps if an employer views the profile, and if that user had saved any of that employer’s jobs, we could show “This candidate is interested in \[Your Job Title\]” – which would be a cool highlight to the employer that it’s a mutual interest. * But for simplicity, we might have a toggle where a user can display a list of “Open to these roles: X, Y, Z” which is basically summarizing their saved jobs or preferences. ### Profile Edit and Maintenance Users can edit their profile anytime (maybe via a Profile Edit section with the same fields as onboarding). If they gain new experience or change preferences, updating is encouraged and might trigger the AI to adjust. We should consider verifying or endorsing profiles: * Perhaps a verification badge if they connected their LinkedIn or provided an ID (for trust). * Or allow adding an “Open to work” badge, etc. ### Profile Example Layout (for web UI) Let's outline how a profile page might look in Markdown (for concept): ``` [Profile Photo] Jane Doe (headline: Senior UX Designer) Location: Paris, France | Experience: 8 years | Education: M.Sc in Design Availability: Immediately | Preferred Salary: (hidden or shown if allowed) ABOUT ME Passionate UX Designer with a background in front-end development... [Video Introduction play button] WORK EXPERIENCE - **Senior UX Designer,** XYZ Corp (2019–Present, Remote) - Led the redesign of e-commerce platform, resulting in 25% higher conv. rate. - Managed a team of 4 designers in a remote setting. - **UX Designer,** Acme Inc (2016–2019, On-site) - ... EDUCATION - M.Sc. in Human-Computer Interaction, University of ABC (2014–2016) - B.A. in Graphic Design, Institute of DEF (2010–2014) SKILLS [User Research] [Wireframing] [Figma] [HTML/CSS] [JavaScript] [French: Fluent] CERTIFICATIONS - Nielsen Norman Group UX Certification (2017) - Scrum Alliance CSM (Scrum Master) (2018) PORTFOLIO - Case Study: E-commerce Redesign (link) - Personal portfolio: janedesign.com (link) - GitHub: github.com/janedoe (link) INTERESTS Photography, Travel, Volunteer Teaching LOOKING FOR - Roles: Senior Product Designer, UX Lead, Design Manager - Industries: Open to FinTech, EdTech, and Healthcare. - Remote Setup: Available for EU or US time zones, flexible hours. ``` On the right or top, for an employer viewing: * A “Contact Candidate” button. * Possibly a “Like” or “Shortlist” button for the employer’s use (to add to their list). * If the employer has a relevant job open, maybe “Invite to apply for \[Job\]”. ### Employers Viewing Profiles and Contacting If an employer is logged in and views a candidate profile: * They might see some extra info if candidate allowed (like contact email or a “Request Contact” button). * We might have an internal messaging system: e.g., if employer hits “Contact”, it could open a message dialog where they write a message that gets emailed to the candidate through our system (we relay it). * Or if the user allowed showing email, they might email directly. Probably safer to use an internal messaging first (for privacy and to log interactions). So we can create a message thread between employer and candidate in the platform (like how LinkedIn messaging works). We’ll have to incorporate such messaging in design if we choose that route: * Possibly a “Messages” section for users to see incoming employer inquiries (especially if they aren’t using the follow-up AI or even with it). * For MVP, maybe simpler: we send the candidate an email saying “Employer X is interested and left you a message: \[text\]. Reply via email to contact them or log in to see details.” and we reveal the employer’s contact if needed. Anyway, profile availability and contact is the goal. ### Significance of Profiles The profile is essentially the user’s “identity” on the platform. We are effectively building a mini-LinkedIn but focusing purely on remote job fit. The profile allows: * AI to do its job matching. * Employers to find candidates and know their qualifications. * Users to apply easily (profile info populates applications). * Networking: though not mentioned, possibly users could see each other’s profiles if they choose (like a community aspect), but that’s out of scope for now. We should ensure the profile looks professional and is easy to read: Use consistent formatting (maybe using Tailwind to style sections, e.g., section headers with distinct styling). Using shadcn components: * We might use between sections, * Use proper text styles (maybe or just classes). * Possibly use if we had multiple tabs on profile (like one for “Profile info”, maybe another for “Activity” or “Saved jobs” if we show that publicly). Now, let’s move to the **Employer Side of the Platform**, which includes their feed of candidates, job posting, etc., to complete the picture. ## Employer Features and Feed (Finding Candidates) Remote Work Engine is not just for job seekers; it also provides tools for employers and recruiters to find qualified remote candidates. In many ways, the employer side mirrors the features available to job seekers, but with content flipped (candidates instead of jobs). ### Employer Account and Onboarding Employers (or recruiters/hiring managers) can sign up for an Employer account. This likely involves: * Providing company information (name, website, location of headquarters or remote, size, industry). * Verifying their email (possibly using a company domain email to validate authenticity). * Possibly a review by RWE staff to prevent fake employers (for quality control). * Optionally adding a company logo and description for the profile. * If they plan to post jobs, maybe billing info if job posting is a paid feature (not mentioned, but often job boards charge employers; however, maybe RWE’s revenue comes from job seekers’ premium mostly). Let’s assume posting and basic use is free for now, focusing on features. ### Company Profile (Employer Profile) Each employer has a profile page showcasing: * Company name, logo, banner image maybe. * Description of the company (mission, products). * Details on remote work culture/policy (they might specify “We are 100% remote” or “Hybrid but open to remote for these roles” etc.). * Possibly stats like number of employees, founded year. * List of current open positions (jobs they have posted on RWE). * Maybe testimonials or benefits offered. This is useful for candidates who click on an employer’s name via a job posting to learn more about them (like Glassdoor-ish style, but our scope is limited to what employer provides). ### Job Posting for Employers Employers can post jobs directly on RWE: * A form to create a new job listing with fields: title, location (likely “Remote” plus perhaps eligible regions), job type, salary or rate, description, requirements, how to apply (if through RWE or an external link). * Once posted, these jobs become part of the RWE database that the AI can recommend to candidates. * They can manage postings: edit, close, or mark as filled. If RWE charges for postings or has premium employer accounts, that could be managed here, but not in the prompt, so skip. ### Employer Candidate Feed Just as users have a job feed, employers have a **candidate feed**. This feed shows potential candidates that might fit roles the employer is trying to fill or overall profiles that match their typical criteria. How do we generate it? * If an employer posted specific jobs, the system can look at those jobs’ requirements and find candidates whose profiles match. Then in the feed, group by job or just list “Candidates you may want to contact.” * If an employer hasn’t posted a job, perhaps based on general preferences (like if they indicated what kind of candidates they usually seek, or maybe based on industries they’re in). * It could also show actively job-seeking candidates first (maybe those who signaled open to work). * The AI can personalize this over time if the employer likes or skips candidates. **Candidate Card in Feed:** * Shows candidate’s name (or anonymous ID if they chose to hide? But likely name and maybe current role). * Key skills, experience level, location/timezone. * Perhaps a snippet from their “About Me” or their desired role. * Buttons: Like (shortlist), Dislike, and Contact. *Example concept of an employer’s view of a candidate feed (conceptual illustration). Each candidate card might show a profile photo, name, title (or desired title), location, experience summary, and skills, with actions to save or contact the candidate.* *(Image source: conceptual design for a talent feed interface, showing how candidate profiles could be listed with key info and an option to contact or save the profile.)* **Shortlist and Contact:** * “Like” on a candidate might add them to a “Shortlisted Candidates” list for a particular job or in general. * Perhaps when they click Like, we ask “Shortlist for which job?” if they have multiple openings, or just a general shortlist if not. * “Dislike” will hide that candidate (maybe the recruiter is not interested or already reviewed them). * “Contact” opens a messaging interface or shows the candidate’s email if available. * Possibly “Invite to Apply” if the employer wants that candidate to apply to a specific job. If they have an open job, they can send an invite which could trigger an email to the candidate like “Company X invites you to apply for \[Job\].” We could incorporate swipe for employers too if they use mobile (swipe right to shortlist, left to pass etc.), making it Tinder-for-talent from their side. ### Candidate Search Employers can also search the candidate database: * Filters might include: Skills, job title or keywords (in profiles), years of experience, education level, location/time zone, languages, etc. * They could search by name if they met someone or have a reference. The search results would list candidates with similar card layout or as a list. They can then view full profiles of candidates of interest. We should consider only showing candidates who are open to being contacted: * Perhaps in user settings, they can mark themselves “visible to employers” or not. Some might use RWE just for searching jobs but not want unsolicited contacts. So an opt-out of appearing in employer searches might exist. * It might default to on for those actively looking, and off if they choose (similar to LinkedIn’s “open to work” flag). Alternatively, since RWE is for job seekers actively, we assume they’re open to employer outreach. But having the option is good. ### Communication: Employer to Candidate We need a mechanism for employers to reach out: * **Messaging System:** A built-in messaging where messages appear in both user and employer inboxes on RWE. This keeps everything in platform. * It could be real-time (like chat) or just like email (but within platform). * We could notify via email when a new message arrives (“You have a new message from Employer X on RWE”). * **Email Relay:** Alternatively, we can email the candidate on behalf of the employer (and give the employer no direct contact until the candidate replies). For example, when an employer sends a message, we deliver it to the candidate’s email. If the candidate replies via email, we forward it to the employer’s email, acting as a relay (like Craigslist communication proxies). This avoids forcing them to log in to RWE to communicate, but can get complicated. A simpler first version is to keep it on-site with email notifications. * If the candidate provided a public contact (some might just list their email on profile), the employer could directly reach out off-platform. That’s okay but we lose tracking of that then. Perhaps better to encourage using the RWE messaging for initial contact, both for user privacy and for analytics. ### Employer Daily Candidate Alerts Analogous to job alerts, an employer could set up daily alerts for new candidate sign-ups or updates that match criteria: * e.g., “Alert me when a new Data Scientist with \>5 years experience joins.” * Or “Daily digest of top new candidates in Design.” This is not mentioned in prompt but could be an extrapolated feature, making it truly two-sided. However, maybe not needed initially since candidate base might be smaller. ### Premium Employer Features If monetization involves employers: * Possibly paying for contacting candidates (some platforms require a subscription to message candidates, like LinkedIn InMails). * Or paying to post jobs. * The prompt doesn’t explicitly mention it, but an employer feed being free might be part of the attraction. Perhaps RWE’s revenue is mostly from job seekers’ subscriptions. But in reality, job boards often charge employers. We might not delve into that, focusing on functional features. ### Bulk Actions for Employers If an employer likes many candidates, maybe they can send a bulk invite to apply to all shortlisted ones for a role. But that’s advanced; likely one-by-one messaging is fine for now. ### Employer Experience Summary Let's illustrate an employer’s journey: * **Onboarding:** ACME Corp signs up. They fill in their profile, maybe post a job “Remote Marketing Manager”. * They go to their candidate feed. It shows profiles like “John Doe – Marketing Specialist, 5 years exp, located in USA” etc. The feed algorithm knows they have a Marketing Manager opening, so it shows marketing folks, perhaps with 5-7 years experience, etc. * They swipe through or scroll – when they see one they like, they hit “Invite to Apply” for their job. The candidate gets notified. * They also use search to find “SEO expert” because they might need that skill; finds some candidates, etc. * In one profile, they click “Contact” and write a message “Hi, we have a role that fits you, can we chat?” * Later, they check “My Job Postings” and see how many applied (some might be from our RWE users clicking apply). * They can view applicants as well (if someone applied directly through RWE, we should show the employer that application, with the candidate’s profile attached). Yes, an important feature: **Managing Applications** for posted jobs: * For jobs posted on RWE, we’ll have an applicant tracking interface (simple version): a list of who applied (with profile snapshot and status). * Employers can mark statuses (e.g., “reviewed”, “interviewing”, “rejected”, etc., maybe just for their own tracking). * Possibly message applicants from there or schedule interviews. * We won’t build a full ATS, but basic functionality is good. ### Use of AI for Employers We can also apply AI to help employers: * Recommend which candidates to contact (like the feed does). * Possibly automatically suggest matches when they post a job: “10 candidates from our database match this job; invite them?”. * Or even an AI screening of applicants (like analyzing resume vs job description to sort them). * This is beyond core, but could mention as an idea. Given the scope, we’ll keep it to feed \+ search \+ messaging for employers. Now we have covered both user and employer main flows. Next sections should address any remaining details like the technical architecture, database design, AI algorithms, and non-functional requirements in detail to guide a junior developer through implementation. ## Technical Architecture and Implementation Details Now that we’ve outlined the features and user flows, we will dive into how to actually build Remote Work Engine. This section will cover the suggested technology stack, system components, data models, and how various pieces (like the AI recommendation engine, the PWA, etc.) come together. The aim is to give a junior developer a blueprint of the system’s architecture, along with some example code and guidance on key technical challenges. ### Overall Architecture Overview Remote Work Engine can be designed as a modern web application with a modular, service-oriented architecture: * **Front-End:** A responsive web application (PWA) built with **React** (likely Next.js or a similar framework for server-side rendering and routing) and styled with **Tailwind CSS**, using the **shadcn/ui** component library for UI components. This provides the interactive UI for users and employers. * **Back-End:** A set of RESTful (or GraphQL) **API** endpoints, or a monolithic server application, to handle all the business logic (user authentication, profile management, job search, recommendations, etc.). This could be built with **Node.js (Express or Next.js API routes)** for ease of using the same language on front-end and back-end (JavaScript/TypeScript). Alternatively, a Python back-end (with Django/Flask/FastAPI) could be used especially if leveraging Python’s ML libraries for the recommendation engine. We can also consider microservices (one for core app, one for the recommender, one for scraping jobs, etc.) if scaling demands it. * **Database:** A **PostgreSQL** (relational) database for storing structured data (user profiles, job listings, applications, messages, etc.). Postgres is reliable and familiar. We might also use additional storage: * **Elasticsearch** for powering advanced search queries on job postings and profiles, to handle full-text search efficiently. * **Redis** for caching frequent queries or managing session data. * **Vector database or embeddings store** (like Pinecone or even Postgres with vector extension) if we employ embedding-based similarity for recommendations (e.g., storing vector representations of job descriptions and user profiles to do semantic matching). * **AI/ML Components:** * A recommendation service or module that uses machine learning. Initially, it could be rule-based (based on weights we define from preferences), and gradually evolve into a ML model (like a collaborative filtering model or a content-based ranking model using algorithms such as matrix factorization or a learning-to-rank model). * If using NLP for parsing job descriptions or generating summaries, we might integrate with a library like spaCy for keyword extraction, or even OpenAI’s API (GPT) for advanced tasks (like generating interview prep summaries). * For the email follow-up AI, likely integrate with an LLM API or a fine-tuned model hosted by us. * **Integrations:** * External Job APIs (for fetching jobs): We might integrate with services like the LinkedIn Jobs API (if available for remote jobs) or other remote job aggregators. If not, we could have a scraping service that periodically scrapes popular remote job boards (with permission or as allowed) to feed our database. * Email/SMS services: Use something like **SendGrid** or **Amazon SES** for sending emails (alerts, notifications). Use **Twilio** for SMS if we send texts. * For PWA push notifications: use the Push API and possibly a service like OneSignal for easier cross-browser support. * File storage: if users upload resumes, images, videos, we need storage like **AWS S3** or similar to save those files and serve them. * **Security & Auth:** * User authentication via sessions or JWT. Possibly using NextAuth (if Next.js) for ease of OAuth and sessions. * Password hashing (bcrypt or argon2). * Roles: differentiate Job Seeker vs Employer accounts with role-based access control (simple flags). * Protect sensitive data (like salary prefs, contact info) on back-end so even if an API is called, only authorized (the user themselves or an employer who has permission) gets it. * Use HTTPS everywhere (PWA requirement and for security). * **Scaling Considerations:** * The architecture should allow scaling horizontally: e.g., separate instances for the back-end behind a load balancer, a separate instance for the recommendation engine if needed (or use a cloud service). * Database should handle potentially a large number of job listings (millions) and user interactions (likes/dislikes). * Use background job queues (like BullMQ for Node or RQ for Python) for heavy tasks: e.g., sending bulk emails, running the daily auto-apply tasks, computing recommendations in batch, etc. * **Progressive Web App specifics:** * We will have a **Service Worker** to cache static assets and possibly cache some API calls for offline access. For example, cache the last fetched feed and profile so the user can open the app offline to see something. * The service worker will also handle push notification events (receiving push messages and showing notifications). * We will include a web app manifest (name, icons, theme color, offline page) so users can install the app on their home screen[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=3). Let’s break down some of these components in more detail for implementation: ### Data Model (Database Schema) We will outline key database tables and their fields: * **User:** (id, name, email, password\_hash, role \[seeker or employer\], location, etc., and flags like email\_verified, premium\_tier, etc.) * **UserProfile:** (user\_id FK, headline, summary, experience\_years, availability, desired\_salary, etc. plus flags like show\_email\_to\_employers, profile\_visibility) * Possibly we merge User and UserProfile for simplicity (one table), but logically separated for clarity. * **Experience:** (id, user\_id FK, title, company, start\_date, end\_date, description, location) * **Education:** (id, user\_id FK, degree, institution, year, field\_of\_study, etc.) * **Skill:** (id, name) – a table of skill names to normalize perhaps. * **UserSkill:** (user\_id, skill\_id, level maybe) * **JobListing:** (id, title, company\_id (if posted by an employer on platform), external\_company\_name (if scraped), description, requirements, location, salary\_min, salary\_max, currency, job\_type, experience\_level, posted\_date, apply\_link, etc., and flags like is\_active, source \[internal/external\]). * **Company (Employer):** (id, name, description, website, location, size, industry, logo\_url, profile fields, etc.) * **EmployerUser:** (id, user\_id, company\_id, role\_in\_company) – if we allow multiple users managing one company profile (like recruiters), but maybe initially one user \= one company. * **Application:** (id, job\_id, user\_id, application\_date, status \[applied, employer\_viewed, interviewed, rejected, hired\], source \[manual, bulk, auto\], resume\_url, cover\_letter\_text, etc.) * **SavedJob (Likes):** (user\_id, job\_id, saved\_date, liked (boolean), applied (boolean)). * We might unify “liked” and “saved” in one table with a flag or separate them. Perhaps: * If free user likes something, we save it here with liked=true, but if not premium, we might not show the saved list UI, but we still keep it for recommendation logic. * If premium saves (which is basically like as well but also visible in their saved list), same record. * Dislikes might be recorded similarly or in a separate table, or we can put a liked=false (or rating \= \-1 for dislike, \+1 for like, 0 for neutral). * **UserPreferencesKeywords:** (user\_id, keyword, sentiment \[+1 or \-1\]) – to store the liked/disliked keywords from the cloud. * **ReportSubscriptions:** (id, user\_id, type \[daily\_profile, custom\], search\_filters (maybe JSON of the filter criteria), frequency, last\_sent). * **Messages:** (id, sender\_user\_id, receiver\_user\_id, timestamp, content, job\_id (optional, if context of a specific job), has\_read etc.). This for in-platform messaging. * **Notifications:** (id, user\_id, type, message, link, is\_read, created\_at) – for notifying within app (like “Employer X invited you...”). * **CandidateRecommendations:** (maybe a table caching recommended job ids for each user and their scores, updated daily). * **CandidateFeedSeen:** (user\_id, job\_id, seen\_date, liked/disliked status) – we might log what was shown and actions for analytics. And for employer side: * **CandidateShortlist:** (employer\_user\_id or company\_id, candidate\_user\_id, job\_id (if associated with a job), saved\_date, decision\_made\_flag?). * We could reuse messages and notifications for employer-candidate communications similarly. This schema can become complex, but these are the major entities. ### Recommendation Engine Implementation For a junior dev, start simple: * Use the data we have: For each user, when generating feed: * Filter jobs by must-haves: * If user has location preference (say only US), filter out jobs that are not open to US (if we have that info). * If min\_salary is X, filter out jobs with max\_salary \< X (or unknown salary possibly include but rank lower). * etc. These filters ensure relevance. * Score remaining jobs: * Start with base score 0\. * If job’s title or description contains any of user’s liked keywords, \+ some points. * If contains disliked keywords, big negative points (or exclude it entirely). * If job’s field/industry matches user’s interest, \+. * If job requires skills that the user has, \+ for each match (e.g., job asks for JavaScript and user has it, \+2). * If job is at a company user liked (maybe they liked “Google” keyword), \+. * If job type matches preference (e.g., user wants full-time and it is full-time) \+. * Also incorporate popularity: if many similar users liked this job (we can define “similar” loosely or use a global like count), maybe \+ a small amount to surface trending good jobs (serendipity factor). * Possibly random small variation to not always show the exact same ordering, adding some variety. * Sort jobs by score descending. * That gives a personalized ranking. This approach is a heuristic content-based algorithm. It’s understandable and tunable for a junior dev. Over time, one could replace the scoring with a machine learning model: * For example, a logistic regression or gradient boosting model that predicts “likelihood user will like/apply” based on features of (user, job). But that requires training data of likes/dislikes. * Or collaborative filtering: treat it like a recommender where users have liked certain jobs, recommend jobs liked by similar users. But since jobs turnover, content-based is more practical. We also can incorporate an **embedding approach**: * Use a transformer model (like SBERT or similar) to embed job descriptions and user profiles into vectors, and then recommend based on cosine similarity (content-based on semantics). That might capture subtle fits (like matching skills, etc. in a vector way). This might be advanced but is something an AI-powered system might do behind the scenes. * There are even open source models for job-career matching or one can train a simple neural network. For now, the rule-based approach suffices and can be gradually improved. We also maintain feedback: * Each like/dislike can adjust some weights. For example, if user disliked a job with “Sales” in title, we could add “Sales” to their negative keywords implicitly. * If they like many jobs in a certain salary range or company size, the model might learn to favor those. This gets into machine learning; a junior dev might not design that from scratch, but understanding the concept is good. ### PWA Implementation Details To ensure RWE is a true PWA: * Add a `manifest.json` with app name, icons (we’ll create various sized icons from our logo), theme color, offline page fallback, etc. * Create a `service-worker.js`: * Use Workbox or manual approach to cache static assets (CSS, JS files). * Maybe cache the most recent API responses for feed and profile, so if offline the user can see last known data. * Use the service worker to handle push notifications: * The back-end will use a Web Push library to send notifications via the Push API to subscribed devices (we’ll obtain user’s push subscription via front-end and store it). * E.g., when a new message comes from an employer, push a notification to the user’s device: “New message from X”. The service worker will receive that and show a notification. * Possibly implement background sync: if the user likes some jobs offline, queue those and sync when online. * Test PWA installability: ensure site served over HTTPS, has correct service worker and manifest, passes Lighthouse PWA checks (like can be added to home screen, works offline for at least some content). ### Example Code: Service Worker Registration In our React app entry, we’d register SW: ```javascript if ('serviceWorker' in navigator) { window.addEventListener('load', () => { navigator.serviceWorker.register('/service-worker.js') .then(reg => console.log('ServiceWorker registered', reg)) .catch(err => console.log('ServiceWorker registration failed', err)); }); } ``` And the service-worker.js might use Workbox routes: ```javascript // Precache files precacheAndRoute(self.__WB_MANIFEST || []); // Cache API responses for feed and profile (network first to get fresh data) registerRoute( ({url}) => url.pathname.startsWith('/api/feed') || url.pathname.startsWith('/api/profile'), new NetworkFirst({ cacheName: 'api-data', plugins: [new ExpirationPlugin({ maxEntries: 50, maxAgeSeconds: 60 * 60 })], }) ); // Cache static assets (CSS/JS/images) with a stale-while-revalidate or cache-first registerRoute( ({request}) => request.destination === 'script' || request.destination === 'style' || request.destination === 'image', new StaleWhileRevalidate({ cacheName: 'static-resources' }) ); // Push notification event self.addEventListener('push', event => { const data = event.data.json(); event.waitUntil( self.registration.showNotification(data.title, { body: data.body, icon: '/icons/icon-192.png', data: data.url // perhaps include a URL to open when clicked }) ); }); self.addEventListener('notificationclick', event => { event.notification.close(); if (event.notification.data) { clients.openWindow(event.notification.data); } }); ``` This is a simple outline. A junior dev could use Workbox CLI to generate a lot of this. ### Example Code: Basic Recommendation (Pseudo-code) ```py # Pseudo-code for generating job recommendations for a user def recommend_jobs_for_user(user_id): user = db.get_user(user_id) profile = db.get_profile(user_id) user_prefs = db.get_user_preferences(user_id) # liked/disliked keywords, etc. # Fetch candidate jobs (that are active and not applied by user already) jobs = db.query_jobs(active=True) matches = [] for job in jobs: # Filter by must-haves: if profile.location_restriction and job.location_region not in profile.location_restriction: continue if profile.min_salary and job.max_salary and job.max_salary < profile.min_salary: continue # ... other filters score = 0 # content matches: title = job.title.lower() desc = (job.description or "").lower() # Positive keyword matches for kw in user_prefs.liked_keywords: if kw in title or kw in desc: score += 5 # Negative keywords skip = False for kw in user_prefs.disliked_keywords: if kw in title or kw in desc: skip = True break if skip: continue # Skill matching for skill in profile.skills: if skill.lower() in desc: score += 2 # Industry match if profile.target_industries and job.industry in profile.target_industries: score += 3 # Experience level match if profile.experience_level and job.experience_level: # e.g., if user is senior and job is senior, +, if mis-match, small adjust if profile.experience_level == job.experience_level: score += 2 else: score -= 1 # Company preference if job.company_name and job.company_name in user_prefs.liked_companies: score += 4 if job.company_name and job.company_name in user_prefs.disliked_companies: continue # skip # Freshness days_old = (today - job.posted_date).days if days_old < 1: score += 2 # favor fresh jobs slightly # Possibly incorporate global popularity (not implemented here) matches.append((score, job)) # sort by score descending matches.sort(key=lambda x: x[0], reverse=True) # return top N return [job for score, job in matches[:50]] ``` This is a simplistic algorithm but covers the basis. Over time, one could incorporate more data (like actual user feedback signals by adjusting keyword lists or adding more weights if the user consistently likes certain patterns). ### Handling Bulk Apply and Auto Apply Programmatically **Bulk Apply:** We would implement an endpoint like `POST /api/bulk_apply` for authenticated users. It would: * Retrieve the user’s saved jobs where not already applied. * For each, call a function `apply_to_job(user, job)`: * If job.company\_id exists (an internal posting), create Application record, possibly email the employer contact with the resume or add to their applicant list. * If job.apply\_link is external: * If we have an integration function for that domain, call it (e.g., `integrations.apply_greenhouse(user, job)`). * Else, perhaps launch a headless browser process (if we have infrastructure) to simulate a form submission, or just open a browser tab for user (which can’t be done from server side obviously). * As a fallback, we mark it as “could not auto-apply, manual action needed” and return that info to the client so the UI can inform the user (like we did in progress list). * Mark each job as applied in `Application` table. * Return a summary of successes/failures. We should do this asynchronously if many jobs (to not time out the request): * Possibly the API just enqueues a background job to do all the applications and immediately responds “Bulk apply started, you’ll get an email when done” or keeps WebSocket connection to update progress. For simplicity, maybe do synchronously up to say 10 jobs, beyond that require background. **Full Auto:** * This likely runs on server side on a schedule (cron daily). * Pseudo-code: ```py for user in db.get_users_with_full_auto_enabled(): matches = recommend_jobs_for_user(user.id) new_jobs = [job for job in matches if job.posted_date > user.last_full_auto_run] for job in new_jobs: apply_to_job(user, job) user.last_full_auto_run = now db.save(user) # Send summary email with list of applied jobs. ``` We would incorporate safety to not spam apply too much: * Maybe limit to, say, 5 applications per day via full-auto by default (user could tweak). * We could also allow user to approve some queued auto applications if they want more control, but by the description, full auto means fully automatic. **Follow-up Emails:** * Likely implemented as: * Hook into incoming emails or ask user to BCC a certain address for all job emails (complicated). * Or easier: when an application is submitted via RWE, we know the contact email for that job (if internal posting). We can track if employer responds via our platform messaging or email. * For external, we rely on user’s email. So maybe instruct them to forward any job-related emails to a special RWE address that our system monitors and associates to them. This is advanced, might skip detailed implementation here. Instead, possibly simpler: * Provide the user with templated follow-ups and info via the site, but let them send it themselves. ### Ensuring a Junior Developer-Friendly Approach Given this document is for a junior dev, we avoid over-complicating initial implementation: * Start with core features (profile, posting, search, feed with manual weights). * Use known libraries (don’t write a new ML algorithm, use existing packages or straightforward code). * For any AI complexity like NLP, consider using third-party APIs (like if need to parse resume or job, could use something like a ML API or skip). * Emphasize writing tests for important logic (like recommendation function, application function) to catch issues early. ### Component Libraries (shadcn) in Implementation Using shadcn/ui: * It’s basically a library of pre-built Tailwind components. We’d copy the component code into our project as needed (shadcn provides a CLI to add a component). * For example, for the multi-step form, we might use the `Tabs` or `Accordion` component to split sections. * For the feed, use `Card`, `Button`, `ScrollArea`. * For modals like messaging or filter sheets, use `Dialog` or `Sheet`. * The library ensures accessibility (keyboard navigation, proper ARIA labels etc.) which is great for compliance. * We would customize styling via Tailwind if needed but defaults likely fine. ### Security Considerations We should mention: * Prevent XSS by sanitizing any user-generated content (like job descriptions from external sources, or profile summaries). * Use parameterized queries/ORM to avoid SQL injection. * Hash passwords, and perhaps implement 2FA for accounts for security. * Rate-limit certain actions (like login attempts, or how many messages can be sent in a minute) to prevent abuse. * For privacy: comply with GDPR if global (allow user to delete account & data, etc.). ### Performance and Scalability As usage grows: * We might implement caching for expensive operations. For instance, caching the recommendation results for a user so we’re not recalculating too often. Maybe recalc when profile changes or new jobs come in, otherwise serve cached for feed scroll. * Sharding database or using read replicas could come if a lot of read traffic (job browsing is heavy read). * Our architecture can be deployed on cloud (AWS/GCP/Azure). Possibly use AWS: * EC2 or ECS for server, * RDS for Postgres, * Elasticache for Redis, * S3 for file storage, * etc. * Logging and monitoring: implement logs (for debugging issues) and performance monitoring (like track how long recommendation takes, etc.). ### Code Example: Contact via Platform (simplified) If implementing messaging: ``` // A simple messaging dialog using shadcn Dialog function ContactCandidate({ candidate }) { const [message, setMessage] = useState("") const sendMessage = async () => { await api.post("/messages", { to: candidate.id, body: message }) // handle response, close dialog, etc. } return ( ) } ``` This uses shadcn’s Dialog and Textarea to quickly scaffold a modal for messaging. ### Wireframes and Layouts Recap We have described UI in words. In practice, a developer might sketch the wireframes: * **Home/Feed screen:** Navigation bar at bottom (on mobile) or side (desktop) with icons: Home (feed), Search, Saved (premium), Messages, Profile. * Feed shows cards, etc. * **Search screen:** Search bar on top, filters side or top, results list. * **Profile (user) screen:** sections as described, maybe a vertical layout with headings. * **Profile (employer view)** similar but with contact button. * **Employer dashboard:** could have a top menu: Candidates, Post a Job, My Jobs, etc. * Candidates \= feed/search of candidates. * My Jobs \= list of jobs posted, clicking one shows applicants. * **Messages screen:** a list of conversations, clicking one shows the thread (like a basic chat interface). Given the complexity, focusing on key flows (feed, search, profile, posting, applying) first is wise, then layering messaging, alerts, etc. ### Testing and Quality We should instruct that after building features, test them: * Write unit tests for functions like recommendation scoring. * Do integration tests for critical APIs (register user, create profile, find jobs). * Manually test PWA features (simulate offline, push notifications). * Use Lighthouse or similar to ensure PWA compliance. ### Continuous Improvement Make it clear the platform can evolve: * The AI can get smarter with more data (could implement A/B tests on recommendation changes). * Add more features like analytics for users (like how many views their profile got). * Community features (maybe allow users to refer jobs to friends). * But those are beyond initial scope. This technical section is detailed, but a junior dev reading it should glean: * Which technologies to use and why. * How to structure front-end vs back-end. * How data flows from database to UI (through APIs). * Some specific examples of code for tricky parts (service worker, recsys). * Emphasis on using existing tools (like shadcn, libraries) rather than reinventing everything. We have now a comprehensive view of how to implement RWE. The final step may be to summarize any remaining non-functional requirements (performance, security, etc. which we have done) and conclude. ## Non-Functional Requirements and Final Considerations Beyond the core features and architecture, Remote Work Engine must satisfy various non-functional requirements to ensure it is a robust, user-friendly, and trustworthy platform. We outline these considerations below: ### Performance and Scalability * **Responsive Performance:** The application should load quickly and respond to user interactions without noticeable lag. This means optimizing database queries (using indexes especially on fields like job title, location, etc.), using caching for frequently accessed data (like common job lists or profile info), and leveraging CDN for static assets. Aim for page loads under a few seconds at most, and instant responses on button clicks (use optimistic UI updates when liking jobs, for instance). * **Scalability:** The system should handle an increasing load as the user base grows. This includes: * Designing the database with efficient queries so it can handle thousands of concurrent users searching or swiping. We might have to scale vertically (a more powerful DB server) or horizontally (read replicas, sharding by region perhaps for jobs). * The stateless nature of the front-end and API means we can run multiple server instances behind a load balancer to share the traffic. * The recommendation engine should be able to update recommendations for potentially millions of users. We might use batch processing or incremental updates. For example, update the recommendations for active users daily in batch, rather than recalculating from scratch on every page load. * Real-time features (like messaging) should use technologies (WebSocket or polling) that can scale. Perhaps using a service like Firebase for chat is an option if we want to offload real-time infra, or a dedicated WebSocket server cluster. * **Elasticity:** During peak times (maybe early morning when daily emails go out or evenings when users browse), ensure the system can auto-scale (if on cloud) to handle the spike and scale down during off-peak to save cost. * **Job Data Volume:** As we intend to aggregate remote jobs broadly, the jobs table could become very large (tens of thousands of active jobs at a time). Use efficient text search (Elasticsearch or full-text indices) to handle queries, and archive old/expired jobs out of the main table to keep it lean. ### Security * **Authentication & Authorization:** All API endpoints should verify the user’s identity (via session token or JWT). Ensure that users can only access their own data (e.g., one user cannot fetch another’s saved jobs via API, employers can only see candidates who opted in, etc.). Use role checks to restrict employer-specific endpoints from normal users and vice versa. * **Data Encryption:** All network communication is over HTTPS. For sensitive data at rest (passwords are hashed of course), consider encrypting highly sensitive fields in the database if any (not many need it here, maybe contact info if we worried about internal threat). * **Password Management:** Enforce strong passwords on sign up (min length, mix of characters). Possibly integrate haveibeenpwned API to prevent known leaked passwords. Allow users to reset password via secure token emailed to them. * **Preventing Injection Attacks:** Use parameterized queries or ORM to avoid SQL injection. Also, sanitize inputs for search (if directly putting into queries). * **Cross-Site Scripting (XSS):** Since we display user-generated content (like profile summary, messages, maybe job descriptions fetched from external sources), we must sanitize any HTML or scripts. Use a library to strip or escape HTML tags in user inputs. Similarly, encode content in our React app (which React does by default for content, but if using dangerouslySetInnerHTML or raw HTML, sanitize it). * **CSRF:** If using cookies for auth, implement CSRF tokens on state-changing requests or use SameSite cookies to mitigate cross-site request forgery. * **Rate Limiting and Abuse Prevention:** Put rate limits on endpoints like login (to prevent brute force), messaging (to avoid spam by employers or users), and any expensive operations. Possibly integrate a CAPTCHA for critical actions if abuse is detected (like too many login attempts). * **Audit Logging:** Keep logs of important actions (e.g., bulk apply actions executed, auto apply emails sent) so we can trace what happened if there’s a dispute or problem. * **Privacy Compliance:** Allow users to delete their account and personal data. Be transparent in a privacy policy about what data is collected (which is a lot, including potentially sensitive things like disability status – ensure we handle that data with extra care and only use it for its intended purpose of matching accommodations). * **Email Security:** If we implement email integration (for follow-ups), ensure we use secure protocols (IMAP/SMTP over TLS) and store OAuth tokens or app passwords securely (perhaps encrypted in DB). * **File Uploads:** If users upload resume files or images, virus-scan them (using a service or antivirus library) to avoid storing malicious files. Also serve them in a way that prevents executing any script (serve with correct content-type, maybe from a separate domain or with Content-Security-Policy restricting scripts). ### Usability and Accessibility * **Ease of Use:** The platform should be intuitive even for those not tech-savvy: * Use clear labels and placeholders in forms (e.g., in intake form, label “Preferred Schedule” with help text explaining options). * Provide tooltips or help icons where needed, especially for complex features like explaining what Bulk Apply does before they use it (maybe a confirmation “This will submit your application to all selected jobs. Make sure your profile is up to date.”). * The design we outlined with headings, short sections, etc., is aimed to avoid overwhelming the user. Continue that principle: break up content, use modals or accordions to hide advanced options unless needed. * **Mobile Friendly:** As a PWA, many will use it on mobile. Ensure all pages are tested on small screens. Use responsive design (Tailwind’s utility classes for different screen sizes). Swipe gestures and scroll need to be tested on actual devices. * **Accessibility (a11y):** Follow WCAG guidelines: * Ensure proper semantic HTML: headings, lists, labels for inputs, alt text for images (profile pics could have alt “Profile picture of \[Name\]”). * Keyboard navigation: all interactive elements (buttons, links, form fields) should be reachable and operable via keyboard (shadcn components are built on Radix which ensures a11y in components like Dialog, Select, etc.[ui.shadcn.com](https://ui.shadcn.com/docs#:~:text=Next)). * Color contrast: use a design that meets contrast requirements for text vs background (Tailwind can help but be mindful when customizing colors). * Provide skip links or logical focus management (e.g., when modal opens, focus inside it). * Test with a screen reader for key flows (ensure that the feed updates are announced, etc.). * **Internationalization:** Not explicitly requested, but if this goes global, we might need to support multiple languages and date formats. The data itself (jobs) likely mostly in English if aiming broad remote jobs. However, we can design with i18n in mind (use a library for strings, not hard-code text). * **Offline Support:** Since it’s a PWA, try to give some offline functionality: * Maybe allow writing draft profile changes or saving a job while offline and sync later. * At least show the last loaded content (like feed) offline. * Provide an offline page or message (“You’re offline. Connect to load new jobs.”). * **User Feedback and Help:** Integrate means for users to get help or give feedback. Maybe a help center or even a chatbot for support (not AI for jobs, but help answering platform questions). This is an extra, but a simple FAQ page might be good. ### Monitoring and Maintenance * **Analytics:** Track usage data (in a privacy-respecting way) to see how features are used. E.g., track if users use Bulk Apply, or where drop-off in onboarding happens (maybe many users quit at the big intake form? That’s important to know to simplify it if needed). Use tools like Google Analytics or self-hosted Matomo. * **Error Tracking:** Integrate an error tracking service (Sentry, etc.) to catch front-end and back-end errors in the wild and fix bugs proactively. * **Maintenance:** Set up routine tasks: * Clean up old job listings or mark them expired after some time. * Remove or archive inactive user accounts (or at least their data if they haven’t logged in for X years, for privacy). * Keep the tech stack updated (especially for security patches in dependencies). * **Testing:** Before each release, run regression tests. Particularly test the critical flows: sign up, search, apply, etc., to avoid breaking things as new features added. * **Backup:** Regularly backup the database (especially important because we hold user profile data, applications history – losing it would be very bad). Also backup any file storage. * **Deployment:** Use a CI/CD pipeline to test and deploy. For instance, when code is pushed, run tests, then deploy to staging, run some integration tests, then to production. This ensures we don’t break the live site. ### Future Extensions (for perspective) While not needed at launch, the architecture we built allows adding new features relatively easily: * A mobile native app using the same APIs (though PWA might suffice, some users may still prefer an App Store app). * Integration with calendars for interview scheduling. * AI interview bots to practice with (could be an idea, given we have AI theme). * More advanced filtering, like filtering jobs by company ratings (if we integrate Glassdoor data, etc.). * Community features: forums or chat groups for remote workers (to share tips, but that’s more of a pivot). * Endorsements/Recommendations: colleagues can endorse a skill on a profile, etc., to enrich data. * Verified status: verify companies and candidates (e.g., background checks, or at least LinkedIn verified info) so trust is built. * Monetization ideas: job seekers pay for premium (as described), employers could pay to unlock ability to message more candidates or to have their job posts promoted in feeds (sponsored jobs). * Machine Learning improvements: perhaps train a model on successful placements data to better predict which candidate-job pairs lead to hires, and use that to refine recommendations. ### Conclusion This Project Requirements Document has provided a comprehensive guide to building **Remote Work Engine (.com)**, an AI-powered remote job platform. We covered: * A thorough breakdown of features from both user and employer perspectives. * UI/UX design guidelines with Markdown structured sections and example images for clarity. * Step-by-step flows for complex interactions like profile setup, job feed swiping, searching, and automated applying. * In-depth technical architecture, including usage of modern web development frameworks, databases, and AI components, with example code to illustrate implementations. * Considerations for maintaining performance, security, and usability at a high standard. By following this document, a junior developer (with some support and learning along the way) should be able to implement the key components of the platform. They will also understand not just the “what” but the “why” behind design decisions – for instance, why we combine vertical scroll with swipe (to allow both quick scanning and deliberate decisions), or why PWA is chosen (to reach users on all devices easily with offline and push capabilities[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=PWA%20stands%20for%20%E2%80%9CProgressive%20Web,benefits%20of%20a%20web%20application)[onesignal.com](https://onesignal.com/blog/what-is-a-pwa/#:~:text=Push%20notifications%20are%20arguably%20the,even%20when%20they%27ve%20exited%20a)). Remote Work Engine aims to make the job search and hiring process smarter and more efficient for the remote work era. By leveraging detailed user data and AI, it creates a personalized experience that saves time for job seekers (applying to jobs with one click, getting curated daily leads) and helps employers pinpoint the right talent faster. The implementation will undoubtedly involve iterative improvement and fine-tuning (especially the recommendation logic as we gather user feedback), but the foundation laid out in this document provides a clear path forward. With careful development, testing, and refinement, Remote Work Engine can become a **powerful platform that truly makes “getting hired not feel like a second job”[dribbble.com](https://dribbble.com/shots/26185414-HireHub-Job-Feed-Role-Details-Message#:~:text=Getting%20hired%20shouldn%E2%80%99t%20feel%20like,a%20second%20job) – turning the arduous process of remote job hunting into a seamless, even enjoyable, journey for all parties involved.** [image1]: --- ## AI Services Business Directory **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/ai-services-business-directory **Description:** Developer Product Requirements Document (PRD) Last Updated: October 2, 2025 Target Audience: Junior Developer (Beginner Level) Project Goal: Build an automat... # **AI Services Business Directory** ## **Developer Product Requirements Document (PRD)** **Version:** 1.0 **Last Updated:** October 2, 2025 **Target Audience:** Junior Developer (Beginner Level) **Project Goal:** Build an automated system to collect, validate, and maintain 50,000 AI services businesses across the United States --- ## **Table of Contents** 1. [Project Overview](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#project-overview) 2. [System Architecture](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#system-architecture) 3. [Technical Stack](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#technical-stack) 4. [Database Design](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#database-design) 5. [n8n Workflow Implementation](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#n8n-workflow-implementation) 6. [API Integration Details](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#api-integration-details) 7. [Data Quality & Validation](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#data-quality-validation) 8. [Automatic Update System](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#automatic-update-system) 9. [Implementation Roadmap](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#implementation-roadmap) 10. [Testing Procedures](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#testing-procedures) 11. [Deployment Guide](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#deployment-guide) 12. [Troubleshooting](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#troubleshooting) 13. [Glossary](https://claude.ai/chat/4730940c-e3da-4578-b074-139619ed9679#glossary) --- ## **1\. Project Overview** ### **1.1 What We're Building** We're creating an **automated business directory** containing 50,000 companies that provide AI services (artificial intelligence consulting, machine learning development, chatbot creation, etc.) across the United States. **Key Features:** * Organized by **State → County → City** hierarchy * Top 100 highest-income cities per state (5,000 cities total) * Average 10 businesses per city * Automatically collects business information from APIs * Validates data quality (checks emails, phones, addresses) * Updates information automatically every 90 days ### **1.2 Success Criteria** * **Quantity:** 50,000 verified AI services businesses * **Quality Standards:** * 95%+ valid email addresses * 90%+ valid phone numbers * 95%+ accurate addresses * 80%+ overall data completeness * **Organization:** Properly categorized by location hierarchy * **Maintenance:** Automatic quarterly updates ### **1.3 Timeline** * **Week 1:** Setup and configuration * **Week 2-3:** Pilot test (1,000 businesses) * **Week 4-6:** Full production (49,000 remaining businesses) * **Week 7:** Quality assurance * **Week 8:** Finalization and documentation * **Ongoing:** Automatic updates every 90 days --- ## **2\. System Architecture** ### **2.1 High-Level Overview** Think of our system like a **factory assembly line**: 1. **Raw Materials (Input):** City names and locations 2. **Machines (n8n Workflows):** Automated processes that collect data 3. **Quality Control (Validation):** Check that data is correct 4. **Storage (Database):** Keep all the verified information 5. **Maintenance (Auto-Update):** Refresh old information regularly ### **2.2 Component Diagram** ``` ┌─────────────────────────────────────────────────────────────┐ │ ORCHESTRATOR WORKFLOW │ │ (Main controller that manages everything) │ └───────────────────────┬─────────────────────────────────────┘ │ ┌───────────────┼───────────────┐ │ │ │ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ WORKER #1 │ │ WORKER #2 │ │ WORKER #3 │ │ (Batch 1-100)│ │(Batch 101-200│ │(Batch 201-300│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ └────────────────┼────────────────┘ │ ▼ ┌───────────────────────────────┐ │ DATA VALIDATION │ │ (Check quality of data) │ └───────────────┬───────────────┘ │ ▼ ┌───────────────────────────────┐ │ POSTGRESQL DATABASE │ │ (Store all verified data) │ └───────────────────────────────┘ ``` ### **2.3 Why This Architecture?** **Orchestrator Pattern Benefits:** * **Parallel Processing:** Multiple workers run simultaneously (faster) * **Resilience:** If one worker fails, others continue * **Memory Efficient:** Process small batches instead of all 50,000 at once * **Resumable:** Can restart from failure point, not from beginning --- ## **3\. Technical Stack** ### **3.1 Core Technologies** | Component | Technology | Cost | Why We Choose It | | ----- | ----- | ----- | ----- | | **Automation Platform** | n8n (self-hosted) | $10/month VPS | Visual workflow builder, 400+ integrations, no code required | | **Database** | PostgreSQL | $20-50/month | Handles millions of records easily, free and open-source | | **Primary Data Source** | Apollo.io API | $79/month \+ credits | Best AI/tech company coverage, 275M contacts | | **Geographic Data** | SimpleMaps | $199 one-time | Pre-calculated income rankings, saves weeks of work | | **Email Validation** | ZeroBounce API | \~$200 for 50k | Ensures emails are deliverable | | **Phone Validation** | Twilio Lookup API | \~$250 for 50k | Verifies phone numbers are real | | **Hosting** | DigitalOcean Droplet | $10/month | Simple VPS for n8n | **Total One-Time Cost:** \~$650 **Monthly Cost During Collection:** \~$110/month **Monthly Cost After Collection:** \~$30/month (database \+ hosting) ### **3.2 Free Alternatives (If Budget Constraints)** * **Database:** SQLite (free, but slower) * **Email Validation:** Hunter.io free tier (50 checks/month) * **Geographic Data:** US Census API (free, but requires more processing) * **Hosting:** Oracle Cloud Free Tier (limited resources) ### **3.3 Development Tools** * **Code Editor:** VS Code (free) * **API Testing:** Postman (free tier) * **Database Management:** DBeaver (free) * **Version Control:** Git \+ GitHub (free) --- ## **4\. Database Design** ### **4.1 Understanding Databases (Beginner Explanation)** A **database** is like a giant Excel spreadsheet that computers can read extremely fast. Instead of one giant table, we organize data into multiple related tables. **Key Concepts:** * **Table:** Like one sheet in Excel * **Row:** One record (e.g., one business) * **Column:** One piece of information (e.g., business name) * **Primary Key:** Unique ID for each row (like a social security number) * **Foreign Key:** Links to another table (like a reference) ### **4.2 Database Schema** We'll create 4 main tables: #### **Table 1: `states`** Stores information about all 50 US states. ```sql CREATE TABLE states ( state_code VARCHAR(2) PRIMARY KEY, -- 'CA', 'NY', 'TX', etc. state_name VARCHAR(100) NOT NULL, -- 'California', 'New York' total_cities INTEGER DEFAULT 0, -- How many cities we're tracking total_businesses INTEGER DEFAULT 0, -- How many businesses collected last_updated TIMESTAMP -- When we last updated this state ); ``` #### **Table 2: `cities`** Stores the top 100 cities per state (5,000 cities total). ```sql CREATE TABLE cities ( city_id SERIAL PRIMARY KEY, -- Auto-incrementing unique ID city_name VARCHAR(100) NOT NULL, -- 'San Francisco', 'Austin' state_code VARCHAR(2) NOT NULL, -- Links to states table county_name VARCHAR(100), -- 'San Francisco County' latitude DECIMAL(10, 7), -- 37.7749295 longitude DECIMAL(10, 7), -- -122.4194155 income_median INTEGER, -- $112,449 (median household income) population INTEGER, -- 873,965 (city population) rank_in_state INTEGER, -- 1-100 (income ranking) target_businesses INTEGER DEFAULT 10, -- How many businesses we want collected_businesses INTEGER DEFAULT 0, -- How many we've collected last_scraped TIMESTAMP, -- When we last searched this city FOREIGN KEY (state_code) REFERENCES states(state_code) ); -- Index for faster lookups CREATE INDEX idx_cities_state ON cities(state_code); CREATE INDEX idx_cities_rank ON cities(rank_in_state); ``` #### **Table 3: `businesses`** Stores all business information (our main table with 50,000 records). ```sql CREATE TABLE businesses ( -- PRIMARY IDENTIFIERS business_id UUID PRIMARY KEY DEFAULT gen_random_uuid(), record_hash VARCHAR(64) UNIQUE, -- For deduplication -- BASIC INFORMATION business_name VARCHAR(255) NOT NULL, doing_business_as VARCHAR(255), -- Alternative name (DBA) description TEXT, website_url VARCHAR(500), -- CONTACT INFORMATION email VARCHAR(255), email_verified BOOLEAN DEFAULT FALSE, phone VARCHAR(20), -- Format: +1-555-123-4567 phone_verified BOOLEAN DEFAULT FALSE, -- LOCATION INFORMATION street_address VARCHAR(255), city_id INTEGER NOT NULL, -- Links to cities table state_code VARCHAR(2) NOT NULL, -- Links to states table zip_code VARCHAR(10), latitude DECIMAL(10, 7), longitude DECIMAL(10, 7), location_type VARCHAR(20), -- 'physical', 'remote', 'hybrid' -- AI-SPECIFIC INFORMATION ai_service_types TEXT[], -- Array: ['AI Consulting', 'ML Development'] technologies_used TEXT[], -- Array: ['TensorFlow', 'PyTorch', 'OpenAI'] industry_verticals TEXT[], -- Array: ['Healthcare', 'Finance'] target_clients TEXT[], -- Array: ['Enterprise', 'SMB', 'Startups'] use_cases TEXT[], -- Array: ['Chatbots', 'Predictive Analytics'] -- COMPANY INFORMATION employee_count INTEGER, employee_range VARCHAR(20), -- '11-50', '51-200', etc. founded_year INTEGER, funding_stage VARCHAR(50), -- 'Seed', 'Series A', 'Bootstrap' total_funding_usd DECIMAL(15, 2), -- SOCIAL PRESENCE linkedin_url VARCHAR(500), twitter_url VARCHAR(500), github_url VARCHAR(500), -- DATA QUALITY METRICS completeness_score INTEGER, -- 0-100 (percentage of fields filled) quality_tier VARCHAR(20), -- 'Excellent', 'Good', 'Sufficient' data_source VARCHAR(50), -- 'Apollo', 'Google Places', etc. -- METADATA status VARCHAR(20) DEFAULT 'pending', -- 'pending', 'validated', 'duplicate' created_at TIMESTAMP DEFAULT NOW(), updated_at TIMESTAMP DEFAULT NOW(), last_verified TIMESTAMP, FOREIGN KEY (city_id) REFERENCES cities(city_id), FOREIGN KEY (state_code) REFERENCES states(state_code) ); -- Indexes for performance CREATE INDEX idx_businesses_city ON businesses(city_id); CREATE INDEX idx_businesses_state ON businesses(state_code); CREATE INDEX idx_businesses_hash ON businesses(record_hash); CREATE INDEX idx_businesses_status ON businesses(status); CREATE INDEX idx_businesses_email ON businesses(email); CREATE INDEX idx_businesses_updated ON businesses(updated_at); ``` #### **Table 4: `collection_logs`** Tracks our progress and errors. ```sql CREATE TABLE collection_logs ( log_id SERIAL PRIMARY KEY, city_id INTEGER, execution_type VARCHAR(50), -- 'initial_collection', 'update', 'validation' records_processed INTEGER, records_added INTEGER, records_updated INTEGER, duplicates_found INTEGER, errors_count INTEGER, error_details TEXT, execution_time_seconds INTEGER, created_at TIMESTAMP DEFAULT NOW(), FOREIGN KEY (city_id) REFERENCES cities(city_id) ); CREATE INDEX idx_logs_city ON collection_logs(city_id); CREATE INDEX idx_logs_created ON collection_logs(created_at); ``` ### **4.3 Setting Up the Database** **Step-by-Step Instructions:** 1. **Install PostgreSQL:** ```shell # On Ubuntu/Debian sudo apt update sudo apt install postgresql postgresql-contrib # On macOS (using Homebrew) brew install postgresql brew services start postgresql ``` 2. **Create Database:** ```shell # Log in to PostgreSQL sudo -u postgres psql # Create database CREATE DATABASE ai_directory; # Create user CREATE USER directory_app WITH PASSWORD 'your_secure_password_here'; # Grant permissions GRANT ALL PRIVILEGES ON DATABASE ai_directory TO directory_app; # Exit \q ``` 3. **Run Schema Creation:** ```shell # Save all CREATE TABLE commands above to a file: schema.sql psql -U directory_app -d ai_directory -f schema.sql ``` 4. **Verify Setup:** ```shell psql -U directory_app -d ai_directory # List tables \dt # You should see: states, cities, businesses, collection_logs ``` --- ## **5\. n8n Workflow Implementation** ### **5.1 Understanding n8n (Beginner Explanation)** **n8n** is a visual automation tool where you connect "nodes" (boxes) together to create workflows. **Think of it like Lego blocks:** * Each block (node) does one specific task * You connect blocks in sequence * Data flows from one block to the next * No programming required (mostly) **Example Simple Workflow:** ``` [Trigger] → [Get Data from API] → [Transform Data] → [Save to Database] ``` ### **5.2 Installing n8n** **Option A: Docker (Recommended for Beginners)** ```shell # Install Docker first (if not installed) # Visit: https://docs.docker.com/get-docker/ # Run n8n docker run -d \ --name n8n \ -p 5678:5678 \ -v ~/.n8n:/home/node/.n8n \ n8nio/n8n # Access n8n at: http://localhost:5678 ``` **Option B: npm (If you have Node.js)** ```shell npm install -g n8n n8n start ``` ### **5.3 Workflow Architecture** We'll build **3 main workflows:** 1. **Orchestrator Workflow** (Main controller) 2. **Data Collection Workflow** (Worker) 3. **Validation Workflow** (Quality checker) 4. **Update Workflow** (Automatic refresh) --- ### **5.4 WORKFLOW \#1: Orchestrator** **Purpose:** Controls the entire collection process, manages batches, tracks progress. **Nodes in Order:** ``` 1. Schedule Trigger (runs daily at 2 AM) ↓ 2. PostgreSQL: Get Next Batch of Cities ↓ 3. Split Into Batches (100 cities per batch) ↓ 4. Loop Through Batches ↓ 5. HTTP Request: Call Data Collection Workflow (webhook) ↓ 6. Wait (5 minutes between batches for rate limiting) ↓ 7. PostgreSQL: Update Progress Log ↓ 8. Check if Complete → Loop or End ``` **Detailed Node Configuration:** #### **Node 1: Schedule Trigger** ``` Type: Schedule Trigger Settings: - Trigger Interval: Days - Days Between Triggers: 1 - Trigger at Hour: 2 - Trigger at Minute: 0 - Timezone: America/New_York ``` #### **Node 2: PostgreSQL \- Get Cities** ``` Type: PostgreSQL Operation: Execute Query Query: SELECT city_id, city_name, state_code, latitude, longitude, target_businesses, collected_businesses FROM cities WHERE collected_businesses < target_businesses ORDER BY rank_in_state, state_code LIMIT 100; Connection: Host: localhost (or your database server) Database: ai_directory User: directory_app Password: [your password] Port: 5432 ``` #### **Node 3: Split in Batches** ``` Type: Split In Batches Settings: - Batch Size: 10 (process 10 cities at a time) - Options: Reset (check this box) ``` #### **Node 4: Loop Through Each City** ``` Type: Code (JavaScript) Code: // This node processes each city in the batch const cities = $input.all(); const processedCities = []; for (const city of cities) { processedCities.push({ city_id: city.json.city_id, city_name: city.json.city_name, state_code: city.json.state_code, lat: city.json.latitude, lng: city.json.longitude, needed: city.json.target_businesses - city.json.collected_businesses }); } return processedCities.map(city => ({ json: city })); ``` #### **Node 5: HTTP Request \- Trigger Worker** ``` Type: HTTP Request Method: POST URL: http://localhost:5678/webhook/collect-businesses Headers: Content-Type: application/json Body (JSON): { "city_id": "\{\{ $json.city_id \}\}", "city_name": "\{\{ $json.city_name \}\}", "state": "\{\{ $json.state_code \}\}", "coordinates": { "lat": "\{\{ $json.lat \}\}", "lng": "\{\{ $json.lng \}\}" }, "count_needed": "\{\{ $json.needed \}\}" } ``` #### **Node 6: Wait Between Batches** ``` Type: Wait Settings: - Time: 5 - Unit: Minutes - Reason: Prevents API rate limiting ``` #### **Node 7: Log Progress** ``` Type: PostgreSQL Operation: Insert Table: collection_logs Columns: - city_id: \{\{ $json.city_id \}\} - execution_type: 'batch_processing' - records_processed: \{\{ $json.records_added \}\} - created_at: NOW() ``` --- ### **5.5 WORKFLOW \#2: Data Collection Worker** **Purpose:** Collects business data for one city from Apollo.io API. **Nodes in Order:** ``` 1. Webhook Trigger (receives city info from orchestrator) ↓ 2. Apollo.io API: Search for AI Businesses ↓ 3. Loop Through Results ↓ 4. Transform Data (map API fields to our database schema) ↓ 5. Generate Record Hash (for deduplication) ↓ 6. PostgreSQL: Check if Duplicate ↓ 7. IF Block: Is Duplicate? ├─ YES → Skip └─ NO → Continue to validation ↓ 8. Email Validation (ZeroBounce API) ↓ 9. Phone Validation (Twilio API) ↓ 10. Calculate Completeness Score ↓ 11. PostgreSQL: Insert Business Record ↓ 12. Return Success Response ``` **Detailed Node Configuration:** #### **Node 1: Webhook Trigger** ``` Type: Webhook Settings: - HTTP Method: POST - Path: collect-businesses - Response Code: 200 - Response Mode: Wait for Webhook Response ``` #### **Node 2: Apollo.io API Search** ``` Type: HTTP Request Method: POST URL: https://api.apollo.io/v1/mixed_people/search Headers: Content-Type: application/json X-Api-Key: [Your Apollo.io API Key] Body (JSON): { "q_organization_keyword_tags": ["artificial intelligence", "machine learning", "AI services", "deep learning"], "organization_locations": ["\{\{ $json.city_name \}\}, \{\{ $json.state \}\}"], "page": 1, "per_page": 25, "organization_num_employees_ranges": ["1,10", "11,50", "51,200", "201,500", "501,1000", "1001,10000"], "person_titles": ["CEO", "Founder", "CTO", "VP"] } Settings: - Response Format: JSON - Pagination: - Pagination Mode: Update a Parameter - Parameter Name: page - Max Requests: 10 ``` #### **Node 3: Loop Through Results** ``` Type: Item Lists Operation: Split Out Items Settings: - Field Name: organizations (or whatever Apollo returns) ``` #### **Node 4: Transform Data** ``` Type: Code (JavaScript) Code: // Map Apollo.io response to our database schema const org = $input.item.json; // Extract AI service types from keywords function extractServiceTypes(keywords) { const services = []; if (keywords.includes('consulting')) services.push('AI Consulting'); if (keywords.includes('machine learning')) services.push('ML Development'); if (keywords.includes('chatbot')) services.push('Conversational AI'); // Add more logic as needed return services; } // Extract technologies from tech stack function extractTechnologies(techStack) { const tech = []; if (techStack.includes('tensorflow')) tech.push('TensorFlow'); if (techStack.includes('pytorch')) tech.push('PyTorch'); if (techStack.includes('aws')) tech.push('AWS AI/ML'); // Add more logic as needed return tech; } return { json: { business_name: org.name, website_url: org.website_url, email: org.email || org.primary_email, phone: org.phone, street_address: org.street_address, city_id: $node["Webhook"].json.city_id, state_code: $node["Webhook"].json.state, zip_code: org.postal_code, latitude: org.latitude, longitude: org.longitude, description: org.short_description, ai_service_types: extractServiceTypes(org.keywords || []), technologies_used: extractTechnologies(org.technologies || []), employee_count: org.employee_count, employee_range: org.employee_range, founded_year: org.founded_year, funding_stage: org.funding_stage, linkedin_url: org.linkedin_url, twitter_url: org.twitter_url, data_source: 'Apollo.io' } }; ``` #### **Node 5: Generate Record Hash** ``` Type: Code (JavaScript) Code: const crypto = require('crypto'); // Create unique hash from key fields const hashString = [ $json.business_name.toLowerCase().trim(), $json.website_url, $json.email, $json.phone ].filter(x => x).join('|'); const hash = crypto.createHash('sha256').update(hashString).digest('hex'); return { json: { ...$json, record_hash: hash } }; ``` #### **Node 6: Check for Duplicates** ``` Type: PostgreSQL Operation: Execute Query Query: SELECT business_id FROM businesses WHERE record_hash = '\{\{ $json.record_hash \}\}' LIMIT 1; ``` #### **Node 7: IF Duplicate Exists** ``` Type: IF Conditions: - \{\{ $json.business_id \}\} is not empty If TRUE: Connect to Skip node If FALSE: Connect to Email Validation ``` #### **Node 8: Email Validation** ``` Type: HTTP Request Method: GET URL: https://api.zerobounce.net/v2/validate Query Parameters: - api_key: [Your ZeroBounce API Key] - email: \{\{ $json.email \}\} Settings: - Continue On Fail: true Response Mapping: - Save status to email_verified field ``` #### **Node 9: Phone Validation** ``` Type: HTTP Request Method: GET URL: https://lookups.twilio.com/v1/PhoneNumbers/\{\{ $json.phone \}\} Authentication: - Type: Basic Auth - User: [Your Twilio Account SID] - Password: [Your Twilio Auth Token] Settings: - Continue On Fail: true Response Mapping: - Save valid status to phone_verified field ``` #### **Node 10: Calculate Completeness Score** ``` Type: Code (JavaScript) Code: // Calculate what percentage of fields are filled const data = $json; const requiredFields = ['business_name', 'website_url', 'email', 'phone', 'street_address']; const recommendedFields = ['description', 'ai_service_types', 'employee_range']; const optionalFields = ['linkedin_url', 'founded_year', 'funding_stage']; let score = 0; // Required: 50 points requiredFields.forEach(field => { if (data[field]) score += 10; }); // Recommended: 30 points recommendedFields.forEach(field => { if (data[field] && data[field].length > 0) score += 10; }); // Optional: 20 points optionalFields.forEach(field => { if (data[field]) score += 6.67; }); // Determine quality tier let tier = 'Insufficient'; if (score >= 90) tier = 'Excellent'; else if (score >= 75) tier = 'Good'; else if (score >= 50) tier = 'Sufficient'; return { json: { ...data, completeness_score: Math.round(score), quality_tier: tier } }; ``` #### **Node 11: Insert to Database** ``` Type: PostgreSQL Operation: Insert Table: businesses Columns: (map all fields from $json to corresponding database columns) Settings: - Continue On Fail: true - Return Fields: business_id, created_at ``` #### **Node 12: Respond to Orchestrator** ``` Type: Respond to Webhook Settings: - Response Code: 200 - Response Body: { "success": true, "business_id": "\{\{ $json.business_id \}\}", "business_name": "\{\{ $json.business_name \}\}" } ``` --- ### **5.6 WORKFLOW \#3: Validation & Quality Check** **Purpose:** Performs deeper validation on collected data. **Trigger:** Runs once per day after collection. **Nodes:** ``` 1. Schedule Trigger (daily at 6 AM) ↓ 2. Get Unvalidated Records (status = 'pending') ↓ 3. Split Into Batches (500 records per batch) ↓ 4. Validate Email Deliverability (batch API call) ↓ 5. Validate Phone Numbers (batch API call) ↓ 6. Geocode Addresses (confirm coordinates) ↓ 7. Update Validation Status ↓ 8. Generate Quality Report ↓ 9. Send Email Notification (if quality below threshold) ``` --- ### **5.7 WORKFLOW \#4: Automatic Update System** **Purpose:** Refreshes business data every 90 days to keep directory current. **Nodes:** ``` 1. Schedule Trigger (runs weekly) ↓ 2. Get Businesses Needing Update (WHERE last_verified < NOW() - INTERVAL '90 days') ↓ 3. Split Into Daily Batches (700 businesses per day) ↓ 4. For Each Business: ├─ Re-query Apollo.io for updated info ├─ Compare with existing data ├─ IF significant changes: Update record ├─ IF business closed: Mark as inactive └─ Update last_verified timestamp ↓ 5. Log Update Results ↓ 6. Generate Weekly Update Report ``` **Detailed Update Logic:** ```javascript // Node: Check for Changes const existing = $node["Get Existing Business"].json; const fresh = $node["Apollo API Update"].json; function hasSignificantChanges(old, new) { // Check critical fields const criticalChanges = [ old.email !== new.email, old.phone !== new.phone, old.website_url !== new.website_url, old.street_address !== new.street_address ]; return criticalChanges.some(change => change === true); } function businessStillActive(apiResponse) { // Check if business is still operational return apiResponse.status !== 'closed' && apiResponse.status !== 'inactive'; } const needsUpdate = hasSignificantChanges(existing, fresh); const stillActive = businessStillActive(fresh); return { json: { business_id: existing.business_id, needs_update: needsUpdate, is_active: stillActive, changes_detected: needsUpdate ? ['email', 'phone'] : [], updated_data: fresh } }; ``` --- ## **6\. API Integration Details** ### **6.1 Apollo.io Setup** **Step 1: Create Account** 1. Visit https://www.apollo.io/ 2. Sign up for Professional plan ($79/month) 3. Navigate to Settings → API 4. Generate API key (keep this secret\!) **Step 2: Test API Connection** Using Postman or curl: ```shell curl -X POST https://api.apollo.io/v1/mixed_people/search \ -H "Content-Type: application/json" \ -H "X-Api-Key: YOUR_API_KEY" \ -d '{ "q_organization_keyword_tags": ["artificial intelligence"], "organization_locations": ["San Francisco, CA"], "page": 1, "per_page": 10 }' ``` **Step 3: Understanding the Response** Apollo returns JSON like this: ```json { "organizations": [ { "id": "12345", "name": "AI Innovations Inc", "website_url": "https://aiinnovations.com", "primary_phone": { "number": "+1-415-555-0123" }, "primary_email": "info@aiinnovations.com", "street_address": "123 Market St", "city": "San Francisco", "state": "California", "postal_code": "94103", "employee_count": 50, "founded_year": 2018, "keywords": ["machine learning", "consulting"] } ], "pagination": { "page": 1, "per_page": 10, "total_entries": 250 } } ``` **Step 4: Rate Limits** * **Free Tier:** 50 searches/month * **Professional:** Unlimited searches, 12,000 email credits/year * **Best Practice:** Add 500ms delay between requests ### **6.2 SimpleMaps Setup** **Step 1: Purchase Database** 1. Visit https://simplemaps.com/data/us-cities 2. Purchase "Comprehensive" version ($199) 3. Download CSV file **Step 2: Import to Database** ```shell # Using PostgreSQL COPY command psql -U directory_app -d ai_directory \copy cities(city_name, state_code, county_name, latitude, longitude, income_median, population) FROM '/path/to/simplemaps.csv' DELIMITER ',' CSV HEADER; ``` **Step 3: Calculate Rankings** ```sql -- Add rank_in_state column UPDATE cities c1 SET rank_in_state = ( SELECT COUNT(*) + 1 FROM cities c2 WHERE c2.state_code = c1.state_code AND c2.income_median > c1.income_median ); -- Keep only top 100 per state DELETE FROM cities WHERE city_id NOT IN ( SELECT city_id FROM ( SELECT city_id, ROW_NUMBER() OVER ( PARTITION BY state_code ORDER BY income_median DESC ) as rn FROM cities ) ranked WHERE rn <= 100 ); ``` ### **6.3 Validation APIs** #### **ZeroBounce (Email Validation)** **Setup:** 1. Create account at https://www.zerobounce.net/ 2. Purchase credits ($16 per 1,000 validations) 3. Get API key from dashboard **Usage in n8n:** ``` Node: HTTP Request URL: https://api.zerobounce.net/v2/validate Method: GET Query Parameters: - api_key: YOUR_KEY - email: \{\{ $json.email \}\} Response Codes: - valid: Email is deliverable - invalid: Email doesn't exist - catch-all: Domain accepts all emails - unknown: Cannot determine ``` #### **Twilio Lookup (Phone Validation)** **Setup:** 1. Create account at https://www.twilio.com/ 2. Purchase credits ($0.005 per lookup) 3. Get Account SID and Auth Token **Usage in n8n:** ``` Node: HTTP Request URL: https://lookups.twilio.com/v1/PhoneNumbers/\{\{ $json.phone \}\} Method: GET Authentication: Basic Auth - Username: Account SID - Password: Auth Token Response: { "phone_number": "+14155551234", "valid": true, "country_code": "US", "carrier": { "name": "Verizon", "type": "mobile" } } ``` --- ## **7\. Data Quality & Validation** ### **7.1 Multi-Level Deduplication Strategy** **Level 1: Exact Match (100% confidence)** ```sql -- Check for exact duplicates before inserting SELECT COUNT(*) FROM businesses WHERE business_name = 'AI Solutions Inc' AND website_url = 'https://aisolutions.com' AND state_code = 'CA'; ``` **Level 2: Hash-Based Match (99% confidence)** ```javascript // Generate hash from multiple fields const crypto = require('crypto'); function generateHash(business) { const normalized = { name: business.name.toLowerCase().replace(/[^a-z0-9]/g, ''), website: business.website.replace(/https?:\/\/(www\.)?/, ''), phone: business.phone.replace(/[^0-9]/g, '') }; const hashString = Object.values(normalized).join('|'); return crypto.createHash('sha256').update(hashString).digest('hex'); } ``` **Level 3: Fuzzy Match (85-95% confidence)** ```javascript // Levenshtein distance for similar names function levenshteinDistance(a, b) { const matrix = []; for (let i = 0; i <= b.length; i++) { matrix[i] = [i]; } for (let j = 0; j <= a.length; j++) { matrix[0][j] = j; } for (let i = 1; i <= b.length; i++) { for (let j = 1; j <= a.length; j++) { if (b.charAt(i - 1) === a.charAt(j - 1)) { matrix[i][j] = matrix[i - 1][j - 1]; } else { matrix[i][j] = Math.min( matrix[i - 1][j - 1] + 1, matrix[i][j - 1] + 1, matrix[i - 1][j] + 1 ); } } } return matrix[b.length][a.length]; } function calculateSimilarity(name1, name2) { const distance = levenshteinDistance(name1, name2); const maxLength = Math.max(name1.length, name2.length); return 1 - (distance / maxLength); } // Usage const similarity = calculateSimilarity("AI Solutions Inc", "A.I. Solutions Incorporated"); if (similarity > 0.85) { console.log("Likely duplicate!"); } ``` ### **7.2 Data Completeness Scoring** **Formula:** ```javascript function calculateCompletenessScore(business) { let score = 0; let maxScore = 100; // Required Fields (50 points) const required = { business_name: 10, website_url: 10, email: 10, phone: 10, street_address: 10 }; for (const [field, points] of Object.entries(required)) { if (business[field] && business[field].trim() !== '') { score += points; } } // Recommended Fields (30 points) const recommended = { description: 10, ai_service_types: 10, employee_range: 10 }; for (const [field, points] of Object.entries(recommended)) { if (business[field] && business[field].length > 0) { score += points; } } // Optional Enrichment (20 points) const optional = { linkedin_url: 5, founded_year: 5, funding_stage: 5, technologies_used: 5 }; for (const [field, points] of Object.entries(optional)) { if (business[field]) { score += points; } } return { score: score, tier: score >= 90 ? 'Excellent' : score >= 75 ? 'Good' : score >= 50 ? 'Sufficient' : 'Incomplete' }; } ``` ### **7.3 Automated Quality Reports** **SQL Query for Daily Quality Report:** ```sql -- Generate quality metrics SELECT state_code, COUNT(*) as total_businesses, AVG(completeness_score) as avg_completeness, SUM(CASE WHEN email_verified THEN 1 ELSE 0 END) as verified_emails, SUM(CASE WHEN phone_verified THEN 1 ELSE 0 END) as verified_phones, SUM(CASE WHEN quality_tier = 'Excellent' THEN 1 ELSE 0 END) as excellent_records, SUM(CASE WHEN quality_tier = 'Good' THEN 1 ELSE 0 END) as good_records, SUM(CASE WHEN quality_tier = 'Sufficient' THEN 1 ELSE 0 END) as sufficient_records, SUM(CASE WHEN quality_tier = 'Incomplete' THEN 1 ELSE 0 END) as incomplete_records FROM businesses WHERE created_at >= CURRENT_DATE - INTERVAL '1 day' GROUP BY state_code ORDER BY state_code; ``` **n8n Node for Sending Report:** ``` Type: Send Email (Gmail) To: your-email@example.com Subject: Daily AI Directory Quality Report - \{\{ $now.format('YYYY-MM-DD') \}\} Body: Quality Metrics for \{\{ $now.format('YYYY-MM-DD') \}\} Total Records Collected: \{\{ $json.total \}\} Average Completeness: \{\{ $json.avg_completeness \}\}% Email Verification Rate: \{\{ ($json.verified_emails / $json.total * 100).toFixed(2) \}\}% Phone Verification Rate: \{\{ ($json.verified_phones / $json.total * 100).toFixed(2) \}\}% Quality Distribution: - Excellent: \{\{ $json.excellent_records \}\} - Good: \{\{ $json.good_records \}\} - Sufficient: \{\{ $json.sufficient_records \}\} - Incomplete: \{\{ $json.incomplete_records \}\} ``` --- ## **8\. Automatic Update System** ### **8.1 Update Strategy** **Principle:** Keep data fresh without excessive API costs. **Update Schedule:** * **Critical fields (email, phone, website):** Every 90 days * **Nice-to-have fields (funding, employees):** Every 180 days * **Static fields (founded\_year):** Never update ### **8.2 Update Workflow Logic** ```javascript // Node: Determine Update Priority function getUpdatePriority(business) { const daysSinceUpdate = Math.floor( (Date.now() - new Date(business.last_verified)) / (1000 * 60 * 60 * 24) ); // Priority levels if (daysSinceUpdate > 180) return 'urgent'; // 6+ months old if (daysSinceUpdate > 90) return 'high'; // 3-6 months old if (daysSinceUpdate > 30) return 'medium'; // 1-3 months old return 'low'; // <1 month old } function shouldUpdate(business) { const priority = getUpdatePriority(business); const quality = business.quality_tier; // Always update urgent records if (priority === 'urgent') return true; // Update high priority if quality isn't excellent if (priority === 'high' && quality !== 'Excellent') return true; // Update medium priority if quality is insufficient if (priority === 'medium' && quality === 'Insufficient') return true; return false; } // Usage in workflow const needsUpdate = shouldUpdate($json); if (needsUpdate) { // Proceed to API call } else { // Skip this business } ``` ### **8.3 Detecting Closed Businesses** ```javascript // Node: Check Business Status async function verifyBusinessActive(business) { const checks = { website_accessible: false, email_deliverable: false, phone_working: false }; // Check 1: Website returns 200 try { const response = await fetch(business.website_url); checks.website_accessible = response.status === 200; } catch (e) { checks.website_accessible = false; } // Check 2: Email domain has MX records // (This would be done via ZeroBounce API in n8n) // Check 3: Phone number still assigned // (This would be done via Twilio API in n8n) // Business is likely closed if all checks fail const failedChecks = Object.values(checks).filter(x => !x).length; return { status: failedChecks >= 2 ? 'likely_closed' : 'active', checks: checks, confidence: (3 - failedChecks) / 3 }; } ``` ### **8.4 Incremental Update vs Full Refresh** **Incremental (Recommended):** * Update \~700 businesses per day * Spreads API costs over time * Completes full refresh in \~72 days * Less disruptive to live directory **Full Refresh (Emergency only):** * Update all 50,000 in 1-2 weeks * High API costs * Risk of hitting rate limits * Use only when data quality degrades severely **n8n Implementation:** ```sql -- Get businesses for today's update batch SELECT * FROM businesses WHERE last_verified < NOW() - INTERVAL '90 days' ORDER BY last_verified ASC LIMIT 700; ``` --- ## **9\. Implementation Roadmap** ### **Week 1: Setup & Configuration** **Day 1-2: Infrastructure Setup** * \[ \] Provision DigitalOcean Droplet ($10/month) * \[ \] Install Docker and n8n * \[ \] Install PostgreSQL * \[ \] Set up database backups (automated daily) * \[ \] Configure firewall rules **Day 3-4: Database Setup** * \[ \] Create database schema (run all CREATE TABLE commands) * \[ \] Import SimpleMaps data * \[ \] Calculate city rankings * \[ \] Verify data integrity (check row counts) * \[ \] Create database indexes **Day 5: API Account Setup** * \[ \] Create Apollo.io account, verify API access * \[ \] Create ZeroBounce account, purchase credits * \[ \] Create Twilio account, purchase credits * \[ \] Test each API with Postman * \[ \] Document API keys in secure location (password manager) **Day 6-7: Build Test Workflow** * \[ \] Create simple n8n workflow: Get 1 city → Call Apollo → Save to DB * \[ \] Test with San Francisco (should return \~10-20 businesses) * \[ \] Verify data saves correctly to PostgreSQL * \[ \] Check for errors in execution logs **Checkpoint:** By end of Week 1, you should be able to collect 10 businesses manually. --- ### **Weeks 2-3: Pilot Testing** **Week 2: Build Core Workflows** * \[ \] Build Orchestrator Workflow (Days 1-2) * \[ \] Build Data Collection Worker (Days 3-4) * \[ \] Build Validation Workflow (Day 5\) * \[ \] Connect workflows via webhooks (Day 6-7) **Week 3: Run Pilot (1,000 Businesses)** * \[ \] Select 100 cities for pilot (top 2 per state) * \[ \] Run collection workflow (Days 1-3) * \[ \] Expected: \~10 businesses × 100 cities \= 1,000 records * \[ \] Monitor execution logs for errors * \[ \] Run validation workflow (Day 4\) * \[ \] Analyze results (Day 5): * Email deliverability rate * Phone validity rate * Average completeness score * Data quality tier distribution * \[ \] Calculate actual costs (Day 6\) * \[ \] Optimize workflow based on findings (Day 7\) **Success Criteria:** * ✓ At least 800 valid businesses collected * ✓ \>90% email deliverability * ✓ \>85% phone validity * ✓ Average completeness \>75% * ✓ Actual cost \<$100 for 1,000 businesses **If success criteria not met:** Pause and troubleshoot before continuing. --- ### **Weeks 4-6: Full Production** **Strategy:** Collect 10,000 businesses per week **Daily Routine:** 1. Morning (9 AM): Check overnight execution logs 2. Review quality metrics from previous day 3. Address any errors or failures 4. Monitor API credit usage 5. Run validation on yesterday's new records **Week 4: Cities Ranked 1-33 per State** * Expected: \~16,500 businesses * Apollo searches: \~1,650 * Monitor rate limiting **Week 5: Cities Ranked 34-66 per State** * Expected: \~16,500 businesses * Check running costs vs budget * Adjust batch sizes if needed **Week 6: Cities Ranked 67-100 per State** * Expected: \~16,500 businesses * Total should reach \~49,500 * Prepare for final validation **Monitoring Checklist:** ``` Daily: □ Check n8n execution logs for errors □ Verify database row count increasing □ Monitor API credit balance □ Review quality metrics Weekly: □ Generate quality report by state □ Identify data gaps (cities with <10 businesses) □ Calculate cost per business □ Backup database ``` --- ### **Week 7: Quality Assurance** **Day 1: Automated Validation** * \[ \] Run comprehensive validation workflow on all 50,000 records * \[ \] Re-verify emails (sample 1,000) * \[ \] Re-verify phones (sample 1,000) * \[ \] Update quality scores **Day 2-3: Manual Spot Checking** * \[ \] Randomly select 200 businesses across states * \[ \] Visit websites to verify businesses exist * \[ \] Check if AI services are actually offered * \[ \] Document findings **Day 4: Gap Analysis** * \[ \] Identify cities with \<10 businesses * \[ \] Run supplemental searches for low-count cities * \[ \] Consider alternative data sources for gaps **Day 5: Data Enrichment** * \[ \] Fill missing LinkedIn URLs (sample) * \[ \] Add missing descriptions * \[ \] Improve categorization of AI services **Day 6: Generate Reports** ```sql -- Final Quality Report SELECT 'Total Businesses' as metric, COUNT(*)::text as value FROM businesses UNION ALL SELECT 'Average Completeness', ROUND(AVG(completeness_score), 2)::text FROM businesses UNION ALL SELECT 'Verified Emails', ROUND(AVG(CASE WHEN email_verified THEN 100 ELSE 0 END), 2)::text || '%' FROM businesses UNION ALL SELECT 'Verified Phones', ROUND(AVG(CASE WHEN phone_verified THEN 100 ELSE 0 END), 2)::text || '%' FROM businesses; ``` **Day 7: Review & Approve** * \[ \] Present quality report * \[ \] Document known limitations * \[ \] Get approval to proceed --- ### **Week 8: Finalization** **Day 1: Final Deduplication** * \[ \] Run aggressive deduplication * \[ \] Review flagged duplicates manually * \[ \] Merge or remove duplicates **Day 2: Data Export** * \[ \] Export to CSV for backup * \[ \] Export to JSON for API * \[ \] Create state-specific exports **Day 3-4: Set Up Auto-Update** * \[ \] Build automatic update workflow * \[ \] Schedule weekly execution * \[ \] Test update on 100 businesses **Day 5: Documentation** * \[ \] Document all workflows * \[ \] Create operations manual * \[ \] Write troubleshooting guide **Day 6-7: Deployment** * \[ \] Move to production environment (if applicable) * \[ \] Set up monitoring alerts * \[ \] Configure backup schedule * \[ \] Launch\! 🚀 --- ## **10\. Testing Procedures** ### **10.1 Unit Tests (Test Individual Nodes)** **Test 1: Apollo API Connection** ``` Expected Input: City name "San Francisco, CA" Expected Output: JSON array with 10-25 organizations Success Criteria: - HTTP status 200 - Response contains "organizations" key - At least 1 organization returned ``` **Test 2: Email Validation** ``` Test Cases: 1. Valid email: "contact@example.com" → expect "valid" 2. Invalid email: "invalid@notarealdomain.fake" → expect "invalid" 3. Catch-all: "anything@gmail.com" → expect "catch-all" Success Criteria: API returns status within 2 seconds ``` **Test 3: Duplicate Detection** ``` Test Case: Insert same business twice Expected: Second insert should be skipped Verify: Check collection_logs shows "duplicates_found: 1" ``` ### **10.2 Integration Tests (Test Full Workflows)** **Test 1: End-to-End Collection** ```shell # Test collecting 1 city Input: city_id = 1 (e.g., New York, NY) Expected Output: - 10-15 new businesses in database - All have valid record_hash - No duplicates - Completeness score >50% Verification: SELECT COUNT(*), AVG(completeness_score) FROM businesses WHERE city_id = 1 AND created_at > NOW() - INTERVAL '1 hour'; ``` **Test 2: Orchestrator → Worker Communication** ``` Steps: 1. Manually trigger orchestrator 2. Verify webhook calls are made 3. Check worker receives data correctly 4. Confirm responses return to orchestrator Success: All 3 workers process batches without errors ``` **Test 3: Update Workflow** ``` Steps: 1. Mark 10 businesses as needing update (set last_verified to 100 days ago) 2. Run update workflow 3. Verify: - API calls made for those 10 - last_verified timestamp updated - Changes logged in collection_logs Success: All 10 businesses updated, no errors ``` ### **10.3 Load Testing** **Test 1: Batch Processing** ``` Test: Process 1,000 cities in orchestrator Expected: - All batches complete within 24 hours - No memory errors - Database remains responsive Monitor: - n8n execution queue size - PostgreSQL connection count - Server CPU/RAM usage ``` **Test 2: Database Performance** ```sql -- Test query performance on 50,000 records EXPLAIN ANALYZE SELECT * FROM businesses WHERE city_id = 1 AND completeness_score > 80 LIMIT 100; -- Expected: Query time <100ms ``` ### **10.4 Data Quality Tests** **Test 1: Completeness Distribution** ```sql -- All records should have minimum required fields SELECT COUNT(*) as incomplete_records FROM businesses WHERE business_name IS NULL OR email IS NULL OR phone IS NULL; -- Expected: 0 incomplete records ``` **Test 2: Validation Rates** ```sql -- Check validation percentages SELECT ROUND(AVG(CASE WHEN email_verified THEN 100 ELSE 0 END), 2) as email_rate, ROUND(AVG(CASE WHEN phone_verified THEN 100 ELSE 0 END), 2) as phone_rate FROM businesses; -- Expected: email_rate >90%, phone_rate >85% ``` --- ## **11\. Deployment Guide** ### **11.1 Production Environment Setup** **Server Requirements:** * **CPU:** 2 cores minimum (4 recommended) * **RAM:** 4GB minimum (8GB recommended) * **Storage:** 50GB SSD * **Network:** 100Mbps * **OS:** Ubuntu 22.04 LTS **DigitalOcean Droplet Setup:** ```shell # 1. Create Droplet # Go to: https://cloud.digitalocean.com/droplets/new # Select: # - Image: Ubuntu 22.04 LTS # - Plan: Basic ($24/month, 4GB RAM) # - Datacenter: New York (or closest to you) # - Enable: Monitoring # 2. SSH into server ssh root@your_droplet_ip # 3. Update system apt update && apt upgrade -y # 4. Install Docker curl -fsSL https://get.docker.com -o get-docker.sh sh get-docker.sh # 5. Install Docker Compose apt install docker-compose -y # 6. Create n8n directory mkdir -p /opt/n8n cd /opt/n8n # 7. Create docker-compose.yml cat > docker-compose.yml < /etc/nginx/sites-available/n8n < /opt/backup.sh <<'EOF' #!/bin/bash DATE=$(date +%Y%m%d_%H%M%S) BACKUP_DIR="/opt/backups" mkdir -p $BACKUP_DIR # Backup database docker exec postgres pg_dump -U directory_app ai_directory > $BACKUP_DIR/db_$DATE.sql # Backup n8n workflows docker exec n8n tar czf - /home/node/.n8n > $BACKUP_DIR/n8n_$DATE.tar.gz # Keep only last 7 days find $BACKUP_DIR -name "db_*.sql" -mtime +7 -delete find $BACKUP_DIR -name "n8n_*.tar.gz" -mtime +7 -delete echo "Backup completed: $DATE" EOF chmod +x /opt/backup.sh # Add to crontab (daily at 2 AM) (crontab -l 2>/dev/null; echo "0 2 * * * /opt/backup.sh") | crontab - ``` ### **11.4 Monitoring Setup** ```shell # Install monitoring tools apt install htop iotop nethogs -y # Set up alerts (using simple email) cat > /opt/monitor.sh <<'EOF' #!/bin/bash DISK_USAGE=$(df -h / | tail -1 | awk '{print $5}' | sed 's/%//') MEM_USAGE=$(free | grep Mem | awk '{print int($3/$2 * 100)}') if [ $DISK_USAGE -gt 80 ]; then echo "Disk usage is $DISK_USAGE%" | mail -s "Alert: High Disk Usage" your@email.com fi if [ $MEM_USAGE -gt 90 ]; then echo "Memory usage is $MEM_USAGE%" | mail -s "Alert: High Memory Usage" your@email.com fi EOF chmod +x /opt/monitor.sh # Run every hour (crontab -l 2>/dev/null; echo "0 * * * * /opt/monitor.sh") | crontab - ``` --- ## **12\. Troubleshooting** ### **12.1 Common Issues & Solutions** **Issue 1: "API Rate Limit Exceeded"** ``` Symptoms: Workflow fails with 429 error Cause: Too many API requests too quickly Solution: 1. Increase wait time between requests - Change Wait node from 200ms to 500ms 2. Reduce batch size - Change from 100 to 50 items per batch 3. Add exponential backoff: // In HTTP Request node settings Retry On Fail: true Max Tries: 3 Wait Between Tries: 5000 (5 seconds) ``` **Issue 2: "Duplicate Key Violation"** ``` Symptoms: INSERT fails with "duplicate key value violates unique constraint" Cause: Trying to insert business that already exists Solution: 1. Verify hash generation is working: SELECT record_hash, COUNT(*) FROM businesses GROUP BY record_hash HAVING COUNT(*) > 1; 2. Add ON CONFLICT clause: INSERT INTO businesses (...) VALUES (...) ON CONFLICT (record_hash) DO NOTHING; 3. Or use UPSERT: INSERT INTO businesses (...) VALUES (...) ON CONFLICT (record_hash) DO UPDATE SET updated_at = NOW(); ``` **Issue 3: "Out of Memory Error"** ``` Symptoms: n8n crashes or becomes unresponsive Cause: Processing too much data at once Solution: 1. Reduce batch size in Split In Batches node 2. Increase server RAM (upgrade DigitalOcean droplet) 3. Add pagination to large queries: SELECT * FROM businesses LIMIT 1000 OFFSET 0; -- Process 1000 at a time ``` **Issue 4: "Connection Timeout"** ``` Symptoms: PostgreSQL node fails with timeout Cause: Database overloaded or network issues Solution: 1. Increase connection timeout in PostgreSQL node: Timeout: 60000 (60 seconds) 2. Check database connections: SELECT COUNT(*) FROM pg_stat_activity; 3. If >100 connections, add connection pooling: npm install pg-pool ``` **Issue 5: "Invalid Email/Phone Format"** ``` Symptoms: Validation fails, data looks correct Cause: Unexpected format from API Solution: 1. Add data cleaning step before validation: function cleanEmail(email) { if (!email) return null; return email.toLowerCase().trim(); } function cleanPhone(phone) { if (!phone) return null; // Convert to E.164 format let cleaned = phone.replace(/[^0-9]/g, ''); if (cleaned.length === 10) { cleaned = '1' + cleaned; // Add US country code } return '+' + cleaned; } ``` ### **12.2 Debugging Tips** **Enable Debug Mode:** ```shell # In n8n docker-compose down docker-compose up -d -e N8N_LOG_LEVEL=debug ``` **Check Logs:** ```shell # n8n logs docker logs -f n8n # PostgreSQL logs docker logs -f postgres # System logs journalctl -f ``` **Test Individual Nodes:** 1. In n8n editor, click on a node 2. Click "Execute Node" button 3. View output in right panel 4. Check for errors in JSON data **Database Debugging:** ```sql -- Check for orphaned records SELECT city_id, COUNT(*) FROM businesses WHERE city_id NOT IN (SELECT city_id FROM cities); -- Find slow queries SELECT query, mean_exec_time FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 10; -- Check table sizes SELECT schemaname, tablename, pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS size FROM pg_tables WHERE schemaname = 'public' ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC; ``` --- ## **13\. Glossary** **API (Application Programming Interface):** A way for different software programs to talk to each other. Like a waiter taking your order to the kitchen. **Batch Processing:** Processing data in groups (batches) instead of one at a time. Faster and more efficient. **Database:** Organized collection of data stored electronically. Like a giant, super-fast filing cabinet. **Deduplication:** Removing duplicate (identical or similar) records from a database. **Docker:** Software that packages applications in "containers" so they run the same everywhere. **Foreign Key:** A field in a database table that links to the primary key of another table. **Fuzzy Matching:** Finding records that are similar but not exactly identical (e.g., "IBM Corp" vs "IBM Corporation"). **Hash:** A unique fingerprint for data. Same input always produces same hash. ```text **JSON (JavaScript Object Notation):** A format for storing and transmitting data. Looks like: `{"name": "value"}`. ``` **n8n:** Visual workflow automation tool that connects different apps and services. **Node:** In n8n, a single step in a workflow that performs one specific task. **Orchestrator:** A workflow that manages and coordinates other workflows. **PostgreSQL:** A free, open-source database system. Very powerful and reliable. **Primary Key:** A unique identifier for each row in a database table (like a Social Security Number). **Rate Limiting:** Restricting how many requests you can make to an API per time period. **Schema:** The structure of a database \- what tables exist and what columns they have. **SQL (Structured Query Language):** The language used to interact with databases. **UUID (Universally Unique Identifier):** A 128-bit number that's unique across all computers and time. Looks like: `550e8400-e29b-41d4-a716-446655440000`. **VPS (Virtual Private Server):** A virtual computer running in the cloud that you can rent. **Webhook:** A URL that receives data when an event occurs. Like a mailbox that programs can send messages to. **Workflow:** A sequence of automated steps that accomplish a task. --- ## **Appendix A: Cost Breakdown (Detailed)** ### **One-Time Setup Costs** | Item | Cost | Notes | | ----- | ----- | ----- | | SimpleMaps Database | $199 | One-time purchase | | Initial Apollo.io Credits | $79 | First month subscription | | ZeroBounce Credits (50k) | $200 | \~$4 per 1,000 validations | | Twilio Credits (50k) | $250 | $0.005 per lookup | | **Total One-Time** | **$728** | | ### **Monthly Recurring Costs (During Collection)** | Item | Cost | Duration | | ----- | ----- | ----- | | DigitalOcean Droplet | $24 | 2 months | | PostgreSQL (managed) | $15 | 2 months | | Apollo.io Subscription | $79 | 1 month | | **Total Monthly** | **$118** | | | **Total for 2 Months** | **$236** | | ### **Monthly Recurring Costs (After Collection)** | Item | Cost | | ----- | ----- | | DigitalOcean Droplet | $24 | | PostgreSQL | $15 | | **Total Monthly** | **$39** | ### **Total Project Cost** * **Setup Phase:** $728 \+ $236 \= **$964** * **Annual Maintenance:** $39 × 12 \= **$468** * **Total First Year:** **$1,432** --- ## **Appendix B: Example API Responses** ### **Apollo.io Search Response** ```json { "breadcrumbs": [], "partial_results_only": false, "disable_eu_prospecting": false, "partial_results_limit": 10000, "pagination": { "page": 1, "per_page": 25, "total_entries": 247, "total_pages": 10 }, "organizations": [ { "id": "5f7b1234567890abcdef1234", "name": "AI Solutions Inc", "website_url": "https://aisolutions.com", "blog_url": null, "angellist_url": null, "linkedin_url": "https://www.linkedin.com/company/ai-solutions", "twitter_url": "https://twitter.com/aisolutions", "facebook_url": null, "primary_phone": { "number": "4155551234", "source": "Account" }, "languages": [], "alexa_ranking": 1250000, "phone": "4155551234", "linkedin_uid": "12345678", "founded_year": 2018, "publicly_traded_symbol": null, "publicly_traded_exchange": null, "logo_url": "https://logo.clearbit.com/aisolutions.com", "crunchbase_url": null, "primary_domain": "aisolutions.com", "sanitized_phone": "+14155551234", "industry": "Computer Software", "keywords": [ "artificial intelligence", "machine learning", "consulting" ], "estimated_num_employees": 50, "snippets_loaded": true, "industry_tag_id": "5567cdfe7369647540020000", "retail_location_count": 0, "raw_address": "123 Market St, San Francisco, CA 94103", "street_address": "123 Market St", "city": "San Francisco", "state": "California", "postal_code": "94103", "country": "United States", "owned_by_organization_id": null, "suborganizations": [], "num_suborganizations": 0, "seo_description": "AI Solutions provides consulting and ML development services", "short_description": "Enterprise AI consulting", "annual_revenue_printed": "$5M-$10M", "annual_revenue": 7500000, "technologies": [ "Google Analytics", "Amazon AWS", "TensorFlow" ] } ] } ``` --- ## **Appendix C: Sample SQL Queries** ### **Query 1: Find AI Companies in Top 10 Cities by State** ```sql SELECT s.state_name, c.city_name, COUNT(b.business_id) as business_count, AVG(b.completeness_score) as avg_quality FROM states s JOIN cities c ON s.state_code = c.state_code LEFT JOIN businesses b ON c.city_id = b.city_id WHERE c.rank_in_state <= 10 GROUP BY s.state_name, c.city_name ORDER BY s.state_name, c.rank_in_state; ``` ### **Query 2: Identify Data Gaps** ```sql SELECT c.state_code, c.city_name, c.target_businesses, c.collected_businesses, (c.target_businesses - c.collected_businesses) as gap FROM cities c WHERE c.collected_businesses < c.target_businesses ORDER BY gap DESC LIMIT 50; ``` ### **Query 3: Best Quality Businesses** ```sql SELECT business_name, city_name, state_code, completeness_score, email_verified, phone_verified, array_to_string(ai_service_types, ', ') as services FROM businesses b JOIN cities c ON b.city_id = c.city_id WHERE completeness_score >= 90 AND email_verified = true AND phone_verified = true ORDER BY completeness_score DESC LIMIT 100; ``` ### **Query 4: Technology Distribution** ```sql SELECT unnest(technologies_used) as technology, COUNT(*) as company_count FROM businesses WHERE technologies_used IS NOT NULL GROUP BY technology ORDER BY company_count DESC LIMIT 20; ``` --- ## **Final Checklist** Before launching to production, verify: * \[ \] All API keys are configured and tested * \[ \] Database schema is created with all indexes * \[ \] SimpleMaps data is imported and ranked * \[ \] Test workflow successfully collects 10 businesses * \[ \] Pilot test of 1,000 businesses meets quality targets * \[ \] Orchestrator workflow handles batches correctly * \[ \] Worker workflows don't hit rate limits * \[ \] Validation workflows verify data accurately * \[ \] Update workflow runs without errors * \[ \] Backups are automated and tested * \[ \] Monitoring alerts are configured * \[ \] Documentation is complete and clear * \[ \] Junior developer can follow all instructions --- **End of Product Requirements Document** *Version 1.0 \- October 2, 2025* *For questions or clarifications, refer to the troubleshooting section or contact the technical lead.* --- ## aiConnected Platform — Foundation PRD **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-foundation-prd # aiConnected Platform — Foundation PRD **Version:** 1.0 **Date:** March 25, 2026 **Author:** Bob Hunter, Founder/CPO — aiConnected **Status:** Draft --- ## 1. Purpose & Scope This PRD defines the **core foundation** of the aiConnected platform — the shell that everything else is built on. It does not describe any product modules (voice, chat, knowledge base, etc.). Those are first-party modules that will be imported after the foundation is stable. The foundation is not a feature. It is the architecture. When it is complete, a developer should be able to build any module, plug it in, and have it work — without touching the core. **In scope:** - Multi-tenant permission architecture - UI system (shadcn/ui + TweakCN) - Layout Manager + AI Module Creator - Memory system (Neurigraph basics) - Module import system - Module isolation / standalone access **Out of scope (first modules to import, not foundation components):** - Voice AI - Chat Interface - Knowledge Base Generator - Contact Forms / Chat Monitor - Co-Browser --- ## 2. Architectural Philosophy — The Lego Brick Model The single most important requirement for this platform is that every major system is **swappable in isolation**. This is non-negotiable. The core shell is a **central fortress** — it provides identity, routing, permissions, theming, and the event bus. Everything else connects to the fortress via well-defined endpoints. Nothing lives inside the fortress that doesn't have to. No module should be able to break another module. No upgrade should require a full rebuild. In practice this means: - Each foundation component lives in its own package with its own folder - Components communicate through defined contracts (endpoints, events, manifests) — not direct imports - Replacing or upgrading any component (e.g., swapping the UI library, changing the auth provider, updating the memory system) should not require changes to any other component - Every module gets its own container, its own repo, and its own direct-access URL from day one - The complexity is front-loaded into the foundation so that future creation is as frictionless as possible The analogy is Lego: each brick is designed and tested in isolation. Once the brick exists, it can be combined freely. The goal is to reach a point where building a new module is a matter of arranging existing bricks — not inventing new materials. --- ## 3. Foundation Components ### 3.1 Multi-Tenant Permission Architecture #### Overview The platform serves five distinct user layers in a hierarchical structure. Each layer is fully isolated from layers it does not own or manage. ``` Super Admin └── Agency ├── Business │ └── End User (via module, not platform login) └── Developer (sandboxed, separate trust pipeline) Personal (isolated, no sub-users) ``` #### The 13 Permission Types Each of the first four layers (Super, Agency, Business, Developer) supports three internal user roles. Personal has no sub-users — it is always a single private user. | Layer | Admin | Manager | User | |-------|-------|---------|------| | Super | Full platform control | Manages platform-level users | Limited read/support access | | Agency | Full agency control | Manages agency users and businesses | Role-specific (sales, support, finance, etc.) | | Business | Full business account control | Manages business users | Role-specific (assigned accounts, assistants, etc.) | | Developer | Full dev environment control | Manages team collaborators | Contributor access | | Personal | (single user, no sub-roles) | — | — | **Layer Admin** — The account owner. Full permissions for their layer. Cannot exceed their layer's access. **Layer Manager** — Delegated authority. Near-admin permissions. Responsible for user management within the layer. **Layer User** — Individual contributors. Permissions are explicitly assigned. Cannot escalate their own permissions. #### Inheritance Rules - A Super Admin can impersonate any layer for support and testing - An Agency Admin can configure what Business Admins are allowed to do - A Business Admin can configure what their users can do, within the bounds the Agency set - No layer can grant permissions that exceed their own - Personal accounts are entirely isolated — no cross-tenant visibility #### Key Requirements - Permission checks must be enforced at the API layer, not just the UI - Role assignments must be auditable (who granted what, when) - Impersonation sessions must be logged and time-limited - The permission model must be swappable — if the role structure changes, only the permissions package updates #### v1 Reference `packages/permissions/src/auth.js` has the 13 role constants and group helpers. The logic is sound. The v2 version should be rebuilt cleanly from scratch using this as a reference spec only — no code copy. --- ### 3.2 UI System — shadcn/ui + TweakCN #### Overview The entire platform UI is built on shadcn/ui as the component foundation and TweakCN as the theming layer. This means the visual design is completely separated from the functional structure. **shadcn/ui** provides the component library — buttons, inputs, cards, tables, modals, navigation, everything. Every interface element in the platform uses shadcn components. This ensures consistency, accessibility, and rapid development. Developers building modules inherit this library automatically. **TweakCN** provides per-tenant CSS variable theming. Colors, fonts, border radii, shadows, spacing — all controlled via CSS custom properties, not hardcoded values. Each agency gets a theme configuration. Each business can inherit or override within the bounds the agency allows. #### Theming Hierarchy ``` Platform defaults (Super) └── Agency theme (overrides platform defaults) └── Business theme (overrides agency defaults, if permitted) ``` Agency Admins can configure their complete visual identity — brand colors, logo, fonts, border styles — and it propagates automatically to all their business accounts. A business account using an agency's platform will never see the aiConnected brand. #### AI-Promptable Theming The UI system must be designed so that a non-technical user can describe a design change in plain language and the system can execute it. Example: "Make everything more rounded, use a dark navy background, and switch to Inter font." The system AI reads the current theme configuration, interprets the request, updates the CSS variables, and previews the result. This is not a separate product — it is how the theme editor works for anyone who prefers it. #### Key Requirements - All components must come from `@aiconnected/ui` (the platform's shadcn wrapper package) — never imported directly from shadcn or any other library - Hardcoded color values are not permitted anywhere in the codebase - All colors reference CSS variables only - Theme inheritance must be explicit and auditable - Theme changes must be previewable before publishing - The UI package must be versioned independently of everything else #### Module UI Flexibility Modules that are imported from external developers are not required to use shadcn. If a module has its own UI (e.g., a major vendor like HeyGen), it is loaded in an iframe or isolated container. The platform does not break if a module has a foreign UI. However, first-party modules and all modules built inside the platform must use the shadcn system. --- ### 3.3 Layout Manager + AI Module Creator #### Overview The Layout Manager is the platform's visual building tool. It has two functions: 1. **Edit existing screens** — drag, drop, resize, and reconfigure layouts using shadcn components as building blocks 2. **Create new modules** — a conversational AI workflow that builds entire new modules from a description These are not separate tools. They are two tabs inside the same Layout Manager interface. #### Who Has Access | Role | Layout Manager Access | |------|--------------------| | Super Admin | Full access — all screens, all modules, all tenants | | Developer | Limited — their own module scope only | | Agency Admin | Limited — their tenant's screens only, within platform bounds | | Everyone else | No access | #### Tab 1: Modules (Edit Existing) The Modules tab displays a table of all screens and modules in the platform. Clicking any screen opens the drag-and-drop canvas editor. The canvas editor is powered by **Craft.js** — an open-source React drag-and-drop framework built specifically to be embedded inside an application. Every shadcn component in the platform appears as a draggable widget. Admins drag components onto the canvas, resize and reposition them, and configure their props through a sidebar panel. **Key behaviors:** - Layout changes do not go live immediately. They are staged for AI processing. - When an admin saves a layout, the system's orchestration AI interprets the layout change, updates the underlying code, and triggers a redeployment via Dokploy. - The admin receives a notification when the change is live and can preview it before publishing. - Layout changes are versioned. Any change can be rolled back. - A pencil icon appears on every screen when an authorized user is logged in. Clicking it opens the Layout Manager directly to that screen. - The Layout Manager is also accessible from the admin sidebar under "Layout Manager → Modules." #### Tab 2: Create New (AI Module Creator) The Create New tab is a conversational interface for building new modules without leaving the platform. **Workflow:** 1. Admin describes what they want in plain language. Example: "Create a team communications module that works like Slack." 2. The AI researches the functionality — searches for open-source resources, reviews existing endpoints in the SDK, identifies what already exists vs. what needs to be built. 3. The AI presents a plan: here are the endpoints I'll connect to, here are the ones I need to create, here is the user flow, here is what the UI will look like. The admin reviews and approves or requests changes before anything is built. 4. The AI writes the underlying code using existing platform bricks wherever possible. Novel functionality is minimized and logged as new SDK endpoints. 5. The AI builds the UI using the shadcn component library. If a new component is needed, it is built to be shadcn-compatible. 6. The AI creates the screens, wires the interactions, and generates the module manifest. 7. The admin is notified: "Your module is ready for testing." 8. The admin tests interactively — making direct edits via the Modules tab, or communicating changes in natural language through the Create New tab. 9. When satisfied, the admin publishes the module, sets access permissions, and it becomes available to enabled users. **Module release workflow:** - When a new module is published, Agency Admins receive a release notification: what it does, how to enable it, what it costs. - Agency Admins try it, decide whether to make it available to their business accounts. - Business Admins receive their own notification if the Agency has enabled it. - Developers releasing modules through the developer trust pipeline follow a separate review workflow (community sandbox → aiConnected certification). **Key principle:** The Create New tool does not invent new things unnecessarily. It assembles existing bricks. The SDK endpoint registry grows over time, and as it grows, the number of things the tool can build from existing pieces increases. After voice is built, anything needing voice just connects to it. After image generation is built, anything needing images just connects to it. The goal is to front-load the complexity so that future creation is fast and safe. --- ### 3.4 Memory System — Neurigraph Basics #### Overview The platform includes a centralized memory system based on the Neurigraph Memory Architecture. For the foundation, only the core layer is implemented — enough to give the platform and its AI components persistent, structured memory across sessions and users. The full Neurigraph system is a standalone product (Cognition Adaptive LLC) and will be built out separately. #### What "Basics" Means for the Foundation The foundation memory system must support: - **Session memory** — the platform remembers what happened in the current session, including user actions, AI interactions, and module events - **User memory** — persistent preferences, history, and context for each user across sessions - **Tenant memory** — shared context that applies across all users within an agency or business account (e.g., brand voice, common workflows, frequently asked questions) - **Module memory** — each module can read from and write to the shared memory layer, so a call logged by Voice AI is visible to the Chat module and the Knowledge Base #### Key Requirements - Memory is stored in Supabase with row-level security enforcing tenant isolation - The memory API is a shared service — all modules connect to the same memory layer, not separate databases - Memory is structured (typed, queryable) not just a blob of text - The memory layer exposes a clean read/write API that modules can call without knowing the storage implementation - The memory package must be versioned and swappable independently of the modules that use it #### What Is Deferred The full Neurigraph cognitive architecture — multi-tier memory classification, associative retrieval, adaptive context weighting — is out of scope for the foundation. The foundation implements the data layer and API contract that the full system will eventually plug into. --- ### 3.5 Module Import System #### Overview The Module Import System is how externally-developed apps become platform-compatible. A developer brings a GitHub repo. The system reads it, maps its functionality to the platform's SDK, and produces a platform-compatible module. This is the mechanism that allows the platform to grow beyond what aiConnected builds directly — any developer can build a module externally and import it. #### Import Process 1. **Submission** — Developer submits a GitHub repo URL or ZIP archive through the Developer Portal. 2. **Manifest check** — The system looks for a `platform-app.json` manifest. If one exists and is valid, the import skips to step 5. If not, the AI-assisted mapping begins. 3. **Frontend mapping** — The system scans the repo's UI components and maps them to their shadcn equivalents. A normalization report is generated showing what mapped cleanly, what mapped with modifications, and what could not be mapped (warnings, not blockers). 4. **Backend mapping** — The system scans API calls, service integrations, and data schemas. Existing SDK endpoints are matched. New functionality that has no SDK equivalent is flagged as requiring new endpoint creation. 5. **Plan presentation** — The system presents the mapping results to the developer: here is what your app does, here is how it connects to the platform, here is what needs to be built. The developer reviews and approves. 6. **Normalization** — The system rewrites imports from shadcn to `@aiconnected/ui`. It replaces hardcoded hex values with CSS variables. The original code is preserved. A normalized artifact is created alongside it. 7. **Manifest generation** — A `platform-app.json` is generated or validated, declaring the module's inputs, outputs, events, permissions, and capabilities. 8. **Staging** — The module enters the staging registry. It is visible to the developer community for testing and integration. The source code is not exposed — only the capability contract (inputs, outputs, events). 9. **Community review** — Developers in the community can install the staged module into their sandbox environments and test it against their own modules. Weighted votes determine readiness. 10. **aiConnected certification** — Modules that pass community review are submitted to aiConnected for security review and stress testing before being published to the live registry. #### The Living SDK Every time a new module introduces functionality that has no existing SDK endpoint, that endpoint is created and logged in the SDK registry. Over time, the registry grows. Future modules — whether imported or created inside the platform — have an increasingly rich set of building blocks to connect to. The SDK endpoint registry is the platform's most important long-term asset. It compounds. #### Key Requirements - The original submitted code is always preserved. Normalization creates a separate artifact. - Import succeeds even with unmapped components or unrecognized colors — these are warnings, not failures - Every module in the registry is containerized and isolated (Dokploy) - Every module gets a unique direct-access URL from the moment it enters the registry (see 3.6) - The manifest format is defined in `packages/app-sdk` (extended from v1 with `events_emitted` and `events_consumed` fields) --- ### 3.6 Module Isolation — Standalone Access #### Overview Every module must be accessible without the full platform from the moment it is registered. A module is not just a feature inside the platform — it is a standalone product that happens to also integrate with the platform. #### Why This Is a Day-One Requirement - Demos — show a single module without exposing the full platform - Campaigns — link directly to a module as a standalone product experience - Licensing — grant a third-party company access to a module's output without giving them platform access - A/B testing — test variations of a module independently - Open source — publish a module under an MIT license as a public tool - Embedding — drop a module into another company's product #### How It Works When a module is added to the system, two things are created automatically: 1. **Standalone URL** — a direct endpoint (e.g., `modules.sec-admn.com/kb-studio` or a custom domain) that gives access to the module with configurable authentication (public, token-gated, invite-only, or full auth) 2. **Standalone repository** — the module's own Git repo, independent of the platform monorepo The module is not aware of whether it is being accessed through the full platform or via its standalone URL. It simply operates. The platform shell (sidebar, navigation, billing) is conditionally rendered — present when accessed through the platform, absent when accessed standalone. #### Authentication Options for Standalone Access | Mode | Description | |------|-------------| | Public | No login required. Anyone with the URL can access. | | Token | A unique token in the URL grants one-time or time-limited access. | | Invite | Access by email invite only. | | Full auth | Standard platform login required. Same as in-platform access. | Access mode is configured per module by the Super Admin or Agency Admin. #### Key Requirements - Standalone URL and repo are created automatically at module registration — not a manual step - The module's container runs independently — it does not depend on other modules being online - Billing for standalone access is tracked separately and routes through the same Stripe infrastructure - Standalone access does not bypass the permission model — access mode is set by the admin, not the end user --- ## 4. What This Foundation Enables When these five components are complete and working together, the platform can do the following without any additional core development: - An Agency Admin logs in, customizes their brand in 10 minutes using the AI-prompted theme editor, and shares a branded link with their client - A developer builds a new module externally, submits it, and the import system maps it to the platform in one session - Bob describes a new module in plain language inside the Create New tab, and the AI builds and deploys it - A module is shared as a public demo via its standalone URL — no login required - A partner company integrates a single module's output into their own product via its standalone endpoint - When the memory system is updated or the UI library is replaced, no modules need to be touched --- ## 5. Individual PRDs Required Each of the following will require its own detailed PRD before development begins: 1. **Multi-Tenant Permission Architecture** — role model, inheritance rules, impersonation, audit logging 2. **UI System** — shadcn/ui wrapper package, TweakCN integration, theme inheritance, AI-promptable theme editor 3. **Layout Manager** — Craft.js integration, canvas editor, AI-processed layout saves, redeploy pipeline 4. **AI Module Creator** — conversational workflow, research layer, plan review, code generation, testing workflow, publish flow 5. **Memory System** — Neurigraph basics layer, data schema, read/write API, tenant isolation 6. **Module Import System** — manifest spec, frontend normalization, backend mapping, staging registry, community review, certification 7. **Module Isolation** — standalone URL generation, standalone repo creation, authentication modes, billing tracking --- ## 6. Build Sequence The foundation components must be built in dependency order: | Phase | Component | Dependency | |-------|-----------|------------| | 1 | Monorepo structure + shared packages | None | | 2 | Permission system | None | | 3 | Supabase schema + memory layer basics | Permission system | | 4 | UI system (shadcn wrapper + TweakCN) | None | | 5 | Platform shell (auth, routing, navigation, billing hooks) | Permissions + UI system | | 6 | Module manifest spec + SDK endpoint registry | Platform shell | | 7 | Module isolation (standalone URLs + repos) | Module manifest | | 8 | Module import system | Module manifest + isolation | | 9 | Layout Manager — canvas editor | UI system + platform shell | | 10 | Layout Manager — AI Module Creator | Layout Manager + SDK registry + memory | Voice, Chat, and Knowledge Base are imported after Phase 10 as the first live test of the import system. --- ## 7. v1 Reference Notes The v1 platform (`platform.sec-admn.com-2`) contains reference material that informed this PRD. **No v1 code should be copied.** The logic is sound in places; the implementation is not trustworthy. Reference only (do not copy): - `packages/permissions/src/auth.js` — 13 role constants, group helpers - `packages/app-sdk/src/manifests.js` — manifest spec baseline - `packages/branding/src/index.js` — `mergeTheme()` logic and token names The v2 versions of these must be written cleanly from scratch, using the v1 files as a specification reference, not a code source. --- ## 8. Definition of Done The foundation is complete when: - [ ] All 13 permission types are enforced at the API layer (not just UI) and tested - [ ] A theme can be changed for a tenant without touching another tenant's theme - [ ] A layout change in the canvas editor deploys to production without manual developer intervention - [ ] A new module can be described in plain language and the AI builds and deploys it end-to-end - [ ] An externally-built module can be imported, normalized, and staged without manual code editing - [ ] Every registered module has a working standalone URL accessible without a platform login - [ ] Any foundation component (UI system, permission system, memory layer) can be replaced without changes to modules that depend on it --- *This PRD covers the foundation only. First-party module PRDs (Voice AI, Chat Interface, Knowledge Base Generator, Contact Forms, Co-Browser) will be written separately after the foundation is complete.* --- ## aiConnected Platform — MVP Specification **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-mvp-specification # aiConnected Platform — MVP Specification **Version 1.0** *Prepared for development team engagement — March 2026* --- ## TL;DR aiConnected is a white-label SaaS platform that agencies buy, rebrand as their own product, and sell to business clients — primarily for AI-powered sales tooling. Think GoHighLevel, but open at the code level so third-party developers can build and publish modules that compound on top of each other over time. The MVP delivers a working agency platform with five interconnected sales modules: a Knowledge Base Generator (scrapes a business's website and builds a comprehensive AI knowledge source that powers everything else), a Voice AI Hub (handles all inbound and outbound voice interactions via LiveKit), a Chat Interface (a fully branded sales funnel in chat form, embeddable as a full-screen window or bubble), a Contact Forms module (intercepts form submissions and converts them into AI-engaged leads in real time), and a Chat Monitor (gives the business a live view of conversations with the ability to step in or guide the AI when a lead goes warm). A Co-Browser add-on extends the chat to every page of a website with full page-context awareness. Agencies pay nothing until they generate revenue. aiConnected earns a 10% platform tax on whatever agencies charge their clients, plus a 10% markup on AI API costs. All billing flows through aiConnected's Stripe infrastructure automatically. The architecture is the more important story. The MVP is built as a shell with pluggable, containerized modules that communicate exclusively through a shared event bus and a declared capability registry. The developer ecosystem — community sandbox, trust pipeline, registry UI — is not an MVP deliverable, but the architecture must support it from day one. Every first-party module ships with a compliant manifest. The event bus is designed for modules it hasn't seen yet. The API gateway routes dynamically. Nothing about the MVP architecture can require rework when the developer layer is built on top of it. The full platform spec, billing model, and architecture document are included in this package. This document defines the MVP scope and what done looks like. --- ## Purpose of This Document This document defines the Minimum Viable Product for the aiConnected platform rebuild. It is written for senior engineers and technical collaborators who need a precise understanding of what is being built, why the architectural decisions were made the way they were, and what "done" looks like for the MVP. The platform is being rebuilt from scratch. An earlier version exists and may be referenced for logic or salvage, but it is not recommended as a foundation. This specification represents the authoritative target. --- ## 1. What We Are Building aiConnected is a white-label SaaS operating system for agencies. It functions the way GoHighLevel functions in its interconnectedness and multi-tenant white-label model — but unlike GoHighLevel, it is architecturally open. Third-party developers can extend the platform at the code level, with new capabilities compounding on top of existing ones over time. The closest mental model is this: **WordPress core + Crocoblock-style interconnected plugins, built as a modern SaaS platform, sold to agencies who rebrand and resell it to their business clients.** Every module on the platform shares the same data layer, the same identity infrastructure, and the same event system. A voice call log is visible to the chat module. A knowledge base update immediately powers AI interactions everywhere. No module is an island. The platform's long-term differentiator is its developer extensibility model — any developer can build a module, submit it through a governed trust pipeline, and have its capabilities published to a shared registry that all future developers can build upon. Development progress on the platform compounds rather than accumulates in silos. This is described in detail in Section 6.4, and the architecture must support it from day one — though it will not be marketed or sold in the MVP phase. --- ## 2. Market Positioning The AI SaaS market has largely split into two camps: marketing automation platforms (GoHighLevel, ActiveCampaign, HubSpot) and general-purpose AI tools (ChatGPT, Claude, Gemini wrappers). Neither camp owns sales. aiConnected occupies the sales lane specifically. Every MVP module is chosen because it serves the sales process: - Converting website visitors into identified leads - Warming leads through intelligent AI interaction - Qualifying leads in real time before a human ever gets involved - Giving sales teams visibility into lead behavior before a call - Handling inbound voice and chat interactions around the clock This positioning means aiConnected agencies are not competing with GoHighLevel resellers. They are bringing a category of tooling — AI-powered sales infrastructure — that does not exist as a cohesive, white-label product in the market today. --- ## 3. User Types The platform serves five distinct user classes. The MVP focuses exclusively on the first three. **Super User (aiConnected Admin)** The platform operator. Has visibility into all agencies, all billing, all system health. The super user does not interact with the product the way agencies and businesses do — they manage the ecosystem. **Agency User** The primary commercial customer. An agency signs up for aiConnected, configures it as their own branded product, and deploys it to their business clients. From their clients' perspective, the agency built the platform. aiConnected is invisible. The agency sets their own pricing, creates their own packages, and controls which modules their clients access. **Business User** The agency's end client. A business user logs into what appears to be the agency's proprietary platform. They configure their AI tools, manage their contacts, and monitor their leads. They have no awareness of aiConnected as the underlying infrastructure. **Personal User** *(post-MVP)* Individual users accessing the platform outside of an agency context. Architecture must not preclude this user type, but it is not an MVP deliverable. **Developer** *(post-MVP)* Third-party developers who build and publish modules to the platform. The module manifest system and capability registry must be architected for this user type from day one, but the developer-facing portal and trust pipeline are not MVP deliverables. --- ## 4. The Architecture ### 4.1 The Core Shell The shell is the platform's permanent, stable foundation. Every module lives inside it. The shell itself never contains business logic — that lives entirely in modules. **Shell responsibilities:** - User authentication and session management (Supabase Auth) - Multi-tenant provisioning — agency accounts, sub-accounts (business clients), and super admin - Navigation and routing infrastructure - Billing infrastructure (Stripe, platform tax collection, module activation) - Module registry — the live directory of all installed and available modules - Event bus — the system-wide communication channel between modules - API gateway — routes requests between modules, enforces permissions, handles rate limiting - Theme engine — applies per-tenant TweakCN configuration **What the shell explicitly does not do:** It contains no CRM logic, no voice logic, no chat logic, no AI inference calls. All of that lives in modules. --- ### 4.2 UI Foundation — shadcn/ui + TweakCN The entire platform UI is built on shadcn/ui. This decision has three direct benefits: **1. Development velocity.** Building new interfaces means organizing existing components, not making design decisions from scratch. Module developers inherit a complete, consistent design system without writing a line of custom CSS. **2. True white-label.** TweakCN enables full CSS-level customization per tenant — colors, typography, border radius, shadows, spacing, borders, backgrounds, hover states. Agencies can configure every visual element to match their brand. The result is that two agencies running the same platform look nothing alike. GoHighLevel's weakness — that its branded deployments are immediately recognizable as GoHighLevel — is structurally prevented. **3. Developer ergonomics.** Third-party developers who build modules using shadcn/ui components get platform-native styling automatically. Developers who bring a custom UI (for instance, a company integrating an existing product that was not designed with shadcn) are accommodated — the system must not break for non-shadcn UIs. But shadcn is the strongly encouraged default, and the path of least resistance. Business users may also be granted UI customization access at the agency's discretion. This represents a potential upsell — agencies can charge clients for the ability to personalize their own experience. --- ### 4.3 Shared Data Layer A single Supabase (PostgreSQL) instance serves as the platform's unified data foundation. **Core shared entities:** | Entity | Description | |---|---| | `workspaces` | The tenant record. Every module references this. | | `contacts` | The universal entity. Every module reads from and writes to contacts. | | `users` | Platform users with roles and permissions scoped to their workspace. | | `events` | The shared event log. This is the interconnection mechanism. | | `module_registry` | Live directory of installed modules and their capability contracts. | **Module data:** Each module owns its own database tables, namespaced by module (e.g., `voice_calls`, `chat_conversations`, `kb_entries`). Modules may read from shared entities. They write to their own tables and emit events to the shared event log. **The interconnection mechanism:** When the voice module completes a call, it writes to `voice_calls` and emits a `voice.call.completed` event. The chat module, the contacts module, and any automation subscribed to that event receives it and responds accordingly. No module ever reaches directly into another module's tables. All cross-module communication flows through the event bus and declared API contracts. This is what makes the platform's capabilities genuinely interconnected rather than just co-located. --- ### 4.4 The Module System Every capability on the platform — whether built by aiConnected or a third party — is a module. Modules are self-contained and follow a common manifest specification. **Module manifest (example):** ```json { "id": "voice-hub", "name": "Voice AI Hub", "version": "1.0.0", "developer": "aiConnected", "routes": ["/voice", "/voice/calls", "/voice/settings"], "sidebar": { "label": "Voice", "icon": "phone", "position": 3 }, "permissions": ["contacts.read", "contacts.write", "events.emit"], "capabilities": { "inputs": ["contact_id", "script", "voice_profile_id"], "outputs": ["call_record", "transcript", "call_status"], "events_emitted": ["voice.call.started", "voice.call.completed", "voice.call.failed"], "events_consumed": ["contact.updated", "kb.updated"] }, "data_schemas": ["voice_calls", "voice_profiles", "transcripts"] } ``` The platform reads the manifest and automatically registers routes, adds sidebar navigation, grants declared permissions, subscribes the module to its declared events, and publishes its capabilities to the registry. This manifest-first approach is what makes the module system extensible. Adding a new module does not require touching the shell. --- ### 4.5 Container Architecture Every module — including all first-party aiConnected modules — runs in its own isolated container. This is not a post-MVP architectural improvement. It is a day-one requirement. **Why this is non-negotiable:** - A failing module cannot affect the platform or any other module - A compromised module cannot reach into the core or other containers - Each module can be updated, rolled back, or restarted independently - Resource usage per module is monitored and enforceable - Security audits are scoped to individual containers Modules communicate with each other exclusively through the API gateway. Direct container-to-container calls are not permitted. The API gateway handles routing, authentication enforcement, rate limiting, and anomaly detection. Infrastructure: DigitalOcean, orchestrated via Dokploy. --- ### 4.6 Developer Ecosystem Foundation *(Architectural Requirement — Not MVP Product)* The platform must be architected for third-party developer extensibility from day one. The developer-facing portal, community sandbox, capability registry UI, and trust pipeline workflow are not MVP deliverables and will not be marketed or sold in the MVP phase. However, the architecture must not require significant rework to support them later. This means the following must be in place at MVP: - The module manifest specification must be final and enforced - The capability registry database schema must be in place, even if the UI for browsing it is not - The event bus must be designed to accommodate modules it has not yet seen - The API gateway must be designed to route to containers dynamically, not hardcoded to known modules - Tenant isolation must be scoped in a way that supports future developer-submitted modules The developer ecosystem will be a significant commercial layer on top of this foundation. Its viability depends entirely on the foundation being correctly built in the MVP. This is the single most important architectural constraint in this document. --- ## 5. MVP Modules The MVP ships with five modules and one add-on. All five are deeply interconnected and all serve the sales process. --- ### 5.1 Knowledge Base Generator **Role:** The brain of the platform. Every interaction-based module draws from it. The Knowledge Base Generator builds a comprehensive, structured intelligence document for a business — covering their services, pricing, FAQs, target customers, ideal and non-ideal use cases, delivery timelines, what clients can expect during and after service, and supplemental market context. This becomes the knowledge source that powers all AI interactions on the platform: chat responses, voice conversations, automated follow-ups, and service card presentations. **How it works:** 1. The system scrapes and reads the client's entire website 2. It identifies gaps — information that should exist but doesn't appear on the site — and fills those gaps with AI-assisted research 3. It generates the structured knowledge base and presents it to the business user through a management UI 4. The business user reviews, edits, supplements, and approves the knowledge base 5. The approved knowledge base is published and immediately available to all connected modules 6. The business user can configure a regeneration schedule to keep the knowledge base current as their offerings evolve **Module connections:** `kb.updated` event triggers re-indexing in Voice AI and Chat Interface. All AI modules query the knowledge base via the API gateway rather than maintaining their own copies. --- ### 5.2 Voice AI Hub **Role:** Powers all voice interactions across the platform. Any module that requires voice — inbound phone calls, outbound calls, voice mode within the chat interface — routes through Voice AI Hub. It is the single voice infrastructure layer for the entire platform. **Core capabilities:** - Inbound call handling: AI answers calls for the business, handles questions using Knowledge Base data, qualifies callers, books appointments, or routes to a live agent - Outbound calls: AI-initiated calls for lead follow-up, appointment reminders, and outreach sequences - In-chat voice: Powers the voice communication mode inside the Chat Interface (see 5.3) - Phone number management: Supports adding and managing business phone numbers within the platform **Technology:** Built on LiveKit for real-time voice communication. **Module connections:** Consumes `contact.updated` and `kb.updated` events. Emits `voice.call.started`, `voice.call.completed`, `voice.call.failed` events consumed by Contacts, Chat Monitor, and automation layers. --- ### 5.3 Chat Interface **Role:** The business's AI-powered sales funnel in chat form. This is not a generic chatbot. The Chat Interface is a fully branded, white-label sales experience that a business deploys on their website. It is powered by the Knowledge Base Generator and presents information in rich interactive formats rather than plain text responses. **Deployment options:** - Full-screen chat window (comparable to a Claude.ai or ChatGPT interface experience, embedded directly on the business's site) - Traditional chat bubble in the corner of any page **What it does:** A prospective customer opens the chat and begins asking questions. The AI responds using the business's Knowledge Base — answering questions about services, pricing, timelines, and suitability. Services are presented as structured cards with relevant details, not buried in paragraphs. The interface supports text and voice modes (Voice AI Hub powers the voice mode). As the conversation progresses, the Chat Monitor (see 5.5) watches lead warmth in real time. When a lead crosses a warmth threshold, the business receives a notification and can choose to step in, allow the AI to guide the lead toward booking, or route to a live agent. **Module connections:** Pulls from Knowledge Base on every response. Emits lead events to Chat Monitor. Invokes Voice AI for voice-mode sessions. Writes conversation records and contact data to the shared `contacts` and `chat_conversations` tables. --- ### 5.4 Contact Forms Module **Role:** Converts traditional form submissions into qualified, AI-engaged leads. Most contact forms on business websites are dead ends — a submission goes into a CRM and waits for a human to follow up. The Contact Forms module intercepts that process. **What it does:** 1. **Submission validation:** Before anything else, the system evaluates whether a submission is genuine or spam/bot. This alone provides significant value — businesses receive only real leads. 2. **Intent classification:** Real submissions are classified by intent. Is this a purchase inquiry? A support question? A refund request? An information request? Classification determines the response path. 3. **AI-assisted engagement:** Based on intent, the submitter is routed into an appropriate AI interaction. A purchase inquiry might open a targeted chat asking qualifying questions. A support question might be resolved immediately with Knowledge Base answers. A warm lead might be prompted toward booking an appointment. **The key distinction from the Chat Interface:** The Chat Interface catches people who chose to engage. Contact Forms catches people who never intended to chat — they filled out a form and expected to wait. The Contact Forms module meets them in that moment with immediate, intelligent engagement, dramatically improving conversion rates and reducing time-to-contact. **Module connections:** Writes to `contacts`. Emits `contact.form_submitted`, `contact.qualified`, and `contact.warm` events. Can invoke the Chat Interface for follow-on engagement and Voice AI for callback initiation. --- ### 5.5 Chat Monitor **Role:** Gives the business real-time visibility and control over live AI sales conversations. The Chat Monitor is the business-facing backend companion to the Chat Interface. While AI handles conversations autonomously, the Chat Monitor watches for signals that a lead is warming — increased engagement, questions about pricing or next steps, repeated visits to specific service pages — and surfaces those signals to the business user. **What the business can do from the Chat Monitor:** - Receive real-time notifications when a lead crosses a warmth threshold - View the full conversation in progress - Step into the conversation directly (live agent takeover) - Push a back-end instruction to the AI — guiding it to move toward booking, offer a specific promotion, or escalate the conversation — without the customer knowing a human is now involved This gives businesses the efficiency of full AI automation combined with the option of human judgment at the moments that matter most. **Module connections:** Subscribes to lead warmth events from Chat Interface and Contact Forms. Writes agent intervention records to the shared event log. --- ### 5.6 Co-Browser *(Add-on / Upsell)* **Role:** Extends AI chat to every page of a website with full contextual awareness. The Co-Browser is a floating input bar that follows the user across every page of the business's website. Unlike the Chat Interface (which is a dedicated chat destination), the Co-Browser is ambient — it is always present without requiring the user to navigate anywhere. **What makes it different:** - The AI has awareness of which page the user is currently on, and uses that context in its responses. A user on the HVAC maintenance service page asking "how long does this take?" gets an answer specific to that service, not a generic response. - Voice mode is the primary intended interaction method. A user browsing through multiple pages can carry on a natural conversation without typing on each page. - Page visit data is recorded throughout the session. Before a sales call happens, the business knows exactly which pages this prospect viewed, in what order, how long they spent, and what questions they asked. This is high-value pre-call intelligence. The Co-Browser carries a higher inference cost than the standard Chat Interface due to continuous page context processing. It is priced as an add-on above the standard chat subscription. **Module connections:** Leverages Voice AI for voice mode. Writes page visit and conversation data to the contact record. Emits events that Chat Monitor can act on. --- ## 6. Business Model The platform operates on a zero-barrier, performance-aligned revenue model. Agencies access the platform at no cost and pay nothing until they generate revenue for themselves. **The three-party structure:** Every transaction involves aiConnected (infrastructure operator), the Agency (white-label service provider), and the Business Client (end user). All billing flows through aiConnected's Stripe infrastructure — this is the mechanism by which the platform tax is automatically collected. **Platform Tax (Revenue Share)** aiConnected charges 10% of whatever the agency charges their clients, collected automatically at the point of transaction. If an agency generates no revenue, aiConnected earns nothing from that agency. The platform tax scales directly with agency success. **Floor Pricing** Every module carries a minimum floor price set by aiConnected. Agencies may not charge below the floor. Above the floor, agencies set whatever price they choose — $200/month or $2,000/month for the same module. aiConnected takes 10% regardless. **API Resale Model** AI inference is sourced through OpenRouter, giving access to all major model providers (Anthropic, OpenAI, Google, Mistral, Meta, and others) through a single integration. aiConnected applies a 10% markup on API costs and resells credits to agencies. Agencies then apply their own markup when selling AI usage to clients. The platform tax applies on top of this at the client billing level. Agencies may also opt into BYOK (Bring Your Own Key) if they have existing direct API relationships. BYOK removes the API markup but does not exempt the agency from the platform tax — the platform tax compensates for platform infrastructure and white-label capability, not API access. **Revenue streams summary:** | Stream | Type | Rate | |---|---|---| | Platform tax on client billing | Variable | 10% of agency charges | | API resale markup | Variable | 10% of API cost | | Platform tax on BYOK usage | Variable | 10% of agency charges | | Customer Success — Starter | Flat monthly | $600–$800/month | | Customer Success — Part-Time | Flat monthly | $1,500–$1,700/month | | Customer Success — Full-Time | Flat monthly | $3,000–$3,500/month | Customer Success is an optional white-label service offering where aiConnected provides dedicated human success managers who engage with business clients on the agency's behalf. All CS activity is fully white-labeled — clients interact with who they believe is the agency's team. --- ## 7. Technical Stack | Layer | Technology | |---|---| | Frontend framework | Next.js 14 | | Monorepo management | Turborepo | | UI component system | shadcn/ui | | Tenant theming | TweakCN | | Database | Supabase (PostgreSQL) | | Authentication | Supabase Auth | | Realtime / event bus | Supabase Realtime + custom event log table | | Voice infrastructure | LiveKit | | AI inference | OpenRouter (unified API layer across all major providers) | | Container orchestration | Dokploy | | Automation / workflow layer | n8n (self-hosted, existing infrastructure) | | API gateway | Custom — Next.js middleware + edge functions | | Billing | Stripe (all client billing flows through aiConnected's Stripe) | | Infrastructure | DigitalOcean | --- ## 8. MVP Scope — What's In, What's Out, What Must Be Architected For ### In scope for MVP - Core shell (auth, multi-tenant provisioning, billing, module registry, event bus, API gateway, theme engine) - shadcn/ui component foundation + TweakCN theming engine - Super user, Agency user, and Business user access layers - All five MVP modules: Knowledge Base Generator, Voice AI Hub, Chat Interface, Contact Forms, Chat Monitor - Co-Browser as an add-on module - Stripe billing integration with platform tax collection and floor price enforcement - Agency white-label configuration (branding, custom domain, module selection, client management) ### Out of scope for MVP (post-MVP) - Developer portal and developer account management - Community sandbox and trust pipeline UI - Capability registry browsing interface - Personal user account type - Visual builder (Craft.js drag-and-drop interface builder) - GitHub import / module compatibility translation layer ### Must be architected for — not shipped, but cannot be an afterthought - Module manifest specification: must be finalized and enforced from day one. Every first-party module ships with a compliant manifest. - Capability registry schema: database schema must exist in MVP even if no UI exposes it yet - Event bus: must be designed to handle events from unknown future modules, not hardcoded to MVP module list - API gateway: must route dynamically to module containers, not via hardcoded routing tables - Container isolation: every module, including first-party modules, runs in its own container from day one - Tenant data isolation: must be designed to accommodate third-party module data schemas without architectural rework --- ## 9. What MVP Completion Looks Like MVP is complete when the following is true end-to-end: An agency signs up, configures their white-label branding (logo, colors, domain, typography), sets up their Stripe billing, and creates their first business client account. That business client logs into what appears to be the agency's product, runs the Knowledge Base Generator against their website, reviews and approves the generated knowledge base, and publishes it. The Chat Interface goes live on the business's website — both as a bubble and as a full-screen embed. A site visitor opens the chat, asks questions about the business's services, receives AI responses drawn from the knowledge base, and is presented with formatted service cards. They switch to voice mode and continue the conversation by speaking. The business user sees this conversation happening in the Chat Monitor. The lead's warmth score increases. The business receives a notification, reviews the conversation, and decides to let the AI guide the lead toward booking. The appointment is set. Separately, a visitor submits a contact form on the business's website. The Contact Forms module validates the submission, classifies the intent as a purchase inquiry, and initiates a follow-up AI interaction that qualifies the lead and routes them toward a consultation booking. All of the above happens within a white-label environment that bears zero visible relationship to aiConnected. The agency looks like the product company. That is the MVP. --- *aiConnected Platform — MVP Specification v1.0* *March 2026* *For development team engagement — confidential* --- ## aiConnected Platform — Overview **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-overview-non-technical # aiConnected Platform — Overview **What We're Building and Why It Matters** *March 2026* --- ## The Short Version aiConnected is a platform that gives agencies their own AI-powered software product to sell — without building one from scratch. An agency signs up, puts their brand on it, sets their own prices, and sells it to their business clients as if they made it. The agency keeps most of the money. aiConnected runs silently in the background. The tools on the platform are built specifically to help businesses win more sales — not market better, not manage projects, not send newsletters. Close more deals. That focus is deliberate, and it's the gap in the market. --- ## The Problem We're Solving Two groups of people have the same problem from different directions. **Agencies** — marketing agencies, consulting firms, service businesses — are increasingly expected to offer AI tools to their clients. The problem is that building software is expensive, slow, and not what agencies do. So they either skip it entirely, or they stitch together a dozen different subscriptions and hope it holds. Neither approach is a real product. **Businesses** — the agencies' clients — are drowning in AI hype and short on practical results. Every tool they try is either too complex to use or too disconnected from everything else. A chatbot that doesn't know what the business actually does. A voice system that can't pass notes to the sales team. Tools that were never meant to work together. aiConnected solves both problems with one platform. Agencies get a real product to sell. Businesses get tools that actually work together. --- ## What the Platform Does Every tool on aiConnected is built around one idea: help a business turn more strangers into customers. **The Knowledge Base** is the starting point for every business. The platform reads through the business's entire website, researches any gaps in the information, and builds a comprehensive AI knowledge source — covering every service, every price, every frequently asked question, every "is this right for me?" scenario. This becomes the brain that powers every other tool. When the AI answers a question, it's answering from this knowledge base. It knows what the business does, who it's for, what it costs, and how it works. **The AI Chat** is what the business's customers interact with. It lives on the business's website — either as a full-screen experience or a chat bubble in the corner — and it's branded entirely to the business. There's no mention of aiConnected anywhere. A visitor asks a question, the AI answers using the knowledge base, services are presented in clean card formats, and the conversation moves toward booking or buying. If a visitor wants to talk instead of type, the chat switches to voice instantly. **The Voice AI** handles phone calls the same way. Inbound calls are answered by an AI that knows the business inside and out. It can answer questions, qualify callers, book appointments, and route people to the right person — around the clock, without a receptionist. Outbound calls — follow-ups, reminders, outreach — work the same way. **The Contact Forms tool** does something simple but valuable. When someone fills out a form on a business's website, they're usually left waiting for a response. This tool intercepts that moment. It validates that the submission is real, figures out why the person reached out, and immediately starts a helpful conversation — answering their question on the spot, qualifying them as a lead, or booking them an appointment. Most businesses lose leads in that waiting period. This closes the gap. **The Sales Monitor** gives the business a live view of what's happening in their AI conversations. If a prospect is asking detailed questions and showing signs of being ready to buy, the business gets a notification. They can watch the conversation, step in to take over, or quietly guide the AI to move the prospect toward a next step — all without the prospect knowing anything changed. It's the difference between handing the business a set of tools and giving them a full picture of their sales activity in real time. **The Co-Browser** is an add-on that follows a visitor across every page of a website with a floating conversation window. The AI knows which page the visitor is on and responds with that context in mind. A visitor on the pricing page gets different engagement than one on the about page. Most people browsing a website don't want to type — so this works just as well by voice. And throughout the session, the business can see exactly which pages the visitor looked at and what questions they had. That's valuable intelligence before any sales conversation ever happens. --- ## How the Business Model Works Agencies join the platform for free. There are no upfront fees, no setup costs, and no monthly charges just to have an account. The only time money changes hands is when the agency is making money from their clients. When an agency charges a client for any service on the platform, aiConnected takes a small percentage of that transaction automatically. That's it. If an agency isn't generating revenue, neither is aiConnected. The incentives are fully aligned. Agencies set their own prices. One agency might charge $200 a month for the AI chat. Another might charge $2,000 for the same tool. That's entirely their business. aiConnected takes its percentage either way. The practical result: an agency can go from signing up to offering a complete AI sales product to their clients — under their own brand, at their own price point — with no technical staff, no infrastructure costs, and no upfront investment. They only pay when they're already earning. --- ## Why This Is Different **GoHighLevel** is the most obvious comparison — it's the dominant white-label agency platform. But GoHighLevel is a closed system. Nobody can build new tools for it from the outside. What GoHighLevel offers today is what it will always offer, barring their own internal roadmap. aiConnected is built to be open. Outside developers can build new tools for the platform, submit them through a governed review process, and make them available to every agency on the platform. A developer who builds a video avatar tool today makes it available to every aiConnected agency. A developer who builds an outbound prospecting tool tomorrow can connect it to the voice AI that already exists and the knowledge base that already exists — they don't have to start from zero. The platform gets more capable with every addition. **GoHighLevel owns marketing automation.** There's no real competition with them on that turf. aiConnected is built for sales — prospecting, qualifying, following up, closing. That's a gap the market hasn't filled with a cohesive, white-label product. That's the lane. --- ## The Opportunity Agencies are the distribution channel. Every agency that joins the platform becomes a sales force for aiConnected tools — selling them under their own brand to their own clients. The platform doesn't need a large direct sales team. It needs agencies who see the value and want to build a recurring revenue stream from tools they didn't have to build. The businesses those agencies serve are exactly the businesses that need what's on the platform. They're not enterprise companies with dedicated tech teams. They're service businesses — insurance agencies, law firms, contractors, medical practices, consulting firms — that need to be better at sales and don't have the people or the time to run a proper sales operation. AI does that for them, at a fraction of the cost. The compounding element — each new tool building on top of what already exists — means the platform's value to both agencies and their clients grows over time without requiring proportional growth in aiConnected's own team. --- *aiConnected, Inc. — Atlanta, Georgia* *aiconnected.ai* --- ## aiConnected Platform — Overview **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-overview # aiConnected Platform — Overview **What We're Building and Why It Matters** _March 2026_ --- ## The Short Version aiConnected is a platform that gives agencies their own AI-powered software product to sell — without building one from scratch. An agency signs up, puts their brand on it, sets their own prices, and sells it to their business clients as if they made it. The agency keeps most of the money. aiConnected runs silently in the background. The tools on the platform are built specifically to help businesses win more sales — not market better, not manage projects, not send newsletters. Close more deals. That focus is deliberate, and it's the gap in the market. --- ## The Problem We're Solving Two groups of people have the same problem from different directions. **Agencies** — marketing agencies, consulting firms, service businesses — are increasingly expected to offer AI tools to their clients. The problem is that building software is expensive, slow, and not what agencies do. So they either skip it entirely, or they stitch together a dozen different subscriptions and hope it holds. Neither approach is a real product. **Businesses** — the agencies' clients — are drowning in AI hype and short on practical results. Every tool they try is either too complex to use or too disconnected from everything else. A chatbot that doesn't know what the business actually does. A voice system that can't pass notes to the sales team. Tools that were never meant to work together. aiConnected solves both problems with one platform. Agencies get a real product to sell. Businesses get tools that actually work together. --- ## What the Platform Does Every tool on aiConnected is built around one idea: help a business turn more strangers into customers. **The Knowledge Base** is the starting point for every business. The platform reads through the business's entire website, researches any gaps in the information, and builds a comprehensive AI knowledge source — covering every service, every price, every frequently asked question, every "is this right for me?" scenario. This becomes the brain that powers every other tool. When the AI answers a question, it's answering from this knowledge base. It knows what the business does, who it's for, what it costs, and how it works. **The AI Chat** is what the business's customers interact with. It lives on the business's website — either as a full-screen experience or a chat bubble in the corner — and it's branded entirely to the business. There's no mention of aiConnected anywhere. A visitor asks a question, the AI answers using the knowledge base, services are presented in clean card formats, and the conversation moves toward booking or buying. If a visitor wants to talk instead of type, the chat switches to voice instantly. **The Voice AI** handles phone calls the same way. Inbound calls are answered by an AI that knows the business inside and out. It can answer questions, qualify callers, book appointments, and route people to the right person — around the clock, without a receptionist. Outbound calls — follow-ups, reminders, outreach — work the same way. **The Contact Forms tool** does something simple but valuable. When someone fills out a form on a business's website, they're usually left waiting for a response. This tool intercepts that moment. It validates that the submission is real, figures out why the person reached out, and immediately starts a helpful conversation — answering their question on the spot, qualifying them as a lead, or booking them an appointment. Most businesses lose leads in that waiting period. This closes the gap. **The Sales Monitor** gives the business a live view of what's happening in their AI conversations. If a prospect is asking detailed questions and showing signs of being ready to buy, the business gets a notification. They can watch the conversation, step in to take over, or quietly guide the AI to move the prospect toward a next step — all without the prospect knowing anything changed. It's the difference between handing the business a set of tools and giving them a full picture of their sales activity in real time. **The Co-Browser** is an add-on that follows a visitor across every page of a website with a floating conversation window. The AI knows which page the visitor is on and responds with that context in mind. A visitor on the pricing page gets different engagement than one on the about page. Most people browsing a website don't want to type — so this works just as well by voice. And throughout the session, the business can see exactly which pages the visitor looked at and what questions they had. That's valuable intelligence before any sales conversation ever happens. --- ## How the Business Model Works Agencies join the platform for free. There are no upfront fees, no setup costs, and no monthly charges just to have an account. The only time money changes hands is when the agency is making money from their clients. When an agency charges a client for any service on the platform, aiConnected takes a small percentage of that transaction automatically. That's it. If an agency isn't generating revenue, neither is aiConnected. The incentives are fully aligned. Agencies set their own prices. One agency might charge $200 a month for the AI chat. Another might charge $2,000 for the same tool. That's entirely their business. aiConnected takes its percentage either way. The practical result: an agency can go from signing up to offering a complete AI sales product to their clients — under their own brand, at their own price point — with no technical staff, no infrastructure costs, and no upfront investment. They only pay when they're already earning. --- ## Why This Is Different **GoHighLevel** is the most obvious comparison — it's the dominant white-label agency platform. But GoHighLevel is a closed system. Nobody can build new tools for it from the outside. What GoHighLevel offers today is what it will always offer, barring their own internal roadmap. aiConnected is built to be open. Outside developers can build new tools for the platform, submit them through a governed review process, and make them available to every agency on the platform. A developer who builds a video avatar tool today makes it available to every aiConnected agency. A developer who builds an outbound prospecting tool tomorrow can connect it to the voice AI that already exists and the knowledge base that already exists — they don't have to start from zero. The platform gets more capable with every addition. **GoHighLevel owns marketing automation.** There's no real competition with them on that turf. aiConnected is built for sales — prospecting, qualifying, following up, closing. That's a gap the market hasn't filled with a cohesive, white-label product. That's the lane. --- ## The Opportunity Agencies are the distribution channel. Every agency that joins the platform becomes a sales force for aiConnected tools — selling them under their own brand to their own clients. The platform doesn't need a large direct sales team. It needs agencies who see the value and want to build a recurring revenue stream from tools they didn't have to build. The businesses those agencies serve are exactly the businesses that need what's on the platform. They're not enterprise companies with dedicated tech teams. They're service businesses — insurance agencies, law firms, contractors, medical practices, consulting firms — that need to be better at sales and don't have the people or the time to run a proper sales operation. AI does that for them, at a fraction of the cost. The compounding element — each new tool building on top of what already exists — means the platform's value to both agencies and their clients grows over time without requiring proportional growth in aiConnected's own team. --- _aiConnected, Inc. — Atlanta, Georgia_ _aiconnected.ai_ --- ## Platform v1 Audit — Save vs. Trash **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-audit # Platform v1 Audit — Save vs. Trash **Assessed against v2 MVP requirements — March 2026** --- ## The Short Answer The apps are trash. The packages are gold. The v1 repo has two distinct layers: a set of shared `packages` containing real, working business logic — and a set of `apps` (platform, chat, kb-studio) containing a broken, inconsistent UI layer built without a coherent design system. The packages should be carried forward almost entirely. The apps should be rebuilt from scratch on shadcn/ui. This is actually good news. The hardest parts — the AI logic, the KB engine, the manifest system — are already built and working. What needs rebuilding is the shell and the UI presentation layer, which is time-consuming but straightforward. --- ## Package-by-Package Assessment --- ### `packages/kb-engine` — **SAVE ENTIRELY** This is the most valuable thing in the repo. It is the brain of the Knowledge Base Generator module and it is substantive, working code. | File | Lines | What it does | Status | |---|---|---|---| | `scraper.js` | 161 | Website crawler via Crawl4AI — crawls, batches, follows internal links | Save | | `researcher.js` | 172 | Takes raw service data and generates comprehensive educational content via AI | Save | | `compiler.js` | 338 | Compiles scraped + researched content into a structured KB | Save | | `extractor.js` | 99 | Extracts structured service/business data from raw page content | Save | | `generate.js` | 138 | Orchestrates the full KB generation pipeline | Save | | `ai.js` | 266 | AI inference layer (chatJSON, structured responses) | Save | | `runtime.js` | 487 | KB runtime — handles queries, retrieval, context assembly | Save | | `system-prompt.js` | 111 | Generates KB-aware system prompts for AI interactions | Save | | `starters.js` | 95 | Conversation starter generation from KB content | Save | | `concern-mapper.js` | 109 | Maps customer concerns to KB content sections | Save | | `quiz.js` | 151 | Service qualification quiz logic | Save | **v2 action:** Port as the `kb-engine` module package. The logic is correct. Wrap it in the v2 module manifest format. The UI that presents it gets rebuilt in shadcn — the engine underneath does not change. --- ### `packages/chat-core` — **SAVE ENTIRELY** Over 3,000 lines of chat AI logic. This is the engine that powers conversations, lead capture, structured responses, and runtime configuration. None of it is UI — it is all pure logic. | File | Lines | What it does | Status | |---|---|---|---| | `runtime-config.js` | 1,092 | Full chat configuration model — every setting, validation, default | Save | | `lead-capture.js` | 987 | Lead form templates, field types, capture logic | Save | | `lead-delivery.js` | 436 | Delivers captured leads to configured destinations | Save | | `knowledge.js` | 135 | Connects chat runtime to KB for context retrieval | Save | | `ai-config.js` | 150 | AI model configuration, provider routing | Save | | `system-prompt.js` | 110 | Chat system prompt assembly | Save | | `structured-response.js` | 67 | Formats AI responses into structured card/message format | Save | | `rate-limit.js` | 107 | Rate limiting logic | Save | | `composio.js` | 289 | Composio integration layer | Save — review for v2 adapter pattern | **v2 action:** Port as the `chat-core` package. Same approach as kb-engine — logic stays, UI gets rebuilt. --- ### `packages/permissions` — **SAVE ENTIRELY** The role and permission system is clean, correct, and already maps exactly to the v2 user type model. ``` SUPER_ADMIN / SUPER_ADMIN_STAFF AGENCY_ADMIN / AGENCY_STAFF BUSINESS_ADMIN / BUSINESS_STAFF ``` Role groups, helper functions (`isSuperAdmin`, `isAgencyUser`, `isBusinessUser`) — all correct. This is already the v2 permission model in code. **v2 action:** Copy as-is into v2 `packages/permissions`. --- ### `packages/app-sdk` — **SAVE THE MANIFEST SPEC** The manifest system in `manifests.js` is the most architecturally significant thing in the repo. It already implements the module manifest concept with `inputs`, `outputs`, `extensionPoints`, `permissions`, and `capabilities` — exactly what the v2 module system requires. This wasn't vibe-coded; this was thoughtfully designed. The existing manifests for Chat and KB Studio can be used as the template for the v2 manifest spec and as starting points for those modules' v2 manifests. **v2 action:** The manifest format should become the official v2 module manifest spec. Extend it to add `events_emitted` and `events_consumed` fields (which the v2 event bus requires), then use it as the foundation for every v2 module. --- ### `packages/branding` — **SAVE THE LOGIC, REPLACE THE TOKENS** The `mergeTheme` function and the theme inheritance model are correct. The specific CSS variable names don't map to TweakCN tokens, so the token definitions themselves get replaced — but the merge pattern is reusable. **v2 action:** Keep `mergeTheme`. Replace `DEFAULT_PLATFORM_THEME` token names with TweakCN-compatible equivalents. --- ### `packages/db` — **SAVE THE PATTERNS, AUDIT THE SCHEMA** The Supabase client setup, browser/server split, and connection patterns are reusable. The actual database schema needs a full audit before v2 — it accumulated migrations in an uncontrolled way and likely has inconsistencies. But the connection architecture is correct. **v2 action:** Keep the Supabase client setup. Define the v2 schema cleanly from scratch using the v2 data model (workspaces, contacts, users, events, module_registry). Do not migrate the v1 schema — start fresh. --- ## App-by-App Assessment --- ### `apps/platform` — **TRASH** The main platform admin UI. Built without a consistent design system — mix of custom CSS, Tailwind, partial shadcn adoption, and Plasmic artifacts. The Plasmic integration files (`plasmic-init.ts`, `plasmic-init-client.ts`) are dead weight. Seventeen sidebar links go to "Coming Soon." Core pages (subscriptions, invoices, payments) are empty. The dashboard shows mock data. **v2 action:** Delete. Rebuild the platform shell from scratch on shadcn/ui. The business logic it calls into (from packages) is being saved — only the UI layer is being discarded. --- ### `apps/chat` — **TRASH THE UI, KEEP NOTHING** The chat interface UI deviates from the original design, has broken component styling, a non-functional mobile view, and configuration settings that apply styles to the wrong elements. The underlying logic that powers it lives in `packages/chat-core` — which is being saved. The UI presentation layer itself has no salvageable parts. **v2 action:** Delete. Rebuild entirely on shadcn/ui using chat-core as the logic engine. --- ### `apps/kb-studio` — **TRASH THE UI, KEEP NOTHING** Same pattern. The KB Studio UI has the wrong colors, broken onboarding flow, an API key error that persists despite successful connections, and missing navigation. The engine underneath it (`packages/kb-engine`) is being saved. The UI is not. **v2 action:** Delete. Rebuild entirely on shadcn/ui using kb-engine as the logic engine. --- ### `apps/capabilities` — **REFERENCE ONLY** Contains the capabilities system PRD (22-step build plan) and related planning documents. This is context, not code. The approach it describes is being superseded by the v2 module import system architecture. **v2 action:** Keep as reference. Do not build from it. --- ## Migration Decisions | Item | Decision | Reason | |---|---|---| | Supabase instance | **Keep** | Existing data, connected infrastructure | | Supabase schema | **Rebuild** | Accumulated inconsistencies, v2 needs clean model | | Crawl4AI integration | **Keep** | Working scraper infrastructure at crawl.sec-admn.com | | DigitalOcean / Dokploy | **Keep** | Working deployment infrastructure | | n8n workflows | **Keep** | Separate conversion effort, not blocking v2 | | OpenRouter integration | **Keep** | Already the AI inference layer | | Stripe setup | **Keep** | Existing payment infrastructure | | Plasmic integration | **Trash** | Abandoned approach | | Custom CSS/styling | **Trash** | Replaced by shadcn/ui + TweakCN | --- ## Summary for v2 Build **Start fresh:** Shell (platform app), Chat UI, KB Studio UI, database schema. **Carry forward:** kb-engine package, chat-core package, permissions package, app-sdk manifest format, branding merge logic, Supabase client patterns, all existing infrastructure (DigitalOcean, Crawl4AI, OpenRouter, Stripe, n8n). **The honest assessment:** Three months of vibe-coded UI work gets discarded. Three months of AI logic, KB engine, lead capture, manifest architecture, and permissions work gets carried forward. The rebuild is faster than it looks because the intellectual heavy lifting is already done. --- ## aiConnected Platform — MVP Specification **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v1-mvp-specification # aiConnected Platform — MVP Specification **Version 1.0** *Prepared for development team engagement — March 2026* --- ## TL;DR aiConnected is a white-label SaaS platform that agencies buy, rebrand as their own product, and sell to business clients — primarily for AI-powered sales tooling. Think GoHighLevel, but open at the code level so third-party developers can build and publish modules that compound on top of each other over time. The MVP delivers a working agency platform with five interconnected sales modules: a Knowledge Base Generator (scrapes a business's website and builds a comprehensive AI knowledge source that powers everything else), a Voice AI Hub (handles all inbound and outbound voice interactions via LiveKit), a Chat Interface (a fully branded sales funnel in chat form, embeddable as a full-screen window or bubble), a Contact Forms module (intercepts form submissions and converts them into AI-engaged leads in real time), and a Chat Monitor (gives the business a live view of conversations with the ability to step in or guide the AI when a lead goes warm). A Co-Browser add-on extends the chat to every page of a website with full page-context awareness. Agencies pay nothing until they generate revenue. aiConnected earns a 10% platform tax on whatever agencies charge their clients, plus a 10% markup on AI API costs. All billing flows through aiConnected's Stripe infrastructure automatically. The architecture is the more important story. The MVP is built as a shell with pluggable, containerized modules that communicate exclusively through a shared event bus and a declared capability registry. The developer ecosystem — community sandbox, trust pipeline, registry UI — is not an MVP deliverable, but the architecture must support it from day one. Every first-party module ships with a compliant manifest. The event bus is designed for modules it hasn't seen yet. The API gateway routes dynamically. Nothing about the MVP architecture can require rework when the developer layer is built on top of it. The full platform spec, billing model, and architecture document are included in this package. This document defines the MVP scope and what done looks like. --- ## Purpose of This Document This document defines the Minimum Viable Product for the aiConnected platform rebuild. It is written for senior engineers and technical collaborators who need a precise understanding of what is being built, why the architectural decisions were made the way they were, and what "done" looks like for the MVP. The platform is being rebuilt from scratch. An earlier version exists and may be referenced for logic or salvage, but it is not recommended as a foundation. This specification represents the authoritative target. --- ## 1. What We Are Building aiConnected is a white-label SaaS operating system for agencies. It functions the way GoHighLevel functions in its interconnectedness and multi-tenant white-label model — but unlike GoHighLevel, it is architecturally open. Third-party developers can extend the platform at the code level, with new capabilities compounding on top of existing ones over time. The closest mental model is this: **WordPress core + Crocoblock-style interconnected plugins, built as a modern SaaS platform, sold to agencies who rebrand and resell it to their business clients.** Every module on the platform shares the same data layer, the same identity infrastructure, and the same event system. A voice call log is visible to the chat module. A knowledge base update immediately powers AI interactions everywhere. No module is an island. The platform's long-term differentiator is its developer extensibility model — any developer can build a module, submit it through a governed trust pipeline, and have its capabilities published to a shared registry that all future developers can build upon. Development progress on the platform compounds rather than accumulates in silos. This is described in detail in Section 6.4, and the architecture must support it from day one — though it will not be marketed or sold in the MVP phase. --- ## 2. Market Positioning The AI SaaS market has largely split into two camps: marketing automation platforms (GoHighLevel, ActiveCampaign, HubSpot) and general-purpose AI tools (ChatGPT, Claude, Gemini wrappers). Neither camp owns sales. aiConnected occupies the sales lane specifically. Every MVP module is chosen because it serves the sales process: - Converting website visitors into identified leads - Warming leads through intelligent AI interaction - Qualifying leads in real time before a human ever gets involved - Giving sales teams visibility into lead behavior before a call - Handling inbound voice and chat interactions around the clock This positioning means aiConnected agencies are not competing with GoHighLevel resellers. They are bringing a category of tooling — AI-powered sales infrastructure — that does not exist as a cohesive, white-label product in the market today. --- ## 3. User Types The platform serves five distinct user classes. The MVP focuses exclusively on the first three. **Super User (aiConnected Admin)** The platform operator. Has visibility into all agencies, all billing, all system health. The super user does not interact with the product the way agencies and businesses do — they manage the ecosystem. **Agency User** The primary commercial customer. An agency signs up for aiConnected, configures it as their own branded product, and deploys it to their business clients. From their clients' perspective, the agency built the platform. aiConnected is invisible. The agency sets their own pricing, creates their own packages, and controls which modules their clients access. **Business User** The agency's end client. A business user logs into what appears to be the agency's proprietary platform. They configure their AI tools, manage their contacts, and monitor their leads. They have no awareness of aiConnected as the underlying infrastructure. **Personal User** *(post-MVP)* Individual users accessing the platform outside of an agency context. Architecture must not preclude this user type, but it is not an MVP deliverable. **Developer** *(post-MVP)* Third-party developers who build and publish modules to the platform. The module manifest system and capability registry must be architected for this user type from day one, but the developer-facing portal and trust pipeline are not MVP deliverables. --- ## 4. The Architecture ### 4.1 The Core Shell The shell is the platform's permanent, stable foundation. Every module lives inside it. The shell itself never contains business logic — that lives entirely in modules. **Shell responsibilities:** - User authentication and session management (Supabase Auth) - Multi-tenant provisioning — agency accounts, sub-accounts (business clients), and super admin - Navigation and routing infrastructure - Billing infrastructure (Stripe, platform tax collection, module activation) - Module registry — the live directory of all installed and available modules - Event bus — the system-wide communication channel between modules - API gateway — routes requests between modules, enforces permissions, handles rate limiting - Theme engine — applies per-tenant TweakCN configuration **What the shell explicitly does not do:** It contains no CRM logic, no voice logic, no chat logic, no AI inference calls. All of that lives in modules. --- ### 4.2 UI Foundation — shadcn/ui + TweakCN The entire platform UI is built on shadcn/ui. This decision has three direct benefits: **1. Development velocity.** Building new interfaces means organizing existing components, not making design decisions from scratch. Module developers inherit a complete, consistent design system without writing a line of custom CSS. **2. True white-label.** TweakCN enables full CSS-level customization per tenant — colors, typography, border radius, shadows, spacing, borders, backgrounds, hover states. Agencies can configure every visual element to match their brand. The result is that two agencies running the same platform look nothing alike. GoHighLevel's weakness — that its branded deployments are immediately recognizable as GoHighLevel — is structurally prevented. **3. Developer ergonomics.** Third-party developers who build modules using shadcn/ui components get platform-native styling automatically. Developers who bring a custom UI (for instance, a company integrating an existing product that was not designed with shadcn) are accommodated — the system must not break for non-shadcn UIs. But shadcn is the strongly encouraged default, and the path of least resistance. Business users may also be granted UI customization access at the agency's discretion. This represents a potential upsell — agencies can charge clients for the ability to personalize their own experience. --- ### 4.3 Shared Data Layer A single Supabase (PostgreSQL) instance serves as the platform's unified data foundation. **Core shared entities:** | Entity | Description | |---|---| | `workspaces` | The tenant record. Every module references this. | | `contacts` | The universal entity. Every module reads from and writes to contacts. | | `users` | Platform users with roles and permissions scoped to their workspace. | | `events` | The shared event log. This is the interconnection mechanism. | | `module_registry` | Live directory of installed modules and their capability contracts. | **Module data:** Each module owns its own database tables, namespaced by module (e.g., `voice_calls`, `chat_conversations`, `kb_entries`). Modules may read from shared entities. They write to their own tables and emit events to the shared event log. **The interconnection mechanism:** When the voice module completes a call, it writes to `voice_calls` and emits a `voice.call.completed` event. The chat module, the contacts module, and any automation subscribed to that event receives it and responds accordingly. No module ever reaches directly into another module's tables. All cross-module communication flows through the event bus and declared API contracts. This is what makes the platform's capabilities genuinely interconnected rather than just co-located. --- ### 4.4 The Module System Every capability on the platform — whether built by aiConnected or a third party — is a module. Modules are self-contained and follow a common manifest specification. **Module manifest (example):** ```json { "id": "voice-hub", "name": "Voice AI Hub", "version": "1.0.0", "developer": "aiConnected", "routes": ["/voice", "/voice/calls", "/voice/settings"], "sidebar": { "label": "Voice", "icon": "phone", "position": 3 }, "permissions": ["contacts.read", "contacts.write", "events.emit"], "capabilities": { "inputs": ["contact_id", "script", "voice_profile_id"], "outputs": ["call_record", "transcript", "call_status"], "events_emitted": ["voice.call.started", "voice.call.completed", "voice.call.failed"], "events_consumed": ["contact.updated", "kb.updated"] }, "data_schemas": ["voice_calls", "voice_profiles", "transcripts"] } ``` The platform reads the manifest and automatically registers routes, adds sidebar navigation, grants declared permissions, subscribes the module to its declared events, and publishes its capabilities to the registry. This manifest-first approach is what makes the module system extensible. Adding a new module does not require touching the shell. --- ### 4.5 Container Architecture Every module — including all first-party aiConnected modules — runs in its own isolated container. This is not a post-MVP architectural improvement. It is a day-one requirement. **Why this is non-negotiable:** - A failing module cannot affect the platform or any other module - A compromised module cannot reach into the core or other containers - Each module can be updated, rolled back, or restarted independently - Resource usage per module is monitored and enforceable - Security audits are scoped to individual containers Modules communicate with each other exclusively through the API gateway. Direct container-to-container calls are not permitted. The API gateway handles routing, authentication enforcement, rate limiting, and anomaly detection. Infrastructure: DigitalOcean, orchestrated via Dokploy. --- ### 4.6 Visual Builder The platform includes a native drag-and-drop interface builder — the equivalent of Elementor, but for React components, running inside the platform itself. **The problem it solves:** Building new module interfaces currently requires writing React code. This creates a bottleneck: every UI change, every new layout, every new module screen requires a developer. The Visual Builder removes that bottleneck entirely. New interfaces can be assembled visually, using the same shadcn/ui components that power the rest of the platform, without touching code. **Foundation:** Craft.js — an open-source React drag-and-drop framework designed to be embedded natively inside an application rather than running as a standalone tool. **How it works:** - Every shadcn/ui component is registered as a drag-and-drop block - Any component from an imported library can be registered as a block - Developers and platform admins configure component props visually — layout, spacing, content, behavior — without writing JSX - The builder outputs real, production React components that live inside the module - Non-technical tenant admins can use it for page and layout customization within their white-label environment **Scope boundary:** The Visual Builder handles UI composition — what things look like and how they are arranged. Business logic lives in the module's backend. The builder has no access to backend logic and cannot create or modify data schemas. **Why this is foundational:** The platform's long-term extensibility depends on new interfaces being buildable without a full engineering cycle for every screen. This is especially critical for the third-party developer model — a developer should be able to assemble a functional, platform-native UI for their module without becoming a React expert. The Visual Builder is also the foundation for the tenant customization story: agencies and businesses who want to adjust layouts, restructure pages, or create custom views do so here. --- ### 4.7 Module Import System The Module Import System is the mechanism by which capabilities that exist outside the platform are brought inside it and made platform-native. It is a core infrastructure component, not a future feature. **The problem it solves:** The platform's value compounds as more modules are added. But most useful software already exists in other forms — GitHub repositories, WordPress plugins, n8n automation workflows, third-party applications. Requiring every external capability to be rebuilt from scratch inside the platform would be prohibitively slow. The Module Import System creates a conversion pipeline instead. **What it handles:** *GitHub Repository Import* A developer provides a GitHub repository URL. The import system reads the codebase, identifies what the application does, maps its inputs and outputs to the platform's capability contract format, and generates a module manifest. The developer reviews and refines the generated manifest, the module is containerized, and it enters the developer trust pipeline. The developer does not need to rewrite their application — the import layer wraps it in platform-compatible structure. *WordPress Plugin Conversion* WordPress plugins represent decades of accumulated functionality. The plugin conversion pathway reads a plugin's PHP codebase, identifies its hooks, filters, and data structures, and generates the equivalent module logic in the platform's architecture. Not every plugin is a candidate for direct conversion — those with deep WordPress core dependencies require a more manual process — but the converter handles the common cases automatically and produces a translation scaffold for the rest. *n8n Workflow Conversion* The platform operates a library of 2,000+ n8n automation workflows. These represent an enormous inventory of pre-built capabilities — each workflow is, effectively, a module waiting to be formalized. The n8n converter reads a workflow's node structure, maps its trigger conditions and outputs to the module manifest format, wraps the workflow execution in a containerized runtime, and registers it as a platform capability. Workflows converted this way become first-class modules: they appear in the capability registry, they emit and consume events through the event bus, and they are available to all tenants and future developers as building blocks. **What the import system is not:** It is not a magic button that instantly makes any external code platform-compatible. It is a structured conversion pipeline that does the heavy lifting for the common cases and produces a working scaffold for the complex ones. Human review is always part of the process — the import system reduces the effort, it does not eliminate it. **Why this is foundational:** The platform's capability library starts with what aiConnected builds directly. The Module Import System is what causes that library to grow at a pace that no single development team could sustain. It is the mechanism that connects the existing ecosystem of software — GitHub, WordPress, n8n — to the platform's compounding model. --- ### 4.8 Developer Ecosystem Foundation *(Architectural Requirement — Not MVP Product)* The platform must be architected for third-party developer extensibility from day one. The developer-facing portal, community sandbox, capability registry UI, and trust pipeline workflow are not MVP deliverables and will not be marketed or sold in the MVP phase. However, the architecture must not require significant rework to support them later. This means the following must be in place at MVP: - The module manifest specification must be final and enforced - The capability registry database schema must be in place, even if the UI for browsing it is not - The event bus must be designed to accommodate modules it has not yet seen - The API gateway must be designed to route to containers dynamically, not hardcoded to known modules - Tenant isolation must be scoped in a way that supports future developer-submitted modules The developer ecosystem will be a significant commercial layer on top of this foundation. Its viability depends entirely on the foundation being correctly built in the MVP. This is the single most important architectural constraint in this document. --- ## 5. MVP Modules The MVP ships with five modules and one add-on. All five are deeply interconnected and all serve the sales process. --- ### 5.1 Knowledge Base Generator **Role:** The brain of the platform. Every interaction-based module draws from it. The Knowledge Base Generator builds a comprehensive, structured intelligence document for a business — covering their services, pricing, FAQs, target customers, ideal and non-ideal use cases, delivery timelines, what clients can expect during and after service, and supplemental market context. This becomes the knowledge source that powers all AI interactions on the platform: chat responses, voice conversations, automated follow-ups, and service card presentations. **How it works:** 1. The system scrapes and reads the client's entire website 2. It identifies gaps — information that should exist but doesn't appear on the site — and fills those gaps with AI-assisted research 3. It generates the structured knowledge base and presents it to the business user through a management UI 4. The business user reviews, edits, supplements, and approves the knowledge base 5. The approved knowledge base is published and immediately available to all connected modules 6. The business user can configure a regeneration schedule to keep the knowledge base current as their offerings evolve **Module connections:** `kb.updated` event triggers re-indexing in Voice AI and Chat Interface. All AI modules query the knowledge base via the API gateway rather than maintaining their own copies. --- ### 5.2 Voice AI Hub **Role:** Powers all voice interactions across the platform. Any module that requires voice — inbound phone calls, outbound calls, voice mode within the chat interface — routes through Voice AI Hub. It is the single voice infrastructure layer for the entire platform. **Core capabilities:** - Inbound call handling: AI answers calls for the business, handles questions using Knowledge Base data, qualifies callers, books appointments, or routes to a live agent - Outbound calls: AI-initiated calls for lead follow-up, appointment reminders, and outreach sequences - In-chat voice: Powers the voice communication mode inside the Chat Interface (see 5.3) - Phone number management: Supports adding and managing business phone numbers within the platform **Technology:** Built on LiveKit for real-time voice communication. **Module connections:** Consumes `contact.updated` and `kb.updated` events. Emits `voice.call.started`, `voice.call.completed`, `voice.call.failed` events consumed by Contacts, Chat Monitor, and automation layers. --- ### 5.3 Chat Interface **Role:** The business's AI-powered sales funnel in chat form. This is not a generic chatbot. The Chat Interface is a fully branded, white-label sales experience that a business deploys on their website. It is powered by the Knowledge Base Generator and presents information in rich interactive formats rather than plain text responses. **Deployment options:** - Full-screen chat window (comparable to a Claude.ai or ChatGPT interface experience, embedded directly on the business's site) - Traditional chat bubble in the corner of any page **What it does:** A prospective customer opens the chat and begins asking questions. The AI responds using the business's Knowledge Base — answering questions about services, pricing, timelines, and suitability. Services are presented as structured cards with relevant details, not buried in paragraphs. The interface supports text and voice modes (Voice AI Hub powers the voice mode). As the conversation progresses, the Chat Monitor (see 5.5) watches lead warmth in real time. When a lead crosses a warmth threshold, the business receives a notification and can choose to step in, allow the AI to guide the lead toward booking, or route to a live agent. **Module connections:** Pulls from Knowledge Base on every response. Emits lead events to Chat Monitor. Invokes Voice AI for voice-mode sessions. Writes conversation records and contact data to the shared `contacts` and `chat_conversations` tables. --- ### 5.4 Contact Forms Module **Role:** Converts traditional form submissions into qualified, AI-engaged leads. Most contact forms on business websites are dead ends — a submission goes into a CRM and waits for a human to follow up. The Contact Forms module intercepts that process. **What it does:** 1. **Submission validation:** Before anything else, the system evaluates whether a submission is genuine or spam/bot. This alone provides significant value — businesses receive only real leads. 2. **Intent classification:** Real submissions are classified by intent. Is this a purchase inquiry? A support question? A refund request? An information request? Classification determines the response path. 3. **AI-assisted engagement:** Based on intent, the submitter is routed into an appropriate AI interaction. A purchase inquiry might open a targeted chat asking qualifying questions. A support question might be resolved immediately with Knowledge Base answers. A warm lead might be prompted toward booking an appointment. **The key distinction from the Chat Interface:** The Chat Interface catches people who chose to engage. Contact Forms catches people who never intended to chat — they filled out a form and expected to wait. The Contact Forms module meets them in that moment with immediate, intelligent engagement, dramatically improving conversion rates and reducing time-to-contact. **Module connections:** Writes to `contacts`. Emits `contact.form_submitted`, `contact.qualified`, and `contact.warm` events. Can invoke the Chat Interface for follow-on engagement and Voice AI for callback initiation. --- ### 5.5 Chat Monitor **Role:** Gives the business real-time visibility and control over live AI sales conversations. The Chat Monitor is the business-facing backend companion to the Chat Interface. While AI handles conversations autonomously, the Chat Monitor watches for signals that a lead is warming — increased engagement, questions about pricing or next steps, repeated visits to specific service pages — and surfaces those signals to the business user. **What the business can do from the Chat Monitor:** - Receive real-time notifications when a lead crosses a warmth threshold - View the full conversation in progress - Step into the conversation directly (live agent takeover) - Push a back-end instruction to the AI — guiding it to move toward booking, offer a specific promotion, or escalate the conversation — without the customer knowing a human is now involved This gives businesses the efficiency of full AI automation combined with the option of human judgment at the moments that matter most. **Module connections:** Subscribes to lead warmth events from Chat Interface and Contact Forms. Writes agent intervention records to the shared event log. --- ### 5.6 Co-Browser *(Add-on / Upsell)* **Role:** Extends AI chat to every page of a website with full contextual awareness. The Co-Browser is a floating input bar that follows the user across every page of the business's website. Unlike the Chat Interface (which is a dedicated chat destination), the Co-Browser is ambient — it is always present without requiring the user to navigate anywhere. **What makes it different:** - The AI has awareness of which page the user is currently on, and uses that context in its responses. A user on the HVAC maintenance service page asking "how long does this take?" gets an answer specific to that service, not a generic response. - Voice mode is the primary intended interaction method. A user browsing through multiple pages can carry on a natural conversation without typing on each page. - Page visit data is recorded throughout the session. Before a sales call happens, the business knows exactly which pages this prospect viewed, in what order, how long they spent, and what questions they asked. This is high-value pre-call intelligence. The Co-Browser carries a higher inference cost than the standard Chat Interface due to continuous page context processing. It is priced as an add-on above the standard chat subscription. **Module connections:** Leverages Voice AI for voice mode. Writes page visit and conversation data to the contact record. Emits events that Chat Monitor can act on. --- ## 6. Business Model The platform operates on a zero-barrier, performance-aligned revenue model. Agencies access the platform at no cost and pay nothing until they generate revenue for themselves. **The three-party structure:** Every transaction involves aiConnected (infrastructure operator), the Agency (white-label service provider), and the Business Client (end user). All billing flows through aiConnected's Stripe infrastructure — this is the mechanism by which the platform tax is automatically collected. **Platform Tax (Revenue Share)** aiConnected charges 10% of whatever the agency charges their clients, collected automatically at the point of transaction. If an agency generates no revenue, aiConnected earns nothing from that agency. The platform tax scales directly with agency success. **Floor Pricing** Every module carries a minimum floor price set by aiConnected. Agencies may not charge below the floor. Above the floor, agencies set whatever price they choose — $200/month or $2,000/month for the same module. aiConnected takes 10% regardless. **API Resale Model** AI inference is sourced through OpenRouter, giving access to all major model providers (Anthropic, OpenAI, Google, Mistral, Meta, and others) through a single integration. aiConnected applies a 10% markup on API costs and resells credits to agencies. Agencies then apply their own markup when selling AI usage to clients. The platform tax applies on top of this at the client billing level. Agencies may also opt into BYOK (Bring Your Own Key) if they have existing direct API relationships. BYOK removes the API markup but does not exempt the agency from the platform tax — the platform tax compensates for platform infrastructure and white-label capability, not API access. **Revenue streams summary:** | Stream | Type | Rate | |---|---|---| | Platform tax on client billing | Variable | 10% of agency charges | | API resale markup | Variable | 10% of API cost | | Platform tax on BYOK usage | Variable | 10% of agency charges | | Customer Success — Starter | Flat monthly | $600–$800/month | | Customer Success — Part-Time | Flat monthly | $1,500–$1,700/month | | Customer Success — Full-Time | Flat monthly | $3,000–$3,500/month | Customer Success is an optional white-label service offering where aiConnected provides dedicated human success managers who engage with business clients on the agency's behalf. All CS activity is fully white-labeled — clients interact with who they believe is the agency's team. --- ## 7. Technical Stack | Layer | Technology | |---|---| | Frontend framework | Next.js 14 | | Monorepo management | Turborepo | | UI component system | shadcn/ui | | Tenant theming | TweakCN | | Database | Supabase (PostgreSQL) | | Authentication | Supabase Auth | | Realtime / event bus | Supabase Realtime + custom event log table | | Voice infrastructure | LiveKit | | AI inference | OpenRouter (unified API layer across all major providers) | | Container orchestration | Dokploy | | Automation / workflow layer | n8n (self-hosted, existing infrastructure) | | API gateway | Custom — Next.js middleware + edge functions | | Billing | Stripe (all client billing flows through aiConnected's Stripe) | | Infrastructure | DigitalOcean | --- ## 8. MVP Scope — What's In, What's Out, What Must Be Architected For ### In scope for MVP - Core shell (auth, multi-tenant provisioning, billing, module registry, event bus, API gateway, theme engine) - shadcn/ui component foundation + TweakCN theming engine - Visual Builder (Craft.js) — foundational UI composition layer - Module Import System — GitHub repo, WordPress plugin, and n8n workflow conversion pipelines - Super user, Agency user, and Business user access layers - All five MVP modules: Knowledge Base Generator, Voice AI Hub, Chat Interface, Contact Forms, Chat Monitor - Co-Browser as an add-on module - Stripe billing integration with platform tax collection and floor price enforcement - Agency white-label configuration (branding, custom domain, module selection, client management) ### Out of scope for MVP (post-MVP) - Developer portal and developer account management - Community sandbox and trust pipeline UI - Capability registry browsing interface - Personal user account type - Composio integration layer - Neurigraph Memory Architecture - System-level orchestration chat ### Must be architected for — not shipped, but cannot be an afterthought - Module manifest specification: must be finalized and enforced from day one. Every first-party module ships with a compliant manifest. - Capability registry schema: database schema must exist in MVP even if no UI exposes it yet - Event bus: must be designed to handle events from unknown future modules, not hardcoded to MVP module list - API gateway: must route dynamically to module containers, not via hardcoded routing tables - Container isolation: every module, including first-party modules, runs in its own container from day one - Tenant data isolation: must be designed to accommodate third-party module data schemas without architectural rework - Module Import System: the conversion pipelines (GitHub, WordPress, n8n) must produce manifests that are fully compliant with the same spec first-party modules use. Imported modules are not second-class citizens. - Visual Builder component registry: shadcn/ui components must be registered as builder blocks from day one. Adding new blocks later must require no core changes — only registration. --- ## 9. What MVP Completion Looks Like MVP is complete when the following is true end-to-end: An agency signs up, configures their white-label branding (logo, colors, domain, typography), sets up their Stripe billing, and creates their first business client account. That business client logs into what appears to be the agency's product, runs the Knowledge Base Generator against their website, reviews and approves the generated knowledge base, and publishes it. The Chat Interface goes live on the business's website — both as a bubble and as a full-screen embed. A site visitor opens the chat, asks questions about the business's services, receives AI responses drawn from the knowledge base, and is presented with formatted service cards. They switch to voice mode and continue the conversation by speaking. The business user sees this conversation happening in the Chat Monitor. The lead's warmth score increases. The business receives a notification, reviews the conversation, and decides to let the AI guide the lead toward booking. The appointment is set. Separately, a visitor submits a contact form on the business's website. The Contact Forms module validates the submission, classifies the intent as a purchase inquiry, and initiates a follow-up AI interaction that qualifies the lead and routes them toward a consultation booking. All of the above happens within a white-label environment that bears zero visible relationship to aiConnected. The agency looks like the product company. That is the MVP. --- *aiConnected Platform — MVP Specification v1.0* *March 2026* *For development team engagement — confidential* --- ## aiConnected Platform v2 — Build Plan **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/aiconnected-platform-v2-build-plan # aiConnected Platform v2 — Build Plan **Sequenced execution for a small development team** *March 2026* --- ## Guiding Principles 1. **Foundation before features.** The shell, event bus, module registry, and manifest spec must be solid before any module is built. A module built on a shaky foundation requires rework. 2. **Validate the pattern early.** Port one full module end-to-end (KB Studio) and confirm it works within the shell before building the others. This proves the architecture before committing to it at scale. 3. **Packages first, apps second.** Set up all shared packages before any app is written. Apps should have nothing to build until the packages they depend on exist. 4. **shadcn/ui is non-negotiable.** Every component in every app comes from `@aiconnected/ui`. No exceptions. Custom CSS is only written for TweakCN token overrides. 5. **Manifest compliance is a Day 1 requirement.** Every module, including the first one built, ships with a fully compliant manifest. The manifest spec does not evolve retroactively. --- ## Phase 0 — Environment Setup **Estimated: 1 day | Owner: Lead developer** - [ ] Initialize new Turborepo monorepo (`platform-v2`) - [ ] Configure `turbo.json` with correct pipeline (build, dev, lint, test) - [ ] Configure `package.json` workspaces for `apps/*` and `packages/*` - [ ] Set up ESLint, Prettier, TypeScript config shared packages - [ ] Initialize Git repo, connect to GitHub, set up main/dev branch protection - [ ] Provision fresh Supabase project (separate from v1 — do not touch v1 until v2 is live) - [ ] Confirm access to DigitalOcean, Dokploy, Crawl4AI, OpenRouter, Stripe - [ ] Copy `.env.example` from v1, document all required v2 env vars **Gate: Team can run `turbo dev` and see an empty workspace.** --- ## Phase 1 — Shared Packages **Estimated: 2–3 days | Owner: Lead developer** Set up all shared packages before any app is written. Apps should have nothing to build until their dependencies exist. ### 1A — Port existing packages (no logic changes) - [ ] Copy `packages/permissions` → v2 (as-is) - [ ] Copy `packages/kb-engine` → v2 (as-is) - [ ] Copy `packages/chat-core` → v2 (as-is, flag composio.js for gateway review) - [ ] Copy `packages/app-sdk` → v2, then extend manifests with `events_emitted` / `events_consumed` - [ ] Port `packages/branding` merge logic → v2, replace token names with TweakCN equivalents ### 1B — Build new packages - [ ] Create `packages/ui` — initialize shadcn/ui, install core components (button, card, dialog, input, form, table, badge, avatar, dropdown, sheet, tabs, toast, sidebar) - [ ] Export all components from `packages/ui/src/index.ts` - [ ] Write `packages/db` client/server Supabase split (port patterns from v1) ### 1C — Define v2 database schema - [ ] Write migration 001: core shared tables (`workspaces`, `users`, `contacts`, `events`, `module_registry`) - [ ] Write migration 002: permissions and RLS policies for all core tables - [ ] Write migration 003: Stripe billing fields on workspaces - [ ] Apply migrations to fresh Supabase project - [ ] Verify RLS policies enforce tenant isolation **Gate: All packages build cleanly. `packages/ui` exports all components. Core schema is live in Supabase.** --- ## Phase 2 — Platform Shell **Estimated: 4–5 days | Owner: Lead developer + 1 developer** Build the shell that all modules live inside. This is the most critical phase. Every design decision made here is inherited by every module. ### 2A — Authentication - [ ] Supabase Auth integration (email/password + Google OAuth) - [ ] Login page (shadcn/ui) - [ ] Signup / invite flow - [ ] Password reset flow - [ ] Session management and protected route middleware - [ ] Role assignment on signup (agency_admin default for self-signup) ### 2B — Shell layout - [ ] Authenticated layout: sidebar + header + main content area - [ ] Sidebar component — dynamic menu from `packages/permissions` role - [ ] Header component — user menu, workspace switcher, notifications placeholder - [ ] Responsive layout (desktop primary, tablet-functional) - [ ] Loading states and skeleton screens ### 2C — Multi-tenant routing - [ ] Super admin dashboard and routes (`/admin/*`) - [ ] Agency dashboard and routes (`/agency/[agencyId]/*`) - [ ] Business dashboard and routes (`/business/[businessId]/*`) - [ ] Impersonation context (super admin → agency → business drill-down) - [ ] Tenant isolation: verify no agency can access another agency's data ### 2D — Module registry integration - [ ] Module registry table in Supabase - [ ] API endpoint: `GET /api/modules` — returns installed modules for current workspace - [ ] Shell sidebar dynamically builds nav from registered modules - [ ] Module slot rendering — shell renders a placeholder for each installed module's routes ### 2E — TweakCN theming - [ ] TweakCN installed and configured - [ ] CSS variable injection on workspace load (applies agency theme to entire shell) - [ ] Agency branding configuration page — logo, colors, typography, radius - [ ] Business-level theme override (inherits from agency, can be customized if permitted) - [ ] Preview mode — agency can preview their theme before publishing ### 2F — Billing foundation - [ ] Stripe Connect setup for agency billing - [ ] Webhook handler for payment events - [ ] Platform tax calculation on transactions (10%) - [ ] Module activation gating — check billing status before granting module access - [ ] Basic billing dashboard (super admin: all revenue; agency: their clients) **Gate: An agency can sign up, configure their brand, see their dashboard, and add a business client account. Billing is wired. Theme applies correctly.** --- ## Phase 3 — Event Bus + API Gateway **Estimated: 2–3 days | Owner: Lead developer** This phase is invisible to users but essential for module interconnection. Build it before any module is built. ### 3A — Event bus - [ ] `events` table schema with indexes on `workspace_id`, `event_type`, `created_at` - [ ] `platform.emit(eventName, payload, workspaceId)` — writes to events table, triggers Supabase Realtime - [ ] `platform.subscribe(eventName, handler, workspaceId)` — subscribes module to event type - [ ] Event schema validation — emitted events must match the module's declared `events_emitted` contract - [ ] Event delivery logging and retry logic for failed handlers ### 3B — API gateway - [ ] Request routing layer — routes `/api/modules/[moduleId]/*` to correct module container - [ ] Permission enforcement — validates module has declared permission before allowing data access - [ ] Rate limiting per module per workspace - [ ] `platform.call(moduleId, capability, params)` — cross-module capability invocation - [ ] Gateway health check — returns status of each registered module **Gate: Two modules can communicate via events. The gateway routes correctly. A module that hasn't declared a permission cannot access data it didn't claim.** --- ## Phase 4 — KB Studio Module (Validation Phase) **Estimated: 3–4 days | Owners: 1–2 developers** Build KB Studio first because it is the foundation that every other module depends on. If this module's manifest, events, and API surface work correctly, the pattern is proven for all subsequent modules. ### 4A — Module manifest - [ ] Write `kb-studio` manifest (extend v1 manifest with `events_emitted`: `['kb.published', 'kb.updated']`) - [ ] Register in module registry - [ ] Verify shell sidebar picks up KB Studio automatically from registry ### 4B — Database - [ ] Migration: `kb_projects`, `kb_entries`, `kb_sections`, `kb_schedule` - [ ] RLS: business users can only access their own KB projects ### 4C — Onboarding flow (shadcn/ui) - [ ] Step 1: Enter website URL - [ ] Step 2: Crawl progress indicator (calls `kb-engine/scraper.js`) - [ ] Step 3: Review extracted services (calls `kb-engine/extractor.js`) - [ ] Step 4: AI research in progress (calls `kb-engine/researcher.js`) - [ ] Step 5: Review + edit generated KB content (calls `kb-engine/compiler.js`) - [ ] Step 6: Publish — emits `kb.published` event ### 4D — KB Editor (shadcn/ui) - [ ] Service cards with edit capability - [ ] FAQ section management - [ ] Add/remove KB sections - [ ] Schedule configuration (re-crawl frequency) - [ ] Publish / unpublish toggle ### 4E — API surface - [ ] `POST /api/kb/generate` — triggers full pipeline - [ ] `GET /api/kb/[projectId]` — returns compiled KB - [ ] `POST /api/kb/[projectId]/publish` — publishes and emits event - [ ] Capability: `knowledge-base.search` — responds to cross-module search queries **Gate: A business user can run the KB generator against their website, review and edit the output, publish it, and have the `kb.published` event appear in the event log. Another module subscribed to that event receives it.** --- ## Phase 5 — Chat Interface Module **Estimated: 4–5 days | Owners: 2 developers** ### 5A — Module manifest - [ ] Write `chat` manifest (extend v1 with full `events_emitted` / `events_consumed`) - [ ] Register in module registry ### 5B — Database - [ ] Migration: `chat_configs`, `chat_conversations`, `chat_messages`, `chat_leads` - [ ] RLS policies ### 5C — Chat configuration UI (shadcn/ui) - [ ] Design tab: colors, fonts, radius, layout (via TweakCN) - [ ] Experience tab: conversation starters, greeting, service cards, voice mode toggle - [ ] Lead capture tab: form fields, delivery settings - [ ] Floating save button (sticky) - [ ] Live preview panel ### 5D — Chat runtime (shadcn/ui) - [ ] Full-screen chat interface (`/chat/[businessId]`) - [ ] Chat bubble widget (`/widget/[businessId]`) - [ ] AI response endpoint (uses `chat-core/ai-config.js`, queries KB via `platform.call`) - [ ] Service card rendering - [ ] Voice mode toggle (placeholder for Voice AI Hub integration) - [ ] Lead form trigger and submission ### 5E — Embed system - [ ] `widget.js` embed script (injected via ` ``` ### Script Features * Loads widget and assistant UI dynamically * Pulls branding, welcome prompts, and voice settings from Supabase via the site ID * Tracks user interactions, scroll targets, highlights, and form submissions * Establishes socket or polling connection to maintain co-browsing state ### Installation Platforms * **WordPress:** Plugin wrapper that auto-injects the script in `` * **Shopify:** Theme snippet and admin console helper app (Phase 2\) * **Custom Sites:** Copy-paste embed code --- ## 6.3 Hosting Infrastructure | Component | Platform | Purpose | | :---- | :---- | :---- | | Frontend Embed Script | DigitalOcean CDN | Fast delivery of siteGuide widget across all sites | | Widget UI & Assets | DO App Platform | HTML/CSS/JS for assistant, voice overlay, chat interface | | Backend API | DO App Platform | Handles session tracking, actions, lead collection | | Database | Supabase (Postgres) | Stores user sessions, memory data, leads, preferences | | Auth/Access Control | Supabase | Role-based access to Admin Panel | | Admin Panel | DO App Platform (Next.js) | Business-facing control dashboard | | Persistent Vector Store | Supabase Edge Functions | Lightweight embeddings for ongoing memory recall | | AI Model Runtime | Local LLM or hosted endpoint (Phase 2\) | Low-latency response generation | | Analytics | Supabase \+ Logflare | Event tracking and funnel analysis | --- ## 6.4 Technical Stack Overview ### Frontend (Client-Facing) * **Language:** JavaScript (ES6+) * **Framework:** Vanilla JS \+ Stimulus/AlpineJS (lightweight control) * **Voice:** Web Speech API or ElevenLabs (if enabled) * **UI Styling:** TailwindCSS, CSS custom properties injected per site * **Browser Storage:** `localStorage`, `sessionStorage`, and optional IndexedDB ### Backend (Server-Facing) * **Runtime:** Node.js (API and sync calls) * **Database:** Supabase (PostgreSQL \+ RLS) * **Authentication:** Supabase Auth with JWT * **Realtime:** Supabase Channels (WebSockets for memory refresh, voice sync) * **Serverless Logic:** Supabase Edge Functions (Python/Node handlers) ### Admin Panel * **Frontend:** Next.js with Tailwind and ShadCN components * **State Mgmt:** React Context \+ SWR * **API Calls:** Supabase JS SDK * **Deployment:** DO App Platform CI/CD --- ## 6.5 Project Environment Structure ``` /siteguide-core /src /embed # JS loaded into client site /assistant # Chat assistant logic /scrolling # Scroll and highlight handlers /voice # Voice controls + speech handling /navigation # Path prediction and page changes /forms # Lead form UI & validation /admin-panel /pages # Next.js Admin Routes /components # Configurable dashboards /utils # API + local state helpers /api /functions # Supabase Edge or DO API functions ``` --- ## 6.6 Continuous Deployment Workflow | Action | Toolchain | | :---- | :---- | | Code pushed to main branch | GitHub | | Build triggered | DO App Platform CI | | Admin panel deployed | Static Next.js output auto-pushed | | Embed script redeployed | Bundled & uploaded to DigitalOcean CDN | | Supabase migrations | Auto-run via CLI (SQL schema \+ RLS enforcement) | | Error logging | Sentry (widget) \+ Logflare (backend) | --- ## 6.7 Environment Configuration | Key Setting | Environment Variable | Notes | | :---- | :---- | :---- | | Supabase Project URL | `SUPABASE_URL` | Required for all API calls | | Supabase Anon Key | `SUPABASE_ANON_KEY` | Read access for front-end | | Admin Auth Secret | `ADMIN_JWT_SECRET` | For role-based Admin Panel | | CDN Base URL | `CDN_BASE_URL` | Script delivery \+ assets | | SiteGuide Instance ID | `SITE_ID` | Passed via script tag per client | | Voice API Key (Optional) | `ELEVENLABS_API_KEY` or TTS Provider | Only needed for premium voice | --- ## 6.8 Success Criteria | Metric | Threshold | | :---- | :---- | | Time to deploy on new client site | \< 2 minutes via script or plugin | | Script load time (embed \+ UI) | \< 800ms over 4G | | Admin Panel load time | \< 1.5s first contentful paint | | Supabase API response latency | \< 250ms average | | Real-time co-browsing sync events | 99.5% delivered within 500ms | | Deployment errors per release | Zero regressions in script loader | --- # 7\. Data, Privacy, and Security This section outlines how siteGuide manages user data, protects personal information, and ensures full compliance with privacy laws such as GDPR, CCPA, and other international standards. Given that siteGuide operates on public-facing websites and can collect lead data, interaction data, and usage history, strict security and transparency standards are required at every layer. --- ## 7.1 Data Types Collected siteGuide collects and stores a mix of behavioral, contextual, and optionally, personally identifiable information (PII). These are categorized into three tiers: ### Tier 1: Anonymous Session Data (Always Collected) * Site ID * Session UUID (auto-generated, anonymized) * Pages visited (URL paths) * Time spent per page * Clicked buttons, scrolled sections * AI assistant prompts and responses * Device type, browser, and location (city/country only) ### Tier 2: Behavioral Memory Data (Optional, if enabled) * Previous session interactions (persisted via Supabase) * Scroll targets and FAQ clicked history * Assistant confidence scores or misfires * Tracked goals (e.g., clicked “book now” or submitted a form) ### Tier 3: Personally Identifiable Information (Optional, Explicit) * Name (via lead capture) * Email address (for follow-ups or persistent sessions) * Phone number (if captured in form fields) * Business name, industry (if provided) --- ## 7.2 Consent & User Control ### Anonymous Mode (Default) * All tracking is non-personal unless the user engages the assistant and chooses to leave information. * No cookies are required for basic session tracking. ### Explicit Consent for PII * Users are only asked for PII when initiating a lead submission or selecting “resume session via email”. * All PII entry points are accompanied by: * A consent checkbox (e.g., “I agree to receive follow-up emails from this business.”) * Link to the privacy policy * PII is stored only after consent is given and includes a timestamped consent log. ### Session Persistence Disclosure * The first time a user revisits a site with active memory, the assistant displays: * “Welcome back\! I remember your last visit. Would you like me to resume where we left off?” * Options: Yes / No, start fresh * If “Yes” is selected, session UUID is reused. If “No,” a new session is generated. --- ## 7.3 Data Storage and Retention ### Primary Storage: Supabase PostgreSQL * Role-based access enforced via RLS (Row Level Security) * Business owners can only view data for their own site ID * All leads and PII stored with AES-256 encryption at rest ### Session History / Memory Storage * Persisted sessions stored in structured JSON blobs * Indexed by session ID and optionally by email hash * Sessions auto-purge after 90 days of inactivity unless marked as "active lead" ### Vector Memory Embeddings (Optional Feature) * If enabled, past interactions are stored in vector format for memory recall * Stored in Supabase Edge Functions or local Pinecone-compatible store * Only assistant prompts/responses are embedded — no raw PII --- ## 7.4 Data Transmission and Encryption | Transmission Context | Encryption Protocol | | :---- | :---- | | Embed script from CDN | HTTPS (TLS 1.2 or higher) | | Supabase API calls (client) | HTTPS | | Realtime updates (WebSockets) | WSS with token auth | | Voice recording / playback | HTTPS streaming (TTS only) | | Admin dashboard login | Supabase Auth \+ JWT | All data-in-transit uses modern TLS protocols. Authentication tokens are scoped per role and expire after 12 hours. --- ## 7.5 Data Access and Permissions | Role | Access Scope | | :---- | :---- | | Anonymous visitor | No access to stored data beyond own session | | Business Owner | Only data from sessions on their own site ID | | Admin (internal) | Full access for support and debugging only | ### Admin Panel Restrictions * No raw PII can be exported unless explicitly authorized * All export/download buttons must include a GDPR notice * Audit logs must be stored for all admin data access --- ## 7.6 Legal Compliance ### GDPR * Consent-based data capture * Right to access, update, or delete data supported via email or admin interface * Data Protection Officer contact listed in privacy policy ### CCPA * Opt-out banner for California visitors * “Do Not Sell My Info” link embedded in assistant’s settings menu ### International Data Protection * Supabase supports global hosting, fallback plan includes EU-region storage if required * Client-specific data location setting can be added in Phase 2 --- ## 7.7 User Rights & Removal * **Delete my data** request form available in assistant settings and on host site privacy policy * Users can enter their email address and receive a confirmation link to delete stored data * Admin tools include “Forget Session” and “Forget User” functions to fully wipe records * All deletions are hard-deleted, not just flagged --- ## 7.8 Breach Mitigation and Logging * Daily audit logs of all data accesses and exports * Error and anomaly detection on spike in PII access * Internal alerts (Slack/email) for: * Failed auth attempts * Abnormal access patterns * Large export operations In case of breach: * Affected businesses are notified within 72 hours * Users are notified by the host business (not aiConnected) * Full forensics retained and logged --- ## 7.9 Success Criteria | Metric | Target | | :---- | :---- | | User PII stored without consent | 0 incidents | | Average time to fulfill deletion request | \< 48 hours | | % of sessions tracked anonymously | ≥ 90% unless lead is captured | | Admin exports logged and auditable | 100% | | Compliance review status | GDPR \+ CCPA certified policies | --- # 8\. Admin Tools and Business Dashboard This section details the full feature set of the administrative dashboard provided to business owners who install siteGuide. It defines how users (businesses) can configure, monitor, and optimize their assistant, view session replays, manage leads, and adjust behavior to better match their conversion goals. The admin panel is hosted by aiConnected and accessed via secure login at `dashboard.aiConnected.ai`. --- ## 8.1 Authentication and Access ### Login * Secure login via Supabase Auth (email \+ password or OAuth) * Optional 2FA via email or authenticator app (Phase 2\) * Each business user account is linked to one or more websites via a unique `site_id` ### User Roles * **Owner:** Full access to all data and settings for a given site * **Editor:** Can modify assistant behavior and branding * **Viewer:** Read-only access to leads, transcripts, and analytics --- ## 8.2 Site Onboarding and Setup Upon first login, the user is taken through a 4-step assistant setup process: 1. **Site Details** * Site name * Industry category * Public URL 2. **Assistant Configuration** * Select use-case focus: Lead Generation, FAQ Help, Navigation, or All * Upload up to 5 key pages (for initial semantic parsing) 3. **Branding** * Upload logo (used in chat bubble) * Pick assistant color scheme * Set assistant greeting (e.g., “Hi\! Need help finding anything?”) 4. **Embed Script** * One-line JS snippet provided (customized with `site_id`) * Includes step-by-step WordPress instructions * Includes check for script installation (active/inactive status) All assistant settings are editable later in the dashboard. --- ## 8.3 Real-Time Interaction Feed Business users can view a live feed of interactions on their site. ### Features * Scrollable timeline of sessions, labeled by: * Session UUID * Entry page (e.g., `/pricing`) * Time of visit * Assistant topic (e.g., “Asked about refund policy”) * Toggle to view chat transcript per session * “Highlight in replay” option for scroll & click actions ### Filters * By date range * By action type (clicked button, submitted form, etc.) * By page (e.g., all sessions on `/contact`) --- ## 8.4 Lead Management siteGuide automatically saves leads captured by the assistant. ### View Leads * Table view with: * Name, email, phone, timestamp * Assistant summary (e.g., “Interested in monthly subscription plan”) * Lead source (page and session ID) * Click to view full transcript of interaction ### Actions * Export to CSV * Push to CRM (Zapier or webhook) * Mark as contacted * Delete or redact lead ### Smart Tags * Auto-generated tags (e.g., “Pricing Inquiry,” “Booking Request”) * Searchable and filterable by tag * Option to assign custom tags --- ## 8.5 Assistant Customization Within the dashboard, users can fine-tune the assistant’s: ### Greeting * Change default greeting based on page context * Set greeting delay (e.g., greet after 15s on site) ### Lead Prompt Behavior * Set “When should the assistant offer to collect contact info?” * After 2+ questions * After goal reached (e.g., visited booking page) * After 60+ seconds of activity ### Tone of Voice * Options: Friendly, Professional, Casual, High-Energy * Future: Custom fine-tuning per business (e.g., import brand tone document) ### Language Support * Choose one default language * Option to auto-detect browser language (Phase 2\) --- ## 8.6 Analytics and Performance Tracking ### Key Metrics * Total sessions * Avg. session duration * Leads generated * Lead conversion rate (% of total sessions) * Most clicked elements (based on scroll & highlight) ### Conversion Goals * Define conversion goals (e.g., clicked “Book Now” or submitted form) * View goal completions over time * AI will learn which phrases and paths lead to conversion and adjust behavior ### Funnel View * Visualization of how users navigated via the assistant * Drop-off points highlighted * Common click paths mapped --- ## 8.7 Session History and Replay Each session is stored with: * Page paths visited * AI actions (scrolls, highlights, clicks) * Full assistant transcript * Lead form status * Dwell time and exit page Business users can replay sessions in real-time or scrub through a timeline to analyze drop-offs and assistant accuracy. --- ## 8.8 Privacy Controls * “Forget this user” option per session (deletes memory and transcript) * Toggle assistant memory on/off per site * Set default session expiry duration (e.g., forget after 30 days) --- ## 8.9 Success Criteria | Functionality | Success Definition | | :---- | :---- | | Assistant installed | \>95% of registered users complete embed | | Leads captured | ≥15% of sessions yield lead or booking | | Business user login frequency | 2+ logins per week | | Customization usage | \>50% of users change at least 2 default settings | | Export/download compliance | 100% consent and access logs recorded | --- # 9\. Multisite Support and Scalability This section outlines how siteGuide will support businesses with multiple websites, teams, or assistant configurations, while ensuring robust infrastructure performance and clear segmentation of data. This is especially important for agencies, franchises, and enterprise clients managing multiple domains or regional sites. --- ## 9.1 Multisite Support ### Overview Each business user account can create and manage multiple “Sites.” A **Site** represents a single domain or subdomain with its own assistant configuration, memory, and analytics. ### Use Case Examples * A marketing agency installs siteGuide on 50 client websites. * A franchise business operates 10 local domains with distinct offerings. * An enterprise has different language sites (e.g., `us.example.com`, `de.example.com`). ### Site Independence * Each site has: * Its own `site_id` * Separate assistant memory * Unique branding, prompts, lead fields, and settings * Separate analytics dashboard ### Switching Sites * Admin users can switch between sites in the dashboard via a dropdown. * Each session and assistant instance reports to the correct site via `site_id` embedded in the JS snippet. --- ## 9.2 Multi-User Team Management (Future) **Not required at launch**, but the architecture must support future team permissions per site: | Role | Permissions | | :---- | :---- | | Owner | Full access across all sites under their account | | Site Admin | Full access to one site | | Assistant Editor | Modify assistant prompts only | | Lead Viewer | View leads and transcripts only | Admin panel UX must be built with this future expansion in mind, using componentized RBAC (role-based access control) logic. --- ## 9.3 Namespace Isolation Each `site_id` creates a namespace for: * Supabase tables (e.g., `leads_site_abc123`) * Vector memory storage * AI context injection (no bleed between sites) * Session cookies (stored as `siteguide_{site_id}_session`) Isolation is critical to prevent: * Cross-site data leakage * Confused memory injection * Duplicate analytics across different domains --- ## 9.4 Performance Scaling Strategy siteGuide must remain performant even when installed on thousands of websites with concurrent usage. The architecture supports this by offloading responsibilities: ### On-Page Load * Assistant assets (JS, CSS, UI logic) are served via CDN * Only lightweight UI bundle is loaded on client * Memory and reasoning are cloud-based (via aiConnected APIs) ### Interaction Workflow * Frontend sends prompts → aiConnected API handles reasoning * aiConnected returns next action (chat reply, scroll, highlight, etc.) * Local browser executes the action; no blocking behavior ### Storage * Supabase handles: * Session metadata * Leads and transcripts * Interaction logs * Vector memory stored separately per site for AI retrieval ### Load Management * All API endpoints and memory functions are stateless * Persistent memory is stored externally, only loaded when needed * No live WebSocket unless co-browsing view is active (very rare) --- ## 9.5 Deployment Strategy for Large Clients For enterprise or agency-level installations: * Provide white-label version of the dashboard * Allow API access to pull leads into external CRM * Custom subdomains per client (`clientname.aiConnected.ai`) * Dedicated memory instance per enterprise tenant Optional: Offer service-level guarantees for uptime, replay storage, and assistant memory limits via SLAs. --- ## 9.6 Success Criteria | Goal | Success Metric | | :---- | :---- | | Cross-site stability | Zero data leakage between sites | | Time to add new site | Under 5 minutes with full configuration | | Site switching usage | 70% of agency/franchise users manage 2+ sites | | Performance degradation threshold | No slowdown up to 10,000 simultaneous sessions | --- # 10\. Data Retention, Privacy, and Security This section defines how siteGuide handles all user and business data with strict regard for security, privacy compliance (e.g., GDPR, CCPA), and retention policies. It ensures that siteGuide can be confidently deployed on high-trust websites — including healthcare, finance, legal, and education — without risk of data compromise or misuse. --- ## 10.1 Data Categories The platform interacts with the following categories of data: ### 1\. Visitor Data (End User) * Session ID (UUID) * Page visits * Clicks, scrolls, highlight paths * Chat transcript with the assistant * Lead capture data (e.g., name, email, phone) ### 2\. Business Data (Site Owner) * Assistant configuration * Uploaded brand assets (logo, colors) * Custom prompts and overrides * Lead management records ### 3\. System Metadata * Time stamps * API logs (request/response) * Browser/user agent * Memory vector keys (hashed) No sensitive credit card or health data is ever collected by default. --- ## 10.2 Data Retention Rules ### For Visitor Sessions: * Active memory: 30 days by default * Transcript: 90 days stored (configurable per business) * Full replays (scroll/click): 30–60 days (configurable, auto-expiry) * Leads: Stored indefinitely unless deleted by user or business ### For Business Accounts: * Configurations and assistant settings are stored until account closure * Deletion of a site permanently removes assistant memory and leads for that site Businesses may configure auto-expiry rules per data category. --- ## 10.3 Privacy Tools for Website Visitors siteGuide complies with privacy regulations by offering the following end-user protections: ### GDPR/CCPA Banner Integration * Auto-detects cookie banner tools (e.g., Cookiebot, Termly) * Delays assistant activation until consent is granted ### Data Access & Deletion * In-chat message: “Forget my data” triggers memory and transcript wipe * Link in the siteGuide assistant footer: “Privacy Settings” * Supabase triggers delete logs and scrubs all indexed vectors for session ID ### Opt-Out Mechanisms * Memory-free mode (temporary session, no persistence) * Ability for businesses to turn off memory or auto-delete after each session --- ## 10.4 Encryption Standards ### In Transit * All API communication encrypted via HTTPS/TLS 1.3 * All websocket or push-based updates encrypted via secure channels ### At Rest * Supabase database encrypted with AES-256 * Vector memory storage encrypted at disk level * Passwords stored using bcrypt (Supabase default) --- ## 10.5 Security Architecture ### Access Controls * Role-based access system per site and user * Tokens for assistant instances scoped to `site_id` * No cross-site access possible ### API Protection * Rate-limited public endpoints * Token auth (JWT) with auto-refresh * All read/write operations scoped to authorized `site_id` ### Admin Monitoring * Admin audit logs for every assistant update or lead export * IP logging for dashboard activity * Alerts for unusual data export volumes ### Hosting Security (DigitalOcean) * Hosted behind firewall * Backups run daily with encrypted snapshots * Auto-scaling infrastructure with DDOS mitigation via CDN --- ## 10.6 Compliance and Certifications | Standard | Compliance Status | | :---- | :---- | | GDPR | Fully compliant | | CCPA | Fully compliant | | HIPAA | Not covered (future add-on) | | SOC 2 | Planned via DigitalOcean infra roadmap | | WCAG | AA-level accessible assistant UI | --- ## 10.7 Success Criteria | Objective | Measurable Indicator | | :---- | :---- | | User privacy control | 100% compliance with deletion and opt-out requests | | Security incidents | Zero breaches or unpatched vulnerabilities | | Encryption coverage | 100% of stored PII encrypted at rest and in transit | | Business adoption in sensitive fields | At least 10% of users from regulated industries | --- # 11\. Optional Enhancements and Future Features This section outlines advanced capabilities that are not part of the core MVP for siteGuide but represent high-value additions for future iterations. These features aim to deepen personalization, streamline integrations, and expand the assistant’s utility across more complex customer journeys. --- ## 11.1 Persistent Cross-Device Memory (User-Level Identity) ### Overview Currently, session memory is stored per browser via session cookies and optionally resumed via email input. Future updates will enable: * Memory that persists across different devices (mobile, desktop, tablet) * Seamless recall of past conversations regardless of browser or IP ### Implementation * Add user account creation for site visitors (email \+ OTP, no password) * Upon login, assistant retrieves full memory tied to that user across all sessions * Memory entries will now use `user_id` in addition to `session_id` ### Benefit * Enables deeper personalization (e.g., “Welcome back, here’s where we left off.”) * Ideal for e-commerce (cart recovery), SaaS onboarding, and service industries --- ## 11.2 CRM/Inbox Memory Training ### Overview SiteGuide could eventually use historical data (e.g., past customer emails, CRM conversations, FAQs) to train the assistant’s tone, knowledge, and objection handling. ### Implementation * Allow business to connect Gmail, HubSpot, Salesforce, or import CSVs * N8N workflow processes text content → cleans → indexes into memory * System adds tagged knowledge as non-user memory into vector database ### Use Cases * Customer support pretraining * Personalized onboarding flows * Sales conversation reference material --- ## 11.3 Sentiment-Aware Conversation Routing ### Overview The assistant can monitor sentiment during a live conversation and take specific actions based on tone or urgency. ### Examples * Angry tone → escalate to human * Hesitation or doubt → offer clarification or schedule a callback * Excitement → accelerate toward conversion (e.g., direct booking link) ### Implementation * Sentiment detection via OpenAI or local model * Assign confidence scores to emotional state * Trigger conditional responses in chat flow --- ## 11.4 Event-Based Assistant Behavior ### Overview Let the assistant react to specific user behaviors, such as: * Inactivity for 15 seconds → assistant re-engages * Scrolls to bottom of page → assistant offers help * Copies coupon code → assistant logs intent * Leaves a form half-filled → assistant offers to resume later ### Implementation * Small JS listener library bundled with siteGuide script * Events forwarded to assistant via n8n node or native web socket * Assistant modifies behavior contextually --- ## 11.5 Custom Action Buttons ### Overview Businesses can configure reusable call-to-action buttons that appear contextually in the chat (e.g., “Download Brochure,” “Book a Demo,” “Request a Quote”). ### Features * Buttons tied to tracked actions (downloads, form opens, calendar launches) * Trigger scripts, open URLs, or emit custom DOM events * Responses can vary based on page URL or user attributes --- ## 11.6 Multilingual Support ### Overview Enable automatic detection of the user’s preferred language (via browser locale or explicit choice) and localize: * Assistant UI * Voice output (with accent control) * Chat responses with translated memory ### Tech * Translation memory index per language * Optional integration with DeepL or OpenAI multilingual model * Supabase row-level localization support --- ## 11.7 AI-Powered Dynamic Product Tours ### Overview Assistant visually guides the user through onboarding or product education by: * Moving across multiple pages * Highlighting specific UI elements * Narrating what each feature does * Waiting for user input before advancing ### Use Cases * SaaS onboarding * Guided demos for apps * Product walkthroughs for e-commerce --- ## 11.8 Advanced Lead Routing Rules ### Overview Lead data from conversations can be conditionally routed to different destinations: * Sales rep assignment based on region * Different CRM pipelines for product categories * Instant Slack alerts for “hot” leads only ### Configuration * Rules defined in dashboard (If/Then UI) * N8N integrations execute delivery --- ## 11.9 Success Criteria for Future Feature Rollouts | Feature | Success Indicator | | :---- | :---- | | Cross-device memory | 30% increase in user return-to-chat rates | | CRM memory training | 25% reduction in live agent transfers | | Sentiment routing | 40% faster lead escalation | | Event triggers | 10% increase in lead engagement rates | --- # 12\. Roadmap and Development Milestones This section defines the phased development plan for siteGuide, breaking the project into achievable milestones with clear deliverables. It ensures alignment between technical teams, product leads, and business stakeholders by mapping each stage of the platform’s rollout — from initial prototype to full feature maturity. --- ## 12.1 Phase 0: Internal Proof of Concept (Weeks 1–2) **Objective:** Prove feasibility of real-time DOM interaction, voice control, and persistent session memory using minimal stack. **Deliverables:** * Embeddable JS snippet that attaches AI to a test website * Working co-browsing overlay (mouse follow \+ highlight) * Basic chat window with GPT-powered responses * DOM element targeting for text highlight and scrolling * Session memory stored in localStorage and Supabase * Voice input test (Web Speech API) and text-to-speech (ElevenLabs or fallback) **Success Criteria:** * Assistant can read and highlight a paragraph on command * Page reload does not lose the session transcript * Voice interaction succeeds in \>90% of test cases --- ## 12.2 Phase 1: MVP Beta (Weeks 3–6) **Objective:** Deliver a fully functional co-browsing assistant with persistent memory, working chat interface, and voice interaction on any WordPress site. **Key Features:** * AI overlay with chat UI and draggable co-browsing assistant * DOM scanning and tag-based element detection * Smooth scrolling and mouse-follow animation * Persistent session memory (local and Supabase) * Voice input/output (toggleable) * Email-based session resumption * Page-to-page memory continuity **Technical Setup:** * Supabase instance for storage, auth, and vector memory * Next.js management dashboard for site owners * Embedded JS loader script (deferred, async-ready) * n8n orchestration for memory, triggers, lead routing **Success Criteria:** * Installable via 1-line script on any WordPress site * Leads successfully captured and stored * Memory persists across navigation and logout/login * Works with \>80% of tested themes and site builders --- ## 12.3 Phase 2: Public Launch (Weeks 7–10) **Objective:** Launch siteGuide as a production-ready AI assistant with basic customization options and onboarding workflow. **New Features:** * Assistant appearance configuration (avatar, colors, tone) * Memory viewer for business owners * Lead export tools * Activity log (visits, transcripts, heatmaps) * Usage-based billing integration **Platform Stability Goals:** * 99.9% uptime for API and Supabase * Secure authentication and encryption standards * No memory loss or duplication bugs **Success Criteria:** * 100 active businesses onboarded within first 30 days * \<1% session loss rate * CSAT \>90% for assistant UX across test users --- ## 12.4 Phase 3: Expansion (Weeks 11–14) **Objective:** Begin adding optional modules and partner integrations for advanced use cases. **Expansion Modules:** * CRM/email inbox memory training * Cross-device persistent identity * Event-based engagement triggers * Custom action buttons * Full language localization * Zapier/Make.com integration **Developer Support:** * SDK or plug-in points for external developers * API access for programmatic lead retrieval **Success Criteria:** * CRM integration used by at least 25% of active customers * Average lead volume per business increases \>30% over beta * Third-party developer contributions submitted --- ## 12.5 Maintenance & Support Cycle (Ongoing) **Responsibilities:** * Weekly check-in on Supabase logs and memory usage * Monthly security audit of token/auth layers * Proactive UI updates for browser compatibility * Quarterly feature reviews based on customer feedback **Ongoing Metrics to Monitor:** * Assistant open rate per visitor * Drop-off points in conversations * Percentage of leads converted from assistant --- ## ✅ Missing or Underdeveloped Areas ### 1\. **Security & Compliance Guidelines** **What’s missing:** A clear, dedicated section on how to handle: * User data encryption (at rest and in transit) * Cross-site scripting (XSS) and injection protections in the chat overlay * Secure handling of memory/session data * Supabase row-level security policies * Optional GDPR/CCPA compliance for data deletion or user export **Why it matters:** Investors, enterprise clients, and CTOs will expect clarity around data security — especially since siteGuide stores identifiable memory and possibly voice data. --- ### 2\. **Analytics & Insight Framework** **What’s missing:** A description of what will be tracked, where it will be stored, and how businesses will view it: * Heatmaps (page areas most highlighted or requested) * Assistant usage stats (open rates, most clicked responses, voice usage) * Lead funnel performance (drop-offs, completions) * Session replay or text playback options **Why it matters:** Data reporting is a huge competitive differentiator, and analytics are essential to prove ROI for small business clients. --- ### 3\. **Unit Tests & QA Expectations** **What’s missing:** A brief QA/testing protocol section specifying: * What should be tested (UI components, memory persistence, DOM targeting) * Acceptable test coverage threshold * Bug classification and triage priorities (e.g., memory loss \= P0, misaligned scroll \= P2) * How often regression testing occurs (especially for DOM updates on client sites) **Why it matters:** Even junior developers benefit from seeing what “done” means in code quality and test resilience. --- ### 4\. **Browser & Device Compatibility Matrix** **What’s missing:** Explicit list of: * Minimum browser versions (Chrome, Safari, Firefox, Edge) * Supported devices (desktop, iPad/tablet, mobile) * Voice input/output compatibility (e.g., Safari on iOS may block mic access) **Why it matters:** This prevents confusion and support tickets when customers say "the assistant isn’t talking to me on my iPhone.” --- ### 5\. **Disaster Recovery & Failover Handling** **What’s missing:** Scenarios and protocols for: * Supabase outage * GPT model failure or API timeout * Frontend script failure due to site conflicts * Session loss or memory desync **Why it matters:** Even if just briefly noted, having recovery mechanisms planned builds trust in the system’s resilience. --- ### 6\. **In-Chat Context Menu / Tooltips** **What’s missing:** A UI addition that lets users: * Click a highlighted term for more info * View why a certain element was selected * Hover over past memory or assistant replies to expand context **Why it matters:** Improves user transparency and makes the AI feel more explainable — especially important for trust and legal/sensitive use cases. --- ### 7\. **Developer Environment Setup Instructions** **What’s missing:** The current PRD assumes the dev will figure out how to start. You should include: * GitHub repo structure * Initial command-line setup * Environment variable list (`.env.example`) * Recommended deployment environment (e.g., DigitalOcean droplet \+ Supabase project \+ Vercel frontend) **Why it matters:** Reduces ramp-up time and ensures developer onboarding is smooth — especially helpful if you later outsource pieces of the work. --- ### 8\. **Glossary of Terms** **What’s missing:** A simple glossary defining: * Co-browsing * Session memory * Highlighting * DOM targeting * Rehydration * Vector memory * Supabase (if junior developers are unfamiliar) **Why it matters:** Removes ambiguity, aligns the team’s mental model, and prevents incorrect assumptions during buildout. --- ## ✅ Must-Have Components Already Covered The PRD **already does** an excellent job defining: * AI overlay and chat interface * DOM targeting and element highlighting * Smooth co-browsing via scrolling and auto-focusing * Voice input and output with fallback behavior * Persistent session memory using Supabase/localStorage * Page-to-page continuity and assistant UI hydration * Email-linked session resumption * Developer roadmap, milestone plan, and fallback behavior These are the **core capabilities**. Nothing essential to the app's core promise has been omitted in design. --- ## ❗Remaining Gaps That Could Block or Break the Build These are *the last few real blockers* that, if not addressed, could cause the app to fail in live use or break user expectations. --- ### 1\. **Universal Page Context Restoration** **Problem:** After clicking to a new page, the assistant must **instantly restore** the exact scroll position, memory log, and highlight state. **Gap:** The PRD touches on this concept but doesn’t define a technical spec for: * Re-scanning DOM after page load * Reapplying the last command (e.g., re-highlighting paragraph 3\) * Rehydrating open conversation state in the UI **Why it matters:** If the AI clicks "Learn More" and the user lands on a new page with a blank assistant and lost memory, the illusion is broken. **Solution:** Define a **reinitialization protocol**: * Snapshot last action (DOM selector, command, scroll pos) * Reapply it after `window.onload` * Restore chat UI with `sessionId` --- ### 2\. **DOM Targeting Consistency** **Problem:** Live websites often use dynamic classes or DOM mutations (e.g., from page builders, sliders, or animations). Relying on `querySelector` alone is brittle. **Gap:** There is no fallback or adaptive targeting strategy if selectors fail. **Why it matters:** AI might “click” something that doesn’t exist anymore or highlight the wrong element — causing user confusion or failure to complete an action. **Solution:** * Use multiple DOM targeting strategies: static selectors \+ text match fallback \+ XPath * Store not just the selector, but the **text content \+ position index** for fuzzy recovery * Gracefully degrade with a message like: “It looks like this section changed — let me find the new version for you” --- ### 3\. **Race Conditions in DOM Rendering** **Problem:** If the AI tries to scroll/highlight/click before the DOM is fully hydrated (e.g., on SPA sites or heavy WordPress themes), the action will silently fail. **Gap:** There’s no defined method for detecting **DOM readiness** before performing assistive actions. **Why it matters:** Some client sites will appear “broken” because the AI moves too quickly after navigation or user commands. **Solution:** * Use `MutationObserver` or wait for specific element load before interaction * Add retry logic for element-based actions (e.g., scroll \+ highlight up to 3x with delay) --- ### 4\. **WordPress Script Isolation** **Problem:** Many WordPress sites inject tons of JS (e.g., Elementor, WPBakery, Divi) that can conflict with your script or override events. **Gap:** The PRD doesn’t define how to **sandbox** or isolate the AI’s scripts from common WordPress clashes. **Why it matters:** You may see bugs that are hard to debug because other plugins intercept clicks, hijack styles, or reset DOM state. **Solution:** * Wrap assistant inside a **Shadow DOM** * Use CSS prefixing for isolation * Avoid assuming control over `window`, `document`, or global classes --- ### 5\. **Fail-Safe UI Behavior** **Problem:** If the AI crashes or stalls, there’s no guidance yet on how to gracefully recover or notify the user. **Gap:** No status indicators or fallback UI states are defined. **Why it matters:** An invisible or frozen assistant \= a completely broken experience. Especially problematic in customer-facing websites. **Solution:** * Add a UI status pill: “Thinking...”, “Listening...”, “Connection lost” * Retry on GPT/Supabase failure * Trigger a self-heal or refresh prompt if assistant is unresponsive for 10+ seconds --- ### 6\. **Cross-Origin Memory Behavior** **Problem:** You mention expanding to “all websites” — but the PRD assumes you’ll always have access to embed a script on the target site. **Gap:** There’s no plan for supporting co-browsing across sites you **don’t own**, or across multiple domains (e.g., subdomains, client portals). **Why it matters:** This becomes relevant once you expand beyond single-site deployments and want full session memory across apps (e.g., WordPress \+ Shopify). **Solution:** * For now, scope to same-origin sessions * Flag this as a future milestone: **Cross-Origin Shared Session Layer**, possibly using OAuth \+ client-side session tokens --- ## ✅ Summary: Build is Feasible with Minor Additions You are **95% complete** in design. The remaining \~5% are critical execution details that, if skipped, would cause: * Inconsistency across real-world websites * Broken assistant behavior after navigation * Perceived bugs due to race conditions or DOM conflict * Loss of user trust due to UI crashes or incorrect highlights Once you address the above 6 points explicitly in the PRD (even briefly), a developer — junior or senior — should be able to build the **exact experience you envision**, end-to-end. Would you like me to revise the PRD to include these, or just generate the add-on sections to paste in? --- Powered by [ChatGPT Exporter](https://www.chatgptexporter.com) --- ## aiConnected Platform Industry Templates: The Complete Explanation **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/industry-templates/concept-overview # aiConnected Platform Industry Templates: The Complete Explanation ## Introduction: What is This Feature? Imagine you’re a real estate agent, and you just signed up for a new software platform. You log in and see a blank dashboard. There are dozens of buttons, settings, features, and configuration options. You have to decide: - Which features should I turn on? - What doesn’t apply to my business? - How do successful real estate agents set this up? - Will this work for my specific type of real estate work (land sales? residential homes? commercial)? You’re now faced with decision paralysis. Even though the platform might be perfect for you, the setup friction is so high that you might just give up and go use something else. **aiConnected Platform Industry Templates solves this problem.** Instead of a blank slate, when you log in you see: - “Real Estate” (industry) - Within Real Estate, you see: “Land Sales,” “Residential Home Sales,” “Commercial Real Estate,” “Build-to-Rent,” “M&A,” etc. - You click “Land Sales” - You see a pre-configured, battle-tested setup that’s already been proven by 1.2 million land sales agents - You click “Install” - You’re immediately productive That’s it. That’s the core feature. Pre-configured, community-proven starting points for different industries and specializations. ----- ## The Problem It Solves ### The Cold Start Problem When any new platform launches, it faces a critical challenge: **new users need to see immediate value.** Without Industry Templates, new agencies face: **1. Setup Friction** - Blank slate is intimidating - “Where do I even start?” paralysis - Configuration decisions are unclear - Takes hours/days to get productive - Many give up before finding value **2. Configuration Uncertainty** - No reference for what works - “Am I setting this up right?” - Trial-and-error approach - Suboptimal configurations - Users don’t see platform’s full potential **3. Feature Overwhelm** - Dozens of features, modules, integrations - Not all are relevant to every industry - Users turn off features they need; enable ones they don’t - Result: mediocre experience **4. Community Knowledge Gap** - Successful setups exist somewhere - But new users can’t see them - Each new user re-invents the wheel - Institutional knowledge stays siloed ### The Business Impact Without solving this, the platform experiences: - **High churn**: Users leave before getting value - **Poor activation**: Users don’t reach “aha moment” - **Low retention**: Users who did get set up leave anyway - **Weak network effects**: Each user builds in isolation; no shared knowledge ----- ## How Industry Templates Solves It ### 1. Eliminates Setup Friction **Before**: “I need to configure this platform from scratch. This could take days.” **After**: “I’m a real estate agent selling land. There’s a Land Sales configuration. I install it. I’m productive in 5 minutes.” This is the core value: **immediate productivity.** ### 2. Provides Social Proof When you see “Land Sales Configuration - 1.2M active businesses, 4.8 stars, updated 2 weeks ago,” you know: - This is proven (1.2 million real agents use it) - It’s maintained (updated 2 weeks ago, not abandoned) - It’s trusted (4.8 stars is high) This is **confidence**. You’re not experimenting; you’re adopting a known-good setup. ### 3. Democratizes Expert Knowledge The best configurations don’t stay locked in one agency. They get shared with the community. An agency that figured out the perfect setup for med spa owners shares it. Now every med spa owner benefits from that expertise. This is **knowledge sharing**. Best practices spread. ### 4. Creates Network Effects As more people adopt configurations: - More feedback is generated - Configurations improve - New developers want to build features for proven use cases - Features improve - Configurations improve further - More adoption This is a **virtuous cycle** that strengthens the entire platform. ----- ## Why It’s Important: Strategic Value ### For Users (Agencies & Their Clients) **Immediate value**: - Get productive in minutes, not days - Don’t reinvent the wheel - Benefit from thousands of other users’ experience - Have ongoing support (community maintains configurations) **Long-term value**: - Configurations evolve as platform evolves - Best practices stay relevant - Easy onboarding for new clients - Professional setups for specific industries ### For aiConnected (The Platform) **Activation & Retention**: - New users get value immediately → higher activation - Friction eliminated → higher conversion - Users stick around → higher retention **Differentiation**: - Competitors have blank platforms - aiConnected has pre-configured, proven templates - Significant competitive advantage **Scaling Knowledge**: - Best practices automatically shared - Platform knowledge accumulates - Every new user benefits from collective experience **Developer Ecosystem Growth**: - Developers see proven use cases - They want to build features for Real Estate, Legal, Med Spa, etc. - More developers build more features - More features → better configurations - Flywheel effect **Community Ownership**: - Users contribute configurations - Community votes on improvements - No central curation burden - Self-governing ecosystem ----- ## How It Actually Works (Simple Example) ### Scenario: Sarah is a Real Estate Agent **Day 1: Signup** Sarah signs up for aiConnected. Instead of a blank dashboard, she sees Industry Templates. **Day 1: Browse** She clicks “Real Estate.” She sees: - Land Sales Configuration (1.2M users, 4.8 stars) - Residential Home Sales Configuration (850K users, 4.6 stars) - Commercial Real Estate Configuration (320K users, 4.7 stars) She reads the Land Sales description: “Complete automation setup for land agents. Automated lead capture, property research, skip tracing, client intake, document generation.” This is exactly what she needs. **Day 1: Install** She clicks “Install.” A review screen shows her what will be set up: - Paralegal AI persona (to handle intake & documents) - Automated lead capture - Property research tools - Skip tracing (find property owners) - SMS automation - Document generation She reviews it. It looks good. She clicks “Apply.” **Day 1: Configure Access** She decides: “I have 3 agents on my team. All should get this setup.” She sets pricing: Base setup is free. Skip tracing add-on is $49/month. She clicks “Done.” **Day 2: Productive** Sarah’s team is now set up and productive. They’re using a configuration that’s been proven by 1.2 million users. Everything works because it’s been battle-tested. If something breaks, Sarah knows it’s not her setup (it’s proven). She reports it. The platform fixes it. Everyone benefits. ### The Community Part A few weeks later, Sarah’s team figures out an optimization: “If we adjust the lead intake workflow like THIS, we close 10% more deals.” Sarah thinks: “This could help other land agents.” She exports her modified configuration as “Land Sales - Optimized for Closing” and submits it to the community. It goes into a 30-day testing period. Other land agents test it. If they like it, they vote it up. If it doesn’t work for them, they vote it down. After 30 days, it gets accepted (342 upvotes, 28 downvotes). Now it’s available as an alternative configuration. Other land agents can see: “This version gets better closing rates.” They try it. If they like it, they adopt it. If not, they stick with the original. This is **crowdsourced optimization**. Sarah helped hundreds of agents close more deals. ----- ## The Architecture: Why It’s Designed This Way ### Why “Industry = Collection of Configurations” (Not a Hierarchy) Some might think: “Why not have one ‘Real Estate’ template with specializations under it?” That’s a hierarchy model. It doesn’t work because: **Problem with hierarchy**: - Implies some things are “base” and others are “specialized” - Creates dependencies - New agents selling land think: “I need the Real Estate base, then customize for land” - But maybe they need to remove things that don’t apply - End result: more customization, more friction **Why “collection of configurations” works better**: - Each use case (Land Sales, Residential, Commercial) is co-equal - No hierarchy, no “base + specialization” thinking - Agent selling land picks “Land Sales” and they’re done - No mental model of “base + customization” - Simpler, clearer, less friction **Real-world analogy**: Think of WordPress themes. WordPress doesn’t have a “Blog base theme” with “E-commerce specialization.” It has separate themes: “Blog theme,” “E-commerce theme,” “Portfolio theme.” Each is purpose-built. You pick the one that fits. No hierarchy, no customization needed. Same principle here. ### Why “No Auto-Apply, Ever” Some platforms force updates. When a new feature is available, it automatically turns on. Industry Templates **never** force anything. Why? **Because agencies know their clients better than we do.** An agency might say: “The new skip-tracing feature is great, but my clients don’t need it. I’m not enabling it.” Or: “My Premium-tier clients should get this. My Basic-tier clients shouldn’t.” Or: “I’m going to test this with one client before rolling out to everyone.” We trust agencies to make these decisions. We don’t force features on them. We say: “Here’s what’s new. You decide if and when to enable it. If you do, you set the price.” This is **agency autonomy**. It’s fundamental to the platform philosophy. ### Why “Community Voting” Who decides if a configuration is good? Not aiConnected. The community. **Why?**: - 1.2 million users have more collective wisdom than a small team - Community is diverse (different needs, different use cases) - Voting is simple and transparent - Best practices win naturally **How it works**: - Someone submits a configuration - Community votes thumbs up/down for 30 days - Votes are tallied - If enough upvotes, it becomes available - If mostly downvotes, feedback is provided; submitter can improve No editorial curation. No “this is our official config.” Just: “This is what the community found to work.” ----- ## Why This Is Necessary for Platform Growth ### 1. Solves the Activation Problem **Fact**: 70% of users never reach “first aha moment” in enterprise software. Why? Setup friction. Too many decisions. Unclear path to value. Industry Templates eliminates this friction. Users see immediate value within minutes. This dramatically improves activation. **Platform Impact**: Higher activation → higher survival rate → more users stick around. ### 2. Creates Defensible Competitive Advantage Competitors have platforms. aiConnected has platforms + proven, community-maintained configurations. This is hard to copy: - Can’t just import another platform’s configs (they’re specific to that platform) - Takes time to build community contributions - Network effects compound (more users → better configs → more users) This is **moat**. Hard for competitors to catch up. ### 3. Enables Developer Ecosystem Growth Developers look at blank platforms and think: “What should I build?” Developers look at Industry Templates and think: “I see 1.2M land agents using this config. They need better property research. I’ll build a feature for that.” This aligns developer incentives with real needs. More developers build more features. Ecosystem grows. **Platform Impact**: From zero developer activity → thriving marketplace. ### 4. Scales Institutional Knowledge Without Industry Templates, knowledge about “what works” is scattered: - In one agency’s setup - In another agency’s setup - In Slack conversations - Nowhere centralized With Industry Templates, best practices are: - Documented (in the configuration) - Transparent (visible to all) - Maintained (community updates them) - Rewarded (well-maintained configs get more adoption) **Platform Impact**: Collective intelligence, not scattered silos. ### 5. Creates Positive Network Effects More users → More configurations → Better features built for those configs → More users adopt This is exponential growth, not linear. **Example**: - Month 1: 100K users, 5 configurations - Month 6: 500K users, 50 configurations, marketplace thriving - Year 1: 5M users, 200 configurations, ecosystem self-sustaining Without Industry Templates, growth is limited by setup friction (many users churn before getting value). With Industry Templates, friction is eliminated. Growth compounds. ----- ## Why Industries Benefit: Three Perspectives ### Perspective 1: The New Agent **Without Industry Templates**: - “I just signed up. I’m lost. This is too complex. I’ll try something simpler.” - → Churn **With Industry Templates**: - “I just signed up. There’s a config for what I do. I installed it. I’m productive.” - → Activation → Retention ### Perspective 2: The Mature Agency **Without Industry Templates**: - “We’ve figured out the perfect setup for our business. But everyone else is struggling.” - → Knowledge stays internal **With Industry Templates**: - “We figured out the perfect setup. We’ll share it. The community will improve it. Everyone benefits.” - → Agency gets credit → community improves → everyone better ### Perspective 3: The Platform **Without Industry Templates**: - New users struggle with setup - Churn is high - Developers don’t know what to build - Knowledge is scattered - Growth is limited by friction **With Industry Templates**: - New users get immediate value - Churn drops - Developers see real use cases, build accordingly - Knowledge is centralized and improves over time - Growth is enabled by eliminating friction ----- ## The Broader Vision: What This Enables ### Immediate (Year 1) **What users see**: - “I pick my industry, I get a proven setup, I’m productive” - Setup friction is gone - Immediate value **What the platform sees**: - Higher activation (more users reach aha moment) - Higher retention (users don’t leave at setup) - More data on what works - Clearer direction for feature development ### Medium-term (Year 1-2) **Configurations evolve**: - Real Estate Land Sales v1.0 → v1.1 → v1.2 → v2.0 - Each version is better based on community feedback - Agencies can upgrade when ready or stay on stable versions **Developer ecosystem grows**: - “Land agents need better property research” → Developer builds it - “Legal teams need contract analysis” → Developer builds it - Features and configs improve together **Community becomes source of truth**: - Best practices documented in configurations - Configurations maintained by community, not central team - Platform gets leverage: improvements from millions of users, not dozens of engineers ### Long-term (Year 2+) **Network effects compound**: - More configurations → More users adopt → More data → Better features → Better configurations - Exponential growth **Marketplace thrives**: - Thousands of configurations - Hundreds of developers building features - Self-governing ecosystem - Platform becomes infrastructure for an ecosystem, not just software ----- ## Real-World Analogy: WordPress WordPress faced the same problem 15 years ago. **In 2005**: WordPress was software you installed. Blank slate. Confusing. **WordPress solved it with**: Themes (pre-configured designs) and Plugins (community-built features). **What happened**: - Non-technical people could use WordPress because themes made it easy - Developers could build plugins because there was a clear platform - Ecosystem exploded (Shopify, WooCommerce, countless others built on WordPress themes/plugins) - WordPress became the dominant web platform Industry Templates is aiConnected’s version of this playbook. Instead of “themes” (visual designs), we have “configurations” (functional setups). Instead of “plugins” (general features), we have “modules and personas” (AI-powered capabilities). Same principle. Different domain. ----- ## Why Now? Why This Feature? ### The Timing Question “Why not build this feature right now?” Because: 1. **Configurations remix features** - A Land Sales configuration uses skip-tracing, document generation, lead capture, etc. - These features need to exist and be stable first - Build configurations when the marketplace has proven, stable features 1. **Community needs to exist** - Configurations are only valuable if community contributes - Need a mature marketplace first (developers building features) - Need agencies using the platform (who would contribute configs) 1. **Not the critical path** - Right now: Platform core needs to be rock solid - Configurations can be added after, leveraging the existing feature set - Better to have solid platform + no templates than broken platform + templates **Timeline**: - Now: Build platform core, stabilize features - 6-12 months: Marketplace thriving, features proven - Then: Launch Industry Templates, leverage marketplace ----- ## The Bottom Line: Why This Matters ### For Users - Get productive in minutes, not days - Benefit from thousands of other users’ experience - Professional setups maintained by community - Industry-specific expertise built in ### For Agencies - Reduce onboarding friction for their clients - Create multiple tier offerings (Base, Pro, Enterprise configs) - Monetize granularly (each feature can be upsold) - Access community knowledge and best practices ### For the Platform - Solve the activation problem (users get immediate value) - Create defensible competitive advantage (hard to copy) - Enable developer ecosystem (real use cases = motivation to build) - Scale institutional knowledge (best practices shared, not siloed) - Generate network effects (more users → better configs → more users) ### For Growth - Friction eliminated → higher conversion - Higher conversion → more users - More users → more contributions → better configurations - Better configurations → more growth (flywheel) **Without Industry Templates**: Platform grows linearly, limited by setup friction. **With Industry Templates**: Platform grows exponentially, enabled by eliminating friction and creating network effects. ----- ## Conclusion **aiConnected Platform Industry Templates** is a simple idea with profound impact: > Pre-configured, battle-tested, community-maintained setups for different industries and specializations. This solves: - Setup friction (users are productive immediately) - Configuration uncertainty (proven setups reduce guesswork) - Feature overwhelm (configurations surface only what’s relevant) - Knowledge silos (best practices are shared and maintained) Why it matters: - Higher user activation and retention - Competitive moat (hard to copy) - Developer ecosystem growth - Network effects (exponential growth potential) - Community ownership (scale without central team burden) This feature doesn’t exist in a vacuum. It’s the natural evolution of a platform that wants to: 1. Eliminate friction for users 1. Scale knowledge across communities 1. Enable developer ecosystem 1. Create defensible advantages 1. Generate exponential growth **That’s why aiConnected Platform Industry Templates is not just nice to have. It’s essential for platform maturity and growth.** --- ## Concept Task List **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/industry-templates/concept-task-list # aiConnected Industry Template Concept Implementation Task List ## Core Concept Clarification **Model B: Industry = Collection of Configurations** Industries are not monolithic templates with specializations underneath. Industries ARE their configurations. Example: Real Estate Industry is: - Land Sales Configuration (1.2M active businesses) - Residential Home Sales Configuration (850k active businesses) - Commercial Real Estate Configuration (320k active businesses) - Build-to-Rent Configuration (45k active businesses) - Real Estate M&A Configuration (12k active businesses) - etc. Agency workflow: “My client does land sales” → Click Real Estate > Land Sales config → Done. No customization needed. Battle-tested setup. Agencies can apply configurations to their entire agency OR to specific business clients (granular control). ----- ## Feature Voting & Governance System - [ ] Build voting interface for template features (thumbs up/down, optional comment field) - [ ] Create AI capability to ingest and process user feedback at scale (no human bottleneck) - [ ] Define voting window parameters (test period: 30 vs 90 days, measure which performs better) - [ ] Create feature acceptance/rejection workflow based on vote totals - [ ] When feature is rejected, feed AI-processed feedback back to contributor for improvement iteration - [ ] Document contribution submission workflow (how agencies/developers propose features) ## Version Management & Non-Forced Updates - [ ] Build version tracking system for each industry template - [ ] Ensure agencies can stay on any version indefinitely (no forced upgrades) - [ ] Create version upgrade opt-in flow (not automatic) - [ ] Implement rollback capability if an agency needs to revert to prior version ## Persona Learning Integration with User Acceptance Gates - [ ] When personas across industry template ecosystem improve via cross-persona learning, create user acceptance flow - [ ] Agencies/businesses can review new skills/capabilities before adoption - [ ] Build reversibility system - users can reject or remove skills that don’t work for their use case - [ ] Document how improved persona baseline gets propagated to template versions ## Cold Start / Initial Template Bootstrap - [ ] Identify 10-20 most popular industry verticals for launch (Real Estate, Legal, Med Spa, Dentistry, Insurance, Construction/Remodeling, Healthcare, etc.) - [ ] For each initial template: document required modules, features to enable, baseline personas needed - [ ] Create contribution guidelines that support BOTH developer AND agency input (not just developers) - [ ] Establish quality bar for initial templates before launch ## Ecosystem & Contribution Framework - [ ] Define roles: agencies vs. developers in contribution model - [ ] Create process for agencies to submit their proven/working configurations as template contributions - [ ] Create process for developers to submit new features/modules to existing templates - [ ] Document conflict resolution if two contributions conflict ## Configurations Sub-Feature (Agency Contributions) - [ ] Build configuration creation workflow - agencies export their working setup as a configuration - [ ] Create configuration submission UI - name, description, target industry, what problem it solves - [ ] **Configuration conflict resolution**: When competing configurations (e.g., two “Land Sales” configs) are submitted, use same voting system (30/90 day test window). If accepted = becomes new version. If rejected = doesn’t propagate, but agencies already using it can continue (no forced changes) - [ ] Build configuration discovery interface - search + browse by industry + use case keywords - [ ] **AI-assisted discovery**: System AI can ask targeted questions to drill down to matching configurations (“You’re in land sales? Here are land sales configurations”) - [ ] Create configuration application flow - apply to entire agency OR to specific business clients (granular control) - [ ] Configuration versioning - agencies can use different versions of same configuration (v1.5 vs v2.0) - [ ] Voting/feedback system for configurations (same thumbs up/down mechanism) - [ ] Configuration reusability - ensure configuration can be applied multiple times across different clients without conflicts - [ ] Reversibility - agencies can remove/rollback a configuration if it doesn’t work for them ## Configuration Testing & Feedback (Beta Testing Model) - [ ] Enable Test Mode toggle for agencies - allows business clients to beta test configurations before production - [ ] Test mode activates 30-day voting window (upvote/downvote feedback system) - [ ] Agencies can enable test mode for specific business clients (granular control) - [ ] **Data visualization needed**: Charts showing configuration voting trends, acceptance likelihood, user sentiment during testing period - [ ] Data display in agency settings showing real-time upvote/downvote counts for configurations in test - [ ] Data display in Industry discovery page showing configurations currently in test with vote data and trends - [ ] Test mode data applies both locally (agency settings) and globally (industry discovery) - [ ] Seamless transition: when configuration completes testing and passes voting, automatically moves to production ## Configuration Application (No Forking Model) - [ ] Agencies apply configurations as-is (snapshot of settings, personas, integrations, workflows, modules) - [ ] After applying, agencies can customize/fine-tune for their specific needs - [ ] No forking required - each agency gets their own instance of the configuration to modify - [ ] Changes made by agencies don’t create new public configurations - [ ] If an agency wants to share their customizations, they submit as new configuration through voting process ## Configuration Discovery & Trust Signals (WordPress Plugin Model) - [ ] Display active businesses/users count on every configuration (e.g., “1.2M active businesses”) - [ ] Display star/rating system for each configuration - [ ] Display “Last Updated” date for each configuration - [ ] Display configuration adoption trends (is it growing or declining?) - [ ] Surface top configurations by category (most active, highest rated, newest) - [ ] Make discovery visual and scannable - agencies can glance and see what’s working vs what’s niche - [ ] Show configuration version history (what changed, when, community votes) ## Access Control & Business Client Configuration Management - [ ] Agencies can toggle “Industry Templates/Configurations” access on/off per business client (similar to marketplace toggle) - [ ] Agencies control which configurations are available to their business clients (whitelisting/blacklisting) - [ ] Agencies can set default configurations per business client type - [ ] **Open question**: Should business clients be able to search and apply configurations themselves, or only agencies can apply on their behalf? - Current thinking: Agencies toggle this on/off per client (agency decides what they allow) - Not forcing one model - agencies make the choice for their workflow ## Feature + Configuration Interaction (ARCHITECTURAL PRINCIPLE) - [ ] **CORE PRINCIPLE: No auto-apply, ever.** Features must go through voting/testing. Agencies are notified of updates but never forced to adopt them. - [ ] When a new feature passes voting (e.g., skip-tracing for Real Estate), agencies get notification: “New skip-tracing feature available. Here’s what it does. Toggle on/off?” - [ ] Agencies decide per-business-client whether to enable the new feature - [ ] If enabled, pricing toggle appears (free or upsell) - [ ] No forced updates, no auto-propagation, no breaking changes imposed on users ## Monitoring & Optimization - [ ] Track voting window duration performance (30 vs 90 day outcomes) - [ ] Monitor adoption rates per industry template - [ ] Measure feature acceptance/rejection ratios over time - [ ] Collect data on version retention (which versions do agencies stay on longest) ## Feature Pricing Integration (CRITICAL) - [ ] For every feature toggle available to agencies (enable/disable for business clients), add Pricing button - [ ] Pricing UI shows when feature toggle is activated - [ ] Agencies can set price at $0 (free upsell) or custom amount (paid upsell) - [ ] Pricing applies per-feature, per-business-client, enabling granular monetization - [ ] Ensure pricing data flows to billing system ----- **Note**: This keeps the business model intact. No changes to platform tax, 90/10 split, or core marketplace. This is scaffolding and collaborative evolution for starting configurations. --- ## Developer Overview **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/industry-templates/developer-overview # aiConnected Platform Industry Templates: The Developer’s Guide ## Overview: What You’re Building You’re building a **configuration-as-code system with community governance, AI-powered feedback processing, and cross-instance learning integration**. In simpler terms: A way for agencies to export their working setups as “configurations,” share them with the community, have the community vote on improvements, and have those improvements automatically propagate back to users (with opt-in controls). But there’s a lot of complexity under that simple description. ----- ## The Core Problem: Setup Friction at Scale ### Why This Matters to Development Here’s the developer problem you’re solving: Right now, when a new agency signs up for aiConnected: 1. They get a blank platform 1. They have to decide: which modules to enable? which personas to use? which workflows to set up? 1. This is high-friction, high-cognitive-load, error-prone 1. Many agencies never reach “aha moment” (they churn before seeing value) **For you as a developer**: This means: - Users don’t stick around long enough to use the features you built - No feedback loop (users leave before they can tell you what’s broken) - Platform doesn’t scale because adoption is bottlenecked by setup friction - You’re building features for users who never actually use them **Industry Templates solves this at the platform level**: - New users see pre-configured setups for their industry - They install in minutes - They reach aha moment immediately - Friction is eliminated system-wide ----- ## The Architecture: What You’re Actually Building This isn’t a simple feature. It’s a system with multiple interconnected components: ### 1. Configuration Export/Import Engine **What it does**: - Captures the current state of an agency’s setup (all settings, modules, personas, integrations, workflows) - Serializes it into a configuration snapshot - Can be re-imported into other agencies **Technical requirements**: - Traverse entire account configuration state - Serialize all enabled modules (document generation, knowledge base, voice AI, etc.) - Serialize all personas and their state (trained skills, memory configurations) - Serialize integration state (which providers are connected, auth tokens are hashed) - Serialize workflow definitions - Handle nested structures, circular references, missing states - Version the snapshot schema (v1, v2, v3 as platform evolves) **Complexity**: - As platform adds features, configuration schema evolves - Old snapshots must still import (backward compatibility) - New snapshots must export features old versions don’t know about - Managing schema migrations is non-trivial **Example**: A configuration from 2 years ago has Document Generation but not “Voice Cloning.” When imported today: - Document Generation imports fine - Voice Cloning isn’t included (wasn’t in original snapshot) - User sees: “Configuration doesn’t include Voice Cloning. Want to add it?” ### 2. Configuration Discovery & Catalog System **What it does**: - Stores all shared configurations - Indexes them by industry, use case, tags - Surfaces them to users based on search/browsing **Technical requirements**: - Database schema for configurations (metadata, snapshot, version, creator, timestamps) - Full-text search (search “skip tracing land sales”) - Filtering (industry: Real Estate, use case: Land Sales, tags: automation) - Scoring algorithm (how to rank configurations? by adoption? by rating? by recency?) - Caching layer (heavy search traffic when platform grows) - Analytics tracking (which configs are searched, installed, tested, etc.) **Complexity**: - Search relevance is hard (need to tune scoring, handle synonyms, etc.) - As configurations grow to thousands, discovery UX becomes critical - Need recommendations engine (“Based on Real Estate, you might like…”) - Need to prevent spam (bad actors submitting thousands of low-quality configs) ### 3. Voting & Feedback System **What it does**: - Users vote thumbs up/down on configurations during test period - Comments are collected and analyzed - After test period, votes determine if configuration is accepted **Technical requirements**: - Voting schema (user, configuration, vote direction, timestamp, comment) - Vote tallying (count upvotes vs downvotes, calculate percentages) - Comment ingestion (store, index, make searchable) - Automatic testing period management (30 days? 90 days? variable?) - Notification system (users get notified about test results) **Complexity**: - Handling concurrent votes (thousands of users voting simultaneously) - Preventing vote manipulation (same user voting twice, coordinated brigades) - Determining acceptance threshold (60%? 70%? formula based on volume?) - Managing edge cases (what if vote is exactly 50/50? tie-breaking logic) ### 4. AI-Powered Feedback Processing **This is the hard part. This is what makes this system work at scale.** **What it does**: - Ingests thousands of user comments on a configuration - Analyzes them without human review - Extracts themes, issues, praise - Provides feedback to configuration creator - Generates summary for decision-making **Why this matters**: Without AI, you’d need humans reviewing every comment. That doesn’t scale. With 100K configurations in test simultaneously, you can’t have 100 people reviewing comments. With AI, the system: - Reads all comments (no matter the volume) - Finds common themes (“Skip tracing is broken on Windows”) - Ranks by frequency and impact - Provides summary to creator: “8 users report skip tracing fails on Windows. Consider adding Windows compatibility before v1.1.” **Technical requirements**: - Comment aggregation pipeline - Sentiment analysis (positive/negative/neutral) - Theme extraction (what are people talking about?) - Issue detection (what’s broken?) - Praise detection (what’s working well?) - Summary generation (human-readable feedback) - No hallucinations (AI must be accurate, not make up issues) **Complexity**: - LLM integration (which model? Claude? Fine-tuned model?) - Prompt engineering (how to make AI extract meaningful themes?) - Quality control (how to prevent AI from misinterpreting comments?) - Scaling (can you process 100K comments in batch overnight?) - Cost (LLM API calls are expensive; how to optimize?) **Example**: Comments submitted: - “Skip tracing doesn’t work on Windows” (1 comment) - “Skip tracing fails on Windows machines” (1 comment) - “Can’t use skip tracing because I’m on Windows” (1 comment) - “Other than the Windows bug, this config is perfect” (1 comment) - “Why isn’t skip tracing available on Windows?” (1 comment) AI analysis: - **Theme**: Windows compatibility issue (5 mentions) - **Severity**: Medium (5 users affected, workaround might exist) - **Praise**: “Perfect setup otherwise” (3 mentions) - **Recommendation**: “Consider adding Windows support in next version” Creator gets notified: “Main feedback: Windows compatibility needed. Otherwise very positive reception.” ### 5. Configuration Versioning & Update Propagation **What it does**: - Tracks configuration versions (v1.0, v1.1, v1.2, v2.0) - When newer version available, notifies agencies - Agencies can opt-in to upgrade - Manages backwards compatibility **Technical requirements**: - Version schema (semver? custom?) - Diff detection (what changed between v1.0 and v1.1?) - Changelog generation (auto-generate what’s new) - Upgrade logic (safely upgrade configuration, handle conflicts) - Rollback logic (can agency go back to v1.0? Yes, always) - Notification system (notify agencies of updates) **Complexity**: - Configuration diff is hard when snapshots are large - Some changes are safe to auto-upgrade (new optional feature) - Some changes require user decision (changed password for integration? need re-auth) - Determining breaking changes (is removing a module a breaking change?) - Handling conflicts (agency modified config + new version = conflict. Who wins?) ### 6. Integration with Persona Learning System (Neurigraph) **This is where it gets really complex.** **What it does**: - When personas improve through cross-persona learning, those improvements flow into configurations - Agencies get notified about persona skill changes - Agencies can opt-in per-client **Technical requirements**: - Hook into Neurigraph persona learning system - Detect when personas acquire new skills - Notify all agencies using that persona - Track which clients have accepted new skills (vs. rejected) - Handle skill propagation (client A has skill, client B doesn’t, even though using same persona) - Reverse skill adoption (user rejects skill, it’s removed) **Complexity**: - Persona learning is asynchronous (happens in background) - Configuration might have persona that learns new skill - Need to detect this and propagate - But don’t force it (opt-in always) - And track per-client adoption (not all clients of an agency adopt same skill) - This requires adding state to personas at the client level **Example**: - Configuration v1.0 includes Paralegal persona - Over time, Paralegal persona learns “Legal Research API Integration” skill (via Neurigraph learning) - Agencies get notified: “Paralegal now has Legal Research Integration. Accept for your clients?” - Agency says: “Yes for Pro clients, no for Basic clients” - System propagates skill to Pro clients only - Configuration versions and persona versions now differ (config v1.0, but Paralegal has learned skills) ----- ## Why This Strengthens the Platform ### 1. Solves the Activation Bottleneck **Current problem**: - User activation is bottlenecked by setup friction - Many users never reach “aha moment” - Platform can’t grow faster than setup friction allows **With Industry Templates**: - Setup friction is eliminated - Users reach aha moment in minutes - No bottleneck - Platform can scale **For you**: Your features get used by more users, faster. You get feedback quicker. You can iterate faster. ### 2. Creates a Feedback Loop **Current problem**: - Users configure in isolation - Best practices stay hidden - You don’t know what configurations work well - Feature development is guesswork **With Industry Templates**: - Configurations are visible and rated - Community votes on what works - You see which configurations succeed - You see what features those configs use - You can build features for proven use cases **For you**: You have a clear signal about what to build next. Build features that unlock high-adoption configurations. ### 3. Enables Developer Ecosystem Growth **Current problem**: - Developers see blank platform. What should I build? - No clear use cases or target audiences - Developer motivation is unclear **With Industry Templates**: - Developers see “1.2M land agents using Land Sales config” - They think: “These land agents need better property research. I’ll build that.” - Clear target, clear motivation, clear success metric **For you**: Other developers are motivated to build. Marketplace grows. Features improve. Your platform becomes valuable. ### 4. Scales Knowledge Without Central Burden **Current problem**: - Best practices exist somewhere - You need to document them centrally - This is expensive (hiring, maintaining docs, etc.) - Doesn’t scale to all industries **With Industry Templates**: - Agencies document best practices by sharing configurations - Community maintains them (if one agency creates config, others improve it) - You don’t maintain configurations; community does - Cost is zero for you; value is infinite **For you**: You build the system once. Community maintains it. Scales to unlimited industries without your involvement. ### 5. Network Effects Compound Platform Value **How it works**: ``` More users → More configurations → Better features built for those configs → More users adopt configs → More users ↑ ↓ └────────────────────────────────── Flywheel ──────────────────────────────────────────────────────┘ ``` **Example**: - Month 1: 100K users, 5 configurations - Developers see Real Estate config with 50K users - They build skip-tracing feature (Real Estate agents need this) - Configuration improves, adoption doubles (100K users) - Other developers see success, build more features - Month 6: 500K users, 50 configurations, marketplace thriving - Flywheel accelerates Without Industry Templates, you’re building features hoping someone uses them. With Industry Templates, you’re building features for configurations with known user counts. **For you**: Platform value compounds exponentially instead of linearly. ### 6. Creates Defensible Competitive Advantage **Why it’s hard to copy**: - Requires community contributions (can’t just launch it) - Takes time to build (network effects don’t happen overnight) - Configurations are platform-specific (can’t port WordPress themes to Drupal easily) - Each day you operate, you get more contributions, wider moat **For you**: Once launched, platform becomes harder to compete with each month. ----- ## The Technical Challenges You’ll Face ### Challenge 1: Configuration Schema Versioning **Problem**: - Day 1: Configuration schema is simple (5 fields) - Year 1: Configuration schema is complex (50+ fields) - Old configs from Day 1 must still import **Solution approaches**: - Semantic versioning for snapshot schema - Migration functions (v1→v2, v2→v3, etc.) - Graceful degradation (missing fields = sensible defaults) - Test coverage (test every upgrade path) **Why it matters**: If you get this wrong, configurations break on upgrades. Users lose trust. ### Challenge 2: Preventing Gaming & Spam **Problem**: - Someone submits 100 low-quality configurations - Someone coordinates vote brigades (1000 accounts vote same thing) - Someone submits configuration that breaks other people’s setups **Solution approaches**: - Rate limiting (limit configuration submissions per account) - Reputation system (new users’ configs start with less visibility) - Vote authentication (can only vote once per config, per account) - Community moderation (top-rated users can flag spam) - Quality gates (config must pass checks before entering test) **Why it matters**: If you don’t prevent gaming, top configurations won’t be trustworthy. Users lose confidence. ### Challenge 3: Managing State Explosion **Problem**: - Configuration has personas with state (trained skills, memories) - Personas are shared across accounts - If one persona learns skill, how does that affect all configurations using it? **Solution approaches**: - Distinguish between “baseline” persona state and “instance” persona state - Baseline: universal persona (everyone’s Paralegal learns together) - Instance: account-specific persona (Smith & Associates’ Paralegal has custom skills) - Syncing: when baseline improves, instance users get notified, can opt-in **Why it matters**: If you don’t manage state properly, you’ll have inconsistent behavior. Some clients have skills, others don’t, same persona. Debugging nightmare. ### Challenge 4: Performance at Scale **Problem**: - 100K configurations in catalog - 10M search queries per day - Voting system handles 100K concurrent votes **Solution approaches**: - Database indexing (full-text search index on configuration metadata) - Caching layer (Redis for popular configurations, search results) - Async processing (vote tallying happens in background, not on request) - Database partitioning (maybe shard by industry) **Why it matters**: If discovery is slow, users leave. If voting is slow, users stop voting. Performance is feature. ### Challenge 5: Ensuring Feedback Quality from AI **Problem**: - AI summarizes 10K comments - Summary is slightly wrong - Creator gets bad feedback, makes wrong decision **Solution approaches**: - Prompt engineering (clear instructions to AI about what to extract) - Human review sampling (spot-check summaries, see if they’re accurate) - Multiple models (get summaries from multiple AI models, find consensus) - User feedback loop (creator can say “your summary was wrong,” retrains AI) **Why it matters**: If AI feedback is wrong, creators make wrong decisions. Configurations get worse. System fails. ### Challenge 6: Configuration Conflicts **Problem**: - Agency applies Land Sales config - Also applies Real Estate M&A config - Both enable skip-tracing module (now enabled twice) - Both set SMS automation to different settings (which wins?) **Solution approaches**: - Conflict detection (warn when applying configs that might conflict) - Resolution rules (explicit rules for which setting wins) - Manual resolution UI (let user decide when conflicts exist) - Rollback (easy to undo if conflict causes problems) **Why it matters**: If configurations conflict, agencies end up with broken setups. They blame platform, not their choices. ----- ## Why This Matters for Platform Architecture ### It Creates a Knowledge Layer Without Industry Templates, the platform is: ``` ┌─────────────────────────────┐ │ Features / Modules │ │ (Skip tracing, Documents, │ │ Voice AI, Knowledge Base) │ └─────────────────────────────┘ ``` With Industry Templates, the platform becomes: ``` ┌─────────────────────────────┐ │ Configurations (Knowledge) │ │ (Land Sales, Residential, │ │ Med Spa, Legal, etc.) │ ├─────────────────────────────┤ │ Features / Modules │ │ (Skip tracing, Documents, │ │ Voice AI, Knowledge Base) │ └─────────────────────────────┘ ``` **Why this matters**: - Features are building blocks - Configurations are knowledge (how to use building blocks) - Knowledge is more valuable than blocks - Knowledge compounds (better configurations → better features → better configurations) ### It Creates Positive Feedback Loops ``` Configuration adoption ↑ ↓ More data on what works ↓ Developers build features for popular configs ↓ Popular configs improve ↓ Configuration adoption ↑ ``` This is exponential growth loop. Without Industry Templates, you don’t have it. ### It Distributes Maintenance Burden **Without Industry Templates**: - You maintain all features - You decide which features matter - You maintain all configurations (if any) - You decide what works for which industries **Cost**: Grows linearly with feature count. **With Industry Templates**: - You maintain core features - Community decides which features matter (through configuration voting) - Community maintains configurations - Community decides what works for which industries **Cost**: Stays constant as feature count grows. **This is leverage**: Your effort scales to unlimited industries without your involvement. ----- ## Why You Should Care About Building This ### It’s an Interesting Technical Problem You’re not just building a feature. You’re building: - A configuration export/import system - A community voting system with AI feedback processing - A distributed version management system - A knowledge distribution layer This is complex, non-trivial, and interesting. ### It’s Force Multiplication Most features you build help some users some of the time. Industry Templates helps every user, right when they need it most: when they’re new and trying to get started. Your effort → Thousands of agencies → Millions of end users. That’s leverage. ### It Enables Others to Build Once you build this system, developers can: - Build features for specific configurations - Know which configurations will use those features - Understand the market size (see adoption numbers) - Build with clear motivation Right now, developers build in the dark. With this, they build in the light. ### It Strengthens the Platform in a Way Nothing Else Can Most features add functionality. This feature: - Increases adoption (eliminates friction) - Increases retention (users reach aha moment) - Increases developer motivation (clear targets) - Increases network effects (exponential growth) - Decreases your maintenance burden (community maintains configs) - Creates defensible moat (hard to copy) No other feature does all of these things. ----- ## The Philosophical Reason: Platforms Need Configuration Layers ### Why WordPress Won WordPress isn’t powerful because it’s the best blog software. It won because: 1. It has a core (blogging) 1. It has plugins (extensibility) 1. It has themes (configurations) The themes were the game-changer. Non-technical people could use WordPress because themes made it simple. Technical people could use WordPress because plugins made it powerful. Win/win. Themes are pre-configured environments. That’s exactly what Industry Templates are. ### What You’re Building You’re building the “themes” layer for aiConnected. - Core: Personas, modules, integrations, workflows - Plugins: Developers build features - Themes: Agencies share configurations Without the themes layer, the platform will grow slowly (limited by setup friction). With the themes layer, the platform can grow exponentially (friction eliminated). ----- ## Summary: Why This Matters **For users**: Setup friction eliminated. Productivity immediate. Industry expertise built-in. **For developers**: Clear signals about what to build. Marketplace thrives. Platform grows exponentially. **For the platform**: Knowledge layer created. Network effects amplified. Maintenance burden distributed. Competitive moat established. **For growth**: Linear growth → Exponential growth. This isn’t a feature. It’s a platform evolution. It transforms aiConnected from “software with features” to “ecosystem with knowledge and network effects.” And you’re building it. --- ## Developer Quick Reference **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/industry-templates/developer-quick-reference # aiConnected Industries - Comprehensive Reference & Requirements ## Project Status **Status**: Ideation/Documentation Phase **Implementation Timeline**: Post-launch, after developer marketplace is thriving and proven **Responsible Party**: TBD developer lead (once assigned) ----- ## Core Architecture ### Model B: Industry = Collection of Configurations Industries are **not** monolithic templates. Industries **ARE** their configurations. Example: Real Estate Industry consists of: - Land Sales Configuration - Residential Home Sales Configuration - Commercial Real Estate Configuration - Build-to-Rent Configuration - Real Estate M&A Configuration - Construction-Side Real Estate Configuration **Agency Workflow**: “My client does land sales” → Real Estate > Land Sales → Apply → Done. No further customization needed (though agencies can customize if desired). ----- ## Configuration Snapshot Anatomy What gets captured when an agency exports a configuration: - Personas (all active personas and their configurations) - Integrations (all connected third-party integrations) - Workflows (all custom/active workflows) - Modules (which modules are enabled/disabled) - Settings (all system settings specific to that setup) - Any other platform-specific configurations **Data Privacy**: All snapshots are anonymized. No PII, no identifiable contacts, no specific client data - only the configuration map. ----- ## Core Principles (Non-Negotiable) ### 1. NO AUTO-APPLY, EVER - Features must complete voting/testing before availability - Agencies receive notifications: “New skip-tracing feature for Real Estate. Here’s what it does. Enable/disable?” - Agencies decide per-business-client whether to adopt - No forced updates - No breaking changes imposed on users ### 2. Agency Control Over Everything - Agencies decide what features are available to business clients - Agencies set pricing for every feature (free or paid upsell) - Agencies choose which configurations to offer - Agencies toggle Industry Templates/Configurations access on/off per client - Agencies control test mode participation ### 3. Voluntary Adoption at Every Level - Configurations are optional starting points - Features are optional additions - Updates are opt-in - Versions can be held indefinitely (no forced upgrades) - Personas can reject new skills/capabilities - All actions are reversible ----- ## Feature Pricing Integration (CRITICAL - Applies Everywhere) **For every feature toggle available to agencies (enable/disable for business clients):** - Add Pricing button adjacent to toggle - When toggle is activated, pricing UI displays - Agencies can set price at $0 (free upsell) or custom amount (paid upsell) - Pricing applies per-feature, per-business-client (granular control) - Pricing data flows to billing system - **This applies to**: - Individual features - Modules - Configurations - Personas/skills - Anything an agency can enable/disable ----- ## Testing & Feedback System ### Configuration Testing (Beta Model) - Agencies enable Test Mode for configurations before production - Test mode allows business clients to beta test configurations - Test mode activates 30-day voting window (upvote/downvote) - Agencies can enable test for specific business clients (granular) ### Data Visualization for Testing **Charts/data needed**: - Configuration voting trends over time - Acceptance likelihood visualization - User sentiment analysis during testing period - Real-time upvote/downvote counts **Display locations**: - In agency settings (showing configurations in test they’re participating in) - In Industry discovery page (showing configurations currently in test with trends) **Transition**: When configuration completes testing and passes voting → automatically moves to production ----- ## Configuration Management & Application ### No Forking Model - Agencies apply configurations as-is (complete snapshot of settings) - After applying, agencies **can** customize/fine-tune for their needs - **No forking required** - each agency gets their own instance to modify - Customizations made by agencies don’t create public versions - If agencies want to share customizations, they submit as new configuration through voting ### Application Granularity - Apply to entire agency (all business clients get config) - Apply to specific business clients (selective rollout) - Mix and match: some clients on v1.5, others on v2.0 (all fine, no forced sync) ### Reversibility & Rollback - Agencies can remove/rollback configurations anytime - Can revert to prior versions indefinitely - Business clients can reject new persona skills/capabilities - All rejections are reversible ----- ## Configuration Discovery & Trust Signals (WordPress Plugin Model) Every configuration displays: - **Active Businesses/Users Count** (e.g., “1.2M active businesses using this”) - **Star/Rating System** (aggregated community ratings) - **Last Updated Date** (transparency on maintenance) - **Adoption Trends** (growing/declining/stable) - **Version History** (what changed, when, community vote outcomes) - **Currently in Test Badge** (if applicable, with test metrics) **Discovery Interface**: - Search + browse by industry + use case keywords - AI-assisted discovery: “I’m in land sales. What configurations work for me?” - Top configurations by category (most active, highest rated, newest) - Visual, scannable design ----- ## Governance & Voting System ### Feature/Configuration Voting - Test window: **30 vs 90 days** (measure which performs better) - Simple voting: Thumbs up / Thumbs down - Optional comment field (for feedback) - AI processes all feedback at scale (no human bottleneck) - Accepted = moves to production / becomes new version - Rejected = doesn’t propagate globally, but current users keep it; feedback sent to contributor for improvement ### Competing Configurations When two agencies submit competing configurations (e.g., two “Land Sales” configs): - Both go through voting - Accepted = becomes available as separate option (both coexist) - Rejected = doesn’t propagate, but submitter can continue using it - Community votes determine which gains adoption over time ----- ## Cold Start / Initial Bootstrap **Timing**: Post-launch, after developer marketplace thrives **Approach**: - Identify 10-20 most popular industry verticals - Build baseline configurations for launch industries - Contribution sources: Developers + Agencies (first collaborative effort mixing both) **Initial Industries (example list)**: - Real Estate - Legal - Med Spa - Dentistry - Insurance - Construction/Remodeling - Healthcare - Attorney - (plus 12+ more based on demand data) ----- ## Access Control for Business Clients ### Agency Control Panel - Toggle “Industry Templates/Configurations” access on/off per business client - Whitelist/blacklist specific configurations per client - Set default configurations per client type - Control test mode participation per client ### Business Client Access (Open Question - Agencies Decide) **Still being evaluated**: Should business clients be able to search/apply configurations themselves, or only agencies? **Current approach**: Agencies toggle this on/off per client (each agency defines their own workflow) ----- ## Persona Learning & Ecosystem Integration ### Cross-Persona Learning When personas across industry template ecosystem improve: - Improved baseline personas are shared (anonymized experience) - Users (agencies/businesses) receive notification: “New skills available for Paralegal persona. Accept/reject?” - Acceptance is optional, not automatic - Users can reject or remove skills that don’t fit their use case - All persona skill adoption is reversible ### Skill Propagation - New skills propagate to configuration baselines gradually - Each configuration version can inherit improved personas OR maintain prior version - Agencies control which versions their clients receive ----- ## Developer/Community Contribution Model ### Who Can Contribute? - **Developers**: New features, modules, integrations, enhancements - **Agencies**: Configurations (proven setups), feature feedback, use case validation ### Contribution Workflows - Developers submit features → voting system - Agencies submit configurations → voting system - Both go through same testing/feedback process - Community votes determine production inclusion - Rejected contributions can be improved and resubmitted ----- ## Monitoring & Metrics Track: - Voting window duration performance (30 vs 90 day outcomes) - Adoption rates per configuration - Feature acceptance/rejection ratios - Version retention patterns (which versions do agencies keep longest?) - Configuration migration patterns (when/why do agencies switch versions?) - Test mode participation rates ----- ## Architecture Decisions Made ✅ **Model B** (Industry = Collection of Configurations) ✅ **No auto-apply, ever** (opt-in at every level) ✅ **No forced updates** (agencies control versions) ✅ **No forking** (apply, then customize locally) ✅ **Feature pricing everywhere** (pricing button on every toggle) ✅ **Community voting** (thumbs up/down, AI feedback processing) ✅ **Reversibility** (everything can be undone) ✅ **Granular access control** (agencies decide per-client what’s available) ----- ## Still Open / Requires Decision ❓ Should business clients have direct access to search/apply configurations? ❓ What are the exact triggers for moving configurations from test → production? ❓ How to handle configuration dependencies (if config requires a module that’s disabled)? ❓ Configuration support/maintenance model - who maintains over time? ❓ Exact structure of “Industry” entity vs “Configuration” in database schema? ----- ## Not in Scope (Shelved for Later) - Modules + community evolution model (separate discussion needed) - Exact technical implementation details - Billing integration specifics - API contracts for configuration snapshots - Database schema design --- ## Industry Templates **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/industry-templates **Description:** Documents in Industry Templates. --- ## Opus PRD Handoff **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/industry-templates/opus-prd-handoff # aiConnected Platform Industry Templates - Comprehensive PRD Handoff ## Project Overview **Feature Name**: aiConnected Platform Industry Templates **Formal Name**: Industry Templates (displayed in UI as “Industry Templates” tab/section) **Status**: Ideation/Documentation Phase **Implementation Timeline**: Post-launch, after developer marketplace is proven and stable **Responsible Party**: TBD developer lead (once assigned) **Vision Owner**: Bob Hunter (aiConnected Founder) ----- ## Context & Vision Statement ### The Problem Being Solved **Current State (Without Industry Templates)**: - New agencies arrive at aiConnected platform - They see blank slate: modules, settings, personas, workflows - They must configure everything from scratch - Configuration decisions are unclear (what goes where? what works?) - High setup friction; no reference for “what works in my industry” - Agencies often get stuck, make suboptimal choices, or leave **Desired State (With Industry Templates)**: - New agencies see “Real Estate,” “Legal,” “Med Spa,” “Insurance,” etc. - They click their industry, see curated configurations (“Land Sales,” “Residential Homes,” “Commercial,” “M&A,” etc.) - Each configuration shows: active business count, ratings, last updated, adoption trend - They see “1.2M active businesses using Land Sales config, 4.8 stars, updated 2 weeks ago” - They click “Install” - They immediately see all settings/modules/personas in a review screen - They can customize on the spot if needed - They apply to their agency, set access controls per client, set pricing - They’re productive immediately; configuration is battle-tested and maintained by community - Configuration improves over time as community learns and contributes **Core Value Proposition**: Eliminate setup friction through community-driven, industry-specific, pre-configured starting points. Agencies get battle-tested setups; new contributors share what works; entire ecosystem improves together. ----- ## Core Architectural Model: Model B ### “Industry = Collection of Configurations” (NOT Hierarchy) This is a critical architectural decision. The distinction changes everything. **Model A (Rejected)**: ``` Real Estate (base template) ├── Land Sales (specialization) ├── Residential (specialization) └── Commercial (specialization) ``` - Implies hierarchical inheritance - Creates “base + customization” friction - Suggests some configurations are “more fundamental” than others - Creates dependency chains **Model B (Chosen)**: ``` Real Estate = { Land Sales, Residential, Commercial, M&A, Build-to-Rent, Construction } ``` - Each configuration is co-equal - No “base” Real Estate template - No hierarchy, no inheritance chains - Agencies pick the exact fit without base customization ### Why Model B is Correct 1. **Reflects Reality**: People specialize. Specializations are not secondary. A real estate agent selling land has completely different needs than one selling residential homes. Both are equally valid. 1. **Reduces Friction**: “I sell land, here’s Land Sales config” beats “Here’s a Real Estate base template, now customize it for land sales.” 1. **Enables Co-Evolution**: All configurations in Real Estate improve together. When one improves via community learning, all benefit. 1. **Prevents Lock-In**: No dependency on “base” means configurations can evolve independently. 1. **Scales Better**: New specialization emerges? Just add a new configuration. No need to modify the “base.” ### What “Real Estate Industry” Actually Is The Real Estate industry **IS** a collection of configurations: - Land Sales Configuration - Residential Home Sales Configuration - Commercial Real Estate Configuration - Build-to-Rent Configuration - Real Estate M&A Configuration - Construction-Side Real Estate Configuration - (+ any others the community contributes) There is no separate “Real Estate base template.” Real Estate IS these configurations. The industry is defined by what works in practice, not by a pre-defined template. ----- ## Configuration Snapshot: Detailed Anatomy ### What Gets Captured When an Agency Exports a Configuration When an agency creates/exports a configuration, they capture a **complete snapshot** of their current setup: ``` Configuration Snapshot { // Personas personas: [ { id: "paralegal-v2", name: "Paralegal", state: "trained", skills: ["legal-research", "document-drafting", "contract-review"], neurigraph_memory: "entire persona memory state" }, { id: "intake-specialist-v1", name: "Intake Specialist", state: "trained", skills: ["intake-forms", "client-intake", "verification"] } // ... more personas ], // Integrations integrations: [ { provider: "clio", auth: "oauth-token-hash", config: { account_id: "XXX", workspace: "YYY" } }, { provider: "westlaw", auth: "api-key-hash", config: { tier: "professional" } }, { provider: "zapier", workflows: ["intake-to-slack", "document-alerts"] } // ... more integrations ], // Workflows workflows: [ { id: "intake-workflow", name: "Client Intake Process", steps: [ { trigger: "new-client-form", action: "paralegal-review" }, { action: "document-generation" }, { action: "notification-to-slack" } ], enabled: true } // ... more workflows ], // Modules modules: { "voice-ai": { enabled: true, config: {} }, "document-generation": { enabled: true, config: { template_library: "legal" } }, "knowledge-base": { enabled: true, config: { docs_count: 250 } }, "skip-tracing": { enabled: false }, "sms-automation": { enabled: true, config: {} } // ... more modules }, // Settings settings: { ai_model: "claude-sonnet", response_tone: "professional-formal", max_token_length: 2000, knowledge_base_upload_frequency: "daily", client_timezone: "US/Eastern" // ... more settings }, // Metadata metadata: { created_at: "2026-04-19", last_updated: "2026-04-19", creator_agency: "Smith & Associates Legal", description: "Paralegal intake and document generation for law firm practice" } } ``` ### Data Privacy & Anonymization - **Everything is anonymized**: No PII, no specific contacts, no specific client data - **Only configuration mapping captured**: Which modules enabled, integration types, workflow patterns, persona types - **Specific values are hashed/obfuscated**: API keys, client emails, account IDs are not stored; only the fact that integration exists - **Knowledge base content is NOT captured**: Only the fact that a knowledge base exists and its metadata (size, update frequency, categories) - **Client-specific data is NOT captured**: Only agency-level configuration This is why GoHighLevel called them “Snapshots” - they’re literally a snapshot of the configuration state, not a copy of the data. ### Submission & Versioning **How an agency creates a configuration**: 1. Agency has been using aiConnected, has a working setup for “Land Sales” 1. They navigate to Settings → Export Configuration 1. They name it: “Land Sales - Full Automation” 1. They write a description: “Complete setup for land sales agents. Includes automated property research, skip tracing, client intake, and document generation.” 1. They tag it: `real-estate`, `land-sales`, `automation`, `full-service` 1. They select target industry: `Real Estate` 1. They select primary use case: `Land Sales` 1. They submit **Submission flow**: 1. Configuration is submitted to community voting/test period (30 or 90 days) 1. It appears in Industry Templates discovery with “BETA” badge 1. Agencies can test it, vote, provide feedback 1. After test period, voting is tallied 1. **If accepted**: Becomes production version, badge removed, vote count becomes “Battle-tested by X businesses” 1. **If rejected**: Doesn’t propagate, but agencies already using it locally keep using it; feedback provided to submitter **Versioning**: - Land Sales Configuration v1.0 (original) - Land Sales Configuration v1.1 (community vote accepted an improvement) - Land Sales Configuration v2.0 (significant feature set change) - Agencies can stay on any version indefinitely - No forced upgrades ----- ## Core Non-Negotiable Principles ### Principle 1: NO AUTO-APPLY, EVER **This is absolute.** Nothing is ever forced on users at any level. **Example 1: New Feature** - A developer contributes “skip-tracing module” for Real Estate - It goes through 30/90-day voting/testing - Once accepted, agencies get notified: “New skip-tracing module available for Real Estate. Automatically find and verify property owners and phone numbers. Enable/disable?” - Agency decides per-business-client - If enabled, pricing button appears (agency can set $0 or $49/month) - No forcing **Example 2: Persona Skill Improvement** - Paralegal personas across the ecosystem improve via cross-persona learning - They acquire “Legal Research API Integration” skill - All agencies using Paralegal persona get notified: “Paralegal persona now has Legal Research API Integration. Better case research. Accept/reject?” - NOT automatic - Agencies choose per-client - If rejected, persona doesn’t gain the skill - Fully reversible **Example 3: Configuration Update** - Land Sales Configuration v1.0 is improved to v1.1 - Agencies using v1.0 get notification: “Land Sales Configuration has an update (v1.0 → v1.1). Includes new skip-tracing integration. Review/accept?” - Agencies can stay on v1.0 indefinitely - Can upgrade when ready - No forced updates **This applies at EVERY level**: - Features don’t auto-apply to configurations - Configurations don’t auto-update when features ship - Personas don’t auto-gain skills - Modules don’t auto-enable - Everything is notification + agency choice ### Principle 2: AGENCY CONTROL OVER EVERYTHING Agencies decide: - What features are available to their business clients - What configurations are available to their business clients - Which configurations to offer to which clients (whitelist/blacklist) - Whether to charge for features (free or paid upsell) - What to charge ($0, $49/month, custom price, per-client variation) - Whether business clients can self-serve apply configurations OR agency applies on their behalf - Whether to enable test mode for specific clients - Whether clients can opt-in to beta features - What personas are available per client - What integrations are enabled per client **This is fundamental.** Agencies are the product owner of their client experience. aiConnected provides infrastructure; agencies control access, pricing, and experience. ### Principle 3: VOLUNTARY ADOPTION AT EVERY LEVEL - Configurations are optional starting points (agencies can still build from scratch) - Features are optional additions (can be rejected or ignored) - Updates are opt-in (no forced upgrades, no sunset dates) - Versions can be held indefinitely (“I like v1.5, staying here”) - Persona skills are optional (can accept or reject each new skill) - All actions are reversible (remove configuration, rollback version, remove skill) **Philosophy**: We provide options. You choose. No assumptions, no forcing, no lock-in. ----- ## Feature Pricing Integration (CRITICAL - APPLIES EVERYWHERE) ### The Requirement **For EVERY feature toggle available to agencies** (enable/disable for business clients): - Display a **Pricing button** immediately adjacent to the toggle - When toggle is activated, pricing UI appears - Agencies can set price at **$0** (free upsell) or **$X** (paid upsell), per-client - Pricing applies **per-feature, per-business-client** (full granularity) - Pricing data flows to billing system; fees are charged to client at end of month ### What “Every Toggle” Means This applies to: - Individual features (e.g., “skip-tracing”) - Modules (e.g., “document-generation”) - Configurations (e.g., applying a configuration unlocks certain capabilities) - Personas (e.g., “Paralegal persona” access) - Skills/capabilities (e.g., “Legal Research Integration”) - Workflows (e.g., custom workflow access) - Any setting or capability that an agency can enable/disable for a client ### Why This Matters This enables agencies to build service tiers without needing external tools: **Example**: - “Basic Legal” tier: Paralegal persona only, $0 - “Pro Legal” tier: Paralegal persona + Legal Research Integration, $49/month - “Enterprise Legal”: All features + priority support, custom pricing per client Agencies can monetize granularly. They don’t have to build separate products. They configure what each tier gets, set prices, and billing is automatic. ### UI Pattern **Toggle + Pricing Pattern**: ``` [Toggle Switch: OFF] "Skip Tracing Module" Description: "Automatically find and verify property owners..." [Button: "Pricing"] ← When clicked or toggle turned ON, pricing panel appears Pricing Panel (slides in from right): ┌─────────────────────────────────┐ │ Skip Tracing Module Pricing │ │ │ │ Price per client per month: │ │ [$0 ▼] (dropdown, or text)│ │ │ │ ☐ Apply to all clients │ │ ☑ Apply to selected clients │ │ (select from list) │ │ │ │ [Apply] [Cancel] │ └─────────────────────────────────┘ ``` This pattern repeats for every toggle. Pricing is not a separate section; it’s integrated into feature management. ----- ## User Interface & User Experience ### Primary Navigation **In main platform navigation**: ``` [Platform Logo] Dashboard Clients Automations Knowledge Base Marketplace [Industry Templates] ← NEW Settings ``` Clicking “Industry Templates” takes agency to the Industry Templates Hub. ### Industry Templates Hub (Primary Screen) **Layout**: ``` ┌─────────────────────────────────────────────────────┐ │ INDUSTRY TEMPLATES │ │ │ │ [Search bar: "Search industries, configurations..."] │ │ │ │ Filter options (left sidebar, optional): │ │ • Real Estate │ │ • Legal │ │ • Med Spa │ │ • Healthcare │ │ • Insurance │ │ • Construction │ │ • More... │ │ │ ├─────────────────────────────────────────────────────┤ │ │ │ GLOBAL INDUSTRY TEMPLATES (Featured) │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Real │ │ Legal │ │ Med Spa │ │ │ │ Estate │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ 3.5M │ │ 1.2M │ │ 560K │ │ │ │ businesses │ │ businesses │ │ businesses │ │ │ │ │ │ │ │ │ │ │ │ [Install] │ │ [Install] │ │ [Install] │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ ├─────────────────────────────────────────────────────┤ │ │ │ REAL ESTATE CONFIGURATIONS (showing all configs │ │ if "Real Estate" industry is selected) │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ Land Sales Configuration │ │ │ │ │ │ │ │ Automated lead capture, property research, │ │ │ │ skip tracing, document generation. Best for │ │ │ │ land agents and raw land specialists. │ │ │ │ │ │ │ │ Tags: #land-sales #automation #leads │ │ │ │ │ │ │ │ 1.2M active businesses │ │ │ │ ★★★★★ 4.8 / 5 (2,340 ratings) │ │ │ │ Last updated: 2 weeks ago │ │ │ │ Trend: ↑ Growing │ │ │ │ │ │ │ │ [Install] [Details] │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ Residential Home Sales Configuration │ │ │ │ │ │ │ │ Turnkey setup for residential home agents. │ │ │ │ MLS integration, buyer/seller management, │ │ │ │ appointment scheduling, automated follow-up. │ │ │ │ │ │ │ │ Tags: #residential #homes #mls │ │ │ │ │ │ │ │ 850K active businesses │ │ │ │ ★★★★☆ 4.6 / 5 (1,890 ratings) │ │ │ │ Last updated: 1 month ago │ │ │ │ Trend: → Stable │ │ │ │ │ │ │ │ [Install] [Details] │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ Commercial Real Estate Configuration │ │ │ │ │ │ │ │ For commercial agents and brokers. Building │ │ │ │ analysis, tenant management, lease tracking,│ │ │ │ ROI calculations. Advanced features. │ │ │ │ │ │ │ │ Tags: #commercial #analysis #advanced │ │ │ │ │ │ │ │ 320K active businesses │ │ │ │ ★★★★★ 4.7 / 5 (645 ratings) │ │ │ │ Last updated: 1 week ago │ │ │ │ Trend: ↑ Growing │ │ │ │ │ │ │ │ [Install] [Details] │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ [+ Show more configurations...] │ │ │ └─────────────────────────────────────────────────────┘ ``` **Card Elements** (for each configuration): - **Title**: Configuration name (e.g., “Land Sales Configuration”) - **Description**: Short paragraph (2-3 sentences) explaining what’s special, what it includes, who it’s for - **Tags**: 2-4 tags describing the use case (#land-sales, #automation, #leads) - **Trust Signals**: - Active businesses count (“1.2M active businesses”) - Rating (⭐ 4.8 / 5) - Number of ratings in parentheses - Last updated date (“2 weeks ago”) - Trend indicator (↑ Growing, → Stable, ↓ Declining) - **Install Button**: Large, prominent button saying “[Install]” - **Details Button**: Secondary button “[Details]” (optional, goes to detail page) **Beta/Test Badge** (if applicable): - If configuration is in test period: Display “BETA - Help shape this” badge - Show upvote/downvote counts (“342 up, 28 down”) - Show time remaining (“12 days left in testing”) ### Installing a Configuration (Step 1: Review Screen) When an agency clicks “[Install]” on a configuration, they go to a **Review & Customize** screen: ``` ┌──────────────────────────────────────────────────────┐ │ INSTALL: Land Sales Configuration │ │ │ │ This will add the following to your agency: │ │ │ ├──────────────────────────────────────────────────────┤ │ │ │ PERSONAS (what's included): │ │ ☑ Paralegal Persona │ │ ☑ Intake Specialist Persona │ │ ☐ Document Reviewer Persona (opt-in, optional) │ │ │ │ MODULES (what will be enabled): │ │ ☑ Document Generation │ │ ☑ Knowledge Base │ │ ☑ Skip Tracing │ │ ☑ SMS Automation │ │ ☐ Video Recording (opt-in, optional) │ │ │ │ INTEGRATIONS (what will be connected): │ │ ☑ Westlaw (Legal Research) │ │ ☑ Zapier (Workflow Automation) │ │ ☑ Slack (Notifications) │ │ │ │ WORKFLOWS (automation patterns): │ │ ☑ Lead Intake Process │ │ ☑ Document Generation Workflow │ │ ☑ Client Notification System │ │ │ ├──────────────────────────────────────────────────────┤ │ CUSTOMIZE (Optional) │ │ │ │ ☑ Enable customization mode to adjust settings │ │ │ │ [Expand Customization Panel] │ │ │ │ (If expanded, shows all settings that can be │ │ modified before applying) │ │ │ ├──────────────────────────────────────────────────────┤ │ │ │ [Cancel] [Review & Apply] │ │ │ └──────────────────────────────────────────────────────┘ ``` **Key Features**: - Clear checklist of what’s being installed - Ability to toggle optional items on/off before applying - Optional customization panel for advanced users - Agency can review the entire snapshot before committing ### Installing a Configuration (Step 2: Access Control & Pricing) After clicking “[Review & Apply]”, they go to **Access Control & Pricing** screen: ``` ┌──────────────────────────────────────────────────────┐ │ CONFIGURE ACCESS: Land Sales Configuration │ │ │ ├──────────────────────────────────────────────────────┤ │ │ │ WHO HAS ACCESS? │ │ │ │ ○ All clients in my agency │ │ (Everyone gets Land Sales setup) │ │ │ │ ○ Specific clients only │ │ [Select clients...] │ │ Selected: Smith Realty, Jones Land Group, ... │ │ │ │ ○ New clients by default │ │ (New clients get this setup unless they opt-out) │ │ │ ├──────────────────────────────────────────────────────┤ │ │ │ PRICING (Upsell or Free?) │ │ │ │ Base Configuration is FREE to all clients │ │ │ │ Add optional upsells: │ │ │ │ [+ Add Pricing Tier] │ │ │ │ Tier 1: Skip Tracing + Document Generation │ │ Price: [$49/month ▼] │ │ Clients: │ │ ☑ Smith Realty │ │ ☐ Jones Land Group │ │ ☐ (auto-apply to new clients) │ │ [Edit] [Remove] │ │ │ │ Tier 2: Full Automation (all features) │ │ Price: [$99/month ▼] │ │ Clients: │ │ ☑ Smith Realty │ │ ☑ Jones Land Group │ │ ☐ (auto-apply to new clients) │ │ [Edit] [Remove] │ │ │ │ [+ Add Pricing Tier] │ │ │ ├──────────────────────────────────────────────────────┤ │ │ │ [Cancel] [Apply Configuration] │ │ │ └──────────────────────────────────────────────────────┘ ``` **Key Features**: - Choose who gets access (all, specific, or new by default) - Add as many pricing tiers as needed - Per-tier control over which clients get what - Auto-apply-to-new-clients option - Everything is optional; can be all free ### Configuration Management (After Installation) After applying, configuration appears in agency’s **Settings → Configurations** section: ``` ┌──────────────────────────────────────────────────────┐ │ CONFIGURATIONS (My Agency) │ │ │ ├──────────────────────────────────────────────────────┤ │ │ │ Active Configurations: │ │ │ │ Land Sales Configuration v1.0 │ │ ├─ Status: Active (890 clients using this) │ │ ├─ Adopted: March 2026 │ │ ├─ Version: v1.0 │ │ ├─ Test Mode: ○ Disabled ○ Enabled (for X clients)│ │ │ │ │ │ Actions: │ │ │ [View Details] [Customize] [Manage Clients] │ │ │ [Enable Test Mode] [Upgrade to v1.1] [Remove] │ │ │ │ │ └─ [Expand to see settings breakdown] │ │ │ │ Residential Configuration v2.0 │ │ ├─ Status: Active (120 clients using this) │ │ ├─ Adopted: February 2026 │ │ ├─ Version: v2.0 (latest) │ │ ├─ Test Mode: ○ Disabled ○ Enabled (for 5 clients)│ │ │ │ │ │ Actions: │ │ │ [View Details] [Customize] [Manage Clients] │ │ │ [Disable Test Mode] [Remove] │ │ │ │ │ └─ [Expand to see settings breakdown] │ │ │ └──────────────────────────────────────────────────────┘ ``` **Key Actions**: - **View Details**: See full configuration snapshot, what’s included - **Customize**: Modify settings, toggle modules/personas on/off, adjust integrations - **Manage Clients**: See which clients are using this, add/remove clients, set pricing per client - **Enable Test Mode**: Beta test a new version before rolling out - **Upgrade Version**: If newer version available, can upgrade (opt-in) - **Remove**: Stop using this configuration (affects affected clients) ### Test Mode Interface When agency enables **Test Mode** for a configuration: **In agency settings**: ``` ┌──────────────────────────────────────────────────────┐ │ TEST MODE: Land Sales v1.1 │ │ │ │ Status: ✓ Active (12 days remaining) │ │ │ │ Clients in test: │ │ • Smith Realty (started 18 days ago) │ │ • Jones Land Group (started 18 days ago) │ │ │ │ VOTING DATA (Live): │ │ │ │ [Graph: Upvotes vs Downvotes over time] │ │ ↑ 342 upvotes │ │ ↓ 28 downvotes │ │ Current sentiment: ▓▓▓▓▓░ 92% positive │ │ │ │ Comments Summary: │ │ • "Skip tracing integration is amazing" (34 +1s) │ │ • "Lag when generating documents" (12 +1s) │ │ • "Perfect setup for our team" (28 +1s) │ │ │ │ [View All Comments] [AI Sentiment Analysis] │ │ │ │ [Disable Test Mode] [Decision: Accept/Reject] │ │ │ └──────────────────────────────────────────────────────┘ ``` **In Industry Templates discovery** (same config shown publicly during test): ``` ┌─────────────────────────────────────────────┐ │ Land Sales Configuration v1.1 [BETA] │ │ │ │ What's new: Skip tracing integration, │ │ improved document generation... │ │ │ │ 342 ↑ 28 ↓ (12 days left in testing) │ │ Trend: ▓▓▓▓▓░ 92% positive │ │ │ │ [Try Beta] [View Comments] │ │ │ └─────────────────────────────────────────────┘ ``` ### Voting Interface (Public) When users vote on a configuration (during test or for feature): ``` ┌──────────────────────────────────────────────────────┐ │ Land Sales Configuration v1.1 │ │ │ │ Still in testing? Rate your experience: │ │ │ │ ☺️ [Thumbs Up] [Thumbs Down] ☹️ │ │ │ │ Any feedback? (optional) │ │ [Text input: "Tell us what you think..."] │ │ │ │ [Submit Vote] │ │ │ └──────────────────────────────────────────────────────┘ ``` **Simple, clean, non-intrusive.** Just thumbs up/down with optional comment. ### Configuration Detail Page When clicking “[Details]” or “[View Details]” on a configuration: ``` ┌──────────────────────────────────────────────────────┐ │ Land Sales Configuration v1.0 │ │ │ │ Description: │ │ Complete automation setup for land sales agents. │ │ Includes lead capture, property research, skip │ │ tracing, client intake, document generation... │ │ │ │ Creator: Smith & Associates (agency) │ │ Submitted: March 2026 │ │ Last Updated: 2 weeks ago │ │ │ │ STATS: │ │ • 1.2M active businesses │ │ • ★★★★★ 4.8 / 5 (2,340 ratings) │ │ • Trend: Growing (+120K new adopters last month) │ │ • Version history: v1.0 (accepted 3 months ago), │ │ v1.1 (in test now) │ │ │ │ WHAT'S INCLUDED: │ │ Personas: Paralegal, Intake Specialist │ │ Modules: Document Gen, KB, Skip Tracing, SMS │ │ Integrations: Westlaw, Zapier, Slack │ │ Workflows: Lead intake, Doc generation, Notify │ │ │ │ COMMUNITY FEEDBACK (Top Rated): │ │ "Skip tracing integration is amazing" (34 reactions)│ │ "Perfect for our team" (28 reactions) │ │ "Saves us 10 hours/week" (23 reactions) │ │ │ │ ISSUES REPORTED (Most Recent): │ │ "Lag when generating documents" (12 reactions) │ │ "Wants Clio integration" (8 reactions) │ │ │ │ [Install] [Install & Customize] │ │ │ └──────────────────────────────────────────────────────┘ ``` ----- ## Configuration Snapshot: Detailed Anatomy (Continued) ### How Configurations Evolve **Version 1.0** (Initial submission): - Submitted by Smith & Associates agency - Goes through 30-day voting - Receives 1,800 upvotes, 40 downvotes - Accepted → becomes production v1.0 **Version 1.1** (Improvement proposal): - Another agency or developer proposes improvement: “Add Clio integration” - Submits as v1.1 - Goes through voting - Receives 340 upvotes, 28 downvotes - Accepted → becomes production v1.1 - Agencies on v1.0 get notification: “Update available. Adds Clio integration. Accept?” **Version 2.0** (Major change): - Complete redesign of workflows - Goes through voting - Agencies can upgrade or stay on v1.1 - No forced migration ----- ## Governance & Voting System ### Feature/Configuration Voting Process **Standard Flow**: 1. **Submission** → Community member (developer or agency) submits feature, configuration, or enhancement 1. **Entry to Test** → Submission automatically enters test period (30 or 90 days - to be measured) 1. **Visibility** → During test, appears in discovery with “BETA” badge 1. **Simple Voting** → Users vote thumbs up/down (optional comment) 1. **AI Feedback Processing** → System AI ingests all comments, finds themes, creates summary (no human bottleneck) 1. **Automatic Tallying** → At end of test window, votes are automatically counted 1. **Acceptance Threshold** → TBD: What % = acceptance? (60%? 70%? Based on vote count?) 1. **Outcome**: - **Accepted** → Moves to production (badge removed), vote data becomes part of history - **Rejected** → Doesn’t propagate globally, but submitter can keep using locally; feedback provided for improvement ### Competing Configurations (Conflict Resolution) **Scenario**: Two agencies both submit “Land Sales” configurations **How it’s handled**: 1. Both Land Sales configs submitted simultaneously 1. Both enter voting 1. Both appear in discovery with “BETA” badges 1. Community votes on both independently 1. Both can be accepted (they coexist as separate options) 1. OR one might significantly out-vote the other 1. Community decides through voting, not curation 1. Top-voted configs sort higher in discovery **No “winner-take-all”**: Multiple “Land Sales” configurations can coexist if community votes both as acceptable. ### Feature Voting When a developer proposes a new feature (e.g., “skip-tracing module”): 1. Feature goes through same voting process 1. 30/90-day test period 1. Appears as “Available for Real Estate industry” 1. Agencies can test it, vote 1. If accepted → becomes available to all Real Estate configurations 1. If rejected → feedback provided, developer can improve and resubmit ----- ## Cold Start / Initial Bootstrap ### Timing - **When**: Post-launch, after developer marketplace is stable and proven - **Why then**: Configurations remix features from marketplace. Need features to exist first. - **Owner**: Assigned developer lead (TBD) ### Initial Industries (10-20 to launch with) **Likely candidates** (based on early adoption patterns): 1. Real Estate (highest demand) 1. Legal (high-value, complex) 1. Med Spa (growing vertical) 1. Dentistry 1. Insurance 1. Construction/Remodeling 1. Healthcare/Clinics 1. Coaching/Consulting 1. E-Commerce 1. SaaS/Tech 1. Fitness/Wellness 1. Accounting/Finance 1. Education/Tutoring 1. Photography/Creative 1. Home Services 1. Insurance Agency 1. Automotive Sales 1. Travel Agency 1. Real Estate Management 1. Law Practice (specific to legal) ### Bootstrap Process 1. **Developers build initial configurations** for 10-20 industries 1. **Agencies immediately contribute variations** (e.g., Smith & Associates submits their Land Sales config) 1. **Community voting begins** (first collaborative contributions) 1. **Rapid iteration** (best configurations rise, weaker ones get feedback and improve) ----- ## Configuration Submission & Community Contribution ### How Agencies Submit Configurations **Step 1: Export** ``` Settings → Export Configuration ┌─────────────────────────────────────┐ │ Export Current Setup as Config │ │ │ │ Name: [Land Sales Automation] │ │ Industry: [Real Estate ▼] │ │ Use Case: [Land Sales ▼] │ │ Description: [What's special...] │ │ Tags: [#automation #leads #real...] │ │ │ │ [Privacy Notice: Anonymized...] │ │ [Export] │ └─────────────────────────────────────┘ ``` **Step 2: Submit to Community** - Configuration exported as snapshot - Submitted to Industry Template hub - Automatically enters 30-day test period - Appears with “BETA” badge - Community can vote and test **Step 3: Voting Period** - 30 days (or 90 days - to be measured) - Agencies test it - Vote thumbs up/down - Provide feedback - Agency creator can see voting data in real-time **Step 4: Outcome** - If accepted → becomes production configuration, badge removed - If rejected → feedback provided, can improve and resubmit - Existing users can keep using locally either way ### Developer Contribution Model Developers can contribute: - **Features** (new modules, integrations, capabilities) - **Enhancements** (improvements to existing features) - **Personas** (new personas, persona improvements) - **Personas improvements** (via cross-persona learning) Same voting process applies. ----- ## Testing & Feedback System ### Configuration Test Mode **When an agency tests a configuration before rolling out**: 1. Agency enables Test Mode for configuration 1. Selects which business clients participate in beta 1. 30-day test period starts 1. Clients use configuration with voting enabled 1. Upvotes/downvotes collected 1. Agency sees real-time voting data in settings 1. Public sees test badge + vote count in discovery 1. Day 30: Test period ends, votes tallied 1. Outcome: Accept (move to production) or Reject (feedback provided) ### Data Visibility During Testing **In agency settings** (testing configuration): ``` Test Mode Active (12 days remaining) Upvotes: 342 ↑ Downvotes: 28 ↓ Trend: ▓▓▓▓▓░ 92% positive Comments: [View all] - "Skip tracing is amazing" (34 reactions) - "Lag on document generation" (12 reactions) ``` **In public discovery** (during test): ``` Land Sales v1.1 [BETA] 342 ↑ 28 ↓ (12 days remaining) Trend: 92% positive [Try Beta] [View Feedback] ``` ### Voting Data Persistence When test completes and configuration moves to production: - Vote count preserved (“Battle-tested by 1.2M businesses”) - Vote breakdown visible in history - Comments accessible via “Details” - Becomes part of configuration’s permanent record ----- ## Persona Learning & Configuration Integration ### How Persona Improvements Flow to Configurations **Example: Paralegal Persona Improvement** 1. Paralegal personas across ecosystem improve via cross-persona learning 1. They acquire “Legal Research API Integration” skill 1. System detects improvement (skill was acquired) 1. Notifications sent to all agencies using Paralegal: - “Paralegal persona has new skill: Legal Research API Integration. Better case research. Accept/reject per client?” 1. Agencies decide per-business-client 1. If accepted, client’s Paralegal gets the skill 1. If rejected, client’s Paralegal doesn’t get it (fully reversible) ### Configuration Versioning with Persona Learning **Land Sales v1.0** includes Paralegal persona - When Paralegal improves, agencies on v1.0 get notified - They can accept improvements per-client - Configuration itself doesn’t force-update - Agencies control what their clients get **OR they can upgrade to v1.1** which includes improved Paralegal baseline - Newer adopters get improved baseline immediately - Existing agencies can migrate when ready - No forced migration ----- ## Access Control & Business Client Management ### Agency Control Panel Agencies control **per-client**: - What configurations they have access to - What specific features/modules are available - What pricing they pay - Whether test features are available - Whether they can self-serve apply configurations ### Example Scenario **Smith & Associates uses Land Sales Configuration**: Client A (Smith Realty): - Gets full Land Sales config - Pricing: $0 (free) - Test mode: ○ Disabled Client B (Jones Land Group): - Gets Land Sales config - Pricing: $49/month (for skip tracing upsell) - Test mode: ✓ Enabled (testing v1.1) Client C (New startup): - Hasn’t chosen yet - Can browse configurations, apply, or agency applies - Depends on what agency allows ----- ## Open Questions & Decisions Pending ❓ **Voting Threshold**: What % of votes = acceptance? 51%? 60%? 70%? ❓ **Test Window Duration**: Is 30 days optimal? Or 90? How to measure? ❓ **Module Dependencies**: If config requires a disabled module, auto-enable? Notify? Block? ❓ **Configuration Maintenance**: Who maintains stale configurations over time? ❓ **Business Client Access**: Should they search/apply themselves, or agency-managed only? ❓ **Featured Tier**: Should top-voted configs be “featured” by aiConnected team? ❓ **Deprecation Policy**: How old is too old for a configuration? ❓ **Configuration Size Limit**: Max size for exported snapshots? Optimize for performance? ❓ **Migration Tools**: If config becomes deprecated, how do agencies migrate clients? ----- ## Key Architectural Decisions (Locked ✅) ✅ **Model B**: Industry = Collection of Configurations (not hierarchy) ✅ **NO AUTO-APPLY**: Opt-in at every level, everywhere ✅ **NO FORCED UPDATES**: Versions held indefinitely ✅ **NO FORKING**: Apply then customize locally ✅ **FEATURE PRICING EVERYWHERE**: Pricing button on every toggle ✅ **COMMUNITY VOTING**: Simple thumbs up/down, AI feedback processing ✅ **WORDPRESS PLUGIN MODEL**: Trust signals, ratings, adoption count ✅ **FULL REVERSIBILITY**: Everything can be undone ✅ **GRANULAR ACCESS CONTROL**: Agencies decide per-client ✅ **TEST MODE WITH LIVE VOTING**: Beta testing + transparency ✅ **POST-LAUNCH TIMELINE**: After marketplace thrives ✅ **MIXED CONTRIBUTIONS**: Developers + Agencies ----- ## Not in Scope (For Later Discussion) - Modules + community evolution (separate feature) - Exact API contracts for import/export - Detailed billing/pricing integration specs - Performance optimization (caching, CDN) - i18n/localization - Configuration versioning semantics (SemVer) - UI mockups (PRD defines requirements, not designs) ----- ## References - Task list: `aiconnected-industries-task-list.md` - Architecture reference: `aiconnected-industries-comprehensive-reference.md` - Platform overview: https://oxfordpierpont.mintlify.app/knowledge-base/aiconnected-business-platform/aiconnected-platform-overview --- ## Layout Manager **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/layout-manager **Description:** Documents in Layout Manager. --- ## Layout Manager Codex PRD **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/layout-manager/layout-manager-codex-prd # aiConnected v2 Layout Manager PRD (Implementation-Grade) Version: `1.0` Date: `March 26, 2026` Status: `Draft for Engineering Handoff` Owner: `Product + Systems (aiConnected v2)` ## 1) Purpose Define the complete MVP-to-build specification for the **aiConnected Layout Manager** subsystem: a platform-native visual + conversational construction environment used by privileged roles to compose UI structure, bind functionality intent, invoke AI for missing capability/component creation, and move drafts through save/preview/test/publish/rollback safely. This PRD is intentionally scoped to Layout Manager and its required interfaces/dependencies, not full platform architecture. --- ## 2) Problem Statement Current workflow is fragmented across external coding tools, code review loops, Git pushes, deployment, and rework due to mismatch between requested UI/behavior and generated implementation. The platform needs a native workflow where: 1. Editing starts on the actual live screen. 2. Structural UI composition happens visually. 3. Functional intent is wired at component level. 4. Missing behavior/components are created through guided AI. 5. Output returns as editable drafts. 6. Publish control remains explicit and safe. --- ## 3) Product Definition The Layout Manager is the platform’s native authoring layer for: 1. Structural composition of screens/pages/modules using registered builder-safe components. 2. Component property editing and functional intent wiring. 3. Data source binding via Connect Existing or Create New. 4. AI-assisted generation of missing functionality and reusable components (MVP). 5. Durable persistence as structured layout + bindings (source of truth). 6. Controlled lifecycle: Save, Preview, Test, Publish, Rollback. Generated code is a **derived artifact**, never the primary editable source. --- ## 4) Goals 1. Reduce iteration time from idea to testable platform-native draft. 2. Eliminate dependency on external vibe-coding loops for routine platform evolution. 3. Ensure reuse-first construction via registered components/capabilities. 4. Keep architecture clean: structure vs aesthetics vs business logic separation. 5. Provide safe operational lifecycle with validation, history, rollback, and role controls. ## 5) Non-Goals 1. Full platform PRD (tenant inheritance, marketplace distribution, full plugin lifecycle). 2. Unrestricted raw CSS/theme authoring in builder. 3. Arbitrary raw-code editing by end users in canvas. 4. Open-ended regular-user access to authoring tools. 5. Auto-publish without privileged review/approval action. --- ## 6) Product Principles 1. **Builder-first UX:** users compose and refine in-platform, on real surfaces. 2. **AI owns technical translation:** user defines intent, AI handles implementation shape. 3. **Reuse before creation:** existing components/capabilities are preferred and surfaced first. 4. **Structured truth:** layout JSON + bindings + metadata is canonical. 5. **Safe extensibility:** AI-generated outputs are draft, testable, and rollback-capable. 6. **Clear separation of concerns:** - Layout Manager = structure/composition/intent wiring - Theme System = aesthetics/brand - Module/Service Layer = business logic --- ## 7) Personas and Role Boundaries ## 7.1 Super User Permissions: 1. Access all Layout Manager entry points. 2. Create/edit module/page layouts in permitted shell zones. 3. Use Data Source Connect Existing/Create New. 4. Trigger AI creation for new functionality/components. 5. Run Preview/Test/Publish/Rollback within authorized scope. ## 7.2 Agency Admin Permissions: 1. Access Layout Manager within agency scope. 2. Edit agency-available module layouts and create scoped layouts. 3. Invoke AI creation within scope. 4. Publish within agency-controlled boundaries (no global shell override). ## 7.3 Developer Mode (Limited) Permissions: 1. Technical inspection and assisted debugging of builder artifacts. 2. Restricted publish unless elevated. 3. Access logs/validation detail views not shown to admins. ## 7.4 Regular Users 1. No unrestricted access to Layout Manager editing, AI generation, publish, or rollback. --- ## 8) Scope Boundaries and Dependencies ## 8.1 In Scope 1. Builder workspace IA and interactions. 2. Component registry integration. 3. Layout source-of-truth schemas. 4. Data source binding UX and contracts. 5. AI orchestration states and returns. 6. Lifecycle operations and validation model. 7. Role-based access and builder-scope security controls. ## 8.2 Out of Scope (Referenced Dependency Only) 1. Full theming subsystem internals. 2. Full backend/module platform lifecycle policies. 3. Full shell routing governance. 4. Tenant inheritance architecture details. 5. Marketplace/export workflows. ## 8.3 Required Dependencies 1. Auth/RBAC service. 2. Component Registry service. 3. Capability/Endpoint Registry service. 4. AI orchestration service. 5. Build/deploy pipeline interface. 6. Theme token service (read-only in builder context). 7. Versioning and release metadata store. --- ## 9) Assumptions (Strong Defaults) 1. Layout Manager persists state in a dedicated `layout_definitions` domain store with version records. 2. Canvas rendering engine is React-based and supports nested tree editing. 3. AI operations are asynchronous jobs with resumable status polling/streaming. 4. Preview/Test run against isolated draft runtime context before Publish. 5. Rollback uses immutable published snapshots. 6. Component registry enforces compatibility metadata before component appears in library. 7. Reusable AI-generated component publication requires validation + privileged approval. --- ## 10) Information Architecture and Entry Points ## 10.1 Entry Point A: In-Context Edit on Live Screens 1. Privileged user sees edit trigger (pencil icon) on supported live screen. 2. Clicking trigger opens Layout Manager with route context, current layout draft/published baseline loaded. 3. User edits in context; Save/Preview/Test/Publish accessible per permission. ## 10.2 Entry Point B: Admin Sidebar Path: `Layout Manager` 1. `Modules` - Browse existing module layouts/screens. - Open existing draft or published version for editing. 2. `Create New` - Start conversational creation for new page/screen/module interface/capability-linked draft. ## 10.3 Workspace IA (Mandatory) 1. **Left Panel:** Component library + sticky actions. - Search, categories, draggable items, recent/favorites (optional but recommended for MVP). - Bottom sticky controls: `Save` and `Preview`, side-by-side, `50/50` width. 2. **Center Panel:** Canvas. - Nested structural editing (sections/containers/components). - Selection, drag/drop, reordering, duplicate, delete, move between valid containers. 3. **Right Panel (Tabbed):** - `Hierarchy` (tree) - `Properties` - `History` (change log + undo/redo) --- ## 11) Detailed UX Flows ## 11.1 Edit Existing Screen Flow 1. Enter from in-context trigger or Modules list. 2. Layout baseline loads, with current draft if exists. 3. User modifies tree on canvas. 4. Properties update for selected node. 5. Validation status updates in real time. 6. User Save -> Preview -> Test -> Publish or continue editing. ## 11.2 Create New Flow (MVP Core) 1. User opens `Layout Manager > Create New`. 2. Conversational intake captures intended module/page/capability. 3. AI clarification interview resolves ambiguity. 4. Reuse check runs against existing capabilities/components. 5. AI prepares plan and generates initial draft structure + bindings. 6. System transitions to builder with returned editable draft. 7. User refines visually, tests, and publishes. ## 11.3 Selected Component Behavior 1. Click component in canvas or hierarchy. 2. Properties tab shows: - Basic settings (label/content/options). - Structural settings (size, placement constraints). - Limited interaction settings. - Data Source section (mandatory when component type supports binding). 3. Changes are logged in History and reversible. ## 11.4 Data Source Flow: Connect Existing 1. Open Data Source section. 2. Select `Connect Existing`. 3. Search capability registry (endpoint/service/workflow/resource). 4. Validate compatibility mapping with component contract. 5. Bind and save; unresolved required mappings become blocking issues. ## 11.5 Data Source Flow: Create New 1. Select `Create New`. 2. User states intended behavior in natural language. 3. AI workflow state sequence executes (defined in Section 15). 4. AI returns draft capability + binding proposal. 5. Builder reopens with editable result and explicit validation state. ## 11.6 AI-Generated Reusable Component Flow 1. Component missing in registry for needed UX pattern. 2. User requests component creation from builder context. 3. AI clarifies behavior, props, structural footprint, accessibility expectations. 4. AI generates component draft package and registry metadata draft. 5. Validation + approval gates. 6. Approved component appears in registry and reusable library. ## 11.7 History / Undo / Redo 1. Every user action and significant AI action emits a history entry with readable label. 2. Undo/redo operates on deterministic layout operations. 3. Branching history is managed per draft session; Publish snapshots remain immutable. --- ## 12) Source of Truth: Data Model and Contracts ## 12.1 Canonical Data Objects 1. `LayoutDefinition` (logical identity and metadata) 2. `LayoutVersion` (immutable snapshot; draft/published/rolled_back) 3. `LayoutNode` (tree node) 4. `ComponentBinding` (data/capability linkage) 5. `AIDraftArtifact` (capability/component draft outputs) 6. `ValidationReport` 7. `HistoryEvent` ## 12.2 JSON Schema Example: LayoutDefinition ```json { "layoutId": "lay_01JX...", "moduleId": "mod_sales_dashboard", "screenId": "screen_pipeline_overview", "status": "DRAFT", "currentVersionId": "lv_01JX...", "publishedVersionId": "lv_01JW...", "createdBy": "usr_123", "updatedAt": "2026-03-26T16:22:11Z", "themeRef": "theme_default_v2" } ``` ## 12.3 JSON Schema Example: LayoutVersion ```json { "versionId": "lv_01JX...", "layoutId": "lay_01JX...", "versionNumber": 14, "state": "DRAFT", "tree": { "nodeId": "root", "type": "Page", "children": [ { "nodeId": "sec_hero", "type": "Section", "props": { "columns": 2 }, "children": [ { "nodeId": "cmp_kpi_1", "type": "KpiCard", "props": { "title": "Open Deals" }, "binding": { "mode": "CONNECT_EXISTING", "targetType": "capability", "targetRef": "cap_sales_open_deals_v1", "mapping": { "value": "$.count" } } } ] } ] }, "aiArtifacts": [], "historyCursor": 223, "createdAt": "2026-03-26T16:24:00Z" } ``` ## 12.4 JSON Schema Example: AI Create-New Draft Artifact ```json { "artifactId": "aid_01JX...", "artifactType": "CAPABILITY_DRAFT", "state": "builder_returned", "workflowState": "builder_returned", "intent": "Need a dial pad input and call initiation action for PowerDialer screen", "reuseCandidates": ["cap_voice_place_call_v2"], "plan": { "decision": "Create new reusable UI component + bind existing voice capability", "steps": [ "Generate PhoneKeypad component draft", "Bind submit action to cap_voice_place_call_v2", "Add validation for phone format" ] }, "draftRefs": { "componentDraftId": "cd_01JX...", "bindingDraftId": "bd_01JX..." }, "errors": [] } ``` --- ## 13) Component Registry Contract (Builder-Compatible) ## 13.1 Required Metadata Fields 1. `componentKey` (stable unique key) 2. `displayName` 3. `category` 4. `version` 5. `source` (`system` | `ai_generated`) 6. `footprint` (block/inline/container requirements) 7. `allowedParents` 8. `allowedChildren` (if container) 9. `propSchema` (typed, editable property model) 10. `supportsDataSource` (boolean) 11. `bindingSchema` (if supports data source) 12. `capabilityCompatibility` (optional list of compatible target types) 13. `a11yContract` (required accessibility guarantees) 14. `status` (`draft` | `approved` | `deprecated`) 15. `deprecationPolicy` link/id ## 13.2 Registry API Contract Example `GET /api/layout-manager/component-registry/search?q=phone&category=input` ```json { "items": [ { "componentKey": "phone_keypad", "displayName": "Phone Keypad", "category": "Input", "version": "1.0.0", "source": "ai_generated", "supportsDataSource": true, "status": "approved" } ] } ``` ## 13.3 Compatibility Rule A component is draggable only when: 1. Registry status is `approved`. 2. Parent-child context is valid by footprint contract. 3. Required prop defaults and validation hooks are available. --- ## 14) API and Interface Contracts ## 14.1 Session and Draft APIs 1. `POST /api/layout-manager/sessions` Input: `screenId | moduleId`, entry source (`in_context|admin_modules|create_new`) Output: session id + loaded draft/published refs. 2. `GET /api/layouts/{layoutId}/draft` 3. `POST /api/layouts/{layoutId}/save` 4. `POST /api/layouts/{layoutId}/autosave` ## 14.2 Lifecycle APIs 1. `POST /api/layouts/{layoutId}/preview` 2. `POST /api/layouts/{layoutId}/test` 3. `POST /api/layouts/{layoutId}/publish` 4. `POST /api/layouts/{layoutId}/rollback` ## 14.3 Binding APIs 1. `GET /api/capabilities/search` 2. `POST /api/layouts/{layoutId}/bindings/connect-existing` 3. `POST /api/layouts/{layoutId}/bindings/create-new/start` 4. `GET /api/layouts/{layoutId}/bindings/create-new/{jobId}` ## 14.4 AI Orchestration APIs 1. `POST /api/layout-manager/ai/jobs` 2. `GET /api/layout-manager/ai/jobs/{jobId}` 3. `POST /api/layout-manager/ai/jobs/{jobId}/approve-plan` 4. `POST /api/layout-manager/ai/jobs/{jobId}/return-to-builder` ## 14.5 Event Stream Interface `layout.session.events` (SSE/WebSocket): 1. `AUTOSAVE_SUCCESS` 2. `VALIDATION_UPDATED` 3. `AI_WORKFLOW_STATE_CHANGED` 4. `HISTORY_APPENDED` 5. `PREVIEW_READY` 6. `TEST_RESULT_READY` 7. `PUBLISH_COMPLETED` 8. `ROLLBACK_COMPLETED` --- ## 15) AI Orchestration Behavior (Mandatory State Machine) Required states: `intent_captured -> clarifying -> reuse_check -> plan_ready -> draft_generated -> builder_returned` ## 15.1 State Definitions 1. `intent_captured` User intent accepted, normalized, and linked to context node/layout. 2. `clarifying` AI asks focused questions to resolve ambiguity. 3. `reuse_check` AI queries component/capability registries; ranks reuse candidates. 4. `plan_ready` AI produces implementation plan and proposed artifacts. 5. `draft_generated` AI creates draft artifacts (bindings/capabilities/components/layout diffs). 6. `builder_returned` Draft merged into editable builder state with validation annotations. ## 15.2 AI Decision Rules 1. Prefer reuse over net-new creation when compatibility score threshold is met. 2. If multiple valid approaches exist, choose least-risk architecture and explain rationale in plan metadata. 3. Never auto-publish. 4. On failure, isolate AI artifact and preserve user draft unchanged. ## 15.3 AI Failure Isolation 1. AI job failure cannot corrupt latest saved draft. 2. Partial artifacts remain quarantined until validated or discarded. 3. User can continue manual structural edits while AI retries. --- ## 16) Lifecycle Definition: Save, Preview, Test, Publish, Rollback ## 16.1 Save 1. Persists current draft snapshot and history cursor. 2. Runs lightweight structural validation. 3. Does not affect live published experience. ## 16.2 Preview 1. Builds renderable preview from draft snapshot + mock/live-safe bindings. 2. Shows UI exactly as composed, including nested structure and resolved component props. 3. Marks unresolved bindings visibly. ## 16.3 Test 1. Executes functional checks for: - Existing capability bindings. - AI-created draft capabilities/components. - Required input/output mapping. 2. Produces pass/fail report with blocking/warning classification. ## 16.4 Publish 1. Requires no blocking issues and valid permission. 2. Creates immutable published snapshot. 3. Triggers downstream implementation sync/deploy pipeline. 4. Maintains audit entry with actor/time/version. ## 16.5 Rollback 1. Select previous published version. 2. System sets selected snapshot as active published state. 3. New rollback event/version recorded. 4. Rollback is atomic and reversible via forward publish. --- ## 17) Validation Model (Blocking vs Warning) ## 17.1 Blocking 1. Invalid layout tree structure. 2. Missing required component props. 3. Broken component references. 4. Required Data Source unset for binding-required component. 5. Unresolved AI draft dependency required for behavior. 6. Publish permission violation. 7. Incompatible parent-child placement by footprint contract. ## 17.2 Warning 1. Deprecated component usage. 2. Suboptimal reuse opportunity detected. 3. Non-critical accessibility improvement recommended. 4. Performance risk patterns (excessive nested heavy components). 5. Optional test coverage missing for non-critical branch. ## 17.3 Validation Output Contract ```json { "summary": { "blocking": 2, "warning": 3 }, "issues": [ { "severity": "blocking", "code": "BINDING_REQUIRED_MISSING", "nodeId": "cmp_table_7", "message": "Data Source is required for Table component.", "resolution": "Connect Existing capability or Create New." } ] } ``` --- ## 18) Testing Requirements (Builder Scope) 1. Visual Preview Tests - Render correctness for nested layouts and component props. 2. Functional Binding Tests - Existing capability response mapping checks. 3. AI-Generated Draft Tests - Draft capability/component smoke tests before publish. 4. Lifecycle Tests - Save/Preview/Test/Publish/Rollback transitions and state integrity. 5. History Integrity Tests - Undo/redo determinism across mixed user + AI actions. 6. RBAC Tests - Role restrictions for edit/publish/rollback. --- ## 19) Performance Requirements (MVP Targets) 1. Canvas drag/drop and selection interactions should feel immediate in normal drafts. 2. Component search results should return near-instantly for typical registry sizes. 3. Property edits should reflect in canvas without perceptible lag. 4. Autosave must complete in background without blocking editing. 5. AI states must stream progress updates; no silent waiting. 6. Preview generation should be fast enough for iterative workflows. --- ## 20) Reliability and Resilience Requirements 1. Autosave with draft recovery after interruption/reload. 2. Coherent history model across session reconnects. 3. AI failure isolation from main draft integrity. 4. Registry stability for approved reusable components. 5. Rollback safety with immutable published snapshots. 6. Idempotent publish/rollback operations. 7. Recovery UX that offers “Restore last autosaved draft” when crash detected. --- ## 21) Security and Governance (Builder Scope) 1. RBAC enforcement at API and UI layers. 2. Scope-aware authorization (global vs agency). 3. Audit logging for Save/Test/Publish/Rollback and AI generation actions. 4. Capability access controls on Connect Existing search results. 5. Prompt and artifact handling with PII-safe logging policy. 6. No unrestricted regular user access to authoring endpoints. --- ## 22) MVP Definition MVP includes: 1. Both entry points (in-context + admin sidebar modules/create new). 2. Full 3-pane workspace with mandated tabs and sticky Save/Preview controls. 3. Nested structural editing and hierarchy tree. 4. Properties panel with mandatory Data Source modes. 5. AI workflow states exactly as specified and integrated into builder return flow. 6. AI-assisted creation for missing functionality and reusable components. 7. Save/Preview/Test/Publish/Rollback lifecycle. 8. Blocking/warning validation model. 9. History log + undo/redo + AI action entries. 10. RBAC boundaries for Super User, Agency Admin, limited Developer mode. --- ## 23) MVP Acceptance Criteria 1. Privileged user can launch Layout Manager from live screen edit trigger and edit current screen in context. 2. Privileged user can launch from `Layout Manager > Modules` and open/edit module screens. 3. Privileged user can launch `Layout Manager > Create New`, complete conversational intake, and receive editable generated draft in builder. 4. Left panel contains searchable component library with sticky `Save` and `Preview` buttons side-by-side at equal width. 5. Center canvas supports nested sections/containers/components with drag/drop, reorder, duplicate, delete. 6. Right panel includes tabs: `Hierarchy`, `Properties`, `History`. 7. Selecting any component updates Properties with valid editable fields for that component. 8. Binding-capable components always expose Data Source with both modes: `Connect Existing` and `Create New`. 9. `Connect Existing` allows searchable capability selection and validated binding mapping. 10. `Create New` runs required AI state sequence: `intent_captured -> clarifying -> reuse_check -> plan_ready -> draft_generated -> builder_returned`. 11. AI-generated result is editable in builder and not locked. 12. Save persists draft without publishing. 13. Preview renders draft representation with binding statuses. 14. Test returns actionable functional report for existing and AI-generated connections. 15. Publish is blocked when blocking issues exist. 16. Publish succeeds only for authorized roles and creates immutable published snapshot. 17. Rollback restores selected published snapshot safely and logs audit event. 18. History shows user and notable AI actions with undo/redo functioning deterministically. 19. Autosave and recovery restore draft after simulated interruption. 20. AI failure does not corrupt saved draft or published state. 21. Regular users cannot access authoring APIs or UI entry points. 22. Reusable AI-generated component, once approved, appears in component library for later reuse. --- ## 24) Phased Roadmap ## Phase 1 (MVP Build) 1. Core builder IA and structural editing. 2. Registry-driven component library. 3. Data Source Connect Existing/Create New. 4. AI workflow core states and draft return. 5. Lifecycle controls and validation model. 6. RBAC + audit + autosave/recovery. ## Phase 2 (Hardening and Scale) 1. Enhanced test harnesses and simulation data tooling. 2. Advanced diff visualization and change review before publish. 3. Better AI plan explainability and comparison options. 4. Component deprecation assistant and migration prompts. ## Phase 3 (Advanced Authoring) 1. Multi-user concurrent editing controls. 2. Richer generated component quality gates. 3. Expanded scoped template kits and reusable flow blueprints. 4. Broader orchestration integrations (still governed by structured source model). --- ## 25) Open Risks and Mitigations 1. Risk: AI creates low-quality or over-complex artifacts. Mitigation: strict plan review metadata, validation gates, draft-only return, approval workflow. 2. Risk: Registry inconsistency causes runtime/editor mismatch. Mitigation: registry contract validation and compatibility checks at load and drop-time. 3. Risk: Scope creep into full theme editor. Mitigation: enforce structural-only property schema and theme-system boundary. 4. Risk: Publish pipeline latency harms confidence. Mitigation: explicit state feedback, progress telemetry, and rollback-first safety. 5. Risk: Role boundary confusion in multi-tenant contexts. Mitigation: central RBAC policies + scope labels in UI + API enforcement. 6. Risk: History complexity with mixed AI/user events. Mitigation: operation-based event model with deterministic undo semantics. --- ## 26) Requirements Traceability Matrix | Requirement ID | Requirement Summary | PRD Section(s) | |---|---|---| | R1 | Layout Manager is core to aiConnected v2 | 1, 3, 22 | | R2 | Platform-native visual + conversational environment | 3, 11, 15 | | R3 | shadcn/ui-compatible default building blocks | 3, 13 | | R4 | Source of truth = structured layout + bindings | 3, 12 | | R5 | AI-assisted missing functionality + reusable components is MVP | 11.5, 11.6, 15, 22, 23 | | R6 | Separation: layout vs theme vs backend logic | 6, 8 | | R7 | Required IA: left/center/right + tabs + sticky controls | 10.3, 23 | | R8 | Required entry points: in-context + admin modules/create new | 10.1, 10.2, 23 | | R9 | Data Source mandatory with Connect Existing/Create New | 11.4, 11.5, 23 | | R10 | Required AI workflow states sequence | 15, 23 | | R11 | Lifecycle: Save, Preview, Test, Publish, Rollback | 16, 23 | | R12 | Role boundaries: Super User, Agency Admin, limited Developer; no unrestricted regular users | 7, 21, 23 | | R13 | Validation model: blocking vs warning | 17, 23 | | R14 | Reliability: autosave/recovery, coherent history, AI failure isolation, rollback safety | 18, 20, 23 | | R15 | Acceptance criteria implementation-verifiable | 23 | | R16 | Include explicit API/interface contracts and schema examples | 12, 14, 17.3 | | R17 | Include architecture boundaries and subsystem dependencies | 8 | | R18 | Include component registry contract | 13 | | R19 | Include phased roadmap and open risks | 24, 25 | | R20 | Focus on Layout Manager subsystem, not full platform PRD | 1, 8.2 | --- ## 27) Final Product Statement The aiConnected Layout Manager is the platform-native subsystem through which privileged users visually compose interface structure, wire functional intent, and invoke conversational AI to create missing capabilities/components, with structured layout+binding artifacts as canonical truth and a governed lifecycle that safely converts draft intent into published platform behavior. --- ## What is the Layout Manager? **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-business-platform/layout-manager/what-is-the-layout-manager ## **Overview** The Layout Manager is an **Elementor-style visual UI composition system** built directly into the platform for **React-based interfaces**, using **pre-registered shadcn/ui-compatible components** as the building blocks. Its purpose is not to let users “design anything from scratch” in an uncontrolled way. Its purpose is to let privileged users visually assemble, rearrange, and configure existing interface structures on live platform screens, then let the system translate those structural edits into actual code updates and deployment actions. So the system is fundamentally: > **live visual editing \+ controlled component library \+ layout persistence \+ AI-assisted code generation/conversion \+ publish workflow** That is the real concept. ## **The problem it is solving** The pain point is very clear in your notes. You do not want to keep explaining UI changes to coding AIs in repeated back-and-forth cycles, then wait for code edits, GitHub pushes, Docker deployments, and testing, only to discover the output is still inaccurate. What you want instead is: - enter an edit mode directly on the exact screen - manipulate the screen visually - work in a familiar WordPress/Elementor-like way - let AI handle the code translation afterward - publish once satisfied That means the Layout Manager is solving a **workflow bottleneck**, not merely a frontend customization problem. ## **Core product definition** From your snippets, the strongest definition would be this: The Layout Manager is a **platform-native, drag-and-drop structural editing environment** that allows authorized users to edit live UI layouts using registered React components, save those layouts as structured configuration, and have the system orchestration layer convert or sync those changes into working code and redeployable platform updates. ## **Important boundary: structure vs aesthetics vs logic** This distinction appears multiple times and is one of the most important parts of the concept. The Layout Manager is for **structure and composition**. It controls: - page sections - containers - layout hierarchy - placement of components - arrangement of screens - visible UI composition - component property values within allowed bounds It does **not** primarily control aesthetics. You were clear that pretty design concerns should be handled centrally through the **TweakCN-based theming system**. It also does **not** primarily control business logic. You were clear that the builder handles what things look like and how they are arranged, while logic lives in the backend/module architecture. That means the PRD should sharply separate: 1. **Layout Manager** = structure 2. **Theme/Theming Menu** = visual style 3. **Module Logic / Backend / Workflows** = behavior and business operations That separation is excellent and should remain. ## **Access model** You already gave a meaningful access boundary. Access should be limited to privileged roles such as: - Super Users with full access - Developers with limited access - Agency Admins with limited access Regular tenant users and normal end users should not have unrestricted access to the system-wide builder. This matters because the feature touches live UI structure and potentially code generation. It is an administrative authoring environment, not a casual end-user preference screen. ## **How edit mode works** Your intended flow is very clear and very usable. A privileged user is on a live page and sees an edit trigger, likely a small pencil icon in the corner of the screen. Clicking it opens the Layout Manager in context for that screen. That means the feature is not just a separate admin tool. It is also an **in-context live editing experience**. There is also a second way to access it from the admin sidebar, where the label is likely **Layout Manager**, with sub-options such as: - Modules - Create New That means the feature has both: - **page-level entry** for editing an existing screen - **admin-level entry** for managing layouts/modules more broadly That is a strong product pattern. ## **Visual builder UI structure** From your notes, the internal UI is taking shape quite clearly. The builder includes: ### **Left sidebar** This contains the draggable component library. These are registered shadcn/ui blocks and possibly other imported compatible components. You also specified an important control detail:\ At the bottom of the left sidebar, there should be a **sticky Save button** and a **Preview button**, placed side by side at 50/50 width. That is a concrete UI requirement and should absolutely go in the PRD. ### **Main canvas** This is the drag-and-drop editing surface where users place containers, sections, and components. You explicitly referenced a **container system**, which suggests the canvas must support structural nesting rather than just dropping loose widgets anywhere. That likely means the builder needs a hierarchy such as: - page - sections - rows or layout groups - containers - components Even if the exact terminology changes, the nesting model is essential. ### **Right sidebar** This is especially important because you do not want a simple properties panel. You want a **tabbed right sidebar** whose first three tabs are: 1. **Layout hierarchy / tree** 2. **Component properties** 3. **Editing history** And the history tab should include: - running list of every change - undo - redo That is more sophisticated than a typical builder and gives the system stronger traceability and safer editing. ## **Component model** Your snippets imply a very important constraint. The user is not writing raw React code inside the builder. Instead, the system exposes **pre-coded components** and the user is assembling layouts using those existing building blocks. That means: - shadcn/ui components are pre-registered - imported library components can also be registered - users drag and drop them into layouts - users configure props visually - users do not directly modify source code in the builder for normal layout work This is a very smart architectural choice because it reduces risk and makes the system more stable. It also aligns with your “lego bricks” metaphor. Existing platform-safe components become reusable structural primitives. ## **Persistence model** The snippets imply that layout edits are not merely cosmetic runtime overlays. They are intended to become part of the actual platform. You said: - when the user saves a layout, the code is updated on the backend automatically - or created automatically for new pages - orchestration AI processes edits, updates the code, and redeploys changes This means the builder needs some intermediate representation. Even if not yet explicitly named, the PRD should likely define a layout schema or layout JSON model that captures: - screen structure - component instances - nesting relationships - prop values - identifiers - version history - layout metadata Then the system can use that structured representation to drive: - preview rendering - save state - history tracking - diffing - code generation - deployment workflows Without an intermediate schema, the whole idea becomes brittle. ## **Role of AI in the Layout Manager** The AI is not the builder itself. The AI is the system’s translation and extension layer. There are two distinct AI roles in your notes. ### **1. Layout-to-code orchestration** After you visually modify the interface, the orchestration AI interprets the changes and translates them into the underlying code changes required for the platform. This is a key part of the concept because it removes the need for you to manually explain every design adjustment to an external coding assistant. ### **2. Conversational creation and extension** The “bonus idea” expands the builder into a broader system-level vibe coding capability where AI can help create entire new modules from within the platform. This part is more ambitious, but your latest note is important: you believe this should be considered part of the same Layout Manager experience, likely as another sidebar tab or adjacent workflow, not a separate product. That tells me the product has **two layers**: - **MVP layer**: visual layout editing for existing screens/modules - **Advanced creation layer**: conversational module creation and platform extension That distinction will matter a lot when we revise the PRD. ## **The “Create New” concept** This is the most expansive part of the idea. From your notes, “Create New” is not just “new page.” It is potentially: - new module - new app-like capability - new screen set - new platform extension - new workflow-enabled interface And the AI should be able to: - accept a conversational description of what the user wants - assess what existing endpoints already exist - identify which endpoints must be created - plan the user flow - assess available UI components - generate new compatible components if needed - produce the initial screens/layouts - notify the admin when ready for testing - allow iterative refinement via Layout Manager - publish and set permissions - optionally announce the module to admins or developer community This is much larger than the builder itself. It is really a **platform extension factory** embedded inside the same authoring environment. That is strategically important, but from an MVP perspective it should almost certainly be treated as a later phase unless you explicitly want the first PRD to include the foundational scaffolding for it. ## **Underlying technical direction already implied** Even without the old PRD, your snippets imply a lot of technical assumptions: - React-based app architecture - shadcn/ui component system - component registration layer - drag-and-drop editing engine - likely Craft.js as the layout framework foundation - backend persistence for layouts - version history and diff support - preview mode - publish workflow - orchestration AI integration - code update and redeploy pipeline - role-based access control - module metadata management So even these scraps are enough to define the system properly. ## **The strongest distilled product statement** If I were compressing your idea into one clean sentence, it would be this: The Layout Manager is a platform-native, Elementor-inspired structural builder that lets authorized users visually compose and modify live React interfaces using registered shadcn-compatible components, then uses system orchestration AI to translate those structural changes into durable code-backed platform updates. ## **What I believe should carry into the revised PRD with high confidence** These feel like core requirements, not optional brainstorm notes: - in-context edit mode from live screens - Elementor-style drag-and-drop builder experience - React/shadcn-based component library - component registration system - container-based structural layout editing - separation of layout, theming, and logic - role-restricted access - canvas \+ left library sidebar \+ right tabbed sidebar - right sidebar tabs for hierarchy, props, and history - undo/redo and visible running change log - sticky Save and Preview buttons at bottom-left - persisted layouts that become real platform assets - orchestration AI for code sync/update and redeploy - admin sidebar access through Layout Manager - Modules and Create New entry points ## **What feels like advanced scope or phase-two scope** These ideas are strong, but they are more expansive and should probably be flagged as later-phase unless you decide otherwise: - fully conversational module/app generation - automatic endpoint gap analysis - open-source research during module creation - automatic module release announcements - developer-community publishing/review workflow - generation of entirely new shadcn-compatible components from scratch - full system-level vibe coding for app creation inside the platform These are not weak ideas. They are just broader than the base Layout Manager. ## **My assessment** The concept is strong. It is not vague anymore. The main thing the old PRD will help us determine is whether the earlier document captured this correctly, or whether it drifted into something too abstract, too broad, or too implementation-heavy in the wrong places. ## **UPDATE:** The earlier interpretation was directionally correct, but it still understated the importance of the Layout Manager and the native vibe-coding layer. With the new context, the right way to understand this system is not as a normal platform with a page builder attached. It is a platform whose **core growth mechanism** is built directly into its own architecture. ## **The clearest overall definition** This system is a **platform-native construction environment** that allows authorized users to build, extend, and refine the platform from inside the platform itself. At the center of that construction environment is the **Layout Manager**, which is not merely a drag-and-drop editor. It is a unified authoring system that combines: - live visual UI editing - structural page and screen composition - component configuration - component-to-functionality binding - conversational AI for new functionality creation - orchestration of code generation and platform updates - testing and publish workflows So the system is best understood as an **internal app-building layer** disguised as an Elementor-style interface builder. That is the real idea. ## **What problem it is actually solving** The pain point is not simply “I want an easier way to edit pages.” The real pain point is that your current workflow is fragmented and inefficient: You explain what you want to an external vibe-coding platform. That platform generates code in a separate environment. The code then has to be corrected, pushed to GitHub, deployed through Docker, tested on the actual platform, and often revised again because the result is inaccurate or disconnected from how the native system really works. That means your current process has several structural problems: The design environment is separate from the real platform. The AI is generating changes without native awareness of the platform’s live component architecture. Connections between interface and functionality are often made after the fact instead of at the moment of creation. The deployment loop is too indirect and too error-prone. What you want instead is a system where the design, structure, functionality, and implementation all begin in the same native environment. That is why this is not just a convenience feature. It is an architectural solution to a repeated platform-building bottleneck. ## **What the Layout Manager really is** The Layout Manager is the platform’s native visual and conversational construction system. It allows a privileged user to enter edit mode on a live screen, directly manipulate the layout using pre-registered React components, configure those components through properties, bind them to existing or new functionality, and then let the system convert those changes into durable code-backed platform updates. That means the Layout Manager serves several roles at once. First, it is a **live interface editor**. Second, it is a **structural composition system** for pages, screens, and layouts. Third, it is a **component wiring environment** where every selected element can be connected to existing data or new functionality. Fourth, it is a **native vibe-coding interface** for platform expansion. Fifth, it is a **publishing and deployment trigger point** for turning those changes into working application behavior. That is much broader than a simple builder, but still coherent because all of those things happen around the same object: the page, screen, and component hierarchy. ## **The platform is meant to be built from the inside** This is one of the most important insights from your new clarification. The system is being built so that the place where the app is used is also the place where the app is created, extended, and refined. That means the Layout Manager is not secondary tooling. It is part of the platform’s foundation. The builder is not there so nontechnical users can casually rearrange blocks, although some controlled customization may exist. Its deeper purpose is to let you and other authorized builders rapidly create real platform-native capabilities without leaving the system and without rebuilding context somewhere else. That is why the vibe-coding capability belongs in the MVP. It is not an add-on. It is one of the platform’s core reasons for existing. ## **The right mental model** The best mental model is this: The platform consists of a set of safe, reusable, prebuilt system primitives, and the Layout Manager is the environment where those primitives are assembled into working products. Those primitives include: registered UI components existing endpoints existing services existing workflows existing module functions existing system data structures existing theme rules existing access rules When possible, the user is rearranging and combining proven “lego bricks” that already exist in the system. This reduces risk, improves speed, and keeps the platform stable. When something truly new is needed, the user should still be able to describe it conversationally, and the AI should extend the system in a structured, platform-native way rather than through disconnected external generation. That combination of reuse and controlled extension is central to the design. ## **Structural editing versus aesthetics versus logic** This distinction still matters, but it now needs a more precise explanation. At the page composition level, the Layout Manager is primarily a **structural authoring system**. It determines what components appear, how they are nested, where they are placed, and how screens are composed. Aesthetics are still primarily handled through the centralized theming layer, which you’ve associated with the TweakCN-driven theme system. That means things like visual polish, design consistency, color system, and style behavior should be centrally managed rather than redefined screen by screen in an uncontrolled way. Business logic is still not supposed to live as ad hoc hand-coded behavior inside the visual canvas. Instead, logic should come from bound system functionality, workflows, module actions, services, or newly generated platform capabilities created through the AI workflow. So the clean separation is: The Layout Manager handles structure and configuration. The theming system handles visual style. The platform service layer handles logic and behavior. The AI orchestration layer bridges user intent to implementation when something new must be created. That is the cleanest architecture. ## **The builder is not just about layout anymore** The most important change from your added context is the introduction of **component-level functional binding** through the properties panel. This is what turns the system from a nice builder into a true internal app-construction tool. When a user adds a component to a page and clicks that component, the properties panel must open. Inside those properties, there must be a **Data Source** area. That Data Source area supports two paths. The first path is that the component connects to something that already exists. This might be an endpoint, a dataset, a workflow, a page, a service, a module function, or another internal resource. The user selects this from a searchable registry. The second path is that the component needs something new that the platform does not yet have. In that case, the user describes what is needed conversationally, and the AI begins a clarification and planning process. Once it understands the intent, it can create the new underlying functionality in a safe, structured way. This means every component is not just visual. Every component is potentially a node of real application intent. A table needs data. A chart needs data. A form needs a destination and processing behavior. A button needs an action. A dashboard card needs a source. A workflow trigger element needs a connected system capability. Because of that, the component properties panel becomes one of the most important parts of the entire product. ## **What happens when a component is selected** This should now be treated as a core behavioral pattern of the system. A user clicks a component on the canvas. That opens the component properties interface. Inside the properties, the user can control standard configuration for that component, but also define the source of its meaning and behavior. That means the component properties system should likely include several classes of configuration: visual properties structural properties interaction properties where appropriate data source or functionality binding history awareness for that component’s changes The Data Source area then becomes the place where the component is either connected to an existing platform capability or used as the starting point for creating a new one. This is the moment where UI and application behavior meet. ## **The AI’s role in the system** The AI is not a decorative assistant sitting beside the builder. It is one of the system’s operating mechanisms. There are several AI roles implied by your design. The first is **clarification**. When a user requests something new, the AI should ask follow-up questions in an interview-like consultation until it properly understands what is needed. The second is **planning**. The AI should assess what already exists, what can be reused, what must be added, and what the likely user flow or system flow should be. The third is **implementation orchestration**. Once the request is understood, the AI should translate that intent into actual system changes, including layouts, connections, logic, and underlying code work. The fourth is **safe extension**. The AI should create new functionality in a way that respects the platform’s architecture rather than producing disconnected or unstable code. The fifth is **return-to-builder feedback**. Once the functionality is generated, the user should be able to continue refining the outcome inside the same Layout Manager environment. So the AI is effectively the platform’s internal development layer, expressed through conversation and driven by user intent. ## **What “vibe coding” means in this system** In this context, vibe coding should not be defined loosely as “talking to AI about code.” In this system, vibe coding means that a privileged user can describe a desired capability, screen, module, page, data behavior, or interaction in natural language, and the platform-native AI can turn that into a real, testable, structurally integrated part of the platform. That includes both UI and functionality. The key difference from external vibe-coding tools is that this AI is operating with native awareness of: the live component system the existing module structure the available endpoints and services the internal conventions the theming rules the permission model the deployment environment the platform’s reusable architecture That native awareness is what makes it useful. ## **What the interface structure should be** The interface you described now has a very clear internal logic. A privileged user can enter the Layout Manager from a live page through an edit trigger, likely a pencil icon, or from the admin sidebar. Inside the builder, there is a three-part workspace. On the left is the component library, containing draggable registered components. In the middle is the canvas, where the page structure is built and edited. On the right is a tabbed panel, not a simple static properties drawer. The right tabbed panel begins with three core tabs: the layout hierarchy or tree the selected component’s properties editing history The history tab includes a running list of changes plus undo and redo controls. At the bottom of the left sidebar are sticky Save and Preview buttons, side by side. This is not a random set of UI preferences. It reflects the actual needs of the product. The left side is for supply. The center is for composition. The right side is for inspection, control, and traceability. That is a sound structure. ## **The underlying page model** The builder cannot work reliably unless layouts are represented as structured system objects rather than temporary visual state. That means pages, module screens, and layouts should exist as persisted structured entities. A page is not just HTML output. It is a composed tree of registered components, containers, and configuration states. Each component instance likely needs a durable record of: its type its position in the layout hierarchy its parent-child relationships its visual configuration its interaction settings its data source mode its bound resource, if existing its new functionality request state, if AI-created its history entries its versioning and publish state This underlying model is what makes preview, undo, diffing, testing, publishing, and AI orchestration possible. Without that layer, the builder would just be a cosmetic editor. With it, the builder becomes a real application-construction system. ## **The role of Craft.js and the component library** Your earlier snippets referenced Craft.js as the likely foundation, and that still makes sense as the drag-and-drop structural engine. But the more important architectural point is not the library itself. It is the **registered component model**. The platform needs a controlled registry of compatible components that can safely be used inside the builder. These components should include the platform’s shadcn/ui-compatible building blocks and any other approved imported components. The user is not meant to drag random arbitrary code into the system. They are using pre-validated interface primitives that are already known to the platform. That is what keeps the builder safe and maintainable. ## **New pages, new screens, and new modules** This is another place where the earlier interpretation needs strengthening. The Layout Manager must support more than editing an existing page. It must support: editing existing pages creating new pages for existing modules creating new pages for new modules creating new module structures through conversational AI That means the builder is not only a live editor. It is also a creation environment. This is why the admin sidebar entry of “Layout Manager” with sub-areas like “Modules” and “Create New” makes sense. “Modules” is the management view for existing module-related layouts and settings. “Create New” is the entry into a guided creation workflow where the user describes what they want to build. This creation flow should remain part of the same overall authoring environment, not a separate disconnected product. ## **How the “Create New” flow should be understood** The “Create New” flow is not merely “new blank page.” It is the conversational front door to platform extension. The user describes a new capability, new module, or new app-like function. The AI evaluates existing system assets. It determines what can be reused. It identifies what is missing. It clarifies requirements. It plans the interactions. It assembles or creates the needed functional pieces. It produces initial layouts and screen structure. It returns those outputs to the user for testing and refinement inside the Layout Manager. That means “Create New” is effectively the platform’s internal module-generation workflow. Because you now clarified that this belongs in the MVP, the PRD should not soften this into a future concept. It should define it as core platform functionality, even if some advanced behaviors are phased within that core. ## **Access and governance** The system is powerful enough that access control has to be explicit. Only privileged roles should be able to structurally alter layouts, create pages, bind components to system capabilities, or trigger AI-based system extension. You already named Super Users, Developers, and Agency Admins as the intended access classes, with different scopes of permission. That fits the architecture. This tool is an authoring and extension environment, not a universal end-user customization layer. The governance side also matters because layout changes and functionality changes should likely move through draft, preview, testing, and publish states rather than immediately altering the live user experience without oversight. ## **Publish, test, and deployment** Saving is not the same thing as publishing. The user should be able to work on a draft layout or a generated capability, preview it, and test it before it becomes part of the active platform experience. Once approved, the orchestration layer should convert the structured edits and AI-generated work into the necessary backend updates, code changes, or deployment-ready artifacts. The important principle here is that the user should feel like they are editing and building visually, but the system should still preserve serious engineering discipline underneath that experience. That is part of what makes the idea strong. It hides complexity from the builder without discarding structure. ## **The best concise summary** If I were rewriting the product definition from scratch based on your updated context, I would describe it like this: The Layout Manager is the platform’s native visual and conversational construction environment. It allows authorized users to edit live screens, compose pages from registered React components, configure each component through a properties system, bind those components to existing or newly created functionality, and use built-in AI orchestration to generate, refine, test, and publish real platform-native pages, screens, and modules without leaving the platform. That is the strongest accurate summary. ## **The most important correction to the earlier analysis** The most important correction is this: The Layout Manager is not a structural builder with a separate “bonus” AI creation concept attached to it. It is a unified system in which structural editing, component configuration, data binding, AI-based capability creation, and native platform extension all belong to the same core authoring workflow. --- ## aiConnected Trademark & Brand Identity Documentation **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-supporting-docs/aiConnected-trademark-and-patents/aiConnected-branding-and-trademark **Description:** Table of Contents 1. Executive Summary 2. Trademark Research & Findings 3. Brand Philosophy & Positioning 4. Visual Identity System 5. Logo Design Specificat... # aiConnected Trademark & Brand Identity Documentation ## Table of Contents 1. [Executive Summary](#executive-summary) 2. [Trademark Research & Findings](#trademark-research--findings) 3. [Brand Philosophy & Positioning](#brand-philosophy--positioning) 4. [Visual Identity System](#visual-identity-system) 5. [Logo Design Specifications](#logo-design-specifications) 6. [Custom Font Strategy](#custom-font-strategy) 7. [Hidden Message Architecture](#hidden-message-architecture) 8. [Trademark Filing Strategy](#trademark-filing-strategy) 9. [Implementation Timeline](#implementation-timeline) 10. [Asset Management & Guidelines](#asset-management--guidelines) --- ## 1. Executive Summary ### 1.1 Overview aiConnected is filing a trademark for a visual brand identity consisting of: 1. **Custom-designed logo** featuring a binary-encoded infinity symbol 2. **Custom-designed typeface** based on public domain fonts with significant modifications 3. **Multi-layered hidden message** encoded at microscopic scale, to be revealed at a major company milestone 4. **Integrated design system** where the logo works at all scales from favicon (16px) to billboard size ### 1.2 Strategic Rationale The trademark strategy serves multiple purposes: **Legal Protection:** - Protects the unique visual identity from competitors - Prevents confusion with other "AICONNECTED" applications (particularly the suspended application for "IT consulting services") - Establishes early priority date (May 2024 target filing) - Creates enforceable rights to the distinctive visual design **Brand Differentiation:** - The binary infinity is *genuinely unique* — no competitor has this exact visual concept - Multi-scale design that reveals complexity at different zoom levels creates memorable differentiation - Hidden message layer creates a founding mythology **Technical Positioning:** - Visual identity reflects the actual product (recursive, fractal intelligence, hidden patterns emerging) - Design communicates philosophical positioning without words - Encoding mechanism signals technical depth and sophistication ### 1.3 Key Achievement By designing the logo entirely from scratch (using public domain font bases + significant custom modifications), we overcome the earlier trademark conflict: **Earlier Conflict:** - Another applicant filed "AICONNECTED" for IT consulting services (May 2022) - Application still suspended as of Nov 2025 - Could potentially block a plain-text trademark filing **Our Solution:** - File a **design mark** (special form drawing) not a word mark - Our mark = custom typeface + binary infinity symbol + visual styling - Their mark = plain text "AICONNECTED" - These are visually distinct and in different service categories (they do consulting, we do SaaS) - Both can coexist without likelihood of confusion --- ## 2. Trademark Research & Findings ### 2.1 Initial Search Results #### **Conflicting Application Identified** **Application Details:** ``` Mark: AICONNECTED Applicant: rios pena juan carlos Address: 1292 baywood dr, petaluma, CA 94954 Filing Date: May 19, 2022 Serial Number: 97419798 Status: LIVE/APPLICATION/Under Examination Current Status: SUSPENDED (as of Nov 8, 2025) ``` **Application History:** ``` Timeline: May 23, 2022 → NEW APPLICATION ENTERED May 25, 2022 → NEW APPLICATION OFFICE SUPPLIED DATA ENTERED Mar. 08, 2023 → NON-FINAL ACTION WRITTEN Mar. 08, 2023 → ASSIGNED TO EXAMINER Mar. 08, 2023 → NOTIFICATION OF NON-FINAL ACTION E-MAILED Mar. 08, 2023 → TEAS RESPONSE TO OFFICE ACTION RECEIVED Mar. 08, 2023 → CORRESPONDENCE RECEIVED IN LAW OFFICE Mar. 09, 2023 → TEAS/EMAIL CORRESPONDENCE ENTERED Mar. 25, 2023 → SUSPENSION LETTER WRITTEN Mar. 25, 2023 → LETTER OF SUSPENSION E-MAILED Mar. 25, 2023 → NOTIFICATION OF LETTER OF SUSPENSION E-MAILED Oct. 23, 2023 → REPORT COMPLETED SUSPENSION CHECK CASE STILL SUSPENDED Apr. 24, 2024 → REPORT COMPLETED SUSPENSION CHECK CASE STILL SUSPENDED Oct. 21, 2024 → REPORT COMPLETED SUSPENSION CHECK CASE STILL SUSPENDED Apr. 24, 2025 → SUSPENSION CHECKED - TO ATTORNEY FOR ACTION May 07, 2025 → REPORT COMPLETED SUSPENSION CHECK CASE STILL SUSPENDED Nov. 08, 2025 → SUSPENSION CHECKED - TO ATTORNEY FOR ACTION Nov. 09, 2025 → REPORT COMPLETED SUSPENSION CHECK CASE STILL SUSPENDED ``` **Why It's Suspended:** The suspension letter (dated March 25, 2023) indicates: > "The pending application(s) below has an earlier filing date or effective filing date than applicant's application. If the mark in the application(s) below registers, the USPTO may refuse registration of applicant's mark under Section 2(d) because of a likelihood of confusion with the registered mark(s)." The application is waiting for two prior applications to resolve: - Serial No. 79350437 - Serial No. 88648468 **Goods/Services:** - Class 42: "IT consulting services" **Key Point:** This application has been stuck in suspension for nearly 3 years (since March 2023). The applicant (an individual in Petaluma, CA) appears inactive. The application may eventually abandon, but we cannot rely on that. #### **Search Results for Variations** **Searched:** - "aiConnected" — 1 result (the suspended application above) - "AI Connected" (two words) — Various results, none blocking - "AiConnect" — Various results, none blocking - "aiConnectOS" — No conflicting registrations **Assessment:** - Plain text "AICONNECTED" is encumbered by the suspended application - Variations and different presentation are clear - Design mark filing is the right strategy ### 2.2 USPTO Trademark Classification #### **Class 42: Software & IT Services** Our filing will be in **Class 42** (Computer and Scientific Services): **Specific Goods/Services Description:** > "Software as a service (SaaS) in the field of artificial intelligence; Providing cloud-based platform for developing, managing, and deploying artificial intelligence applications; Software development services in the field of autonomous systems and robotics; Software licensing and distribution services" **Why Class 42:** - Covers SaaS/software platform services - Covers licensing to third parties (gaming, medical, robotics companies) - Explicitly covers cloud-based deployment - Encompasses the full business model we discussed **Why We Won't Need Other Classes:** - We're not manufacturing robots (no Class 37) - We're not selling robotics as a product category (no Class 12) - We're licensing software to companies that manufacture/use robots - Everything falls under software services in Class 42 ### 2.3 Relationship to Conflicting Application #### **Why Our Design Mark Doesn't Conflict** **Their Application:** - Mark type: Standard character mark (plain text) - Goods: "IT consulting services" - Visual presentation: Generic text formatting - Distinctiveness: Low (the word itself, no design elements) **Our Application:** - Mark type: Special form drawing (design mark) - Goods: Software licensing, SaaS platform services - Visual presentation: Custom typeface + binary infinity symbol - Distinctiveness: High (unique visual composition) **Trademark Analysis:** The USPTO examiner will compare: 1. **Mark similarity** (the visual/phonetic similarity) - Their mark: Plain text "AICONNECTED" - Our mark: Same word but in custom typeface + integrated into infinity symbol - Result: Phonetically identical, but visually distinct due to design elements 2. **Goods/services similarity** (whether they're related) - Their goods: IT consulting (advisory services) - Our goods: SaaS software, licensing, deployment - Result: Related but distinct (they sell services, we sell software) 3. **Overall commercial impression** (would consumers be confused?) - Their brand targets: Companies seeking IT consulting advice - Our brand targets: Companies licensing persistent AI software - Result: Different target audiences, different use cases **Conclusion:** A design mark with distinct visual elements (infinity symbol + custom font) for different goods/services (SaaS vs. consulting) is defensible and should not conflict with their plain text mark for IT consulting. --- ## 3. Brand Philosophy & Positioning ### 3.1 Core Brand Premise aiConnected is not a tool. It's a **team member**. The visual identity must communicate: 1. **Persistence** — The AI doesn't disappear or reset 2. **Intelligence that learns** — Grows and evolves 3. **Recursive complexity** — Meaning emerges at every scale 4. **Hidden depth** — Surface simplicity masks profound capability 5. **Connection** — Bridges between user and intelligence ### 3.2 Visual Metaphors #### **The Infinity Symbol** The infinity symbol (∞) represents: - **Limitless continuity** — The persona persists forever - **Recursion** — Patterns within patterns, infinite depth - **Non-linear growth** — Learning doesn't follow a straight path - **Balance** — Two loops in dynamic equilibrium (human + AI) - **Mathematical perfection** — The brand is built on precision #### **Binary Encoding (Ones & Zeros)** Binary represents: - **The foundation of computing** — We're built on digital principles - **Information density** — Complex meaning from simple elements - **Hidden patterns** — Zooming reveals what was invisible - **Quantum/atomic principle** — Nothing is "solid," everything is relationships - **Code and structure** — The DNA of our system #### **Multi-Scale Design** The logo works across infinite scales: - **Zoomed out (normal viewing):** Clean, professional mark - **Zoomed in (1000x):** Binary code becomes visible - **Extreme zoom (10,000x+):** Individual ones and zeros readable - **At every scale:** The same information is present, just revealed differently This mirrors how our AI works: - **Surface level:** Chat interface, simple conversation - **Deeper level:** Memory and learning visible in repeated interactions - **Atomic level:** Every interaction made up of discrete learned patterns --- ## 4. Visual Identity System ### 4.1 The Binary Infinity Symbol #### **Design Concept** The infinity symbol is composed of ones (1) and zeros (0) representing binary code, creating a fractal-like structure where: ``` Zoomed Out (Normal viewing): ∞ (appears as solid infinity symbol) Zoomed In (2x - 10x): Visual indication of pattern within symbol begins to appear Individual 1s and 0s start to be visible at edges Zoomed Further (100x - 1000x): Clear grid of 1s and 0s fills the entire symbol Can begin reading the binary code Extreme Zoom (10,000x+): Individual binary digits are large Code can be read as readable text strings ``` #### **Technical Specifications** **Rendering Requirements:** - Scalable vector format (SVG, EPS) - Anti-aliasing disabled at extreme zoom to maintain binary grid visibility - Font size of binary digits scales with zoom level - Color: Monochrome (black on transparent, scales to system colors) - No smoothing of binary grid edges **Binary Code Implementation:** The ones and zeros that compose the infinity symbol are: 1. **Visually consistent** — Regular grid of monospace digits 2. **Readable** — At sufficient zoom, individual digits are legible 3. **Meaningful** — The binary encodes actual information (see Section 7) #### **Visual Examples (ASCII Representation)** ``` Level 1 (Extreme zoom - individual characters visible): 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0 0 [continues to form shape of ∞] Level 2 (High zoom - small visible text): 0100000101001001000...0101110000000 0110000100110010000...0001001000001 0011100100001000...00111100000 [continues to form shape] Level 3 (Normal zoom): ∞ [appears solid] ``` ### 4.2 Integration with Text: "aiConnected" The typeface is similarly structured: #### **Letter A Design** ``` Level 1 (Normal zoom): A (appears as solid letter) Level 2 (Zoomed 10x): A with visible pattern inside 01000001 (ASCII for 'A') visible in binary Level 3 (Extreme zoom): 0 1 0 0 0 0 0 1 (Each forming part of the letter shape) ``` The letters "A" and "I" (in "aiConnected") are composed of binary code: - **A** = 01000001 (binary for ASCII 'A') - **I** = 01001001 (binary for ASCII 'I') These binary sequences *literally form the letter shapes when zoomed*. #### **Rendering Characteristics** - At normal viewing distance: Solid, readable text in custom font - At 10x zoom: Binary pattern becomes visible - At 100x+ zoom: Individual binary digits form the letter outlines - Pattern is repeating and fractal-like ### 4.3 Complete Mark Composition #### **Design Assembly** ``` Center Element: Binary Infinity Symbol (∞) Surrounding Text: "aiConnected" in custom typeface Integration: The 'A' and 'I' of "aiConnected" flow into the infinity loops Layout: 'ai' on left loop of ∞ 'Connected' on right loop of ∞ Or: Full word above/below the ∞ Mark Type: Special Form Drawing (Design Mark) Colors: Black on transparent (primary) White on dark backgrounds (secondary) Minimum Size: 16 pixels (favicon size) at full readability Scalability: Infinite (fractal design supports any scale) ``` --- ## 5. Logo Design Specifications ### 5.1 Design Standards & Specifications #### **Color Palette** **Primary Colors:** ``` Background: #1e2328 (Deep navy-gray) Text: #839aac (Muted blue-gray) Accent: #2e95f3 (Bright blue) Secondary Dark: #031c33 (Very dark navy) Alternate: #021220 (Darkest navy) ``` **Rationale:** - Deep navy backgrounds evoke technology and trust - Muted blue-gray text maintains readability while feeling sophisticated - Bright blue accent (2e95f3) represents energy and AI/technology - These colors work across light and dark themes #### **Typography** **Primary Font:** - Montserrat (for body text in brand materials) - Sans-serif, modern, geometric - Used in marketing, documentation, UI **Custom Font:** - Based on public domain fonts with significant modifications - Custom "A" and "I" composed of binary code - See Section 6 for detailed font specifications **Secondary Font:** - DM Sans (used in some contexts) - Geometric, humanist sans-serif - Used for UI elements and secondary information #### **Dimensions & Proportions** **Logo Safe Space (Minimum Clearance):** ``` ┌─────────────────────────────────┐ │ │ │ ┌─────────────┐ │ │ │ │ │ │ │ Logo │ Clearance │ │ │ (1x) │ │ │ │ │ │ │ └─────────────┘ │ │ │ └─────────────────────────────────┘ Minimum clearance = 0.5x logo width/height on all sides (Space between logo and other elements) ``` **Aspect Ratio:** - Logo: 1:1 (square) - Can be embedded in wider compositions - Scales proportionally **Recommended Minimum Size:** - Web: 64px × 64px (comfortable reading) - Print: 0.5" × 0.5" (minimum for quality) - Favicon: 16px × 16px (achieves binary visibility at 10-100x zoom) #### **Background Requirements** **Backgrounds That Work:** - Solid colors (especially dark colors) - Subtle gradients - Photography with high contrast borders - Textured backgrounds if contrast is maintained **Backgrounds to Avoid:** - Busy patterns that compete with logo - Colors too similar to logo (insufficient contrast) - Highly saturated backgrounds that clash **Contrast Testing:** - Verify minimum 4.5:1 contrast ratio (WCAG AA standard) - Test on dark, light, and colored backgrounds - Ensure readability at all intended sizes ### 5.2 Variations & Use Cases #### **Full Horizontal Layout** ``` [Binary ∞] aiConnected ``` **Use:** Business cards, web headers, official documentation **Specifications:** - Logo element: 1 unit - Horizontal spacing: 0.3x logo width - Text height: 0.8x to 1x logo height - Total width: ~3.5x logo height #### **Stacked Layout** ``` [Binary ∞] aiConnected ``` **Use:** Social media profiles, app icons (with just symbol), app home screens **Specifications:** - Logo centered above text - Vertical spacing: 0.2x logo height - Text width: 1.5x to 2x logo width - Total height: ~2.5x logo height #### **Symbol Only** ``` [Binary ∞] ``` **Use:** Favicon, app icon, social media favicons, single mark usage **Specifications:** - Standalone infinity symbol - Works at any scale - Can be scaled down to 16px and remain readable at 10x+ zoom - Maintains binary composition across all scales #### **Monochrome Variations** **Black Version:** - Full black on white or light backgrounds - All binary digits rendered in black - Maintains contrast at all sizes **White Version:** - Full white on dark backgrounds - Useful for dark theme applications - Maintains contrast **Grayscale Version:** - Used when color is not available - Gradient from #333 to #999 to show depth - Used in legal documents, PDFs ### 5.3 DO's and DON'Ts #### **DO:** - Use the logo with adequate clear space (0.5x width/height minimum) - Maintain the same proportions when scaling - Use high-quality file formats (SVG for web, EPS for print) - Ensure sufficient contrast with background - Use the official color palette - Maintain the binary composition integrity - Test readability at intended display size #### **DON'T:** - Stretch or distort the logo - Change colors without approval - Remove or obscure the binary pattern - Use on backgrounds without adequate contrast - Rotate the logo (except 90° increments for specific layouts) - Add effects (gradients, shadows, outlines) without design approval - Use outdated file versions - Combine with incompatible fonts or design elements --- ## 6. Custom Font Strategy ### 6.1 Font Design Philosophy #### **Why Custom Font?** The decision to design a custom font (rather than using Alright Sans) addresses multiple objectives: **Legal:** - Alright Sans is a commercial font requiring a paid license - Custom font = full ownership, no licensing issues - Can be freely integrated into trademark filing - No dependency on third-party IP **Brand:** - Custom font is *unique* — no other company uses it - Strengthens trademark distinctiveness - Communicates technical sophistication - Creates visual consistency across all touchpoints **Technical:** - Binary encoding ("A" and "I" composed of 01000001 and 01001001) is custom to our font - Allows for recursive, fractal-like letter construction - Supports multi-scale rendering with consistent pattern - Opens future possibilities for "hidden message" layering ### 6.2 Font Development Strategy #### **Base Font Selection** We will use **public domain fonts as the foundation**: **Criteria for Base Font:** 1. True public domain (not just free-to-use) 2. Sans-serif, modern geometric style 3. Good character set coverage 4. Suitable for both UI and branding 5. Already supports multiple weights **Candidate Base Fonts (All Public Domain):** - **Inter** — Modern sans-serif, exceptional hinting, open source - **Roboto** — Geometric, highly legible, open source (SIL) - **Source Sans Pro** — Excellent family, open source - **JetBrains Mono** — Technical feel, open source - **IBM Plex Sans** — Professional, comprehensive, open source **Likely Selection:** Inter or Source Sans Pro (both are production-quality, public domain fonts with excellent Unicode support) #### **Font Modification Process** **Step 1: Acquire & License** - Obtain full source files (UFO format preferred) - Verify public domain status - Document licensing in trademark filing **Step 2: Base Modifications** - Adjust baseline kerning and metrics - Refine character spacing - Optimize for screen rendering (hinting) - Create new weight variations if needed **Step 3: Special Characters** - **Custom "A":** Compose using binary digits (01000001) - **Custom "I":** Compose using binary digits (01001001) - **Infinity symbol:** Create from scratch using binary pattern **Step 4: Integration** - Ensure smooth transition between binary letters and regular letters - Maintain consistent font metrics across character set - Optimize for rendering at different scales - Create hinting for small sizes **Step 5: Testing** - Test rendering across platforms (web, print, mobile) - Verify OpenType features work correctly - Test at multiple sizes (8px to 1000px+) - Verify binary digit legibility at zoom ### 6.3 Font Specifications #### **Character Set** **Include:** - Full ASCII character set (A-Z, a-z, 0-9) - Punctuation and symbols - Extended Latin characters (for international use) - Custom "A" and "I" (binary-encoded versions) - Custom infinity symbol **Exclude:** - Decorative elements not relevant to brand - Complex Unicode beyond Latin #### **Font Files to Generate** ``` fontfamily-regular.ttf (TrueType for web) fontfamily-regular.woff2 (Web font format) fontfamily-bold.ttf fontfamily-bold.woff2 fontfamily-italic.ttf fontfamily-italic.woff2 fontfamily-bolditalic.ttf fontfamily-bolditalic.woff2 ``` #### **Font Metrics** ``` Specified: Line height: 1.4 (readable) Baseline: 0 (standard) Cap height: 700 (units) X-height: 500 (units) Descender depth: -200 (units) Ascender height: 800 (units) Kerning: Optimized for technology/brand feel Tracking: Tight (letters feel connected) ``` #### **OpenType Features** Implement optional OpenType features: - `liga`: Ligatures for common combinations (if appropriate) - `cpsp`: Capital spacing - `c2sc`: Small caps from capitals - `smcp`: Small caps ### 6.4 Font Licensing & Distribution #### **Licensing Strategy** The custom font will be: **Proprietary to aiConnected:** - Not distributed publicly - Only used for official brand materials - Embedded in web fonts with proper protection - Version controlled internally **Font Files Provided:** - Licensed copy to: company partners, contractors, distributors - License agreement specifies: - Use only for aiConnected branding - No sublicensing or redistribution - Trademark usage restrictions - Termination upon business separation #### **Web Font Implementation** ```css @font-face { font-family: 'aiConnected'; src: url('/fonts/aiconnected-regular.woff2') format('woff2'), url('/fonts/aiconnected-regular.ttf') format('truetype'); font-weight: normal; font-style: normal; font-display: swap; /* WOFF2 includes subsetting to prevent full font exposure */ } body { font-family: 'aiConnected', sans-serif; } ``` **Protection Measures:** - Use WOFF2 with character subsetting (only deliver necessary characters) - Disable font download if possible (CSS-only delivery) - Include font licensing notice in source code comments - Monitor for unauthorized font redistribution --- ## 7. Hidden Message Architecture ### 7.1 Founding Philosophy & Message #### **The Core Message** The hidden message is a Masonic principle, translated into modern technical language: ``` "The lips of wisdom are closed except to the ears of understanding. All creation bows to one who chooses but one. The code is written by the one who can choose and see. But the eye is closed to infinity." ``` #### **Interpretation for aiConnected** **First Statement - "The lips of wisdom are closed except to the ears of understanding"** - Knowledge is not given; it must be earned through understanding - The hidden message rewards those who seek it with sufficient sophistication - Filters who grasps the philosophy from those who merely consume the product - Frames aiConnected as a platform requiring thoughtfulness, not instant answers **Second Statement - "All creation bows to one who chooses but one"** - The power of focus and singular vision - AI becomes more powerful when it chooses *one* purpose rather than trying everything - Mirrors the business philosophy: focus on persistent intelligence, not generic AI tools - Applies to users: personas that specialize are more valuable than generalized assistants **Third Statement - "The code is written by the one who can choose and see"** - Agency and consciousness: the ability to make choices and observe consequences - Our personas have agency within boundaries - Intelligence emerges from decision-making, not just information processing - Directly applies to aiConnected: personas *choose* how to act **Fourth Statement - "But the eye is closed to infinity"** - The paradox of perception: we can see *some* patterns but never the whole - Infinite complexity is unknowable even while being lived - Accepts limitations while pursuing endless learning - Technical truth: neural networks have bounded understanding despite infinite complexity #### **Why This Message Matters** This message is not inspiration—it's a *lens for understanding the product*. When revealed at a major milestone (IPO, $100M valuation, etc.), it communicates: - We've always understood the philosophy underneath - The founding team was thinking deeply about consciousness and agency - Persistent AI requires humility, focus, and honest boundaries - The lesson is available to those "who have ears to hear" ### 7.2 Message Encoding Strategy #### **Encoding Architecture** The message is encoded at multiple zoom levels, hidden deep within the binary infinity and the letters: **Level 1 (Micro-scale: 10,000x+ zoom):** Specific sequences of ones and zeros spell out: ``` A = 01000001 (ASCII for 'A') I = 01001001 (ASCII for 'I') ``` **Level 2 (Nano-scale: 100,000x+ zoom):** Deeper within the binary grid, specific strings of consecutive ones and zeros encode: ``` Message part 1: "The lips of wisdom are closed except to the ears of understanding" Message part 2: "All creation bows to one who chooses but one" Message part 3: "The code is written by the one who can choose and see" Message part 4: "But the eye is closed to infinity" ``` **Encoding Method:** Each ASCII character has an 8-bit binary representation: ``` 'T' = 01010100 'h' = 01101000 'e' = 01100101 ... ``` The message is encoded as a continuous binary string within the logo's background pattern. #### **Message Placement Strategy** The message is **distributed, not sequential**: **Not:** A simple string of consecutive ones and zeros spelling the message **Instead:** The message is scattered across the infinity symbol at different levels: ``` Example (conceptual - not actual encoding): At 10,000x zoom: Individual binary digits visible At 100,000x zoom: Patterns of digits spell individual characters At 1,000,000x zoom: Sequences spell words At 10,000,000x zoom: The full message emerges The message is embedded *throughout* the infinity symbol, requiring deep investigation to extract the complete phrase. ``` #### **Decoding Requirements** To find and decode the message, someone would need: 1. **Knowledge that the message exists** (revealed at milestone) 2. **Technical capability** to zoom into logo at extreme magnification 3. **Understanding of binary encoding** (ASCII to decimal to character) 4. **Patience and obsession** to search the entire infinity symbol 5. **Pattern recognition** to identify that specific sequences spell words **This is intentional:** - The message doesn't reveal itself to casual observers - It rewards deep technical investigation - It mirrors the product philosophy: depth and patterns at every scale - It creates a challenge/achievement moment for discoverers ### 7.3 Milestone Revelation Plan #### **Trigger Event** The message is revealed when aiConnected reaches a **major business milestone**: **Options (in order of likelihood):** 1. **First $100M valuation** (Series B/C) 2. **IPO filing** (becoming public) 3. **1 million personas deployed** (product adoption milestone) 4. **5-year anniversary** (if earlier milestones not reached) **Announcement Process:** ``` 1. Social Media Teaser (1-2 weeks before reveal) "For the last [years], there's been a message hidden deep in our logo. We promised we'd reveal it when we hit a major milestone. Well, we just did." 2. Official Blog Post (Launch day) "Decoding aiConnected: The Message Hidden in Our Logo" - Full history of the decision - How to decode the binary - The philosophical significance - Recognition of whoever decodes it first (if applicable) 3. Challenge & Prize "We're releasing our logo in vector format. For everyone clever enough to find the hidden message and decode it, we're [prize]. Hint: The message is embedded in binary at extreme zoom levels. Here's the vector file to start your search." 4. Verification Community members attempt to decode First verifiable decode posted publicly Winner(s) announced with recognition ``` #### **Prize Structure** (Suggests) **Immediate Prize:** - Named as "Official Message Decoder" on company blog/history - Limited edition digital certificate with the message - Free lifetime access to aiConnected Pro tier - Public recognition in announcement **Extended Prize:** - Invitation to company milestone celebration (if IPO event) - Feature in company story/documentation - Named decoder in trademark/brand documentation - Company merchandise with logo **Why This Matters:** - Demonstrates technical depth of our community - Creates free marketing (people share the challenge) - Proves the philosophy is real (we really did hide it, we really believed in it) - Becomes part of company founding mythology ### 7.4 Technical Implementation #### **Logo File Preparation for Encoding** **File Format Decisions:** 1. **SVG (Scalable Vector Graphics)** - Maintains infinite scalability - Text-based (can be encoded with actual binary strings in comments) - Supports embedding complex patterns - Supports multiple layers 2. **Design File Format (Illustrator/Figma)** - Create base logo (visible) - Create binary encoding layers (hidden at depth) - Each zoom level gets separate layer(s) - Lock hidden layers to prevent accidental deletion 3. **Final Deliverables** - Master SVG with all layers - Reduced-quality SVG for public use (hidden layers removed) - PNG versions at various sizes - PDF version for print #### **Binary String Storage** **In SVG Source Code (commented):** ```xml 01000001 01001001 01010100... (thousands of digits forming infinity shape) ``` #### **Decoding Instructions** (Released at Milestone) When the message is revealed, the company provides: ``` How to Decode the aiConnected Logo Message 1. Download the logo vector file (SVG) 2. Open in a text editor (NOT a design program) - View the raw SVG source code - You'll see comments indicating the hidden encoding 3. Extract the binary string 4. Convert from binary to ASCII: - Group by 8 bits: 01000001 - Convert each group to decimal: 65 - Convert decimal to ASCII character: 'A' Python helper: binary_string = "01010100..." # paste the extracted binary message = ''.join(chr(int(binary_string[i:i+8], 2)) for i in range(0, len(binary_string), 8)) print(message) 5. Verify you've decoded: "The lips of wisdom are closed except to the ears of understanding..." Example (first few characters): 01010100 = 84 = 'T' 01101000 = 104 = 'h' 01100101 = 101 = 'e' 00100000 = 32 = ' ' (and so on...) ``` --- ## 8. Trademark Filing Strategy ### 8.1 Filing Approach: Design Mark vs. Word Mark #### **Why Design Mark (Not Word Mark)** **Word Mark Option (NOT recommended):** ``` Application Type: Standard character mark Mark: AICONNECTED (plain text) Result: Direct conflict with suspended application (97419798) Status: Likely to be refused under Section 2(d) - likelihood of confusion Reasoning: Phonetically identical, even though goods are different Risk: Even if we won, it's defensible and costs legal resources to fight ``` **Design Mark Option (RECOMMENDED):** ``` Application Type: Special form drawing Mark: Custom typeface "aiConnected" + Binary infinity symbol Visual composition: Integrated design with infinity loops and binary pattern Result: No conflict with plain text "AICONNECTED" Reasoning: Visual distinctiveness + different service categories = no confusion Status: High likelihood of approval Cost: Same filing fee, but defense is much stronger ``` #### **Why They Don't Conflict** **Mark Similarity:** ``` Their mark: AICONNECTED (plain text, standard font) Our mark: aiConnected (custom font with binary digits) integrated with binary infinity symbol Visual comparison: The infinity symbol + custom design elements make our mark visually distinct, even though the word is the same. Analogy: Apple's text mark and Apple's apple symbol are both "Apple" but the symbol is a separate design mark with different scope of protection. ``` **Goods/Services Similarity:** ``` Their goods: Class 42 - IT consulting services (Advisory services - a person/firm consulting with clients) Our goods: Class 42 - Software licensing, SaaS, platform services (Software products - licensed to end users) Analysis: While both in Class 42, these are distinct subcategories. - IT consulting = service of providing advice - Software licensing = product of providing software Target audiences are different: - Companies seeking consulting hire a consulting firm - Companies needing software license a platform Likelihood of confusion: Low (different purchase decision, different context) ``` **Conclusion:** A design mark with distinct visual elements in a different service subcategory is defensible and does not conflict with their plain text word mark. ### 8.2 Filing Process & Timeline #### **Pre-Filing Steps (Weeks 1-4)** **Week 1-2: Final Design** - Complete logo design and vector files - Finalize custom font modifications - Create image representation for filing (PNG screenshot of logo) - Prepare all design specifications **Week 2-3: Trademark Search** - Conduct thorough USPTO trademark search (TESS) - Search all variations: aiConnected, ai-connected, etc. - Search similar visual marks (infinity symbols, binary designs) - Document findings - Determine no blocking marks beyond known application **Week 3-4: Legal Preparation** - Decide between LegalZoom and DIY filing - If DIY: Prepare all required documentation - If LegalZoom: Engage attorney, provide all materials - Write goods/services description in Class 42 #### **Filing (Week 5)** **Required Information for Special Form (Design Mark):** ``` 1. Mark representation (PNG image of logo) 2. Applicant name and address (company incorporation address) 3. Entity type (LLC, C-Corp, etc.) 4. International class(es): 42 5. Goods/services description: [from Section 2.1] 6. Filing basis: Intent-to-Use (1(b)) or Use-based (1(a)) 7. Verified statement (sworn under penalty of perjury) 8. Filing fee: $350 (if one class, standard character) ``` **Filing Basis Decision:** Choose **Intent-to-Use (1(b)):** ``` Filing Type: ITU - Intent to Use Advantage: Can file before officially launching product Cost: Must submit Statement of Use after approval (additional fee) Timeline: 6 months to show use in commerce (extendable to 3 years) Recommended: Yes - we're filing during development phase ``` Alternative **Use-Based (1(a)):** ``` Filing Type: Use - Currently in use in commerce Requirement: Must have actual use in commerce on date of filing Evidence needed: Specimens showing mark in actual use Recommended: Only if we're actively selling by filing date ``` #### **After Filing (Weeks 6-24)** **Weeks 6-12: Examining Attorney Review** - USPTO assigns examining attorney - 3-6 month review period typical - Possible office action with questions/concerns - Most common issues for design marks: relative clarity, disclaimer of non-functional matter **If Office Action Received:** - Respond within 6 months (extendable) - Provide clarifications - Address any refusals - May require iteration **Weeks 12-20: Publication Phase** (if no refusal) - Mark published in Official Gazette - 30-day opposition period - Any party can oppose registration **Weeks 20-24: Registration** (if no opposition) - Receive registration certificate - Full federal trademark protection - Can use ® symbol - Enforceable nationwide and internationally ### 8.3 Cost Comparison #### **Option 1: DIY Filing** ``` Component Cost ───────────────────────────────────── Logo design $0 (done in-house) Custom font modification $0 (done in-house) Trademark search (DIY) $0 (free on USPTO TESS) USPTO filing fee (1 class) $350 Additional surcharge (if needed) $0-400 Attorney review (optional) $500-1000 ───────────────────────────────────── Total: $350-1,750 ``` **Risks:** - No professional review of goods/services description - May miss issues requiring office action response - If refusal occurs, response will be DIY or requires attorney hire - Lower likelihood of smooth approval **Timeline:** - 1-2 weeks preparation - 1 week filing - 12-24 weeks to resolution - Total: 4-7 months #### **Option 2: LegalZoom** ``` Component Cost ───────────────────────────────────── Logo design $0 (done in-house) Custom font modification $0 (done in-house) Trademark search (professional) Included LegalZoom attorney service $899 USPTO filing fee (1 class) $350 Additional surcharge (if needed) $0-400 ───────────────────────────────────── Total: $1,249-1,649 ``` **Benefits:** - Professional trademark attorney review - Optimized goods/services description - Included office action response support - Higher likelihood of approval - 60-day satisfaction guarantee **Timeline:** - 1-2 weeks preparation - 1 week filing - 12-24 weeks to resolution - Total: 4-7 months (same as DIY, but with more support) **Recommendation:** **LegalZoom ($1,249)** is recommended because: 1. Professional review of mark distinctiveness 2. Optimized goods/services description 3. Support if office action is issued 4. Cost is reasonable relative to value of trademark protection 5. Reduces risk of refusal --- ## 9. Implementation Timeline ### 9.1 Project Phases #### **Phase 1: Design Foundation (Weeks 1-8)** **Week 1-3: Logo Concept & Iterations** - Establish binary infinity design - Create multiple variations - Test scaling and readability - Iterate with feedback **Week 3-5: Custom Font Development** - Select public domain base font - Begin modifications - Create custom "A" and "I" (binary-encoded) - Test rendering across platforms **Week 5-8: Integration & Refinement** - Integrate logo with custom font - Create complete brand system - Test multi-scale rendering - Create design specifications document **Deliverables:** - Master logo file (SVG) - Custom font files (TTF, WOFF2, etc.) - Brand style guide - Color specifications #### **Phase 2: Hidden Message Integration (Weeks 8-12)** **Week 8-9: Message Encoding** - Finalize message text - Create binary encoding of full message - Design embedding strategy - Create layer structure in logo files **Week 9-11: Implementation** - Embed binary strings in SVG source - Create hidden layers in design files - Test extraction and decoding - Document encoding method **Week 11-12: Preparation for Reveal** - Create decoding guide - Prepare announcement materials - Document in trademark filing materials - Archive original design files **Deliverables:** - Logo files with hidden message embedded - Decoding guide (for future release) - Technical documentation of encoding #### **Phase 3: Trademark Filing Preparation (Weeks 12-16)** **Week 12-13: Legal Research & Strategy** - Complete trademark search (TESS) - Research conflicting applications - Document findings - Finalize filing strategy (design mark) **Week 13-14: Documentation Preparation** - Write goods/services description - Prepare verified statement - Create logo representation image (PNG) - Gather all required information **Week 14-15: Attorney Engagement** (if using LegalZoom) - Provide all materials to attorney - Discuss goods/services description - Review and refine - Prepare for filing **Week 15-16: Final Review** - Verify all information is correct - Confirm filing authorization - Set filing date target **Deliverables:** - Completed filing package - All supporting documentation - Signed verified statement #### **Phase 4: Trademark Filing & Prosecution (Weeks 16-36)** **Week 16: Filing** - Submit application to USPTO - Receive filing confirmation - Document serial number - Begin monitoring **Weeks 16-24: Examining Attorney Review** - Monitor TSDR (Trademark Status & Document Retrieval) - Respond to any office actions within 6-month deadline - Provide clarifications if needed - Track status **Weeks 24-28: Publication** - Mark published in Official Gazette - 30-day opposition window opens - Monitor for oppositions - Respond if necessary **Weeks 28-36: Registration** - Assuming no opposition: receive registration certificate - Mark becomes federally registered - Enable ® symbol use - Update all materials **Deliverables:** - Trademark registration certificate - Updated brand guidelines (with ® symbol) - Proof of registration #### **Phase 5: Brand Materials & Launch (Weeks 20-36, parallel)** **Week 20-24: Website Integration** - Implement custom font on website - Deploy logo variations - Update brand guidelines on site - Test across browsers **Week 24-28: Marketing Materials** - Update business cards - Create logo lockups for various uses - Prepare social media graphics - Update all brand collateral **Week 28-32: Internal Rollout** - Train team on brand usage - Distribute brand guidelines - Update internal templates - Document in brand book **Week 32-36: Full Launch** - Public announcement of brand identity - Social media campaign - Press release (optional) - Update all public-facing materials **Deliverables:** - Updated website - Brand guidelines document - Marketing materials - Team training completion ### 9.2 Milestone-Based Roadmap ``` Timeline Overview: Week 0 ├─ Current state │ Weeks 1-8 ├─ Logo design & font development │ Weeks 8-16├─ Hidden message integration & trademark filing prep │ Week 16 ├─ FILE trademark application │ Weeks 16-36├─ USPTO examination + brand materials rollout │ Week 36 ├─ Trademark registration (estimated) │ Weeks 20-36├─ Website & marketing launch (parallel) │ Week 36+ ├─ Full brand launch │ Year 2-5+ ├─ Hidden message revelation at major milestone │ (IPO, $100M valuation, or 5-year anniversary) │ Milestone │ └─ Launch hidden message challenge │ └─ Decoder announced/awarded │ └─ Becomes part of company mythology ``` --- ## 10. Asset Management & Guidelines ### 10.1 Brand Asset Files #### **Required Files** ``` Logo Assets/ ├── aiconnected-logo-primary.svg ├── aiconnected-logo-primary.png (1000px) ├── aiconnected-logo-primary-dark.png ├── aiconnected-logo-symbol-only.svg ├── aiconnected-logo-symbol-only.png ├── aiconnected-logo-horizontal.svg ├── aiconnected-logo-stacked.svg ├── aiconnected-logo-with-tagline.svg └── aiconnected-logo-favicon.ico Font Assets/ ├── aiconnected-regular.ttf ├── aiconnected-regular.woff2 ├── aiconnected-bold.ttf ├── aiconnected-bold.woff2 ├── aiconnected-italic.ttf ├── aiconnected-italic.woff2 └── aiconnected-bolditalic.woff2 Documentation/ ├── aiconnected-brand-guidelines.pdf ├── aiconnected-color-palette.acs (Adobe Color Swatches) ├── aiconnected-design-specifications.md ├── trademark-registration-certificate.pdf └── hidden-message-documentation.md (internal only) ``` #### **File Version Control** ``` Version Control Standards: Master Files Location: - Secure cloud storage (Google Drive / OneDrive) - Never distributed directly - Only accessed by brand team Distribution Files: - Reduced-quality versions for public use - Hidden layers removed from SVG - Compressed PNGs for web Naming Convention: aiconnected-[element]-[version]-[status].ext Example: aiconnected-logo-2.0-final.svg aiconnected-logo-2.1-web-optimized.svg Change Log: Every file change documented with: - Version number - Date changed - What changed - Who approved change - Reason for change ``` ### 10.2 Brand Guidelines #### **Core Principles** 1. **Consistency** — Use approved files and specifications always 2. **Clarity** — Ensure proper spacing and sizing for readability 3. **Integrity** — Do not modify, stretch, or alter the design 4. **Respect** — The trademark is the company's protected property 5. **Evolution** — Guidelines may evolve; always use the latest approved version #### **Dos & Don'ts Summary** **DO:** - Use official logo files - Maintain clear space around logo - Use approved color palette - Test readability at intended size - Check trademark guidelines before use - Update materials when new versions released **DON'T:** - Create custom variations without approval - Stretch, skew, or distort logo - Change colors - Remove or obscure design elements - Use outdated versions - Combine logo with competing designs - Use on insufficient contrast backgrounds ### 10.3 International Considerations #### **Future International Filing** The design mark strategy supports international expansion: **Madrid Protocol Filing:** ``` Current Plan: US only (Class 42 SaaS services) Future Expansion (when revenue justifies): ├─ EU (via EUIPO) ├─ UK (post-Brexit) ├─ Canada ├─ Japan └─ Australia (common law protections) Cost per region: $200-500 additional filing fee Timeline: Can be filed after US registration established Strategy: Use Madrid Protocol for efficiency ``` **Language Considerations:** - Logo is language-agnostic (no text, just binary + symbol) - Custom font supports Latin-based alphabets - International version with localized text may be filed separately --- ## Conclusion ### Summary The aiConnected trademark strategy creates a **unique, defensible, visually distinctive brand** that: 1. **Avoids the earlier conflict** by using a design mark rather than plain text 2. **Creates genuine differentiation** through custom font and binary infinity symbol 3. **Communicates product philosophy** through visual design (recursion, fractals, hidden depth) 4. **Embeds founding mythology** through the hidden message at extreme zoom 5. **Supports scaling** from favicon to billboard without losing integrity 6. **Reflects company values** through the intersection of technology, philosophy, and design ### Next Steps 1. **Immediate (Weeks 1-8):** Complete logo design and custom font development 2. **Short-term (Weeks 8-16):** Finalize hidden message integration and prepare trademark filing 3. **Mid-term (Week 16+):** File trademark application with USPTO 4. **Long-term (Weeks 20-36+):** Integrate brand across all materials and await registration 5. **Future (Year 2-5+):** Reveal hidden message at major company milestone The trademark represents not just legal protection, but a thoughtful visual encoding of the company's core philosophy: that intelligence emerges from hidden patterns, that focus creates power, and that understanding requires both eyes and wisdom. --- ## Appendix: Quick Reference ### Filing Summary - **Mark Type:** Special Form Drawing (Design Mark) - **Class:** 42 (Computer and Scientific Services) - **Filing Basis:** Intent-to-Use (1(b)) or Use-Based (1(a)) - **Estimated Cost:** $1,249-1,750 (including LegalZoom) - **Timeline to Registration:** 6-12 months - **Conflict:** Suspended application (97419798) does not conflict due to visual distinctiveness ### Brand Assets - **Custom Logo:** Binary infinity symbol + custom typeface - **Custom Font:** Public domain base + significant modifications - **Color Palette:** #1e2328, #839aac, #2e95f3, #031c33, #021220 - **Typography:** Montserrat (secondary), DM Sans (tertiary) - **Hidden Message:** 4-part Masonic principle encoded at nano-scale ### Key Decision Points - Design mark (not word mark) ✓ - Custom font (full ownership, no licensing) ✓ - LegalZoom filing (professional review + support) ✓ - Hidden message (revealed at IPO/major milestone) ✓ - Multi-scale design (supports all uses) ✓ --- ## AiConnected Trademark And Patents **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-supporting-docs/aiConnected-trademark-and-patents **Description:** Documents in AiConnected Trademark And Patents. --- ## trademark filing preparation **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-supporting-docs/aiConnected-trademark-and-patents/trademark-filing-preparation **Description:** PRE FILING PREP ☐ Conduct a trademark search Search the USPTO database (tmsearch.uspto.gov) for \"aiConnected\" and similar marks Look for confusingly similar... ## **PRE-FILING PREP** **☐ Conduct a trademark search** - Search the USPTO database (tmsearch.uspto.gov) for "aiConnected" and similar marks - Look for confusingly similar names in Class 42 (software/AI services) - Check common law usage and domain registrations - *Cost: Free on USPTO site; detailed professional search ~$100-300* **☐ Decide your filing basis** (choose one) - ☐ **Use-based** — You're already using "aiConnected" in commerce (showing actual sales/licensing) - ☐ **Intent-to-use** — You plan to use it soon but aren't yet (more common for startups) **☐ Gather company information** - ☐ Your legal entity name (LLC, Corp, etc.) - ☐ State of incorporation/organization - ☐ Complete physical business address (not a PO Box) - ☐ Email address - ☐ If LLC/Corp: Citizenship of members/shareholders or state of incorporation --- ## **TRADEMARK DETAILS** **☐ Determine mark type** (choose one) - ☐ **Standard character mark** (plain text: "aiConnected") — *Recommended for your case* - ☐ **Special form mark** (logo, design, specific font/colors) **☐ Create a drawing of your mark** - If standard character: Just the text "aiConnected" - If logo: .jpg/.png file (clean, black-and-white preferred) **☐ Prepare a specimen** (proof of use — only if filing use-based) - Screenshot of your website showing "aiConnected" brand - Marketing materials with the trademark - Invoice or sales document with the mark - Product packaging or service description page - *For intent-to-use: You'll skip this now, submit later after approval* --- ## **GOODS & SERVICES DESCRIPTION** **☐ Identify your Class(es)** - ☐ **Class 42** — Software licensing; AI operating system services; cloud-based platform services for robotics and autonomous systems; artificial intelligence software development **☐ Use USPTO's Trademark ID Manual** (critical to avoid $100 insufficiency fee) - Go to: idm-tmng.uspto.gov/id-master-list-public.html - Search Class 42 for pre-approved descriptions matching your services - Copy exact language from the manual - *Alternative: Custom description = +$200 fee per class* **☐ Write accurate description** - Be specific, clear, and concise - Don't use marketing jargon - Must describe actual goods/services you use (or will use) the mark with - Under 1,000 characters per class (or pay $200 per additional 1,000 chars) **Example for aiConnected:** *"Software as a service (SaaS) in the field of artificial intelligence; Providing cloud-based platform for developing, managing, and deploying artificial intelligence applications; Software development services in the field of autonomous systems and robotics"* --- ## **ACCOUNT & FILING** **☐ Create USPTO.gov account** - Go to: trademark.gov or uspto.gov - Create account with verified identity (online verification ~15 min) - Multifactor authentication required - Link to Trademark Center system **☐ Access Trademark Center** - File through: trademarkcenter.uspto.gov - Upload all documents and information - Review for completeness before submission **☐ Verify all information is complete** - ☐ Owner/applicant name and details - ☐ Mark type and drawing - ☐ Goods/services description (from ID Manual if possible) - ☐ Filing basis selected - ☐ Specimen (if use-based) - ☐ Verified statement signed **☐ Submit and pay** - Pay $350 (base fee for Class 42) - *Possible extra fees: $100 (insufficient info) + $200 (custom description) if needed* - Receive filing receipt immediately (electronic confirmation) --- ## **AFTER FILING** **☐ Monitor your application** - Track status on Trademark Center or TSDR (Trademark Status & Document Retrieval) - Wait 3-6 months for USPTO examining attorney review - **If Office Action issued:** Respond within deadline (usually 6 months) or application abandons **☐ Publication phase** (if approved) - Your mark published in Trademark Official Gazette (weekly) - 30-day opposition period — competitors can challenge - If no opposition, trademark moves to registration **☐ Registration or next step** - **Use-based:** Trademark registers → Certificate issued - **Intent-to-use:** You receive Notice of Allowance → You have 6 months to file Statement of Use (proof you're using it) → Then registration --- ## **MAINTENANCE (After Registration)** **☐ Between years 5-6:** - File Section 8 (Declaration of Use) — $325 - File Section 9 (Renewal) — $325 - Combined: $650 if filed together **☐ Every 10 years:** - File Section 8 + 9 combined — $650 - Keep using the mark in commerce - *If you don't file: Registration cancels* --- ## **COMMON DIY MISTAKES TO AVOID** - ❌ Skipping the trademark search (wastes $350 if it's already registered) - ❌ Using marketing language instead of precise descriptions - ❌ Using a PO Box for business address - ❌ Forgetting the specimen for use-based applications - ❌ Missing USPTO examination deadlines - ❌ Failing to maintain the registration (years 5-6, then every 10 years) - ❌ Using custom descriptions without understanding the +$200 surcharge --- ## **REALISTIC DIY TIMELINE & COST** **Timeline:** - Application to approval: 3-6 months - Publication + opposition period: 3-4 months - Total to registration: 6-12 months (can be longer if office actions issued) **Cost (DIY):** - Trademark search: $0-300 - USPTO filing fee: $350 + possible surcharges ($0-400) - **Total: $350-$1,050** (versus $1,249 with LegalZoom) --- **The real question:** Is saving $200-900 worth the risk of mistakes that could cost you the $350 filing fee, or worse, a rejected application you have to re-file? LegalZoom's $899 mainly buys you attorney review to catch issues before submission. --- ## Data Flow Reference **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/data-flow-reference --- title: "Error" --- --- ## hyperthyme investor overview **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/hyperthyme-investor-overview **Description:** Neurigraph Hyperthyme Artificial Memory Framework Investor Overview By Oxford Pierpont The Simple Explanation Imagine you hired the smartest assistant in the... Neurigraph Hyperthyme Artificial Memory Framework Investor Overview By Oxford Pierpont The Simple Explanation Imagine you hired the smartest assistant in the world. They can write, research, analyze, create, and solve problems better than almost anyone. There's just one catch: every time you finish a conversation with them, they forget everything you discussed. Tomorrow, you have to start from scratch. They don't remember your name, your preferences, what you're working on, or anything you've ever told them. That's how artificial intelligence works today. Hyperthyme fixes this. It gives AI a permanent memory—one that never forgets, never loses information, and works across every conversation, forever. Why This Matters The Problem Everyone Experiences If you've ever used ChatGPT, Claude, or any AI assistant, you've experienced this frustration: You explain your project in detail, and the next day, the AI has no idea what you're talking about You tell the AI your preferences, but it keeps asking the same questions You share important documents, have a great conversation, and then it's all gone You can't pick up where you left off—you're always starting over This isn't a small annoyance. It's a fundamental limitation that makes AI far less useful than it could be. Why AI Forgets AI systems have what's called a "context window"—think of it as short-term memory. It can only hold so much information at once. Once a conversation gets too long, or once you close the chat, the AI loses access to everything. Some companies are trying to fix this by making the context window bigger. But that's like giving someone a slightly larger notepad—eventually, it still fills up. And it doesn't solve the problem of remembering things across different conversations. What People Actually Want People want AI that knows them. They want to say: "Remember that business idea we discussed last month? Let's pick that up." "You helped me with a document last week—can you find it?" "What did we decide about the marketing strategy?" And they want the AI to actually remember. What Hyperthyme Does Hyperthyme is a memory system that sits between the user and the AI. It does three things: 1. It Saves Everything Every conversation is automatically logged and stored. Not just a summary—the complete conversation, including any files or documents that were created or shared. Nothing is ever lost. 2. It Organizes Intelligently Conversations are organized by topic, by project, by date. The system understands what you were talking about and files it appropriately. When you ask about something later, it knows exactly where to look. 3. It Retrieves Instantly When you reference something from the past—whether it was yesterday or a year ago—Hyperthyme finds the relevant information and gives it to the AI. From your perspective, the AI simply "remembers." A Real Example Without Hyperthyme: You: "Hey, can you help me continue working on my app idea?" AI: "I don't have any information about an app idea. Could you tell me more about what you're building?" You: (frustrated) "We spent two hours on this last week..." With Hyperthyme: You: "Hey, can you help me continue working on my app idea?" AI: "Of course! Last week we outlined the fitness tracking app with the social accountability feature. We decided on React Native for the frontend and were working through the database design. Do you want to pick up where we left off on the user authentication flow?" The difference is night and day. The Market Opportunity AI Is Everywhere—But Memory Is Missing The AI industry is exploding. Hundreds of millions of people now use AI assistants regularly. Businesses are integrating AI into every part of their operations. The market is measured in hundreds of billions of dollars. But every major AI system shares the same limitation: they can't remember. OpenAI (ChatGPT): Has experimental memory features, but they're limited and don't preserve full conversations Anthropic (Claude): Same limitations Google (Gemini): Same limitations Every other AI company: Same limitations The first company to solve memory properly captures a foundational piece of AI infrastructure. Why This Is a Big Deal Memory isn't a feature—it's infrastructure. It's the difference between: AI as a tool you use occasionally AI as an assistant that truly knows you Every app, every business, every individual using AI would benefit from persistent memory. The potential market is essentially the entire AI market. Comparable Investment In October 2025, a company called Mem0 raised $24 million to build AI memory infrastructure. Their approach focuses on extracting and summarizing key facts from conversations. Hyperthyme takes a different approach: it preserves everything completely, so nothing is ever lost. This is a more robust, more reliable solution—and one that users actually want. Why This Team Oxford Pierpont brings a unique perspective to this problem: Deep understanding of how AI systems work and where they fail A track record of identifying market opportunities before they become obvious The technical vision to build something that doesn't exist yet A focus on practical, real-world usability rather than academic research This isn't a solution looking for a problem. This is a direct response to a frustration that millions of people experience every day. The Business Model Hyperthyme can generate revenue in multiple ways: For Developers (B2B) API access: Developers pay to integrate Hyperthyme memory into their own AI applications Usage-based pricing: Charge based on storage and retrieval volume Enterprise licenses: Large organizations pay for dedicated infrastructure For Consumers (B2C) Subscription model: Individuals pay monthly for persistent AI memory Freemium tier: Basic memory is free, advanced features require payment For AI Companies (Partnerships) Licensing deals: AI providers license Hyperthyme to enhance their own products White-label solutions: Other companies rebrand Hyperthyme as their own memory feature Why Companies Will Pay Memory is not optional for serious AI use. As AI becomes more integrated into work and life, the inability to remember becomes increasingly unacceptable. Companies will pay because their users demand it. What We're Building Phase 1: Core Memory System A working memory layer that can be integrated with any AI system. Users can save, search, and retrieve past conversations and files. Phase 2: Intelligent Organization Automatic categorization, relationship mapping between topics, and smart retrieval that understands what you're looking for even when you're vague. Phase 3: Universal Integration Works with every major AI platform—ChatGPT, Claude, Gemini, open-source models, and more. Your memory travels with you regardless of which AI you use. Phase 4: Defining Memories Beyond just storing conversations, the system identifies and highlights major decisions, milestones, and life events—creating a timeline of what matters most. The Ask We are raising capital to: Build the core product — Engineering team, infrastructure, development Establish intellectual property — Patents, trademarks, legal protection Go to market — Launch to developers and early adopters Scale — Expand infrastructure to handle growth The Bottom Line AI is transforming how people work, create, and live. But current AI has a fundamental flaw: it can't remember. Hyperthyme fixes this. We're not building a feature. We're building infrastructure that every AI system will eventually need. The question isn't whether AI will have persistent memory—it's who will build it. We intend to be that company. Neurigraph Hyperthyme Artificial Memory Framework By Oxford Pierpont Contact: [To be added] --- ## Neurigraph Hyperthyme Artificial Memory Framework **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/hyperthyme-junior-dev-guide **Description:** Junior Developer Guide By Oxford Pierpont What Is Hyperthyme? Hyperthyme is a memory system for AI. Right now, when you chat with an AI like ChatGPT or Claud... # Neurigraph Hyperthyme Artificial Memory Framework ## Junior Developer Guide **By Oxford Pierpont** --- ## What Is Hyperthyme? Hyperthyme is a memory system for AI. Right now, when you chat with an AI like ChatGPT or Claude, it forgets everything once the conversation ends. Hyperthyme solves this by creating a persistent memory layer that stores, organizes, and retrieves past conversations so the AI can "remember" what you've discussed—even months or years later. Think of it like this: the AI is the brain, and Hyperthyme is the long-term memory that the brain can access whenever it needs to recall something. The name comes from "hyperthymesia"—a rare condition where people remember every single day of their lives in perfect detail. We're building that capability for AI. --- ## The Problem We're Solving ### Context Windows Every AI model has a "context window"—the amount of text it can see at once. For example: - GPT-4 can see about 128,000 tokens (\~100,000 words) - Claude can see about 200,000 tokens (\~150,000 words) This seems like a lot, but it fills up fast. And once the conversation ends, it's gone. The AI has no way to access previous conversations. ### Current Solutions Are Incomplete Some companies offer basic memory features, but they typically: - Only store summaries (losing important details) - Compress information (losing exact wording, code, files) - Don't scale to thousands of conversations - Don't organize information intelligently Hyperthyme takes a different approach: store everything, organize it well, and retrieve only what's needed. --- ## How Hyperthyme Works: The Big Picture ``` ┌─────────────────────────────────────────────────────────────┐ │ USER │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ HYPERTHYME MIDDLEWARE │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │ │ │ Logger │ │ Retriever │ │ Context Injector │ │ │ │ │ │ │ │ │ │ │ │ Saves every │ │ Finds past │ │ Adds relevant │ │ │ │ conversation│ │ memories │ │ memories to prompt │ │ │ └─────────────┘ └─────────────┘ └─────────────────────┘ │ │ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ STORAGE LAYER │ │ │ │ │ │ │ │ Knowledge Graph ←→ RAG Database ←→ Recall Files │ │ │ └─────────────────────────────────────────────────────┘ │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ AI MODEL │ │ (Claude, GPT, Gemini, etc.) │ └─────────────────────────────────────────────────────────────┘ ``` The middleware sits between the user and the AI. It: 1. **Logs** every conversation as it happens 2. **Retrieves** relevant past information when needed 3. **Injects** that information into the AI's context so it can "remember" --- ## Core Components ### 1\. Recall Files The foundation of the system. A Recall File is a folder that contains a snapshot of a conversation segment. **When is a Recall File created?** Every \~50,000 tokens (roughly 35,000-40,000 words), the system creates a new Recall File. This threshold is chosen because: - It's small enough to fit in most AI context windows when retrieved - It's large enough that you don't create thousands of tiny files - It represents roughly 1-3 substantial conversations **What's inside a Recall File?** ``` recall-files/ └── ai-brain-memory-architecture-2025-01-11/ ├── summary.md # AI-generated summary of the conversation ├── keywords.txt # Extracted keywords for fast searching ├── transcript.md # Complete verbatim conversation log └── artifacts.zip # Any files created during this conversation ``` **File Breakdown:** | File | Purpose | Size | | :---- | :---- | :---- | | `summary.md` | Quick overview for search matching | Small (\~500-1000 words) | | `keywords.txt` | Exact-match search terms | Tiny (\~50-100 terms) | | `transcript.md` | Full source of truth | Large (\~50,000 tokens) | | `artifacts.zip` | Code, documents, images created | Variable | **Naming Convention:** ``` {topic-key-subject}-{YYYY-MM-DD}/ ``` Examples: - `funnelchat-stripe-integration-2025-01-03/` - `ai-brain-memory-architecture-2025-01-11/` - `marketing-strategy-q1-planning-2025-01-08/` ### 2\. Knowledge Graph The Knowledge Graph is a database that stores **relationships** between topics. Think of it as a map of everything the user has discussed. **What it stores:** - **Nodes**: Topics, projects, concepts, people, entities - **Edges**: Relationships between nodes **Example Structure:** ``` [AI Brain] ──contains──► [Memory System] │ │ │ ├──relates to──► [Hyperthyme] │ │ │ └──discussed in──► [recall-file-2025-01-11] │ ├──contains──► [Coherence Layer] │ └──contains──► [Storage System] ``` **Why it matters:** When the user asks about "the memory system," the Knowledge Graph instantly knows: - It's part of the AI Brain project - It relates to Hyperthyme - The relevant Recall Files are from January 2025 This narrows the search space from potentially millions of files to just a handful. **Technology options:** - Neo4j (most popular graph database) - Amazon Neptune - PostgreSQL with graph extensions - Lightweight: NetworkX (Python library) for prototyping ### 3\. RAG Database (Vector Store) RAG stands for "Retrieval-Augmented Generation." It's a technique where you: 1. Convert text into numerical vectors (embeddings) 2. Store those vectors in a specialized database 3. Search by finding vectors that are "similar" to a query **How it works in Hyperthyme:** The summaries from Recall Files are embedded and stored in a vector database. When the user asks a question, the question is also embedded, and we find summaries that are semantically similar. ``` User Query: "What was that thing about payment processing?" │ ▼ [Generate Embedding] │ ▼ [Search Vector DB] │ ▼ Matches: "funnelchat-stripe-integration-2025-01-03" "payment-gateway-comparison-2024-12-15" ``` **Why not just use keyword search?** Keyword search finds exact matches. RAG finds **semantic** matches. - Keyword search for "payment processing" won't find a document that only mentions "Stripe integration" - RAG understands that "payment processing" and "Stripe integration" are related concepts **Technology options:** - Pinecone (managed, easy to start) - Weaviate (open source) - Chroma (lightweight, good for prototyping) - pgvector (PostgreSQL extension) - Qdrant (open source, performant) ### 4\. Defining Memories Not all memories are equal. Some conversations are routine; others are significant. **Defining Memories** are flagged moments that represent: - Decisions ("I've decided to focus on the AI marketplace") - Milestones ("We launched the beta today") - Life events ("I'm starting a new job") - Turning points ("This changes everything") **How they're detected:** The system looks for trigger patterns in conversations: ```py DECISION_TRIGGERS = [ "I've decided", "We're going with", "I'm committing to", "Let's do", "Final decision:", ] MILESTONE_TRIGGERS = [ "We launched", "It's done", "I finished", "Completed", "Shipped", ] EVENT_TRIGGERS = [ "I'm starting", "I got the job", "We closed the deal", "I'm getting married", ] ``` **Defining Memory Structure:** ```json { "id": "dm-2025-01-11-001", "type": "decision", "date": "2025-01-11", "summary": "Committed to building Hyperthyme memory system", "context": "After discovering Mem0 raised $24M for a similar approach", "source_recall_file": "ai-brain-memory-architecture-2025-01-11/", "related_nodes": ["AI Brain", "Hyperthyme", "Memory System"], "tags": ["product", "commitment", "startup"] } ``` **Why separate Defining Memories?** When someone asks "When did I decide to start this project?" they don't want to search through 10,000 conversations. They want to hit the Defining Memory index and get an instant answer. Defining Memories are always "warm"—always in memory, always fast to access. --- ## The Search Cascade When the user asks something that requires memory, the system searches in layers: ``` ┌─────────────────────────────────────────────────────────────┐ │ QUERY: "What did we decide about the payment system?" │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ STEP 1: Knowledge Graph Navigation │ │ │ │ "payment system" → relates to → "funnelChat" project │ │ │ │ Result: Scope search to funnelChat-related Recall Files │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ STEP 2: Keyword Search │ │ │ │ Search keywords.txt files for: "payment", "stripe", "billing"│ │ │ │ Result: 3 Recall Files match │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ STEP 3: RAG Search on Summaries │ │ │ │ Embed query, find similar summaries │ │ │ │ Result: Ranked list of most relevant Recall Files │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ STEP 4: Load Transcript │ │ │ │ Read full transcript.md from top-ranked Recall File │ │ │ │ Result: Complete context available │ └─────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ STEP 5: Check Defining Memories │ │ │ │ Were there any decisions about payment systems? │ │ │ │ Result: "On Jan 3, decided to use Stripe Connect" │ └─────────────────────────────────────────────────────────────┘ ``` This cascade is fast because each step narrows the search space: - Knowledge Graph: Millions of files → Thousands (scoped to project) - Keywords: Thousands → Hundreds (exact matches) - RAG: Hundreds → Tens (semantic relevance) - Transcript: Load only what's needed --- ## Storage States: Hot, Warm, Cold Not all memories need to be instantly accessible. Hyperthyme uses a tiered storage system: ### Hot (Active) - Current conversation - Currently loaded Recall Files - Uncompressed, in working memory ### Warm (Recent) - Accessed in the last 7 days - Same project/node as current conversation - Uncompressed, ready to read ### Cold (Long-term) - Not accessed in 7+ days - Artifacts are compressed (zipped) - Keywords and summaries still indexed - Takes slightly longer to retrieve **Warming Process:** When the user starts discussing a topic, the system "warms" related memories: ```py def warm_node(node_id): """ When a topic is touched, warm all related Recall Files """ # Get all Recall Files linked to this node recall_files = knowledge_graph.get_files_for_node(node_id) for file in recall_files: if file.is_cold(): # Decompress artifacts file.decompress_artifacts() # Pre-load transcript into cache file.cache_transcript() # Mark as warm file.set_state("warm") ``` This is **predictive retrieval**—if you're asking about the AI Brain project, you'll probably ask more AI Brain questions, so we prepare. --- ## Making It Model-Agnostic Hyperthyme works with any AI model. Here's how: ### The Middleware Pattern Hyperthyme doesn't modify the AI. It wraps around it: ```py class HyperthymeMiddleware: def __init__(self, ai_client, memory_store): self.ai = ai_client # Could be OpenAI, Anthropic, Google, etc. self.memory = memory_store def chat(self, user_message, user_id): # 1. Search for relevant memories relevant_memories = self.memory.search( query=user_message, user_id=user_id ) # 2. Build enhanced prompt with memories enhanced_prompt = self.inject_memories( user_message, relevant_memories ) # 3. Send to AI (any model works here) response = self.ai.generate(enhanced_prompt) # 4. Log the conversation self.memory.log(user_message, response, user_id) return response def inject_memories(self, message, memories): memory_context = "\n".join([ f"[From {m.date}]: {m.summary}" for m in memories ]) return f""" Relevant context from past conversations: {memory_context} Current message: {message} """ ``` ### Swapping Models Because the middleware handles memory separately, you can swap AI models without losing memory: ```py # Using Claude claude_client = AnthropicClient(api_key="...") hyperthyme = HyperthymeMiddleware(claude_client, memory_store) # Switch to GPT—memory stays the same openai_client = OpenAIClient(api_key="...") hyperthyme = HyperthymeMiddleware(openai_client, memory_store) ``` ### MCP (Model Context Protocol) MCP is an emerging standard that lets AI models call external tools. Hyperthyme can be exposed as an MCP server: ```py @mcp_tool("search_memory") def search_memory(query: str, user_id: str) -> list: """Search user's conversation history""" return memory_store.search(query, user_id) @mcp_tool("get_defining_memories") def get_defining_memories(user_id: str) -> list: """Get user's major decisions and milestones""" return memory_store.get_defining_memories(user_id) ``` Now any MCP-compatible AI can access Hyperthyme memory directly. --- ## Database Schema (Simplified) Here's a starting point for the database design: ### recall\_files ```sql CREATE TABLE recall_files ( id UUID PRIMARY KEY, user_id UUID NOT NULL, folder_name VARCHAR(255) NOT NULL, topic VARCHAR(255), created_at TIMESTAMP NOT NULL, updated_at TIMESTAMP NOT NULL, token_count INTEGER, state VARCHAR(20) DEFAULT 'warm', -- 'hot', 'warm', 'cold' summary_path TEXT, transcript_path TEXT, keywords_path TEXT, artifacts_path TEXT ); ``` ### knowledge\_graph\_nodes ```sql CREATE TABLE knowledge_graph_nodes ( id UUID PRIMARY KEY, user_id UUID NOT NULL, name VARCHAR(255) NOT NULL, node_type VARCHAR(50), -- 'project', 'topic', 'person', 'concept' created_at TIMESTAMP NOT NULL, last_accessed TIMESTAMP ); ``` ### knowledge\_graph\_edges ```sql CREATE TABLE knowledge_graph_edges ( id UUID PRIMARY KEY, source_node_id UUID REFERENCES knowledge_graph_nodes(id), target_node_id UUID REFERENCES knowledge_graph_nodes(id), relationship VARCHAR(100), -- 'contains', 'relates_to', 'discussed_in' created_at TIMESTAMP NOT NULL ); ``` ### recall\_file\_nodes (junction table) ```sql CREATE TABLE recall_file_nodes ( recall_file_id UUID REFERENCES recall_files(id), node_id UUID REFERENCES knowledge_graph_nodes(id), PRIMARY KEY (recall_file_id, node_id) ); ``` ### defining\_memories ```sql CREATE TABLE defining_memories ( id UUID PRIMARY KEY, user_id UUID NOT NULL, memory_type VARCHAR(50), -- 'decision', 'milestone', 'event', 'turning_point' summary TEXT NOT NULL, context TEXT, detected_at TIMESTAMP NOT NULL, source_recall_file_id UUID REFERENCES recall_files(id), tags TEXT[] -- Array of tags ); ``` ### summary\_embeddings ```sql -- For vector search (using pgvector) CREATE TABLE summary_embeddings ( id UUID PRIMARY KEY, recall_file_id UUID REFERENCES recall_files(id), embedding vector(1536), -- OpenAI embedding size created_at TIMESTAMP NOT NULL ); -- Create index for fast similarity search CREATE INDEX ON summary_embeddings USING ivfflat (embedding vector_cosine_ops); ``` --- ## Technology Stack Recommendations ### For Prototyping (MVP) | Component | Recommendation | Why | | :---- | :---- | :---- | | Language | Python | Fastest for AI development | | Database | PostgreSQL \+ pgvector | One database for everything | | File Storage | Local filesystem | Simple, no cloud dependency | | Vector Search | pgvector | Integrated with main DB | | Knowledge Graph | NetworkX (in-memory) | Fast prototyping | | AI Integration | LangChain or direct API | Flexibility | | API Framework | FastAPI | Modern, async, automatic docs | ### For Production | Component | Recommendation | Why | | :---- | :---- | :---- | | Language | Python \+ Go for performance-critical | Balance of speed and AI ecosystem | | Database | PostgreSQL (primary) | Battle-tested, scalable | | File Storage | S3 or equivalent | Scalable, cheap | | Vector Search | Pinecone or Weaviate | Purpose-built, performant | | Knowledge Graph | Neo4j | Industry standard | | Caching | Redis | Fast warming/hot storage | | API Framework | FastAPI behind Kong/Nginx | Production-ready | | Orchestration | Kubernetes | Scalability | --- ## Getting Started: Your First Task If you're building this, here's what to tackle first: ### Week 1: Basic Recall File Creation ```py # Goal: Create Recall Files from conversations def create_recall_file(conversation, user_id): # 1. Generate folder name folder_name = generate_folder_name(conversation) # 2. Save transcript save_transcript(folder_name, conversation) # 3. Generate and save summary (using AI) summary = generate_summary(conversation) save_summary(folder_name, summary) # 4. Extract and save keywords keywords = extract_keywords(conversation) save_keywords(folder_name, keywords) # 5. Register in database register_recall_file(folder_name, user_id) ``` ### Week 2: Basic Search ```py # Goal: Find relevant Recall Files def search_memory(query, user_id): # 1. Keyword search keyword_matches = search_keywords(query, user_id) # 2. Return matching Recall Files return load_recall_files(keyword_matches) ``` ### Week 3: RAG Integration ```py # Goal: Add semantic search def search_memory_with_rag(query, user_id): # 1. Embed the query query_embedding = embed_text(query) # 2. Find similar summaries matches = vector_db.search(query_embedding, user_id) # 3. Load and return return load_recall_files(matches) ``` ### Week 4: Knowledge Graph ```py # Goal: Add topic-based navigation def search_memory_with_graph(query, user_id): # 1. Identify relevant nodes nodes = knowledge_graph.find_nodes(query, user_id) # 2. Get Recall Files for those nodes recall_files = [] for node in nodes: recall_files.extend(node.get_recall_files()) # 3. Rank and return return rank_by_relevance(recall_files, query) ``` --- ## Common Pitfalls to Avoid ### 1\. Storing Too Much in Memory Don't try to keep all transcripts in RAM. Use the hot/warm/cold system. Only load what's needed. ### 2\. Ignoring Token Limits When injecting memories into prompts, count tokens. Don't overflow the AI's context window. ```py def inject_memories(message, memories, max_tokens=4000): injected = [] token_count = 0 for memory in memories: memory_tokens = count_tokens(memory.summary) if token_count + memory_tokens > max_tokens: break injected.append(memory) token_count += memory_tokens return injected ``` ### 3\. Not Handling Multiple Users Always scope queries by user\_id. Never let one user's memories leak to another. ### 4\. Synchronous Everything Recall File creation, embedding generation, and cold storage compression should be async/background jobs. Don't block the user. ### 5\. No Backup Strategy Memories are valuable. Implement backups from day one. --- ## Summary Hyperthyme is a memory layer for AI consisting of: 1. **Recall Files** — Complete conversation snapshots with summaries, keywords, transcripts, and artifacts 2. **Knowledge Graph** — Relationship map between topics for fast navigation 3. **RAG Database** — Semantic search over summaries 4. **Defining Memories** — Index of major decisions and milestones 5. **Middleware** — Model-agnostic layer that handles logging and retrieval The system uses a search cascade (Graph → Keywords → RAG → Transcript) to efficiently find relevant memories, and a tiered storage system (Hot → Warm → Cold) to balance speed and cost. Start simple. Build the Recall File system first. Add intelligence layer by layer. --- **Neurigraph Hyperthyme Artificial Memory Framework** *By Oxford Pierpont* --- ## Neurigraph Hyperthyme Artificial Memory Framework by Oxford Pierpont **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/hyperthyme-master-project-checklist **Description:** Master Project Checklist Project: Hyperthyme — Persistent Memory Layer for AI Systems Goal: Build a fundable product with comprehensive documentation, workin... # Neurigraph Hyperthyme Artificial Memory Framework by Oxford Pierpont ## Master Project Checklist **Project:** Hyperthyme — Persistent Memory Layer for AI Systems **Goal:** Build a fundable product with comprehensive documentation, working prototype, and investment-ready materials **Created:** January 11, 2025 --- ## Phase 0: Foundation (Week 1-2) ### Project Setup - [ ] Create dedicated project repository (GitHub or GitLab) - [ ] Set up project management tool (Linear, Notion, or GitHub Projects) - [ ] Establish folder structure for all documentation - [ ] Create README.md with project overview - [ ] Set up version control and branching strategy - [ ] Register domain name(s) for project ### Core Documentation - [ ] **Technical Architecture Document (TAD)** — Complete system design - [ ] **Product Requirements Document (PRD)** — Features, user stories, specifications - [ ] **Glossary of Terms** — Define all project-specific terminology - [ ] **Design Philosophy Document** — Why Recall exists, core principles --- ## Phase 1: Technical Documentation (Week 2-4) ### System Architecture - [ ] High-level architecture diagram - [ ] Data flow diagrams - [ ] Component interaction maps - [ ] Infrastructure requirements document ### Memory System Specifications - [ ] **Recall File Structure Spec** — Folder structure, file formats, naming conventions - [ ] **Token Threshold Configuration** — 50K token trigger logic - [ ] **Summary Generation Spec** — How summaries are created, what they contain - [ ] **Keyword Extraction Spec** — Extraction methodology, storage format - [ ] **Artifact Handling Spec** — Compression, storage, retrieval of companion files ### Defining Memory Specifications - [ ] **Defining Memory Detection Spec** — Trigger patterns, classification logic - [ ] **Defining Memory Schema** — Data structure for milestone/event memories - [ ] **Defining Memory Index Spec** — How the always-warm index is maintained ### Retrieval System Specifications - [ ] **Knowledge Graph Integration Spec** — Node structure, relationships, navigation - [ ] **RAG Database Spec** — Embedding strategy, vector storage, search methodology - [ ] **Search Cascade Logic** — Keywords → Summary → Transcript → Artifacts - [ ] **Node Warming Spec** — How nodes transition between hot/warm/cold states - [ ] **Cross-Project Search Spec** — How global search differs from scoped search ### Storage & Lifecycle - [ ] **Cold Storage Spec** — Compression triggers, 7-day rule, background processes - [ ] **Warming Process Spec** — How related memories are pre-loaded - [ ] **Storage Estimation Model** — Projected storage needs at scale ### Model Agnostic Layer - [ ] **Middleware API Spec** — Endpoints, request/response formats - [ ] **MCP Integration Spec** — How Recall exposes tools via Model Context Protocol - [ ] **Provider Abstraction Spec** — How to swap between Claude/GPT/Gemini/etc. ### Database Design - [ ] Complete database schema (PostgreSQL recommended) - [ ] Entity-relationship diagrams - [ ] Index strategy document - [ ] Migration scripts template ### API Documentation - [ ] RESTful API specification (OpenAPI/Swagger) - [ ] Authentication and authorization spec - [ ] Rate limiting and quota management - [ ] Webhook specifications for integrations - [ ] SDK design document (Python, JavaScript) --- ## Phase 2: Business Documentation (Week 4-6) ### Market Research - [ ] **Competitive Analysis** — Mem0, Zep, Graphiti, LangChain memory, etc. - [ ] **Market Size Estimation** — TAM, SAM, SOM calculations - [ ] **Target Customer Profiles** — Who buys this and why - [ ] **Pricing Research** — What competitors charge, willingness to pay ### Business Model - [ ] **Monetization Strategy Document** — Pricing tiers, revenue model - [ ] **Unit Economics Model** — CAC, LTV, margins, break-even analysis - [ ] **Financial Projections** — 3-year revenue/expense model (spreadsheet) - [ ] **Go-to-Market Strategy** — Launch plan, channels, partnerships ### Differentiation - [ ] **Unique Value Proposition (UVP) Document** — Why Recall vs. alternatives - [ ] **Positioning Statement** — One paragraph that captures the essence - [ ] **Competitive Moat Analysis** — What's defensible long-term --- ## Phase 3: Legal & Compliance (Week 5-7) ### Intellectual Property - [ ] Provisional patent application (if applicable) - [ ] Trademark search for "Recall" name - [ ] Trademark application filing - [ ] Document all proprietary methodologies ### Legal Structure - [ ] Choose business entity type (LLC, C-Corp, etc.) - [ ] Incorporate the company - [ ] Draft founder agreements (if multiple founders) - [ ] Establish equity structure and vesting schedules - [ ] Create IP assignment agreements ### Compliance Documentation - [ ] **Privacy Policy** — GDPR, CCPA compliant - [ ] **Terms of Service** — Platform usage terms - [ ] **Data Processing Agreement (DPA)** — For enterprise customers - [ ] **Security Whitepaper** — How user data is protected - [ ] **SOC 2 Roadmap** — Plan for eventual compliance certification ### Data Handling - [ ] Data retention policy - [ ] Data deletion procedures (right to be forgotten) - [ ] Encryption standards document - [ ] Backup and disaster recovery plan --- ## Phase 4: Product Development (Week 6-14) ### MVP Definition - [ ] **MVP Scope Document** — Exactly what's in v0.1, what's not - [ ] **User Stories** — Detailed stories for MVP features - [ ] **Acceptance Criteria** — How to know each feature is "done" ### Development Milestones #### Milestone 1: Core Storage (Week 6-8) - [ ] Recall file creation (transcript \+ summary \+ keywords) - [ ] Token counting and threshold triggers - [ ] Basic folder structure and naming - [ ] Manual search functionality #### Milestone 2: Retrieval System (Week 8-10) - [ ] Keyword search implementation - [ ] Summary RAG integration - [ ] Search cascade logic - [ ] Basic API endpoints #### Milestone 3: Knowledge Graph (Week 10-12) - [ ] Node creation and relationship mapping - [ ] Navigation/scoping logic - [ ] Node warming implementation - [ ] Cross-project search #### Milestone 4: Defining Memories (Week 12-13) - [ ] Detection triggers - [ ] Defining memory index - [ ] Timeline/milestone view - [ ] Linking to source recall files #### Milestone 5: Polish & Integration (Week 13-14) - [ ] Cold storage compression - [ ] MCP server implementation - [ ] Multi-model testing (Claude, GPT, Gemini) - [ ] Basic web dashboard ### Quality Assurance - [ ] Unit test suite - [ ] Integration test suite - [ ] Performance benchmarks - [ ] Security audit checklist - [ ] Load testing plan --- ## Phase 5: Fundraising Materials (Week 12-16) ### Pitch Deck - [ ] **Investor Pitch Deck** — 10-15 slides covering: - [ ] Problem statement - [ ] Solution overview - [ ] Market opportunity - [ ] Product demo/screenshots - [ ] Business model - [ ] Traction/validation - [ ] Competitive landscape - [ ] Team - [ ] Financial projections - [ ] Ask and use of funds ### Supporting Documents - [ ] **Executive Summary** — 1-2 page overview for cold outreach - [ ] **One-Pager** — Single page with key highlights - [ ] **Detailed Financial Model** — Spreadsheet with assumptions - [ ] **Product Demo Video** — 2-3 minute walkthrough - [ ] **Technical Deep-Dive Deck** — For technical due diligence ### Data Room Preparation - [ ] Organize all documents in secure data room (DocSend, Google Drive, Notion) - [ ] Cap table (current and pro-forma) - [ ] Incorporation documents - [ ] Any existing contracts or LOIs - [ ] Team bios and LinkedIn profiles - [ ] Technical architecture documents - [ ] Financial projections with assumptions ### Fundraising Logistics - [ ] Target investor list (angels, pre-seed funds, AI-focused VCs) - [ ] Warm introduction tracking spreadsheet - [ ] Email templates for outreach - [ ] FAQ document for common investor questions - [ ] SAFE or convertible note terms prepared --- ## Phase 6: Launch Preparation (Week 14-18) ### Marketing Materials - [ ] Landing page (coming soon / waitlist) - [ ] Product marketing website - [ ] Blog post: "Why We Built Recall" - [ ] Technical blog post: "The Architecture Behind Recall" - [ ] Social media accounts (Twitter/X, LinkedIn) - [ ] Press kit with logos, screenshots, boilerplate ### Community Building - [ ] Discord or Slack community setup - [ ] GitHub discussions enabled - [ ] Documentation site (GitBook, Docusaurus, or similar) - [ ] Developer onboarding guide ### Launch Strategy - [ ] Launch timeline with milestones - [ ] Beta user recruitment plan - [ ] Product Hunt launch preparation - [ ] Hacker News launch post draft - [ ] Influencer/developer outreach list ### Metrics & Analytics - [ ] Define key metrics (MAU, retention, API calls, etc.) - [ ] Analytics implementation plan - [ ] Dashboard for tracking metrics - [ ] Feedback collection system --- ## Phase 7: Post-Launch & Growth (Ongoing) ### Operations - [ ] Customer support system - [ ] Bug tracking and triage process - [ ] Feature request tracking - [ ] Changelog and release notes process ### Iteration - [ ] User feedback synthesis process - [ ] Roadmap prioritization framework - [ ] A/B testing infrastructure - [ ] Performance monitoring and alerting ### Scaling Preparation - [ ] Hiring plan document - [ ] Onboarding documentation for new team members - [ ] Infrastructure scaling playbook - [ ] Enterprise sales playbook (when ready) --- ## Key Documents Summary ### Technical (Must Have for MVP) 1. Technical Architecture Document 2. Product Requirements Document 3. Database Schema 4. API Specification 5. Recall File Structure Spec 6. Defining Memory Spec 7. Retrieval System Spec ### Business (Must Have for Fundraising) 1. Pitch Deck 2. Executive Summary 3. Financial Model 4. Competitive Analysis 5. Go-to-Market Strategy ### Legal (Must Have Before Launch) 1. Privacy Policy 2. Terms of Service 3. Company Incorporation 4. Trademark Filing --- ## Suggested Timeline | Phase | Duration | Focus | | :---- | :---- | :---- | | Phase 0 | Week 1-2 | Foundation & Setup | | Phase 1 | Week 2-4 | Technical Documentation | | Phase 2 | Week 4-6 | Business Documentation | | Phase 3 | Week 5-7 | Legal & Compliance | | Phase 4 | Week 6-14 | Product Development | | Phase 5 | Week 12-16 | Fundraising Materials | | Phase 6 | Week 14-18 | Launch Preparation | | Phase 7 | Week 18+ | Post-Launch Growth | **Total Time to Fundable State:** \~16-18 weeks (4-5 months) **Total Time to Public Launch:** \~18-20 weeks (5 months) --- ## Next Immediate Actions - [ ] Finish Technical Architecture Document (today) - [ ] Create GitHub repository (this week) - [ ] Register domain name (this week) - [ ] Begin MVP development (next week) - [ ] Draft pitch deck outline (within 2 weeks) --- *This checklist is a living document. Update as items are completed and new requirements emerge.* --- ## Hyperthyme Technical Architecture Document (TAD) **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/hyperthyme-technical-architecture **Description:** Created: January 2026 Status: Draft Part of the Neurigraph Product Family What's Included: Section Content : : 1. Document Overview Purpose,... # **Hyperthyme Technical Architecture Document (TAD)** **Version:** 1.0 **Author:** Oxford Pierpont **Created:** January 2026 **Status:** Draft **Part of the Neurigraph Product Family** --- ## What's Included: | Section | Content | | :---- | :---- | | 1\. Document Overview | Purpose, scope, audience, definitions | | 2\. System Purpose & Scope | Problem statement, solution, design philosophy, boundaries | | 3\. Architecture Overview | High-level diagrams, component summary, data flows | | 4\. Component Specifications | API Gateway, Middleware, Logger, Retriever, Injector, KG Manager, Defining Memory Detector | | 5\. Data Models & Schema | Complete PostgreSQL schema, Recall File structure, Python dataclasses | | 6\. APIs & Interfaces | REST API spec, MCP server implementation, SDK examples | | 7\. Retrieval Pipeline | 5-stage cascade with code, performance optimization, caching | | 8\. Storage Management | Hot/Warm/Cold tiers, state transitions, file layout, storage estimates | | 9\. Security & Privacy | Auth, encryption, data isolation, audit logging, deletion | | 10\. Performance Requirements | Latency/throughput targets, availability, resource budgets | | 11\. Deployment Architecture | Infrastructure diagrams, Docker, Kubernetes configs | | 12\. Integration Patterns | Direct API, LangChain, MCP, webhooks | | 13\. Error Handling & Recovery | Error categories, retry logic, circuit breakers, data recovery | | 14\. Monitoring & Observability | Prometheus metrics, structured logging, tracing, alerting | | 15\. Future Considerations | Roadmap, migration, scalability path | ## ## Table of Contents 1. [Document Overview](#1-document-overview) 2. [System Purpose & Scope](#2-system-purpose--scope) 3. [Architecture Overview](#3-architecture-overview) 4. [Component Specifications](#4-component-specifications) 5. [Data Models & Schema](#5-data-models--schema) 6. [APIs & Interfaces](#6-apis--interfaces) 7. [Retrieval Pipeline](#7-retrieval-pipeline) 8. [Storage Management](#8-storage-management) 9. [Security & Privacy](#9-security--privacy) 10. [Performance Requirements](#10-performance-requirements) 11. [Deployment Architecture](#11-deployment-architecture) 12. [Integration Patterns](#12-integration-patterns) 13. [Error Handling & Recovery](#13-error-handling--recovery) 14. [Monitoring & Observability](#14-monitoring--observability) 15. [Future Considerations](#15-future-considerations) --- ## 1\. Document Overview ### 1.1 Purpose This Technical Architecture Document (TAD) defines the complete system design for Hyperthyme, a persistent memory layer for AI systems. It provides the technical foundation required for implementation, serving as the authoritative reference for all development decisions. ### 1.2 Scope This document covers: - System architecture and component design - Data models and storage strategies - API specifications and integration patterns - Performance, security, and operational requirements This document does NOT cover: - Business requirements (see PRD) - User interface design - Marketing or go-to-market strategy - The broader Neurigraph ecosystem (Cognigraph, etc.) ### 1.3 Audience - Software engineers implementing the system - DevOps engineers deploying and operating the system - Technical architects reviewing the design - Integration partners building on the platform ### 1.4 Definitions | Term | Definition | | :---- | :---- | | **Recall File** | A folder containing a complete conversation segment (\~50K tokens) with summary, keywords, transcript, and artifacts | | **Knowledge Graph** | A graph database storing relationships between topics, projects, and Recall Files | | **RAG** | Retrieval-Augmented Generation \- using vector similarity to find relevant content | | **Defining Memory** | A flagged moment representing a decision, milestone, or significant event | | **Hot/Warm/Cold** | Storage tiers based on access recency and retrieval speed requirements | | **Middleware** | The Hyperthyme layer that sits between applications and AI models | --- ## 2\. System Purpose & Scope ### 2.1 Problem Statement Current AI systems (LLMs) operate statelessly. They have no persistent memory across sessions. Users must re-explain context repeatedly, and valuable conversation history is lost. ### 2.2 Solution Hyperthyme provides a persistent memory layer that: 1. **Archives** complete conversations verbatim 2. **Organizes** content via hierarchical knowledge graph 3. **Indexes** content for fast semantic and keyword retrieval 4. **Retrieves** relevant context and injects it into AI prompts 5. **Preserves** significant moments as Defining Memories ### 2.3 Design Philosophy **Principle 1: Summaries are indexes, not storage** - We never discard original content in favor of summaries - Summaries enable fast search; transcripts provide full context **Principle 2: Navigate first, search second** - Knowledge Graph narrows search space before vector search - This maintains performance at scale (millions of Recall Files) **Principle 3: Preserve everything, retrieve selectively** - Storage is cheap; token context is expensive - Store complete archives; inject only what's relevant **Principle 4: Model agnostic** - Works with any LLM (Claude, GPT, Gemini, open-source) - Memory persists even when switching models ### 2.4 System Boundaries **In Scope:** - Conversation logging and archival - Knowledge graph management - Vector and keyword indexing - Memory retrieval and context injection - Defining Memory detection and indexing - Storage lifecycle management - API for integration **Out of Scope:** - The AI model itself (Hyperthyme wraps around it) - User interface (provided by integrating applications) - Real-time collaboration features - Training or fine-tuning AI models --- ## 3\. Architecture Overview ### 3.1 High-Level Architecture ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ CLIENT APPLICATIONS │ │ (Chat apps, IDEs, Voice assistants, etc.) │ └─────────────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────────┐ │ HYPERTHYME API GATEWAY │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ REST │ │ GraphQL │ │ MCP │ │ WebSocket │ │ │ │ Endpoints │ │ Endpoints │ │ Server │ │ (Stream) │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ └─────────────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────────┐ │ HYPERTHYME CORE ENGINE │ │ │ │ ┌───────────────────────────────────────────────────────────────────┐ │ │ │ MIDDLEWARE ORCHESTRATOR │ │ │ │ │ │ │ │ • Request routing • Context assembly │ │ │ │ • User session management • Token budget management │ │ │ │ • Logging coordination • Response handling │ │ │ └───────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ┌────────────────────────┼────────────────────────┐ │ │ ▼ ▼ ▼ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ LOGGER │ │ RETRIEVER │ │ INJECTOR │ │ │ │ │ │ │ │ │ │ │ │ • Capture │ │ • Search │ │ • Build │ │ │ │ • Parse │ │ • Rank │ │ • Format │ │ │ │ • Store │ │ • Expand │ │ • Inject │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ │ │ │ └─────────┼────────────────────────┼────────────────────────┼────────────┘ │ │ │ ▼ ▼ ▼ ┌─────────────────────────────────────────────────────────────────────────┐ │ DATA LAYER │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Knowledge │ │ RAG │ │ Recall │ │ Defining │ │ │ │ Graph │ │ (Vectors) │ │ Files │ │ Memories │ │ │ │ │ │ │ │ │ │ │ │ │ │ Neo4j / │ │ pgvector / │ │ S3 / Local │ │ PostgreSQL │ │ │ │ PostgreSQL │ │ Pinecone │ │ Filesystem │ │ │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────────┐ │ AI MODEL LAYER │ │ │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ │ │ Claude │ │ GPT │ │ Gemini │ │ Local │ │ │ │ API │ │ API │ │ API │ │ (Ollama)│ │ │ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────┘ ``` ### 3.2 Component Summary | Component | Responsibility | Technology Options | | :---- | :---- | :---- | | API Gateway | Request routing, auth, rate limiting | Kong, Nginx, custom FastAPI | | Middleware Orchestrator | Coordinates logging, retrieval, injection | Python (FastAPI) | | Logger | Captures and stores conversations | Python async workers | | Retriever | Finds relevant memories | Python with graph/vector clients | | Injector | Builds context-enhanced prompts | Python | | Knowledge Graph | Topic/project relationships | Neo4j, PostgreSQL with ltree | | RAG (Vector Store) | Semantic similarity search | pgvector, Pinecone, Qdrant | | Recall Files | Complete conversation archives | S3, local filesystem | | Defining Memories | Significant moment index | PostgreSQL | ### 3.3 Data Flow **Write Path (Logging):** ``` User Message → API Gateway → Middleware → Logger │ ┌──────────────────────┼──────────────────────┐ ▼ ▼ ▼ Append to Update KG with Check for active Recall new entities Defining Memory File transcript mentioned triggers │ │ │ └──────────────────────┴──────────────────────┘ │ ▼ If threshold reached (50K tokens): • Finalize Recall File • Generate summary • Extract keywords • Create embeddings • Start new Recall File ``` **Read Path (Retrieval):** ``` User Query → API Gateway → Middleware → Retriever │ ┌──────────────────────┴──────────────────────┐ ▼ ▼ Knowledge Graph Defining Memory Navigation Index Check │ │ ▼ │ Keyword Search │ on candidates │ │ │ ▼ │ RAG Search on │ summaries │ │ │ ▼ │ Load transcripts │ from top matches │ │ │ └──────────────────────┬──────────────────────┘ │ ▼ Injector builds context package │ ▼ Send to AI Model with injected context ``` --- ## 4\. Component Specifications ### 4.1 API Gateway **Purpose:** Single entry point for all client requests. **Responsibilities:** - Request authentication and authorization - Rate limiting per user/tenant - Request routing to appropriate handlers - SSL/TLS termination - Request/response logging - API versioning **Endpoints:** | Endpoint | Method | Purpose | | :---- | :---- | :---- | | `/v1/chat` | POST | Send message with memory-augmented context | | `/v1/search` | POST | Search memory without sending to AI | | `/v1/recall-files` | GET | List user's Recall Files | | `/v1/recall-files/{id}` | GET | Get specific Recall File content | | `/v1/defining-memories` | GET | List user's Defining Memories | | `/v1/graph/nodes` | GET | Query Knowledge Graph nodes | | `/v1/graph/nodes` | POST | Create new node | | `/v1/health` | GET | System health check | **Configuration:** ``` api_gateway: host: 0.0.0.0 port: 8000 rate_limit: requests_per_minute: 60 burst: 10 timeout_seconds: 30 max_request_size_mb: 10 ``` ### 4.2 Middleware Orchestrator **Purpose:** Coordinates all memory operations for a request. **Responsibilities:** - Session management (tracking active conversations) - Routing to Logger, Retriever, Injector - Token budget management - Error handling and fallbacks - Metrics collection **State Management:** Each user has an active session containing: ```py @dataclass class UserSession: user_id: str active_recall_file_id: str current_token_count: int last_activity: datetime warm_nodes: list[str] # KG nodes currently warmed ``` **Token Budget Logic:** ```py def allocate_token_budget( model: str, user_message_tokens: int, system_prompt_tokens: int ) -> dict: """ Determine how many tokens to allocate for memory context. """ model_limits = { "claude-3-opus": 200000, "claude-3-sonnet": 200000, "gpt-4-turbo": 128000, "gpt-4o": 128000, "gemini-1.5-pro": 1000000, } max_context = model_limits.get(model, 100000) reserved_for_response = 4096 available = max_context - user_message_tokens - system_prompt_tokens - reserved_for_response # Allocate up to 25% of available for memory, max 8000 tokens memory_budget = min(available * 0.25, 8000) return { "memory_budget": int(memory_budget), "remaining_for_conversation": available - memory_budget } ``` ### 4.3 Logger Component **Purpose:** Captures, parses, and stores all conversation content. **Responsibilities:** - Append messages to active Recall File transcript - Track token count for threshold detection - Extract entities for Knowledge Graph updates - Detect Defining Memory triggers - Manage Recall File finalization **Message Processing:** ```py async def log_message( user_id: str, role: str, # "user" or "assistant" content: str, artifacts: list[Artifact] = None, metadata: dict = None ) -> LogResult: """ Log a message to the user's active Recall File. """ session = get_session(user_id) # Calculate tokens tokens = count_tokens(content) session.current_token_count += tokens # Append to transcript await append_to_transcript( recall_file_id=session.active_recall_file_id, entry=TranscriptEntry( timestamp=datetime.utcnow(), role=role, content=content, tokens=tokens ) ) # Store artifacts if present if artifacts: await store_artifacts(session.active_recall_file_id, artifacts) # Check for Defining Memory triggers if role == "user": defining_memory = await detect_defining_memory(content) if defining_memory: await store_defining_memory(user_id, defining_memory, session.active_recall_file_id) # Check if threshold reached if session.current_token_count >= RECALL_FILE_TOKEN_THRESHOLD: await finalize_recall_file(session) await start_new_recall_file(session) return LogResult( recall_file_id=session.active_recall_file_id, tokens_logged=tokens, total_tokens=session.current_token_count ) ``` **Recall File Finalization:** ```py async def finalize_recall_file(session: UserSession): """ Complete a Recall File when token threshold is reached. """ recall_file = await get_recall_file(session.active_recall_file_id) # Generate summary using AI transcript = await load_transcript(recall_file.id) summary = await generate_summary(transcript) await save_summary(recall_file.id, summary) # Extract keywords keywords = await extract_keywords(transcript, summary) await save_keywords(recall_file.id, keywords) # Generate embedding from summary embedding = await embed_text(summary) await store_embedding(recall_file.id, embedding) # Update Knowledge Graph entities = await extract_entities(transcript) await update_knowledge_graph(session.user_id, recall_file.id, entities) # Compress artifacts await compress_artifacts(recall_file.id) # Mark as finalized recall_file.status = "finalized" recall_file.finalized_at = datetime.utcnow() await save_recall_file(recall_file) ``` ### 4.4 Retriever Component **Purpose:** Finds relevant memories for a given query. **Responsibilities:** - Execute multi-stage retrieval cascade - Rank and filter results - Load transcript content as needed - Manage retrieval caching **Retrieval Cascade:** ```py async def retrieve_memories( user_id: str, query: str, max_results: int = 5, include_defining: bool = True ) -> RetrievalResult: """ Execute the full retrieval cascade. """ results = [] # Stage 1: Check Defining Memories if include_defining: defining = await search_defining_memories(user_id, query) if defining: results.extend(defining) # Stage 2: Knowledge Graph Navigation relevant_nodes = await find_relevant_nodes(user_id, query) candidate_recall_files = await get_recall_files_for_nodes(relevant_nodes) # Stage 3: Keyword Search if candidate_recall_files: keyword_matches = await keyword_search( query=query, recall_file_ids=[rf.id for rf in candidate_recall_files] ) candidate_recall_files = rerank_by_keywords(candidate_recall_files, keyword_matches) # Stage 4: Semantic Search (RAG) query_embedding = await embed_text(query) semantic_matches = await vector_search( embedding=query_embedding, user_id=user_id, candidate_ids=[rf.id for rf in candidate_recall_files] if candidate_recall_files else None, limit=max_results * 2 ) # Stage 5: Load and Rank for match in semantic_matches[:max_results]: recall_file = await get_recall_file(match.recall_file_id) # Load summary for quick context summary = await load_summary(recall_file.id) # Optionally load relevant transcript section if match.score > 0.85: # High confidence transcript = await load_transcript(recall_file.id) else: transcript = None results.append(MemoryResult( recall_file_id=recall_file.id, topic=recall_file.topic, date=recall_file.created_at, summary=summary, transcript_excerpt=transcript, relevance_score=match.score )) # Warm the neighborhood for future queries if relevant_nodes: asyncio.create_task(warm_neighborhood(relevant_nodes)) return RetrievalResult( memories=results, nodes_searched=len(relevant_nodes), recall_files_considered=len(candidate_recall_files) ) ``` ### 4.5 Injector Component **Purpose:** Builds context-enhanced prompts for AI models. **Responsibilities:** - Format memories for prompt injection - Manage token budget - Structure context for different models - Handle prompt templates **Context Building:** ```py async def build_enhanced_prompt( user_message: str, memories: list[MemoryResult], system_prompt: str, token_budget: int, model: str ) -> EnhancedPrompt: """ Build a prompt with memory context injected. """ # Format memories for injection memory_sections = [] tokens_used = 0 for memory in memories: # Prefer summary if budget is tight if tokens_used + count_tokens(memory.summary) <= token_budget: section = format_memory_section(memory, include_transcript=False) section_tokens = count_tokens(section) # Add transcript if we have budget and it's highly relevant if memory.transcript_excerpt and memory.relevance_score > 0.85: with_transcript = format_memory_section(memory, include_transcript=True) transcript_tokens = count_tokens(with_transcript) if tokens_used + transcript_tokens <= token_budget: section = with_transcript section_tokens = transcript_tokens memory_sections.append(section) tokens_used += section_tokens else: break # Budget exhausted # Build final prompt memory_context = "\n\n".join(memory_sections) enhanced_prompt = PROMPT_TEMPLATE.format( system_prompt=system_prompt, memory_context=memory_context, user_message=user_message ) return EnhancedPrompt( content=enhanced_prompt, memory_tokens_used=tokens_used, memories_included=len(memory_sections) ) PROMPT_TEMPLATE = """ {system_prompt} ## Relevant Context from Previous Conversations {memory_context} --- ## Current Message {user_message} """ ``` ### 4.6 Knowledge Graph Manager **Purpose:** Maintains the hierarchical structure of user knowledge. **Responsibilities:** - Create and update nodes (projects, topics, concepts) - Manage edges (relationships between nodes) - Link Recall Files to nodes - Support graph traversal queries **Node Types:** ```py class NodeType(Enum): PROJECT = "project" # Major work streams TOPIC = "topic" # Subjects within projects CONCEPT = "concept" # Abstract ideas spanning projects ENTITY = "entity" # People, companies, products RECALL_FILE = "recall_file" # Leaf nodes (archives) ``` **Edge Types:** ```py class EdgeType(Enum): CONTAINS = "contains" # Hierarchical parent-child RELATES_TO = "relates_to" # Semantic connection DISCUSSED_IN = "discussed_in" # Links to Recall Files MENTIONS = "mentions" # Entity references SUPERSEDES = "supersedes" # Temporal versioning ``` **Graph Operations:** ```py async def find_relevant_nodes( user_id: str, query: str, max_depth: int = 2 ) -> list[Node]: """ Find nodes relevant to a query. """ # Extract potential topic/entity mentions mentions = await extract_mentions(query) # Find matching nodes matching_nodes = [] for mention in mentions: nodes = await graph_db.find_nodes( user_id=user_id, name_contains=mention, fuzzy=True ) matching_nodes.extend(nodes) # Expand to neighborhood expanded = set() for node in matching_nodes: neighborhood = await graph_db.get_neighborhood( node_id=node.id, depth=max_depth ) expanded.update(neighborhood) return list(expanded) async def get_recall_files_for_nodes(nodes: list[Node]) -> list[RecallFile]: """ Get all Recall Files linked to a set of nodes. """ recall_file_ids = set() for node in nodes: edges = await graph_db.get_edges( source_id=node.id, edge_type=EdgeType.DISCUSSED_IN ) for edge in edges: recall_file_ids.add(edge.target_id) return await batch_get_recall_files(list(recall_file_ids)) ``` ### 4.7 Defining Memory Detector **Purpose:** Identifies and indexes significant moments in conversations. **Detection Triggers:** ```py DEFINING_MEMORY_PATTERNS = { "decision": [ r"I('ve| have) decided", r"we('re| are) going with", r"final decision", r"I('m| am) committing to", r"let's do", r"I choose", ], "milestone": [ r"we launched", r"it's done", r"I finished", r"completed", r"shipped", r"released", r"went live", ], "event": [ r"I('m| am) starting", r"got the job", r"closed the deal", r"signed the contract", r"I('m| am) getting married", r"we('re| are) having a baby", ], "turning_point": [ r"this changes everything", r"I realized", r"from now on", r"never again", r"turning point", ], } async def detect_defining_memory(content: str) -> DefiningMemory | None: """ Check if content contains a defining memory. """ content_lower = content.lower() for memory_type, patterns in DEFINING_MEMORY_PATTERNS.items(): for pattern in patterns: if re.search(pattern, content_lower): # Extract surrounding context context = extract_context_window(content, pattern) # Generate summary using AI summary = await summarize_defining_moment(content, memory_type) return DefiningMemory( type=memory_type, summary=summary, context=context, detected_at=datetime.utcnow(), confidence=0.8 # Pattern-based detection ) return None ``` --- ## 5\. Data Models & Schema ### 5.1 PostgreSQL Schema ```sql -- Enable required extensions CREATE EXTENSION IF NOT EXISTS "uuid-ossp"; CREATE EXTENSION IF NOT EXISTS "pgvector"; CREATE EXTENSION IF NOT EXISTS "pg_trgm"; -- For fuzzy text search -- Users table CREATE TABLE users ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), external_id VARCHAR(255) UNIQUE NOT NULL, -- ID from auth provider email VARCHAR(255), created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), settings JSONB DEFAULT '{}'::jsonb ); CREATE INDEX idx_users_external_id ON users(external_id); -- Recall Files table CREATE TABLE recall_files ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE, folder_name VARCHAR(255) NOT NULL, topic VARCHAR(255), status VARCHAR(50) DEFAULT 'active', -- 'active', 'finalized', 'archived' storage_state VARCHAR(50) DEFAULT 'hot', -- 'hot', 'warm', 'cold' token_count INTEGER DEFAULT 0, -- File paths (relative to user's storage root) summary_path TEXT, keywords_path TEXT, transcript_path TEXT, artifacts_path TEXT, -- Timestamps created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), finalized_at TIMESTAMP WITH TIME ZONE, last_accessed_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), -- Metadata metadata JSONB DEFAULT '{}'::jsonb, CONSTRAINT unique_folder_per_user UNIQUE (user_id, folder_name) ); CREATE INDEX idx_recall_files_user_id ON recall_files(user_id); CREATE INDEX idx_recall_files_status ON recall_files(status); CREATE INDEX idx_recall_files_storage_state ON recall_files(storage_state); CREATE INDEX idx_recall_files_last_accessed ON recall_files(last_accessed_at); CREATE INDEX idx_recall_files_topic ON recall_files USING gin(topic gin_trgm_ops); -- Knowledge Graph Nodes CREATE TABLE kg_nodes ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE, name VARCHAR(255) NOT NULL, node_type VARCHAR(50) NOT NULL, -- 'project', 'topic', 'concept', 'entity' description TEXT, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), last_accessed_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), metadata JSONB DEFAULT '{}'::jsonb, CONSTRAINT unique_node_name_per_user UNIQUE (user_id, name, node_type) ); CREATE INDEX idx_kg_nodes_user_id ON kg_nodes(user_id); CREATE INDEX idx_kg_nodes_type ON kg_nodes(node_type); CREATE INDEX idx_kg_nodes_name ON kg_nodes USING gin(name gin_trgm_ops); -- Knowledge Graph Edges CREATE TABLE kg_edges ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), source_node_id UUID NOT NULL REFERENCES kg_nodes(id) ON DELETE CASCADE, target_node_id UUID NOT NULL REFERENCES kg_nodes(id) ON DELETE CASCADE, edge_type VARCHAR(50) NOT NULL, -- 'contains', 'relates_to', 'discussed_in', etc. weight FLOAT DEFAULT 1.0, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), metadata JSONB DEFAULT '{}'::jsonb, CONSTRAINT unique_edge UNIQUE (source_node_id, target_node_id, edge_type) ); CREATE INDEX idx_kg_edges_source ON kg_edges(source_node_id); CREATE INDEX idx_kg_edges_target ON kg_edges(target_node_id); CREATE INDEX idx_kg_edges_type ON kg_edges(edge_type); -- Recall File to Node mapping CREATE TABLE recall_file_nodes ( recall_file_id UUID NOT NULL REFERENCES recall_files(id) ON DELETE CASCADE, node_id UUID NOT NULL REFERENCES kg_nodes(id) ON DELETE CASCADE, relevance_score FLOAT DEFAULT 1.0, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), PRIMARY KEY (recall_file_id, node_id) ); CREATE INDEX idx_recall_file_nodes_node ON recall_file_nodes(node_id); -- Defining Memories CREATE TABLE defining_memories ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE, memory_type VARCHAR(50) NOT NULL, -- 'decision', 'milestone', 'event', 'turning_point' summary TEXT NOT NULL, context TEXT, source_recall_file_id UUID REFERENCES recall_files(id) ON DELETE SET NULL, confidence FLOAT DEFAULT 1.0, detected_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), occurred_at TIMESTAMP WITH TIME ZONE, -- When the event actually happened tags TEXT[] DEFAULT '{}', metadata JSONB DEFAULT '{}'::jsonb ); CREATE INDEX idx_defining_memories_user_id ON defining_memories(user_id); CREATE INDEX idx_defining_memories_type ON defining_memories(memory_type); CREATE INDEX idx_defining_memories_detected_at ON defining_memories(detected_at); CREATE INDEX idx_defining_memories_tags ON defining_memories USING gin(tags); -- Summary Embeddings (Vector Store) CREATE TABLE summary_embeddings ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), recall_file_id UUID NOT NULL REFERENCES recall_files(id) ON DELETE CASCADE, user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE, embedding vector(1536), -- OpenAI ada-002 dimension created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), CONSTRAINT unique_embedding_per_recall_file UNIQUE (recall_file_id) ); -- Create vector index for similarity search CREATE INDEX idx_summary_embeddings_vector ON summary_embeddings USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100); CREATE INDEX idx_summary_embeddings_user_id ON summary_embeddings(user_id); -- Keywords index (for fast exact-match search) CREATE TABLE recall_file_keywords ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), recall_file_id UUID NOT NULL REFERENCES recall_files(id) ON DELETE CASCADE, keyword VARCHAR(255) NOT NULL, frequency INTEGER DEFAULT 1, CONSTRAINT unique_keyword_per_file UNIQUE (recall_file_id, keyword) ); CREATE INDEX idx_keywords_recall_file ON recall_file_keywords(recall_file_id); CREATE INDEX idx_keywords_keyword ON recall_file_keywords(keyword); CREATE INDEX idx_keywords_keyword_trgm ON recall_file_keywords USING gin(keyword gin_trgm_ops); -- User Sessions (for active conversation tracking) CREATE TABLE user_sessions ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE, active_recall_file_id UUID REFERENCES recall_files(id), current_token_count INTEGER DEFAULT 0, warm_node_ids UUID[] DEFAULT '{}', created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), last_activity_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(), expires_at TIMESTAMP WITH TIME ZONE, metadata JSONB DEFAULT '{}'::jsonb ); CREATE INDEX idx_user_sessions_user_id ON user_sessions(user_id); CREATE INDEX idx_user_sessions_active ON user_sessions(last_activity_at); -- Audit Log CREATE TABLE audit_log ( id UUID PRIMARY KEY DEFAULT uuid_generate_v4(), user_id UUID REFERENCES users(id), action VARCHAR(100) NOT NULL, resource_type VARCHAR(100), resource_id UUID, details JSONB, ip_address INET, created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW() ); CREATE INDEX idx_audit_log_user_id ON audit_log(user_id); CREATE INDEX idx_audit_log_action ON audit_log(action); CREATE INDEX idx_audit_log_created_at ON audit_log(created_at); ``` ### 5.2 Recall File Structure Each Recall File is stored as a folder: ``` /storage/{user_id}/recall-files/{folder_name}/ ├── summary.md # AI-generated summary ├── keywords.txt # Extracted keywords, one per line ├── transcript.md # Complete conversation log └── artifacts/ # Directory for files (or artifacts.zip when cold) ├── code_snippet_001.py ├── document_draft.md └── image_generated.png ``` **summary.md Format:** ``` # Summary: {topic} **Date Range:** {start_date} - {end_date} **Token Count:** {token_count} ## Overview {AI-generated 2-3 paragraph summary} ## Key Points - {bullet point 1} - {bullet point 2} - {bullet point 3} ## Topics Discussed - {topic 1} - {topic 2} ## Artifacts Created - {artifact 1 with description} - {artifact 2 with description} ``` **keywords.txt Format:** ``` hyperthyme memory architecture recall file knowledge graph vector search defining memory ``` **transcript.md Format:** ``` # Conversation Transcript **Recall File:** {folder_name} **Started:** {start_timestamp} **Finalized:** {end_timestamp} --- ## 2026-01-11T08:30:00Z | User {user message content} --- ## 2026-01-11T08:30:45Z | Assistant {assistant response content} --- ## 2026-01-11T08:32:00Z | User {next user message} [... continues ...] ``` ### 5.3 Object Models ```py from dataclasses import dataclass from datetime import datetime from enum import Enum from typing import Optional from uuid import UUID class RecallFileStatus(Enum): ACTIVE = "active" FINALIZED = "finalized" ARCHIVED = "archived" class StorageState(Enum): HOT = "hot" WARM = "warm" COLD = "cold" class NodeType(Enum): PROJECT = "project" TOPIC = "topic" CONCEPT = "concept" ENTITY = "entity" RECALL_FILE = "recall_file" class EdgeType(Enum): CONTAINS = "contains" RELATES_TO = "relates_to" DISCUSSED_IN = "discussed_in" MENTIONS = "mentions" SUPERSEDES = "supersedes" class DefiningMemoryType(Enum): DECISION = "decision" MILESTONE = "milestone" EVENT = "event" TURNING_POINT = "turning_point" @dataclass class User: id: UUID external_id: str email: Optional[str] created_at: datetime settings: dict @dataclass class RecallFile: id: UUID user_id: UUID folder_name: str topic: Optional[str] status: RecallFileStatus storage_state: StorageState token_count: int summary_path: Optional[str] keywords_path: Optional[str] transcript_path: Optional[str] artifacts_path: Optional[str] created_at: datetime updated_at: datetime finalized_at: Optional[datetime] last_accessed_at: datetime metadata: dict @dataclass class KGNode: id: UUID user_id: UUID name: str node_type: NodeType description: Optional[str] created_at: datetime last_accessed_at: datetime metadata: dict @dataclass class KGEdge: id: UUID source_node_id: UUID target_node_id: UUID edge_type: EdgeType weight: float created_at: datetime metadata: dict @dataclass class DefiningMemory: id: UUID user_id: UUID memory_type: DefiningMemoryType summary: str context: Optional[str] source_recall_file_id: Optional[UUID] confidence: float detected_at: datetime occurred_at: Optional[datetime] tags: list[str] metadata: dict @dataclass class SummaryEmbedding: id: UUID recall_file_id: UUID user_id: UUID embedding: list[float] # 1536 dimensions created_at: datetime @dataclass class UserSession: id: UUID user_id: UUID active_recall_file_id: Optional[UUID] current_token_count: int warm_node_ids: list[UUID] created_at: datetime last_activity_at: datetime expires_at: Optional[datetime] metadata: dict ``` --- ## 6\. APIs & Interfaces ### 6.1 REST API Specification **Base URL:** `https://api.hyperthyme.ai/v1` #### 6.1.1 Chat Endpoint **POST /chat** Send a message with memory-augmented context. **Request:** ```json { "message": "Continue working on the payment integration", "model": "claude-sonnet-4-20250514", "include_memories": true, "memory_options": { "max_memories": 5, "token_budget": 4000, "include_defining": true, "time_range": { "start": "2025-01-01T00:00:00Z", "end": null } }, "system_prompt": "You are a helpful coding assistant.", "stream": false } ``` **Response:** ```json { "id": "msg_abc123", "response": "I found our previous work on the payment integration...", "model": "claude-sonnet-4-20250514", "memories_used": [ { "recall_file_id": "rf_xyz789", "topic": "Payment Integration - Stripe", "date": "2025-01-03", "relevance_score": 0.92 } ], "usage": { "prompt_tokens": 1500, "completion_tokens": 350, "memory_tokens": 800 }, "logged_to": "rf_current123" } ``` #### 6.1.2 Search Endpoint **POST /search** Search memories without sending to AI. **Request:** ```json { "query": "payment webhook implementation", "max_results": 10, "include_transcripts": false, "filters": { "date_range": { "start": "2024-01-01", "end": null }, "topics": ["payments", "integration"], "memory_types": ["defining", "regular"] } } ``` **Response:** ```json { "results": [ { "type": "recall_file", "id": "rf_xyz789", "topic": "Payment Integration - Stripe Webhooks", "date": "2025-01-03", "summary": "Implemented webhook handlers for payment events...", "relevance_score": 0.94, "keywords": ["stripe", "webhook", "payment", "handler"] }, { "type": "defining_memory", "id": "dm_abc456", "memory_type": "decision", "summary": "Decided to use Stripe Connect for marketplace payments", "date": "2024-12-15", "relevance_score": 0.87 } ], "total_count": 2, "search_stats": { "nodes_searched": 5, "recall_files_considered": 12, "search_time_ms": 45 } } ``` #### 6.1.3 Recall Files Endpoints **GET /recall-files** List user's Recall Files. **Query Parameters:** - `status`: Filter by status (active, finalized, archived) - `topic`: Filter by topic (fuzzy match) - `limit`: Max results (default 20, max 100\) - `offset`: Pagination offset - `sort`: Sort field (created\_at, updated\_at, last\_accessed\_at) - `order`: Sort order (asc, desc) **Response:** ```json { "recall_files": [ { "id": "rf_xyz789", "folder_name": "payment-integration-stripe-2025-01-03", "topic": "Payment Integration - Stripe", "status": "finalized", "storage_state": "warm", "token_count": 48500, "created_at": "2025-01-03T10:00:00Z", "finalized_at": "2025-01-03T14:30:00Z", "last_accessed_at": "2025-01-10T08:00:00Z" } ], "pagination": { "total": 156, "limit": 20, "offset": 0, "has_more": true } } ``` **GET /recall-files/{id}** Get specific Recall File with content. **Query Parameters:** - `include`: Comma-separated list (summary, keywords, transcript, artifacts) **Response:** ```json { "id": "rf_xyz789", "folder_name": "payment-integration-stripe-2025-01-03", "topic": "Payment Integration - Stripe", "status": "finalized", "storage_state": "warm", "token_count": 48500, "created_at": "2025-01-03T10:00:00Z", "finalized_at": "2025-01-03T14:30:00Z", "summary": "## Overview\n\nImplemented Stripe webhook handlers...", "keywords": ["stripe", "webhook", "payment", "handler", "checkout"], "transcript": "# Conversation Transcript\n\n...", "artifacts": [ { "name": "webhook_handler.py", "type": "text/x-python", "size": 2500 } ], "linked_nodes": [ {"id": "node_123", "name": "Payments", "type": "topic"}, {"id": "node_456", "name": "funnelChat", "type": "project"} ] } ``` #### 6.1.4 Defining Memories Endpoints **GET /defining-memories** List user's Defining Memories. **Query Parameters:** - `type`: Filter by type (decision, milestone, event, turning\_point) - `since`: Filter by date (ISO 8601\) - `limit`: Max results - `offset`: Pagination offset **Response:** ```json { "defining_memories": [ { "id": "dm_abc456", "type": "decision", "summary": "Decided to build Hyperthyme as the memory layer for Neurigraph", "context": "After discovering Mem0 raised $24M...", "detected_at": "2025-01-11T08:00:00Z", "occurred_at": "2025-01-11T08:00:00Z", "source_recall_file_id": "rf_xyz789", "tags": ["product", "strategy", "commitment"], "confidence": 0.95 } ], "pagination": { "total": 23, "limit": 20, "offset": 0, "has_more": true } } ``` #### 6.1.5 Knowledge Graph Endpoints **GET /graph/nodes** Query Knowledge Graph nodes. **Query Parameters:** - `type`: Filter by node type - `name`: Search by name (fuzzy) - `related_to`: Find nodes related to a specific node ID - `depth`: Traversal depth for related queries **Response:** ```json { "nodes": [ { "id": "node_123", "name": "Payments", "type": "topic", "description": "Payment processing and integrations", "recall_file_count": 8, "related_nodes": [ {"id": "node_456", "name": "Stripe", "relationship": "contains"}, {"id": "node_789", "name": "funnelChat", "relationship": "belongs_to"} ] } ] } ``` **POST /graph/nodes** Create or update a node. **Request:** ```json { "name": "New Project", "type": "project", "description": "Description of the project", "parent_id": null } ``` ### 6.2 MCP (Model Context Protocol) Interface Hyperthyme exposes tools for MCP-compatible AI systems. **Tools Exposed:** ```py @mcp_server.tool( name="search_memory", description="Search the user's conversation history for relevant memories" ) async def search_memory( query: str, max_results: int = 5, include_defining: bool = True ) -> list[dict]: """ Search for memories matching the query. Args: query: Natural language search query max_results: Maximum number of results to return include_defining: Whether to include defining memories Returns: List of matching memories with summaries and metadata """ pass @mcp_server.tool( name="get_defining_memories", description="Retrieve the user's major decisions, milestones, and significant events" ) async def get_defining_memories( type_filter: str = None, since: str = None, limit: int = 10 ) -> list[dict]: """ Get defining memories. Args: type_filter: Filter by type (decision, milestone, event, turning_point) since: Only return memories after this date (ISO 8601) limit: Maximum results Returns: List of defining memories """ pass @mcp_server.tool( name="get_recall_file_content", description="Retrieve the full content of a specific conversation archive" ) async def get_recall_file_content( recall_file_id: str, include: list[str] = ["summary", "transcript"] ) -> dict: """ Get content from a specific Recall File. Args: recall_file_id: The ID of the Recall File include: Which components to include (summary, keywords, transcript, artifacts) Returns: Recall File content """ pass @mcp_server.tool( name="list_topics", description="List the user's projects and topics from their knowledge graph" ) async def list_topics( type_filter: str = None, parent_id: str = None ) -> list[dict]: """ List knowledge graph nodes. Args: type_filter: Filter by type (project, topic, concept) parent_id: Only show children of this node Returns: List of nodes with metadata """ pass ``` ### 6.3 SDK Interface ```py # Python SDK Example from hyperthyme import HyperthymeClient # Initialize client client = HyperthymeClient( api_key="sk_...", base_url="https://api.hyperthyme.ai" ) # Chat with memory response = client.chat( message="Continue working on the payment integration", model="claude-sonnet-4-20250514", memory_options={ "max_memories": 5, "token_budget": 4000 } ) print(response.content) print(f"Used {len(response.memories_used)} memories") # Search memories results = client.search( query="payment webhook implementation", max_results=10 ) for result in results: print(f"{result.topic}: {result.summary[:100]}...") # Get defining memories decisions = client.get_defining_memories( type_filter="decision", since="2025-01-01" ) for decision in decisions: print(f"{decision.date}: {decision.summary}") # Direct Recall File access recall_file = client.get_recall_file( "rf_xyz789", include=["summary", "transcript"] ) print(recall_file.transcript) ``` --- ## 7\. Retrieval Pipeline ### 7.1 Pipeline Overview The retrieval pipeline executes a multi-stage cascade designed to efficiently find relevant memories while minimizing computational cost. ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ RETRIEVAL PIPELINE │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ Query: "What was the code for handling payment webhooks?" │ │ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ STAGE 1: Defining Memory Check ~5ms │ │ │ │ │ │ │ │ Check if query relates to a decision/milestone/event │ │ │ │ Result: No direct match (content query, not event query) │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ STAGE 2: Knowledge Graph Navigation ~10ms │ │ │ │ │ │ │ │ Extract entities: ["payment", "webhook", "code"] │ │ │ │ Find matching nodes: [Payments, Webhooks, Stripe] │ │ │ │ Expand neighborhood (depth=2) │ │ │ │ Get linked Recall Files: 15 candidates │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ STAGE 3: Keyword Filtering ~15ms │ │ │ │ │ │ │ │ Search keywords.txt in 15 candidates │ │ │ │ Terms: ["webhook", "payment", "stripe", "handler", "code"] │ │ │ │ Score by keyword overlap │ │ │ │ Result: 6 Recall Files with strong overlap │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ STAGE 4: Semantic Search (RAG) ~30ms │ │ │ │ │ │ │ │ Embed query │ │ │ │ Vector search on 6 candidate summaries │ │ │ │ Rank by cosine similarity │ │ │ │ Result: Top 3 with scores [0.94, 0.87, 0.82] │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ STAGE 5: Content Loading ~20ms │ │ │ │ │ │ │ │ Load summaries for top 3 │ │ │ │ Load transcript for #1 (score > 0.9 threshold) │ │ │ │ Warm neighborhood nodes for future queries │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ Total Time: ~80ms │ │ Result: 3 memories, 1 with full transcript │ │ │ └─────────────────────────────────────────────────────────────────────────┘ ``` ### 7.2 Stage Details #### Stage 1: Defining Memory Check ```py async def check_defining_memories( user_id: str, query: str ) -> list[DefiningMemory]: """ Quick check if query relates to defining memories. Uses keyword matching and optional semantic similarity against the defining memories index (always in memory). """ # Keyword extraction query_keywords = extract_keywords(query) # Check for event-type query patterns event_patterns = [ r"when did (I|we)", r"what (did I|did we) decide", r"(milestone|decision|event)", r"remember when" ] is_event_query = any(re.search(p, query.lower()) for p in event_patterns) if not is_event_query: return [] # Search defining memories index matches = await db.query(""" SELECT * FROM defining_memories WHERE user_id = $1 AND ( summary ILIKE ANY($2) OR tags && $3 ) ORDER BY detected_at DESC LIMIT 5 """, user_id, [f"%{kw}%" for kw in query_keywords], query_keywords) return [DefiningMemory(**m) for m in matches] ``` #### Stage 2: Knowledge Graph Navigation ```py async def navigate_knowledge_graph( user_id: str, query: str, max_depth: int = 2 ) -> tuple[list[KGNode], list[RecallFile]]: """ Find relevant nodes and their linked Recall Files. """ # Extract potential topic/entity mentions mentions = await extract_mentions(query) # NER + keyword extraction # Find matching nodes matching_nodes = [] for mention in mentions: nodes = await db.query(""" SELECT * FROM kg_nodes WHERE user_id = $1 AND ( name ILIKE $2 OR description ILIKE $2 ) """, user_id, f"%{mention}%") matching_nodes.extend(nodes) # Expand to neighborhood (BFS) visited = set() frontier = [n.id for n in matching_nodes] depth = 0 while frontier and depth < max_depth: edges = await db.query(""" SELECT target_node_id FROM kg_edges WHERE source_node_id = ANY($1) UNION SELECT source_node_id FROM kg_edges WHERE target_node_id = ANY($1) """, frontier) new_frontier = [] for edge in edges: node_id = edge['target_node_id'] or edge['source_node_id'] if node_id not in visited: visited.add(node_id) new_frontier.append(node_id) frontier = new_frontier depth += 1 # Get all recall files linked to visited nodes recall_files = await db.query(""" SELECT DISTINCT rf.* FROM recall_files rf JOIN recall_file_nodes rfn ON rf.id = rfn.recall_file_id WHERE rfn.node_id = ANY($1) AND rf.status = 'finalized' """, list(visited)) return matching_nodes, recall_files ``` #### Stage 3: Keyword Filtering ```py async def filter_by_keywords( query: str, candidate_recall_files: list[RecallFile] ) -> list[tuple[RecallFile, float]]: """ Score candidates by keyword overlap. """ query_keywords = set(extract_keywords(query)) scored_candidates = [] for rf in candidate_recall_files: # Get keywords for this recall file rf_keywords = await db.query(""" SELECT keyword FROM recall_file_keywords WHERE recall_file_id = $1 """, rf.id) rf_keyword_set = set(k['keyword'] for k in rf_keywords) # Calculate overlap score if rf_keyword_set: overlap = len(query_keywords & rf_keyword_set) score = overlap / len(query_keywords) if query_keywords else 0 else: score = 0 if score > 0.1: # Minimum threshold scored_candidates.append((rf, score)) # Sort by score descending scored_candidates.sort(key=lambda x: x[1], reverse=True) return scored_candidates ``` #### Stage 4: Semantic Search ```py async def semantic_search( query: str, candidate_ids: list[str], limit: int = 5 ) -> list[tuple[str, float]]: """ Vector similarity search on candidate summaries. """ # Generate query embedding query_embedding = await embedding_model.embed(query) # Search with filtering results = await db.query(""" SELECT recall_file_id, 1 - (embedding <=> $1) as similarity FROM summary_embeddings WHERE recall_file_id = ANY($2) ORDER BY embedding <=> $1 LIMIT $3 """, query_embedding, candidate_ids, limit) return [(r['recall_file_id'], r['similarity']) for r in results] ``` #### Stage 5: Content Loading ```py async def load_memory_content( recall_file_ids: list[str], scores: dict[str, float], transcript_threshold: float = 0.9 ) -> list[MemoryResult]: """ Load content from top-ranked Recall Files. """ results = [] for rf_id in recall_file_ids: rf = await get_recall_file(rf_id) score = scores[rf_id] # Always load summary summary = await load_file(rf.summary_path) # Load transcript only for high-confidence matches transcript = None if score >= transcript_threshold: transcript = await load_file(rf.transcript_path) results.append(MemoryResult( recall_file_id=rf_id, topic=rf.topic, date=rf.created_at, summary=summary, transcript=transcript, relevance_score=score )) # Update last accessed await db.execute(""" UPDATE recall_files SET last_accessed_at = NOW() WHERE id = $1 """, rf_id) return results ``` ### 7.3 Performance Optimization **Caching Strategy:** ```py class RetrievalCache: """ Multi-level cache for retrieval operations. """ def __init__(self, redis_client): self.redis = redis_client self.local_cache = {} # In-memory LRU async def get_embedding(self, text: str) -> list[float]: """Cache embeddings to avoid recomputation.""" cache_key = f"emb:{hash(text)}" # Check local cache first if cache_key in self.local_cache: return self.local_cache[cache_key] # Check Redis cached = await self.redis.get(cache_key) if cached: embedding = json.loads(cached) self.local_cache[cache_key] = embedding return embedding # Compute and cache embedding = await embedding_model.embed(text) await self.redis.setex(cache_key, 86400, json.dumps(embedding)) self.local_cache[cache_key] = embedding return embedding async def get_keywords(self, recall_file_id: str) -> list[str]: """Cache keywords for fast filtering.""" cache_key = f"kw:{recall_file_id}" cached = await self.redis.get(cache_key) if cached: return json.loads(cached) keywords = await load_keywords_from_file(recall_file_id) await self.redis.setex(cache_key, 3600, json.dumps(keywords)) return keywords ``` **Batch Operations:** ```py async def batch_get_recall_files(ids: list[str]) -> list[RecallFile]: """ Fetch multiple Recall Files in a single query. """ if not ids: return [] results = await db.query(""" SELECT * FROM recall_files WHERE id = ANY($1) """, ids) return [RecallFile(**r) for r in results] ``` --- ## 8\. Storage Management ### 8.1 Storage Tiers ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ STORAGE TIERS │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ HOT 0-1 hours │ │ │ │ │ │ │ │ • Currently active Recall File │ │ │ │ • All content in memory │ │ │ │ • Instant access (<10ms) │ │ │ │ • Location: Application memory + local SSD │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ WARM 1h - 7 days │ │ │ │ │ │ │ │ • Recently accessed Recall Files │ │ │ │ • Same KG neighborhood as current topic │ │ │ │ • Transcript cached, artifacts uncompressed │ │ │ │ • Fast access (<100ms) │ │ │ │ • Location: Local SSD / Fast object storage │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ COLD 7+ days │ │ │ │ │ │ │ │ • Infrequently accessed Recall Files │ │ │ │ • Artifacts compressed (zip) │ │ │ │ • Transcript on disk (not cached) │ │ │ │ • Keywords/summaries still indexed │ │ │ │ • Slower access (<1s) │ │ │ │ • Location: Object storage (S3/GCS) with compression │ │ │ └─────────────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────┘ ``` ### 8.2 State Transitions ```py class StorageManager: """ Manages storage tier transitions for Recall Files. """ WARM_THRESHOLD_HOURS = 1 COLD_THRESHOLD_DAYS = 7 async def warm_recall_file(self, recall_file_id: str): """ Transition a Recall File from cold to warm. """ rf = await get_recall_file(recall_file_id) if rf.storage_state == StorageState.COLD: # Decompress artifacts if rf.artifacts_path and rf.artifacts_path.endswith('.zip'): await decompress_artifacts(rf.id) # Pre-cache transcript transcript = await load_file(rf.transcript_path) await cache.set(f"transcript:{rf.id}", transcript, ttl=3600) # Update state rf.storage_state = StorageState.WARM await save_recall_file(rf) async def cool_recall_file(self, recall_file_id: str): """ Transition a Recall File from warm to cold. """ rf = await get_recall_file(recall_file_id) if rf.storage_state == StorageState.WARM: # Compress artifacts if rf.artifacts_path and not rf.artifacts_path.endswith('.zip'): await compress_artifacts(rf.id) # Evict transcript cache await cache.delete(f"transcript:{rf.id}") # Update state rf.storage_state = StorageState.COLD await save_recall_file(rf) async def warm_neighborhood(self, node_ids: list[str]): """ Warm all Recall Files in a KG neighborhood. """ recall_files = await get_recall_files_for_nodes(node_ids) tasks = [ self.warm_recall_file(rf.id) for rf in recall_files if rf.storage_state == StorageState.COLD ] await asyncio.gather(*tasks) class StorageLifecycleJob: """ Background job for storage lifecycle management. """ async def run(self): """ Run nightly to transition warm → cold. """ cutoff = datetime.utcnow() - timedelta(days=7) warm_files = await db.query(""" SELECT id FROM recall_files WHERE storage_state = 'warm' AND last_accessed_at < $1 """, cutoff) storage_manager = StorageManager() for rf in warm_files: try: await storage_manager.cool_recall_file(rf['id']) except Exception as e: logger.error(f"Failed to cool {rf['id']}: {e}") ``` ### 8.3 File Storage Layout ``` /storage/ ├── {user_id}/ │ ├── recall-files/ │ │ ├── payment-integration-stripe-2025-01-03/ │ │ │ ├── summary.md │ │ │ ├── keywords.txt │ │ │ ├── transcript.md │ │ │ └── artifacts/ │ │ │ ├── webhook_handler.py │ │ │ └── test_coverage.png │ │ │ │ │ ├── api-design-session-2025-01-05/ │ │ │ ├── summary.md │ │ │ ├── keywords.txt │ │ │ ├── transcript.md │ │ │ └── artifacts.zip # Compressed (cold) │ │ │ │ │ └── current-session-2025-01-11/ # Active (hot) │ │ └── transcript.md # Being written to │ │ │ └── config/ │ └── user_settings.json │ └── system/ ├── models/ │ └── embedding_model/ └── cache/ ``` ### 8.4 Storage Estimates | Component | Size per Recall File | Notes | | :---- | :---- | :---- | | summary.md | \~2-5 KB | 500-1000 tokens | | keywords.txt | \~0.5-1 KB | 50-100 keywords | | transcript.md | \~150-200 KB | 50K tokens | | artifacts (avg) | \~50-500 KB | Varies widely | | **Total (uncompressed)** | **\~200-700 KB** | | | **Total (compressed)** | **\~50-200 KB** | \~3:1 compression | **Scale Projections:** | Recall Files | Uncompressed | Compressed | | :---- | :---- | :---- | | 1,000 | 200-700 MB | 50-200 MB | | 10,000 | 2-7 GB | 0.5-2 GB | | 100,000 | 20-70 GB | 5-20 GB | | 1,000,000 | 200-700 GB | 50-200 GB | --- ## 9\. Security & Privacy ### 9.1 Authentication & Authorization **Authentication:** - API key authentication for server-to-server - OAuth 2.0 / OIDC for user-facing applications - JWT tokens for session management **Authorization:** - All data is scoped by user\_id - No cross-user data access - Role-based access for admin functions ```py class AuthMiddleware: """ Authentication and authorization middleware. """ async def __call__(self, request: Request, call_next): # Extract auth header auth_header = request.headers.get("Authorization") if not auth_header: raise HTTPException(401, "Missing authorization") # Validate token if auth_header.startswith("Bearer "): token = auth_header[7:] user = await self.validate_jwt(token) elif auth_header.startswith("sk_"): user = await self.validate_api_key(auth_header) else: raise HTTPException(401, "Invalid authorization format") # Attach user to request request.state.user = user return await call_next(request) async def validate_jwt(self, token: str) -> User: try: payload = jwt.decode(token, JWT_SECRET, algorithms=["HS256"]) user = await get_user(payload["sub"]) return user except jwt.ExpiredSignatureError: raise HTTPException(401, "Token expired") except jwt.InvalidTokenError: raise HTTPException(401, "Invalid token") async def validate_api_key(self, api_key: str) -> User: # Hash and lookup key_hash = hashlib.sha256(api_key.encode()).hexdigest() user = await db.query(""" SELECT u.* FROM users u JOIN api_keys ak ON u.id = ak.user_id WHERE ak.key_hash = $1 AND ak.revoked_at IS NULL """, key_hash) if not user: raise HTTPException(401, "Invalid API key") return User(**user[0]) ``` ### 9.2 Data Encryption **At Rest:** - All stored files encrypted with AES-256-GCM - Per-user encryption keys derived from master key - Keys stored in separate key management system **In Transit:** - TLS 1.3 required for all connections - Certificate pinning for mobile SDKs ```py class EncryptionService: """ Handles encryption/decryption of stored data. """ def __init__(self, kms_client): self.kms = kms_client async def encrypt_file(self, user_id: str, content: bytes) -> bytes: # Get or create user data key data_key = await self.get_user_data_key(user_id) # Encrypt content nonce = os.urandom(12) cipher = Cipher(algorithms.AES(data_key), modes.GCM(nonce)) encryptor = cipher.encryptor() ciphertext = encryptor.update(content) + encryptor.finalize() # Return nonce + tag + ciphertext return nonce + encryptor.tag + ciphertext async def decrypt_file(self, user_id: str, encrypted: bytes) -> bytes: # Extract components nonce = encrypted[:12] tag = encrypted[12:28] ciphertext = encrypted[28:] # Get user data key data_key = await self.get_user_data_key(user_id) # Decrypt cipher = Cipher(algorithms.AES(data_key), modes.GCM(nonce, tag)) decryptor = cipher.decryptor() return decryptor.update(ciphertext) + decryptor.finalize() async def get_user_data_key(self, user_id: str) -> bytes: # Derive from master key using HKDF master_key = await self.kms.get_master_key() return HKDF( algorithm=hashes.SHA256(), length=32, salt=user_id.encode(), info=b"hyperthyme-data-key" ).derive(master_key) ``` ### 9.3 Data Isolation **Tenant Isolation:** - Logical isolation via user\_id filtering on all queries - Consider physical isolation (separate databases) for enterprise tier ```py def ensure_user_owns_resource(user_id: str, resource_user_id: str): """ Verify user has access to a resource. """ if user_id != resource_user_id: raise HTTPException(403, "Access denied") # Applied to all resource access @app.get("/recall-files/{recall_file_id}") async def get_recall_file(recall_file_id: str, request: Request): rf = await db.get_recall_file(recall_file_id) ensure_user_owns_resource(request.state.user.id, rf.user_id) return rf ``` ### 9.4 Audit Logging ```py async def audit_log( user_id: str, action: str, resource_type: str, resource_id: str, details: dict = None, ip_address: str = None ): """ Log security-relevant events. """ await db.execute(""" INSERT INTO audit_log (user_id, action, resource_type, resource_id, details, ip_address) VALUES ($1, $2, $3, $4, $5, $6) """, user_id, action, resource_type, resource_id, json.dumps(details), ip_address) # Example usage await audit_log( user_id=user.id, action="recall_file.read", resource_type="recall_file", resource_id=rf.id, details={"include_transcript": True}, ip_address=request.client.host ) ``` ### 9.5 Data Retention & Deletion **Retention Policy:** - Default: Indefinite (user controls) - Configurable per-user retention limits - GDPR/CCPA compliant deletion on request **Deletion Process:** ```py async def delete_user_data(user_id: str, hard_delete: bool = False): """ Delete all user data. Args: user_id: User to delete hard_delete: If True, permanently delete. If False, soft delete with 30-day recovery window. """ if hard_delete: # Delete from all tables await db.execute("DELETE FROM audit_log WHERE user_id = $1", user_id) await db.execute("DELETE FROM defining_memories WHERE user_id = $1", user_id) await db.execute("DELETE FROM summary_embeddings WHERE user_id = $1", user_id) await db.execute("DELETE FROM recall_file_keywords WHERE recall_file_id IN (SELECT id FROM recall_files WHERE user_id = $1)", user_id) await db.execute("DELETE FROM recall_file_nodes WHERE recall_file_id IN (SELECT id FROM recall_files WHERE user_id = $1)", user_id) await db.execute("DELETE FROM recall_files WHERE user_id = $1", user_id) await db.execute("DELETE FROM kg_edges WHERE source_node_id IN (SELECT id FROM kg_nodes WHERE user_id = $1)", user_id) await db.execute("DELETE FROM kg_nodes WHERE user_id = $1", user_id) await db.execute("DELETE FROM user_sessions WHERE user_id = $1", user_id) await db.execute("DELETE FROM users WHERE id = $1", user_id) # Delete files await storage.delete_directory(f"/storage/{user_id}/") else: # Soft delete with recovery window await db.execute(""" UPDATE users SET deleted_at = NOW(), deletion_scheduled_for = NOW() + INTERVAL '30 days' WHERE id = $1 """, user_id) ``` --- ## 10\. Performance Requirements ### 10.1 Latency Targets | Operation | Target (P50) | Target (P99) | Notes | | :---- | :---- | :---- | :---- | | Chat (with memory) | 500ms | 2000ms | Includes retrieval \+ AI response | | Memory search | 50ms | 200ms | Hot/warm storage | | Memory search (cold) | 500ms | 1000ms | Includes decompression | | Recall File creation | 100ms | 500ms | Async summary generation | | Knowledge Graph query | 20ms | 100ms | Graph traversal | | Vector search | 30ms | 100ms | Scoped search | ### 10.2 Throughput Targets | Metric | Target | Notes | | :---- | :---- | :---- | | Requests per second (per node) | 100 RPS | Mix of read/write | | Concurrent users (per node) | 1,000 | Active sessions | | Messages logged per second | 500 | Across all users | | Search queries per second | 200 | Per node | ### 10.3 Availability Targets | Metric | Target | | :---- | :---- | | Uptime | 99.9% (8.76 hours/year downtime) | | RTO (Recovery Time Objective) | \< 1 hour | | RPO (Recovery Point Objective) | \< 5 minutes | ### 10.4 Scalability Requirements **Horizontal Scaling:** - API Gateway: Stateless, scale by adding instances - Core Engine: Stateless workers behind load balancer - PostgreSQL: Read replicas for query scaling - Vector DB: Sharding by user\_id range **Vertical Scaling:** - Start with reasonable instance sizes - Scale up before scaling out for simplicity - Document scaling thresholds ### 10.5 Resource Budgets **Per Request:** ```py REQUEST_BUDGETS = { "max_memory_mb": 512, # Memory per request "max_cpu_seconds": 10, # CPU time "max_file_reads": 20, # File operations "max_db_queries": 50, # Database queries "max_external_calls": 5, # External API calls } ``` **Per User:** ```py USER_LIMITS = { "max_recall_files": 100000, # Total recall files "max_storage_gb": 50, # Total storage "max_active_sessions": 10, # Concurrent sessions "max_requests_per_minute": 60, # Rate limit } ``` --- ## 11\. Deployment Architecture ### 11.1 Infrastructure Overview ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ PRODUCTION ENVIRONMENT │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ LOAD BALANCER │ │ │ │ (AWS ALB / GCP Load Balancer) │ │ │ └─────────────────────────────┬───────────────────────────────────┘ │ │ │ │ │ ┌──────────────────────┼──────────────────────┐ │ │ ▼ ▼ ▼ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ API Server │ │ API Server │ │ API Server │ │ │ │ Node 1 │ │ Node 2 │ │ Node 3 │ │ │ │ │ │ │ │ │ │ │ │ - FastAPI │ │ - FastAPI │ │ - FastAPI │ │ │ │ - Core │ │ - Core │ │ - Core │ │ │ │ Engine │ │ Engine │ │ Engine │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ │ │ │ │ └──────────────────────┼──────────────────────┘ │ │ │ │ │ ┌─────────────────────────────┼───────────────────────────────────┐ │ │ │ DATA LAYER │ │ │ │ │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │ │ │ │ │ PostgreSQL │ │ Redis │ │ Object Storage │ │ │ │ │ │ Primary │ │ Cluster │ │ (S3/GCS) │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ - Users │ │ - Sessions │ │ - Recall Files │ │ │ │ │ │ - KG │ │ - Cache │ │ - Transcripts │ │ │ │ │ │ - Vectors │ │ - Rate │ │ - Artifacts │ │ │ │ │ │ - Metadata │ │ limiting │ │ │ │ │ │ │ └──────┬──────┘ └─────────────┘ └─────────────────────────┘ │ │ │ │ │ │ │ │ │ ▼ │ │ │ │ ┌─────────────┐ │ │ │ │ │ PostgreSQL │ │ │ │ │ │ Replica │ │ │ │ │ │ (Read-only) │ │ │ │ │ └─────────────┘ │ │ │ └──────────────────────────────────────────────────────────────────┘ │ │ │ │ ┌─────────────────────────────────────────────────────────────────┐ │ │ │ BACKGROUND WORKERS │ │ │ │ │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ │ │ Summary │ │ Embedding │ │ Storage │ │ │ │ │ │ Generator │ │ Generator │ │ Lifecycle │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ │ Generates │ │ Creates │ │ Warm→Cold │ │ │ │ │ │ summaries │ │ vectors │ │ transitions │ │ │ │ │ │ when RF │ │ from │ │ and cleanup │ │ │ │ │ │ finalized │ │ summaries │ │ │ │ │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ └──────────────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────────┘ ``` ### 11.2 Container Configuration **Dockerfile:** ``` FROM python:3.11-slim WORKDIR /app # Install dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # Copy application COPY . . # Non-root user RUN useradd -m appuser USER appuser # Environment ENV PYTHONUNBUFFERED=1 ENV PORT=8000 EXPOSE 8000 CMD ["uvicorn", "hyperthyme.main:app", "--host", "0.0.0.0", "--port", "8000"] ``` **docker-compose.yml (Development):** ``` version: '3.8' services: api: build: . ports: - "8000:8000" environment: - DATABASE_URL=postgresql://postgres:postgres@db:5432/hyperthyme - REDIS_URL=redis://redis:6379 - STORAGE_PATH=/data/storage volumes: - ./:/app - storage_data:/data/storage depends_on: - db - redis db: image: pgvector/pgvector:pg16 environment: - POSTGRES_DB=hyperthyme - POSTGRES_USER=postgres - POSTGRES_PASSWORD=postgres volumes: - postgres_data:/var/lib/postgresql/data ports: - "5432:5432" redis: image: redis:7-alpine ports: - "6379:6379" volumes: - redis_data:/data worker: build: . command: celery -A hyperthyme.worker worker --loglevel=info environment: - DATABASE_URL=postgresql://postgres:postgres@db:5432/hyperthyme - REDIS_URL=redis://redis:6379 - STORAGE_PATH=/data/storage volumes: - storage_data:/data/storage depends_on: - db - redis volumes: postgres_data: redis_data: storage_data: ``` ### 11.3 Kubernetes Configuration **Deployment:** ``` apiVersion: apps/v1 kind: Deployment metadata: name: hyperthyme-api spec: replicas: 3 selector: matchLabels: app: hyperthyme-api template: metadata: labels: app: hyperthyme-api spec: containers: - name: api image: hyperthyme/api:latest ports: - containerPort: 8000 resources: requests: memory: "512Mi" cpu: "250m" limits: memory: "2Gi" cpu: "1000m" env: - name: DATABASE_URL valueFrom: secretKeyRef: name: hyperthyme-secrets key: database-url - name: REDIS_URL valueFrom: secretKeyRef: name: hyperthyme-secrets key: redis-url livenessProbe: httpGet: path: /health port: 8000 initialDelaySeconds: 10 periodSeconds: 10 readinessProbe: httpGet: path: /health port: 8000 initialDelaySeconds: 5 periodSeconds: 5 ``` ### 11.4 Environment Configuration ```py # config.py from pydantic_settings import BaseSettings class Settings(BaseSettings): # Database database_url: str database_pool_size: int = 20 database_max_overflow: int = 10 # Redis redis_url: str redis_pool_size: int = 10 # Storage storage_backend: str = "local" # "local", "s3", "gcs" storage_path: str = "/data/storage" s3_bucket: str = None s3_region: str = "us-east-1" # AI Models embedding_model: str = "text-embedding-ada-002" summary_model: str = "gpt-4o-mini" openai_api_key: str = None anthropic_api_key: str = None # Security jwt_secret: str jwt_algorithm: str = "HS256" jwt_expiry_hours: int = 24 # Thresholds recall_file_token_threshold: int = 50000 cold_storage_days: int = 7 # Performance max_concurrent_requests: int = 100 request_timeout_seconds: int = 30 class Config: env_file = ".env" settings = Settings() ``` --- ## 12\. Integration Patterns ### 12.1 Direct API Integration ```py # Example: Integrating Hyperthyme with a chatbot application from typing import AsyncGenerator class ChatbotWithMemory: def __init__(self, hyperthyme_api_key: str, hyperthyme_url: str): self.client = httpx.AsyncClient( base_url=hyperthyme_url, headers={"Authorization": f"Bearer {hyperthyme_api_key}"}, timeout=30.0 ) async def chat( self, user_id: str, message: str, system_prompt: str = "You are a helpful assistant." ) -> str: """ Send a message with memory context. """ response = await self.client.post("/v1/chat", json={ "message": message, "model": "claude-sonnet-4-20250514", "system_prompt": system_prompt, "include_memories": True, "memory_options": { "max_memories": 5, "token_budget": 4000 } }) response.raise_for_status() return response.json()["response"] async def stream_chat( self, user_id: str, message: str ) -> AsyncGenerator[str, None]: """ Stream a response with memory context. """ async with self.client.stream("POST", "/v1/chat", json={ "message": message, "model": "claude-sonnet-4-20250514", "stream": True }) as response: async for chunk in response.aiter_text(): yield chunk ``` ### 12.2 LangChain Integration ```py from langchain.memory import BaseMemory from langchain.schema import BaseMessage, HumanMessage, AIMessage from typing import Dict, List, Any class HyperthymeMemory(BaseMemory): """ LangChain memory backed by Hyperthyme. """ hyperthyme_client: Any user_id: str memory_key: str = "history" @property def memory_variables(self) -> List[str]: return [self.memory_key] def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """ Load relevant memories for the current input. """ query = inputs.get("input", "") # Search Hyperthyme for relevant memories results = self.hyperthyme_client.search( query=query, max_results=5 ) # Format as conversation history messages = [] for result in results: if result.transcript: # Parse transcript into messages for entry in parse_transcript(result.transcript): if entry.role == "user": messages.append(HumanMessage(content=entry.content)) else: messages.append(AIMessage(content=entry.content)) return {self.memory_key: messages} def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """ Save the current interaction to Hyperthyme. Note: This is typically handled automatically by Hyperthyme middleware. """ pass def clear(self) -> None: """Clear memory (no-op for Hyperthyme).""" pass ``` ### 12.3 MCP Server Implementation ```py from mcp import MCPServer, tool, resource class HyperthymeMCPServer(MCPServer): """ MCP server exposing Hyperthyme memory capabilities. """ def __init__(self, hyperthyme_client): super().__init__(name="hyperthyme", version="1.0.0") self.hyperthyme = hyperthyme_client @tool( name="search_memory", description="Search the user's conversation history for relevant memories. Use this when the user references past conversations or when context would be helpful." ) async def search_memory( self, query: str, max_results: int = 5 ) -> list[dict]: results = await self.hyperthyme.search( query=query, max_results=max_results ) return [ { "topic": r.topic, "date": r.date.isoformat(), "summary": r.summary, "relevance": r.relevance_score } for r in results ] @tool( name="get_decisions", description="Retrieve the user's past decisions and major milestones. Use this when the user asks about what they decided or accomplished." ) async def get_decisions( self, type_filter: str = None, limit: int = 10 ) -> list[dict]: memories = await self.hyperthyme.get_defining_memories( type_filter=type_filter, limit=limit ) return [ { "type": m.memory_type, "summary": m.summary, "date": m.detected_at.isoformat() } for m in memories ] @tool( name="get_full_conversation", description="Retrieve the complete transcript of a specific past conversation. Use this when detailed context is needed." ) async def get_full_conversation( self, recall_file_id: str ) -> dict: rf = await self.hyperthyme.get_recall_file( recall_file_id, include=["transcript"] ) return { "topic": rf.topic, "date": rf.created_at.isoformat(), "transcript": rf.transcript } @resource( uri="hyperthyme://topics", name="User Topics", description="List of topics and projects from the user's memory" ) async def get_topics(self) -> list[dict]: nodes = await self.hyperthyme.list_nodes(type_filter="topic") return [{"name": n.name, "type": n.node_type} for n in nodes] ``` ### 12.4 Webhook Integration ```py # For systems that prefer push-based updates @app.post("/webhooks/register") async def register_webhook( url: str, events: list[str], # ["memory.created", "defining_memory.detected", "recall_file.finalized"] request: Request ): """ Register a webhook to receive events. """ user_id = request.state.user.id webhook = await db.execute(""" INSERT INTO webhooks (user_id, url, events, secret) VALUES ($1, $2, $3, $4) RETURNING * """, user_id, url, events, generate_secret()) return { "id": webhook["id"], "secret": webhook["secret"] # For signature verification } async def send_webhook_event(user_id: str, event_type: str, payload: dict): """ Send event to registered webhooks. """ webhooks = await db.query(""" SELECT * FROM webhooks WHERE user_id = $1 AND $2 = ANY(events) AND active = true """, user_id, event_type) for webhook in webhooks: # Sign payload signature = hmac.new( webhook["secret"].encode(), json.dumps(payload).encode(), hashlib.sha256 ).hexdigest() # Send async asyncio.create_task( httpx.post( webhook["url"], json=payload, headers={ "X-Hyperthyme-Signature": signature, "X-Hyperthyme-Event": event_type } ) ) ``` --- ## 13\. Error Handling & Recovery ### 13.1 Error Categories ```py from enum import Enum class ErrorCategory(Enum): VALIDATION = "validation" # Invalid input AUTHENTICATION = "auth" # Auth failures AUTHORIZATION = "authz" # Permission denied NOT_FOUND = "not_found" # Resource doesn't exist RATE_LIMIT = "rate_limit" # Too many requests STORAGE = "storage" # File/storage errors DATABASE = "database" # DB errors EXTERNAL = "external" # External service errors INTERNAL = "internal" # Unexpected errors class HyperthymeError(Exception): def __init__( self, message: str, category: ErrorCategory, code: str, details: dict = None, retryable: bool = False ): super().__init__(message) self.message = message self.category = category self.code = code self.details = details or {} self.retryable = retryable # Specific errors class ValidationError(HyperthymeError): def __init__(self, message: str, field: str = None): super().__init__( message=message, category=ErrorCategory.VALIDATION, code="VALIDATION_ERROR", details={"field": field} ) class RecallFileNotFoundError(HyperthymeError): def __init__(self, recall_file_id: str): super().__init__( message=f"Recall file not found: {recall_file_id}", category=ErrorCategory.NOT_FOUND, code="RECALL_FILE_NOT_FOUND", details={"recall_file_id": recall_file_id} ) class StorageError(HyperthymeError): def __init__(self, message: str, path: str = None): super().__init__( message=message, category=ErrorCategory.STORAGE, code="STORAGE_ERROR", details={"path": path}, retryable=True ) ``` ### 13.2 Error Response Format ```py @app.exception_handler(HyperthymeError) async def hyperthyme_error_handler(request: Request, exc: HyperthymeError): status_codes = { ErrorCategory.VALIDATION: 400, ErrorCategory.AUTHENTICATION: 401, ErrorCategory.AUTHORIZATION: 403, ErrorCategory.NOT_FOUND: 404, ErrorCategory.RATE_LIMIT: 429, ErrorCategory.STORAGE: 503, ErrorCategory.DATABASE: 503, ErrorCategory.EXTERNAL: 502, ErrorCategory.INTERNAL: 500, } return JSONResponse( status_code=status_codes.get(exc.category, 500), content={ "error": { "code": exc.code, "message": exc.message, "category": exc.category.value, "details": exc.details, "retryable": exc.retryable, "request_id": request.state.request_id } } ) ``` ### 13.3 Retry Logic ```py from tenacity import ( retry, stop_after_attempt, wait_exponential, retry_if_exception_type ) class RetryableError(Exception): """Base class for retryable errors.""" pass @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=1, max=10), retry=retry_if_exception_type(RetryableError) ) async def store_file_with_retry(path: str, content: bytes): """ Store a file with automatic retry on transient failures. """ try: await storage.write(path, content) except StorageTransientError as e: raise RetryableError(str(e)) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=0.5, min=0.5, max=5), retry=retry_if_exception_type(RetryableError) ) async def generate_embedding_with_retry(text: str) -> list[float]: """ Generate embedding with retry on API failures. """ try: return await embedding_model.embed(text) except RateLimitError: raise RetryableError("Rate limited, retrying...") except TimeoutError: raise RetryableError("Timeout, retrying...") ``` ### 13.4 Circuit Breaker ```py from circuitbreaker import circuit class ExternalServiceCircuitBreaker: """ Circuit breaker for external service calls. """ def __init__(self, failure_threshold: int = 5, recovery_timeout: int = 30): self.failure_count = 0 self.failure_threshold = failure_threshold self.recovery_timeout = recovery_timeout self.state = "closed" # closed, open, half-open self.last_failure_time = None async def call(self, func, *args, **kwargs): if self.state == "open": if time.time() - self.last_failure_time > self.recovery_timeout: self.state = "half-open" else: raise CircuitOpenError("Circuit breaker is open") try: result = await func(*args, **kwargs) if self.state == "half-open": self.state = "closed" self.failure_count = 0 return result except Exception as e: self.failure_count += 1 self.last_failure_time = time.time() if self.failure_count >= self.failure_threshold: self.state = "open" raise # Usage embedding_circuit = ExternalServiceCircuitBreaker() async def get_embedding_safe(text: str): return await embedding_circuit.call(embedding_model.embed, text) ``` ### 13.5 Data Recovery ```py class RecoveryManager: """ Handles data recovery scenarios. """ async def recover_corrupted_recall_file(self, recall_file_id: str): """ Attempt to recover a corrupted Recall File. """ rf = await get_recall_file(recall_file_id) # Check what's recoverable summary_ok = await self.verify_file(rf.summary_path) keywords_ok = await self.verify_file(rf.keywords_path) transcript_ok = await self.verify_file(rf.transcript_path) if transcript_ok: # Regenerate summary and keywords from transcript transcript = await load_file(rf.transcript_path) if not summary_ok: summary = await generate_summary(transcript) await save_file(rf.summary_path, summary) if not keywords_ok: keywords = await extract_keywords(transcript) await save_file(rf.keywords_path, "\n".join(keywords)) # Regenerate embedding summary = await load_file(rf.summary_path) embedding = await embed_text(summary) await store_embedding(rf.id, embedding) return {"status": "recovered", "regenerated": ["summary", "keywords", "embedding"]} else: # Transcript is primary data - can't fully recover return {"status": "partial", "missing": "transcript", "recoverable": False} async def rebuild_knowledge_graph(self, user_id: str): """ Rebuild KG from Recall Files (disaster recovery). """ recall_files = await get_all_recall_files(user_id) # Clear existing graph await db.execute("DELETE FROM kg_edges WHERE source_node_id IN (SELECT id FROM kg_nodes WHERE user_id = $1)", user_id) await db.execute("DELETE FROM kg_nodes WHERE user_id = $1", user_id) # Rebuild from transcripts for rf in recall_files: transcript = await load_file(rf.transcript_path) entities = await extract_entities(transcript) await update_knowledge_graph(user_id, rf.id, entities) return {"status": "rebuilt", "recall_files_processed": len(recall_files)} ``` --- ## 14\. Monitoring & Observability ### 14.1 Metrics ```py from prometheus_client import Counter, Histogram, Gauge # Request metrics REQUEST_COUNT = Counter( "hyperthyme_requests_total", "Total requests", ["method", "endpoint", "status"] ) REQUEST_LATENCY = Histogram( "hyperthyme_request_latency_seconds", "Request latency", ["method", "endpoint"], buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0] ) # Memory metrics RECALL_FILES_TOTAL = Gauge( "hyperthyme_recall_files_total", "Total recall files", ["user_id", "status"] ) STORAGE_BYTES = Gauge( "hyperthyme_storage_bytes", "Storage used in bytes", ["user_id", "tier"] ) # Retrieval metrics RETRIEVAL_LATENCY = Histogram( "hyperthyme_retrieval_latency_seconds", "Memory retrieval latency", ["stage"], buckets=[0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0] ) RETRIEVAL_RESULTS = Histogram( "hyperthyme_retrieval_results", "Number of results returned", buckets=[0, 1, 2, 5, 10, 20, 50] ) # Error metrics ERRORS_TOTAL = Counter( "hyperthyme_errors_total", "Total errors", ["category", "code"] ) # Middleware to record metrics @app.middleware("http") async def metrics_middleware(request: Request, call_next): start_time = time.time() response = await call_next(request) latency = time.time() - start_time REQUEST_COUNT.labels( method=request.method, endpoint=request.url.path, status=response.status_code ).inc() REQUEST_LATENCY.labels( method=request.method, endpoint=request.url.path ).observe(latency) return response ``` ### 14.2 Logging ```py # Configure structured logging structlog.configure( processors=[ structlog.stdlib.filter_by_level, structlog.stdlib.add_logger_name, structlog.stdlib.add_log_level, structlog.processors.TimeStamper(fmt="iso"), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, structlog.processors.JSONRenderer() ], wrapper_class=structlog.stdlib.BoundLogger, context_class=dict, logger_factory=structlog.stdlib.LoggerFactory(), ) logger = structlog.get_logger() # Usage async def search_memory(user_id: str, query: str): log = logger.bind(user_id=user_id, query=query) log.info("memory_search_started") try: results = await retriever.search(query) log.info( "memory_search_completed", result_count=len(results), top_score=results[0].score if results else None ) return results except Exception as e: log.error( "memory_search_failed", error=str(e), error_type=type(e).__name__ ) raise ``` ### 14.3 Tracing ```py from opentelemetry import trace from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor # Configure tracing trace.set_tracer_provider(TracerProvider()) tracer = trace.get_tracer(__name__) otlp_exporter = OTLPSpanExporter(endpoint="http://jaeger:4317") trace.get_tracer_provider().add_span_processor( BatchSpanProcessor(otlp_exporter) ) # Usage async def retrieve_memories(user_id: str, query: str): with tracer.start_as_current_span("retrieve_memories") as span: span.set_attribute("user_id", user_id) span.set_attribute("query_length", len(query)) # Stage 1: Defining memories with tracer.start_as_current_span("check_defining_memories"): defining = await check_defining_memories(user_id, query) # Stage 2: Knowledge graph with tracer.start_as_current_span("navigate_knowledge_graph"): nodes, candidates = await navigate_knowledge_graph(user_id, query) span.set_attribute("nodes_found", len(nodes)) span.set_attribute("candidates_found", len(candidates)) # Stage 3: Keyword search with tracer.start_as_current_span("keyword_search"): filtered = await filter_by_keywords(query, candidates) # Stage 4: Semantic search with tracer.start_as_current_span("semantic_search"): ranked = await semantic_search(query, [c.id for c in filtered]) span.set_attribute("results_returned", len(ranked)) return ranked ``` ### 14.4 Alerting ``` # Prometheus alerting rules groups: - name: hyperthyme rules: - alert: HighErrorRate expr: | sum(rate(hyperthyme_errors_total[5m])) / sum(rate(hyperthyme_requests_total[5m])) > 0.05 for: 5m labels: severity: critical annotations: summary: "High error rate detected" description: "Error rate is \{\{ $value | humanizePercentage \}\}" - alert: HighLatency expr: | histogram_quantile(0.99, rate(hyperthyme_request_latency_seconds_bucket[5m]) ) > 5 for: 5m labels: severity: warning annotations: summary: "High request latency" description: "P99 latency is \{\{ $value | humanizeDuration \}\}" - alert: StorageNearCapacity expr: | sum(hyperthyme_storage_bytes) / hyperthyme_storage_limit_bytes > 0.9 for: 30m labels: severity: warning annotations: summary: "Storage capacity near limit" - alert: DatabaseConnectionPoolExhausted expr: | hyperthyme_db_connections_available == 0 for: 1m labels: severity: critical annotations: summary: "Database connection pool exhausted" ``` ### 14.5 Health Checks ```py @app.get("/health") async def health_check(): """ Comprehensive health check. """ checks = {} healthy = True # Database try: await db.execute("SELECT 1") checks["database"] = {"status": "healthy"} except Exception as e: checks["database"] = {"status": "unhealthy", "error": str(e)} healthy = False # Redis try: await redis.ping() checks["redis"] = {"status": "healthy"} except Exception as e: checks["redis"] = {"status": "unhealthy", "error": str(e)} healthy = False # Storage try: await storage.check_connectivity() checks["storage"] = {"status": "healthy"} except Exception as e: checks["storage"] = {"status": "unhealthy", "error": str(e)} healthy = False # Embedding service try: await embedding_model.health_check() checks["embedding"] = {"status": "healthy"} except Exception as e: checks["embedding"] = {"status": "degraded", "error": str(e)} # Don't fail health check for embedding - can operate without return JSONResponse( status_code=200 if healthy else 503, content={ "status": "healthy" if healthy else "unhealthy", "checks": checks, "version": VERSION, "timestamp": datetime.utcnow().isoformat() } ) ``` --- ## 15\. Future Considerations ### 15.1 Planned Enhancements **Short-term (3-6 months):** - Multi-language support for summaries and keywords - Custom embedding model fine-tuning - Batch import/export functionality - Advanced search filters (date ranges, sentiment, etc.) **Medium-term (6-12 months):** - Team/organization shared memories - Memory sharing with privacy controls - Real-time collaboration features - Mobile SDK **Long-term (12+ months):** - Federated memory across multiple Hyperthyme instances - On-device memory (edge deployment) - Integration with Cognigraph training system - Memory compression and archival strategies ### 15.2 Migration Considerations **Database Schema Evolution:** - Use Alembic for schema migrations - Maintain backward compatibility for 2 major versions - Document breaking changes **API Versioning:** - URL-based versioning (/v1/, /v2/) - Support previous version for 12 months after deprecation - Provide migration guides ### 15.3 Scalability Roadmap | Users | Architecture | | :---- | :---- | | 1-1,000 | Single instance, single PostgreSQL | | 1,000-10,000 | Multiple API instances, PostgreSQL read replicas | | 10,000-100,000 | Sharded PostgreSQL, dedicated vector DB | | 100,000+ | Regional deployment, global load balancing | --- ## Appendix A: Glossary | Term | Definition | | :---- | :---- | | Context Window | The maximum amount of text an AI model can process at once | | Defining Memory | A flagged significant moment (decision, milestone, event) | | Embedding | A numerical vector representation of text for similarity search | | Knowledge Graph | A graph database storing relationships between entities | | RAG | Retrieval-Augmented Generation \- enhancing AI with retrieved context | | Recall File | A complete conversation archive with summary, keywords, and transcript | --- ## Appendix B: Reference Links - [Neurigraph Product Family](https://neurigraph.ai) - [Hyperthyme API Documentation](https://docs.hyperthyme.ai) - [GitHub Repository](https://github.com/neurigraph/hyperthyme) --- **Document Control:** | Version | Date | Author | Changes | | :---- | :---- | :---- | :---- | | 1.0 | January 2026 | Oxford Pierpont | Initial release | --- *Hyperthyme is part of the Neurigraph product family.* *© 2026 Oxford Pierpont. All rights reserved.* --- ## Neurigraph Hyperthyme Artificial Memory Framework **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/hyperthyme-technical-overview **Description:** Technical Overview for AI Practitioners By Oxford Pierpont Abstract Hyperthyme is a persistent memory architecture for large language models that addresses t... # Neurigraph Hyperthyme Artificial Memory Framework ## Technical Overview for AI Practitioners **By Oxford Pierpont** --- ## Abstract Hyperthyme is a persistent memory architecture for large language models that addresses the fundamental limitations of context windows and session-based interactions. Unlike existing approaches that rely on summarization and extraction (which inevitably lose information), Hyperthyme implements a complete archival system with intelligent retrieval—ensuring that nothing discussed is ever truly forgotten. The architecture combines three complementary systems: a Knowledge Graph for structural navigation, a RAG database for semantic matching, and complete conversation archives (Recall Files) as the source of truth. This layered approach enables efficient retrieval from arbitrarily large memory stores while preserving verbatim access to original content. This document outlines the architectural philosophy, technical implementation, and differentiation from existing memory solutions. --- ## The Problem Space ### Context Windows Are a Bandaid The industry's response to memory limitations has been to expand context windows: | Model | Context Window | Year | | :---- | :---- | :---- | | GPT-3 | 4K tokens | 2020 | | GPT-3.5 | 16K tokens | 2023 | | GPT-4 | 128K tokens | 2023 | | Claude 3 | 200K tokens | 2024 | | Gemini 1.5 | 1M+ tokens | 2024 | This trajectory treats context as an input buffer rather than addressing the fundamental issue: LLMs have no persistent state across sessions. A 1M token context window doesn't help when the conversation ended yesterday. ### Current Memory Approaches Fall Short **Summarization-Based Memory (Mem0, MemGPT, etc.)** These systems extract "memories" from conversations—facts, preferences, decisions—and store them in compressed form. Limitations: - Summarization is lossy by definition - The summarizer decides what's important (often wrong) - Original context is discarded - No access to exact wording, code blocks, or nuanced discussions - Conflicts arise when new information contradicts old summaries **Vector-Only RAG** Embedding all content and retrieving by similarity. Limitations: - No structural understanding of relationships between topics - Poor performance on exact-match queries - Retrieval noise increases with corpus size - No distinction between routine and significant content - Expensive to search at scale without pre-filtering **Session Concatenation** Simply appending previous sessions to context. Limitations: - Quickly exceeds context limits - Wastes tokens on irrelevant history - No intelligent selection of what to include - Scales terribly ### The Real Requirement Users don't want AI that "kind of remembers" or "has a general sense." They want to say: - "What exact code did you give me for the authentication flow?" - "When did I decide to pivot the product strategy, and what was my reasoning?" - "Find that document we created about the Q3 roadmap." This requires: 1. **Complete preservation** — Nothing is lost to summarization 2. **Intelligent retrieval** — Finding the right memory without searching everything 3. **Structural organization** — Understanding relationships between topics 4. **Temporal awareness** — Knowing when things happened and what supersedes what 5. **Distinction of significance** — Separating defining moments from routine exchanges --- ## Hyperthyme Architecture ### Design Philosophy **Summaries are indexes, not storage.** Hyperthyme inverts the typical approach. Instead of storing compressed memories with optional links to sources, we store complete archives with compressed indexes for retrieval. ``` Traditional Approach: Conversation → Summarize → Store Summary → (maybe link to source) ↓ Summary is the memory Hyperthyme Approach: Conversation → Store Complete → Generate Index (summary + keywords) ↓ ↓ Archive is the Index enables source of truth fast retrieval ``` **Navigate first, search second.** At scale (millions of memories), even efficient vector search becomes slow and noisy. Hyperthyme pre-filters using structural navigation before applying semantic search. **Preserve everything, retrieve selectively.** Storage is cheap. Tokens are expensive. Store complete transcripts; inject only what's relevant to the current query. ### System Components ``` ┌─────────────────────────────────────────────────────────────────────┐ │ HYPERTHYME SYSTEM │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ ┌─────────────────────────────────────────────────────────────┐ │ │ │ DEFINING MEMORY INDEX │ │ │ │ │ │ │ │ Always-warm index of decisions, milestones, events │ │ │ │ Detected via linguistic triggers + user confirmation │ │ │ │ Links to source Recall Files for full context │ │ │ └─────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────┐ │ │ │ KNOWLEDGE GRAPH │ │ │ │ │ │ │ │ Nodes: Projects, topics, concepts, entities │ │ │ │ Edges: Relationships (contains, relates_to, discussed_in) │ │ │ │ Function: Structural navigation, scope reduction │ │ │ └─────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────┐ │ │ │ RAG DATABASE │ │ │ │ │ │ │ │ Embeddings of Recall File summaries only (not transcripts) │ │ │ │ Scoped search within KG-selected nodes │ │ │ │ Function: Semantic matching when keywords fail │ │ │ └─────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────────┐ │ │ │ RECALL FILES │ │ │ │ │ │ │ │ Complete conversation archives (50K token segments) │ │ │ │ Structure: summary.md + keywords.txt + transcript.md │ │ │ │ + artifacts.zip │ │ │ │ Function: Source of truth, verbatim retrieval │ │ │ └─────────────────────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────────────────────┘ ``` ### Recall Files: The Source of Truth A Recall File is created every \~50,000 tokens, containing: | Component | Content | Purpose | | :---- | :---- | :---- | | `summary.md` | AI-generated summary (\~500-1000 tokens) | Fast semantic matching | | `keywords.txt` | Extracted entities, terms, names | Exact-match retrieval | | `transcript.md` | Complete verbatim conversation | Source of truth | | `artifacts.zip` | Files created during conversation | Associated deliverables | **Why 50K tokens?** - Fits within retrieval budget for most models - Large enough to contain coherent topic coverage - Small enough for granular retrieval - Represents \~1-3 substantial conversations **Folder Naming Convention:** ``` {primary-topic}-{secondary-topic}-{YYYY-MM-DD}/ ``` This enables both programmatic parsing and human browsability. ### Knowledge Graph Structure The Knowledge Graph provides hierarchical organization of user context: ``` [User Root] │ ┌───────────────┼───────────────┐ │ │ │ [Project A] [Project B] [Personal] │ │ │ ┌────┴────┐ ┌────┴────┐ ┌────┴────┐ │ │ │ │ │ │ [Topic] [Topic] [Topic] [Topic] [Topic] [Topic] │ └──► [Recall File 1] └──► [Recall File 2] └──► [Recall File 3] ``` **Node Types:** - Project: Major work streams - Topic: Subjects within projects - Concept: Abstract ideas that span projects - Entity: People, companies, products mentioned - Recall File: Leaf nodes linking to archives **Edge Types:** - `contains`: Hierarchical relationship - `relates_to`: Semantic connection - `discussed_in`: Links concepts to Recall Files - `supersedes`: Temporal versioning (newer replaces older) **Graph Operations:** ```py # Scope reduction via traversal def get_relevant_recall_files(query_topics: list) -> list: nodes = [] for topic in query_topics: node = graph.find_node(topic) if node: nodes.extend(graph.get_neighborhood(node, depth=2)) recall_files = [] for node in nodes: recall_files.extend(graph.get_recall_files(node)) return deduplicate(recall_files) ``` ### RAG Layer: Semantic Search Within Scope The RAG database contains embeddings of **summaries only**, not full transcripts. This keeps the vector space manageable and search performant. **Search is always scoped:** ```py def semantic_search(query: str, user_id: str, scope: list = None) -> list: query_embedding = embed(query) if scope: # Only search within KG-selected nodes candidate_ids = [rf.id for rf in scope] results = vector_db.search( query_embedding, filter={"id": {"$in": candidate_ids}} ) else: # Fallback: search all user's memories results = vector_db.search( query_embedding, filter={"user_id": user_id} ) return results ``` ### Defining Memories: The Milestone Index Defining Memories are a separate, always-warm index of significant moments: **Detection Triggers:** | Type | Linguistic Patterns | | :---- | :---- | | Decision | "I've decided", "We're going with", "Final decision" | | Milestone | "We launched", "It's done", "Shipped" | | Event | "I'm starting", "Got the job", "Closed the deal" | | Turning Point | "This changes everything", "I realized", "From now on" | **Structure:** ```py @dataclass class DefiningMemory: id: str type: Literal["decision", "milestone", "event", "turning_point"] date: datetime summary: str context: str # Surrounding discussion source_recall_file: str related_nodes: list[str] confidence: float # Detection confidence ``` **Use Cases:** - "When did I decide X?" → Direct lookup, instant response - "What major things happened this quarter?" → Timeline query - "Show me all my product decisions" → Filtered query by type --- ## Retrieval Cascade Queries flow through a multi-stage retrieval cascade, with each stage narrowing the search space: ``` Query: "What was the code for handling payment webhooks?" │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ STAGE 1: Defining Memory Check │ │ │ │ Is this about a decision/milestone? Check defining memory index. │ │ Result: No match (this is a content retrieval, not a decision) │ └──────────────────────────────────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ STAGE 2: Knowledge Graph Navigation │ │ │ │ Identify topics: "payment", "webhooks", "code" │ │ Find nodes: [Payments] → [Stripe Integration] → [Webhooks] │ │ Get linked Recall Files: 12 candidates │ └──────────────────────────────────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ STAGE 3: Keyword Match │ │ │ │ Search keywords.txt in 12 candidates │ │ Terms: "webhook", "stripe", "payment", "handler" │ │ Result: 4 Recall Files have strong keyword overlap │ └──────────────────────────────────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ STAGE 4: Semantic Ranking (RAG) │ │ │ │ Embed query, compare to 4 candidate summaries │ │ Rank by cosine similarity │ │ Result: Top match = funnelchat-stripe-webhooks-2025-01-03 │ └──────────────────────────────────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ STAGE 5: Transcript Retrieval │ │ │ │ Load transcript.md from top-ranked Recall File │ │ Extract relevant section containing webhook code │ │ Result: Exact code block ready for injection │ └──────────────────────────────────────────────────────────────────┘ ``` **Complexity Analysis:** | Stage | Corpus Size | Operation | Time Complexity | | :---- | :---- | :---- | :---- | | Defining Memory | Small (100s) | Index lookup | O(1) | | Knowledge Graph | Medium (1000s nodes) | Graph traversal | O(log n) | | Keyword Match | Reduced (10s-100s) | String matching | O(k × m) | | RAG | Reduced (10s) | Vector similarity | O(1) with index | | Transcript Load | Single file | File read | O(1) | Even with millions of total Recall Files, retrieval remains fast because each stage dramatically reduces the candidate set. --- ## Storage Tiering ### Hot / Warm / Cold Model ``` ┌─────────────────────────────────────────────────────────────────┐ │ HOT │ │ │ │ • Current session's Recall File │ │ • Actively being written to │ │ • All components in memory │ │ • Latency: <10ms │ ├─────────────────────────────────────────────────────────────────┤ │ WARM │ │ │ │ • Accessed in last 7 days │ │ • Same KG neighborhood as current topic │ │ • Transcripts cached, artifacts uncompressed │ │ • Latency: <100ms │ ├─────────────────────────────────────────────────────────────────┤ │ COLD │ │ │ │ • Not accessed in 7+ days │ │ • Artifacts compressed │ │ • Transcripts on disk (not cached) │ │ • Keywords and summaries still indexed │ │ • Latency: <1s │ └─────────────────────────────────────────────────────────────────┘ ``` **Warming Trigger:** When a KG node is accessed, all Recall Files in that node's neighborhood are warmed: ```py async def warm_neighborhood(node_id: str): neighborhood = knowledge_graph.get_neighborhood(node_id, depth=2) for node in neighborhood: for recall_file in node.recall_files: if recall_file.state == "cold": await asyncio.gather( recall_file.decompress_artifacts(), recall_file.cache_transcript(), ) recall_file.state = "warm" ``` **Cold Storage Transition:** Background job runs nightly: ```py async def cold_storage_job(): cutoff = datetime.now() - timedelta(days=7) warm_files = RecallFile.query( state="warm", last_accessed__lt=cutoff ) for recall_file in warm_files: await recall_file.compress_artifacts() await recall_file.evict_transcript_cache() recall_file.state = "cold" await recall_file.save() ``` --- ## Model Agnosticism Hyperthyme operates as middleware, independent of the underlying LLM: ``` ┌─────────────────────────────────────────────────────────────────┐ │ APPLICATION │ └─────────────────────────────┬───────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ HYPERTHYME MIDDLEWARE │ │ │ │ • Intercepts all user messages │ │ • Executes retrieval cascade │ │ • Injects relevant memories into prompt │ │ • Logs response to active Recall File │ │ • Updates Knowledge Graph │ │ • Detects and stores Defining Memories │ └─────────────────────────────┬───────────────────────────────────┘ │ ┌─────────────────┼─────────────────┐ │ │ │ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ Claude │ │ GPT │ │ Gemini │ └─────────┘ └─────────┘ └─────────┘ ``` **API Contract:** ```py class HyperthymeClient: def chat( self, message: str, user_id: str, model: str = "claude-sonnet", include_memories: bool = True, memory_token_budget: int = 4000, ) -> Response: """ Process a message with memory-augmented context. Args: message: User's input user_id: Unique user identifier model: Target LLM (claude-*, gpt-*, gemini-*, etc.) include_memories: Whether to retrieve and inject memories memory_token_budget: Max tokens to allocate for memory context Returns: Response with assistant message and metadata """ pass ``` **MCP Integration:** Hyperthyme exposes tools via Model Context Protocol: ```py @mcp_server.tool() async def search_memory( query: str, user_id: str, max_results: int = 5 ) -> list[MemoryResult]: """Search user's conversation history.""" pass @mcp_server.tool() async def get_defining_memories( user_id: str, type_filter: str = None, since: datetime = None ) -> list[DefiningMemory]: """Retrieve user's decisions, milestones, and events.""" pass @mcp_server.tool() async def get_recall_file( recall_file_id: str, user_id: str, component: str = "transcript" ) -> str: """Retrieve specific Recall File content.""" pass ``` --- ## Comparison with Existing Solutions | Feature | Mem0 | MemGPT | Zep/Graphiti | Hyperthyme | | :---- | :---- | :---- | :---- | :---- | | Storage approach | Extracted facts | Tiered summarization | Graph \+ extraction | Complete archives | | Source of truth | Summaries | Compressed history | Knowledge graph | Verbatim transcripts | | Verbatim retrieval | No | Partial | No | Yes | | Knowledge graph | No | No | Yes | Yes | | Semantic search | Yes | Yes | Yes | Yes | | Keyword search | Limited | No | No | Yes | | Defining memories | No | No | Implicit | Explicit index | | Model agnostic | Yes | No | Yes | Yes | | File/artifact storage | No | No | No | Yes | | Storage tiering | No | Yes | No | Yes (Hot/Warm/Cold) | **Key Differentiator:** Hyperthyme is the only system that guarantees nothing is lost. Other systems trade fidelity for efficiency. We achieve efficiency through intelligent indexing while maintaining complete fidelity in storage. --- ## Implementation Considerations ### Embedding Strategy Embed summaries, not transcripts: - Keeps vector space manageable - Summaries are semantically dense - Full transcripts retrieved on-demand ### Token Budget Management When injecting memories, respect model limits: ```py def build_memory_context( memories: list[Memory], budget: int, model: str ) -> str: context_parts = [] used_tokens = 0 for memory in memories: memory_text = format_memory(memory) memory_tokens = count_tokens(memory_text, model) if used_tokens + memory_tokens > budget: break context_parts.append(memory_text) used_tokens += memory_tokens return "\n\n".join(context_parts) ``` ### Concurrent Access Multiple sessions may access the same user's memory: - Recall File writes use append-only logs - Knowledge Graph updates use optimistic locking - Vector DB supports concurrent reads ### Privacy and Security - All user data is scoped by user\_id - No cross-user data leakage - Encryption at rest for Recall Files - Access tokens required for all operations --- ## Performance Targets | Operation | Target Latency | Notes | | :---- | :---- | :---- | | Memory search (hot) | \<50ms | Cached, in-memory | | Memory search (warm) | \<200ms | Disk read for transcript | | Memory search (cold) | \<1s | Decompression \+ read | | Recall File creation | \<500ms | Async summary generation | | Knowledge Graph update | \<100ms | Incremental | | Vector embedding | \<200ms | Depends on embedding model | ### Scaling Considerations | Corpus Size | Architecture | | :---- | :---- | | \<10K Recall Files | Single PostgreSQL instance | | 10K-100K | PostgreSQL \+ dedicated vector DB | | 100K-1M | Sharded PostgreSQL \+ vector DB cluster | | \>1M | Distributed architecture with regional caching | --- ## Future Directions ### Multi-User Memory Sharing Teams could share memory contexts while maintaining individual privacy boundaries. ### Memory Compression Over Time Old memories could be progressively summarized while maintaining archive links. ### Proactive Memory System suggests relevant memories before being asked. ### Cross-Application Memory Single memory layer serving multiple AI applications (chat, coding assistant, writing tool). --- ## Conclusion Hyperthyme addresses the memory problem not by trying to make AI "smarter" about what to remember, but by ensuring nothing is forgotten and retrieval is intelligent. The architecture recognizes that: 1. Storage is cheap; losing information is expensive 2. Summarization is inherently lossy 3. Users want verbatim access to past content 4. Intelligent indexing beats brute-force search 5. Structural organization enables efficient navigation at scale By combining complete archival storage with a multi-layer retrieval system (Knowledge Graph → Keywords → RAG → Transcript), Hyperthyme provides the memory infrastructure that current LLMs lack—without sacrificing the fidelity that users actually need. --- **Neurigraph Hyperthyme Artificial Memory Framework** *By Oxford Pierpont* For technical inquiries: \[To be added\] Repository: \[To be added\] --- ## Hyperthyme Memory Framework **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework **Description:** Documents in Hyperthyme Memory Framework. --- ## Recall: Persistent Conversational Memory System **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/legacy-memory-recall-overview **Description:** Overview Recall is a memory persistence layer for the AI brain that solves two fundamental limitations in current AI systems: 1. Context window limits — Conv... # Recall: Persistent Conversational Memory System ## Overview Recall is a memory persistence layer for the AI brain that solves two fundamental limitations in current AI systems: 1. **Context window limits** — Conversations eventually exceed what the AI can "see" at once 2. **Session persistence** — Information is lost when a chat ends or a new session begins ## How It Works Recall continuously captures conversation content into simple markdown files at configurable intervals (e.g., every N tokens or based on other metrics). These files serve as a searchable memory archive that exists outside any single conversation. ### The Flow ``` Conversation happens ↓ Every [configured interval], save conversation chunk to .md file ↓ Files accumulate over time as persistent memory ↓ Later: "Do you remember X?" ↓ AI checks current context → Not found ↓ AI searches recall files → Finds relevant file ↓ AI reads file → Now has full context ↓ AI responds with remembered information ``` ### Key Characteristics - **Format**: Plain markdown files (simple, readable, portable) - **Trigger**: Configurable intervals (token count, time, or custom metric) - **Scope**: Works across any chat session—not tied to a single conversation - **Retrieval**: Search-based lookup when current context lacks needed information ## Why This Works Traditional AI memory approaches often involve: - Complex vector databases - Embedding-based semantic search - Summarization that loses detail Recall takes a simpler path: just keep the actual text. When you need it, read it. The AI can process natural language natively, so there's no need to transform the memory into a different format—markdown files are already in the language the AI understands. ## Use Cases - Recalling project decisions made weeks ago - Picking up a topic from a previous session - Cross-referencing information discussed in different chats - Building continuity in long-running projects --- *Part of the AI Brain architecture* --- ## Clean Room Specification 01: MCP Knowledge Graph Memory Server **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/01-MCP-Knowledge-Graph-Memory-Server **Description:** Document Purpose This specification describes a persistent knowledge graph memory server that exposes graph operations (entities, relations, observations) th... # Clean-Room Specification 01: MCP Knowledge Graph Memory Server ## Document Purpose This specification describes a **persistent knowledge graph memory server** that exposes graph operations (entities, relations, observations) through the **Model Context Protocol (MCP)**. An AI coding model should be able to read this document and produce a functionally identical, working implementation without any additional references. --- ## 1. System Overview ### 1.1 What This System Does This is a **single-file TypeScript application** that provides an AI assistant with persistent memory by storing a knowledge graph on disk. The server exposes **9 tools** via MCP that allow an AI to create, query, and delete entities, relations, and observations. All data is stored in a single **JSONL (JSON Lines)** file — one JSON object per line. ### 1.2 Core Architecture ``` ┌─────────────────────────────────────────────────┐ │ MCP Server │ │ (StdioServerTransport — communicates via stdin/ │ │ stdout using JSON-RPC over MCP protocol) │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ 9 Registered MCP Tools │ │ │ │ create_entities, create_relations, │ │ │ │ add_observations, delete_entities, │ │ │ │ delete_observations, delete_relations, │ │ │ │ read_graph, search_nodes, open_nodes │ │ │ └──────────────────┬──────────────────────────┘ │ │ │ │ │ ┌──────────────────▼──────────────────────────┐ │ │ │ KnowledgeGraphManager Class │ │ │ │ │ │ │ │ In-memory state: │ │ │ │ KnowledgeGraph { │ │ │ │ entities: Entity[] │ │ │ │ relations: Relation[] │ │ │ │ } │ │ │ │ │ │ │ │ Methods: │ │ │ │ loadGraph() → read entire JSONL file │ │ │ │ saveGraph() → write entire JSONL file │ │ │ │ createEntities(entities) │ │ │ │ createRelations(relations) │ │ │ │ addObservations(observations) │ │ │ │ deleteEntities(entityNames) │ │ │ │ deleteObservations(deletions) │ │ │ │ deleteRelations(relations) │ │ │ │ searchNodes(query) │ │ │ │ openNodes(names) │ │ │ │ readGraph() │ │ │ └──────────────────┬──────────────────────────┘ │ │ │ │ │ ┌──────────────────▼──────────────────────────┐ │ │ │ JSONL File on Disk │ │ │ │ (default: memory.jsonl in CWD) │ │ │ │ Each line: one JSON object │ │ │ │ Entity lines + Relation lines │ │ │ └─────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────┘ ``` ### 1.3 Key Design Decisions 1. **Full file read/write on every operation**: `loadGraph()` reads the entire file; `saveGraph()` writes the entire file. There is no incremental append or partial update. This is intentional simplicity — the file is small enough for typical use. 2. **No database**: Pure file-based storage. No SQLite, no external dependencies for persistence. 3. **Single-file implementation**: The entire server is one TypeScript file (~300 lines). No separate modules. 4. **JSONL format**: One JSON object per line. Entities first, then relations. Each object has a `type` discriminator field (`"entity"` or `"relation"`). 5. **Deduplication by name**: Entity names are unique identifiers. No numeric IDs. Entity deduplication uses exact string match on `name`. Relation deduplication uses exact match on the tuple `(from, to, relationType)`. 6. **Case-insensitive search**: The `searchNodes` method lowercases the query and compares against lowercased entity fields. 7. **Cascading deletes**: Deleting an entity also removes all relations where that entity appears as either `from` or `to`. --- ## 2. Data Model ### 2.1 Core Types ```typescript interface Entity { name: string; // Unique identifier (e.g., "John_Smith") entityType: string; // Category (e.g., "person", "organization", "concept") observations: string[]; // Array of factual strings about this entity } interface Relation { from: string; // Source entity name (must reference existing entity) to: string; // Target entity name (must reference existing entity) relationType: string; // Describes the relationship (e.g., "works_at", "knows") } interface KnowledgeGraph { entities: Entity[]; relations: Relation[]; } ``` ### 2.2 JSONL File Format The persistence file stores one JSON object per line. Each object includes a `type` discriminator field that is **only present in the file format**, not in the in-memory data structures. **Entity line format:** ```json {"type":"entity","name":"John_Smith","entityType":"person","observations":["Is 30 years old","Works at Acme Corp","Likes hiking"]} ``` **Relation line format:** ```json {"type":"relation","from":"John_Smith","to":"Acme_Corp","relationType":"works_at"} ``` **Complete file example (4 lines):** ``` {"type":"entity","name":"John_Smith","entityType":"person","observations":["Is 30 years old","Lives in Portland"]} {"type":"entity","name":"Acme_Corp","entityType":"organization","observations":["Founded in 2010","Has 500 employees"]} {"type":"relation","from":"John_Smith","to":"Acme_Corp","relationType":"works_at"} {"type":"relation","from":"John_Smith","to":"Acme_Corp","relationType":"knows_about"} ``` **CRITICAL: Write order** — When saving, ALL entity lines are written first, then ALL relation lines. This is the canonical ordering. ### 2.3 Loading and Saving **Loading (`loadGraph`):** 1. Read the entire file as a UTF-8 string 2. Split by newline character (`\n`) 3. Filter out empty lines 4. Parse each line as JSON 5. Use a `reduce` operation to accumulate into a `KnowledgeGraph`: - If `item.type === "entity"`: strip the `type` field, push to `entities` array - If `item.type === "relation"`: strip the `type` field, push to `relations` array ```text 6. If the file doesn't exist, return `{ entities: [], relations: [] }` ``` **IMPORTANT**: When loading, the `type` field is **stripped** from each object. The in-memory `Entity` and `Relation` objects do NOT contain a `type` property. The `type` field only exists in the serialized JSONL format. **Saving (`saveGraph`):** ```text 1. Map each entity to JSON string with `type: "entity"` prepended: `JSON.stringify({ type: "entity", ...entity })` ``` ```text 2. Map each relation to JSON string with `type: "relation"` prepended: `JSON.stringify({ type: "relation", ...relation })` ``` 3. Concatenate all entity lines, then all relation lines, joined by `\n` 4. Write the entire string to the file (overwriting completely) --- ## 3. KnowledgeGraphManager Class — Complete Method Specifications ### 3.1 Constructor and Initialization The class takes a single constructor parameter: the file path (string) for the JSONL storage file. ```typescript class KnowledgeGraphManager { private memoryFilePath: string; constructor(memoryFilePath: string) { this.memoryFilePath = memoryFilePath; } } ``` ### 3.2 `loadGraph(): Promise` Reads and parses the entire JSONL file. **Algorithm:** ``` 1. Try to read file at this.memoryFilePath as UTF-8 string 2. If file does not exist (ENOENT error), return { entities: [], relations: [] } 3. Split the string by "\n" 4. Filter out empty strings (handles trailing newline) 5. Parse each remaining string as JSON 6. Reduce the parsed objects into { entities: [], relations: [] }: For each item: - If item.type === "entity": Create new object WITHOUT the "type" field: { name, entityType, observations } Push to entities array - If item.type === "relation": Create new object WITHOUT the "type" field: { from, to, relationType } Push to relations array 7. Return the KnowledgeGraph ``` ```text **Key detail**: The `type` field is destructured out and discarded. Use object rest/spread: `const { type, ...rest } = item` then push `rest`. ``` ### 3.3 `saveGraph(graph: KnowledgeGraph): Promise` Writes the entire graph to disk. **Algorithm:** ``` 1. Map each entity to: JSON.stringify({ type: "entity", ...entity }) 2. Map each relation to: JSON.stringify({ type: "relation", ...relation }) 3. Concatenate: [...entityLines, ...relationLines].join("\n") 4. Write to this.memoryFilePath (overwrites entire file) ``` ### 3.4 `createEntities(entities: Entity[]): Promise` Adds new entities, skipping any whose name already exists. **Algorithm:** ``` 1. Load the full graph from disk 2. Filter the input entities: keep only those where NO existing entity has the same name - Comparison: exact string match on entity.name (case-SENSITIVE) 3. Append the filtered new entities to graph.entities 4. Save the full graph to disk 5. Return ONLY the newly created entities (the filtered list, not the full graph) ``` **Return value**: The array of entities that were actually created (excluding duplicates). ### 3.5 `createRelations(relations: Relation[]): Promise` Adds new relations, skipping exact duplicates. **Algorithm:** ``` 1. Load the full graph from disk 2. Filter the input relations: keep only those where NO existing relation matches ALL THREE fields: - relation.from === existing.from (exact match) - relation.to === existing.to (exact match) - relation.relationType === existing.relationType (exact match) 3. Append the filtered new relations to graph.relations 4. Save the full graph to disk 5. Return ONLY the newly created relations ``` **Note**: Two relations with the same `from` and `to` but different `relationType` values are NOT duplicates. They are distinct relations. ```text ### 3.6 `addObservations(observations: Array<{entityName: string, contents: string[]}>): Promise>` ``` Adds observation strings to existing entities, skipping duplicate observation strings. **Algorithm:** ``` 1. Load the full graph from disk 2. For each item in the observations array: a. Find the entity where entity.name === item.entityName b. If NOT found: throw an Error with message "Entity with name {entityName} not found" c. Filter item.contents: keep only strings NOT already in entity.observations - Comparison: exact string match (case-SENSITIVE) d. Append the filtered new observations to entity.observations e. Record { entityName: item.entityName, addedObservations: [the filtered new ones] } 3. Save the full graph to disk 4. Return the array of { entityName, addedObservations } records ``` **CRITICAL ERROR BEHAVIOR**: If an entity name is not found, the method throws an error. This aborts the entire operation — no partial saves occur if the error happens mid-iteration (because the save happens after the loop). ### 3.7 `deleteEntities(entityNames: string[]): Promise` Removes entities AND all relations connected to those entities (cascading delete). **Algorithm:** ``` 1. Load the full graph from disk 2. Filter graph.entities: keep entities whose name is NOT in the entityNames array 3. Filter graph.relations: keep relations where NEITHER from NOR to is in the entityNames array - A relation is removed if relation.from is in entityNames OR relation.to is in entityNames 4. Save the full graph to disk ``` **Return value**: void (no return data). ```text ### 3.8 `deleteObservations(deletions: Array<{entityName: string, observations: string[]}>): Promise` ``` Removes specific observation strings from entities. **Algorithm:** ``` 1. Load the full graph from disk 2. For each deletion item: a. Find the entity where entity.name === item.entityName b. If entity is found: Filter entity.observations: keep only those NOT in item.observations c. If entity is NOT found: silently skip (no error thrown) 3. Save the full graph to disk ``` **IMPORTANT DIFFERENCE from `addObservations`**: `deleteObservations` does NOT throw an error when an entity is not found. It silently ignores the deletion request for non-existent entities. ### 3.9 `deleteRelations(relations: Relation[]): Promise` Removes specific relations by exact match on all three fields. **Algorithm:** ``` 1. Load the full graph from disk 2. Filter graph.relations: keep relations where NO item in the input array matches ALL THREE: - relation.from === item.from - relation.to === item.to - relation.relationType === item.relationType 3. Save the full graph to disk ``` ### 3.10 `readGraph(): Promise` Returns the complete graph. **Algorithm:** ``` 1. Load the full graph from disk 2. Return it as-is ``` This is just a passthrough to `loadGraph()`. ### 3.11 `searchNodes(query: string): Promise` Performs case-insensitive substring search across entity names, types, and observations. **Algorithm:** ``` 1. Load the full graph from disk 2. Lowercase the query string 3. Filter entities where ANY of the following contains the lowercased query as a substring: a. entity.name.toLowerCase() b. entity.entityType.toLowerCase() c. ANY string in entity.observations where observation.toLowerCase() contains the query 4. Collect the names of all matching entities into a Set 5. Filter relations where: relation.from is in the matching names Set OR relation.to is in the matching names Set (At least ONE endpoint must be a matching entity) 6. Return { entities: [matching entities], relations: [matching relations] } ``` **Key details:** - The search is SUBSTRING matching, not exact match. If query is "john", it matches "Johnny", "john_smith", etc. - The search is case-INSENSITIVE — both query and target are lowercased before comparison. - Relations are included if EITHER endpoint matches — not just both. ### 3.12 `openNodes(names: string[]): Promise` Retrieves specific entities by exact name match, plus their connected relations. **Algorithm:** ``` 1. Load the full graph from disk 2. Filter entities where entity.name is in the names array (exact match, case-SENSITIVE) 3. Collect the matched entity names into a Set 4. Filter relations where: relation.from is in the matched names Set OR relation.to is in the matched names Set (At least ONE endpoint must be in the requested names) 5. Return { entities: [matched entities], relations: [connected relations] } ``` **Key details:** - Entity lookup is EXACT string match (case-sensitive), unlike `searchNodes` which is case-insensitive substring. - Relations are returned if AT LEAST ONE endpoint matches a requested name. This means you may get relations pointing to/from entities that are NOT in the returned entities list. --- ## 4. MCP Tool Definitions The server registers exactly **9 tools**. Each tool has a name, description, input schema (defined with Zod), and a handler function. Below is the complete specification for each. ### 4.1 `create_entities` **Description:** "Create multiple new entities in the knowledge graph" **Input Schema:** ``` { entities: Array<{ name: string, // The name of the entity entityType: string, // The type of the entity observations: string[] // An array of observation contents }> } ``` **Handler:** 1. Extract `entities` from parsed input arguments 2. Call `manager.createEntities(entities)` 3. Return the result (array of created entities) as a JSON-stringified text content response **Response format:** MCP text content containing JSON array of the newly created entities. ### 4.2 `create_relations` **Description:** "Create multiple new relations between entities in the knowledge graph. Relations are directed edges." **Input Schema:** ``` { relations: Array<{ from: string, // The name of the entity the relation starts from to: string, // The name of the entity the relation points to relationType: string // The type of the relation }> } ``` **Handler:** 1. Extract `relations` from parsed input arguments 2. Call `manager.createRelations(relations)` 3. Return result as JSON-stringified text content ### 4.3 `add_observations` **Description:** "Add new observations to existing entities in the knowledge graph" **Input Schema:** ``` { observations: Array<{ entityName: string, // The name of the entity to add observations to contents: string[] // An array of observation strings to add }> } ``` **Handler:** 1. Extract `observations` from parsed input arguments 2. Call `manager.addObservations(observations)` 3. Return result as JSON-stringified text content **Error case:** If entityName doesn't match any existing entity, this will throw and the MCP framework surfaces the error to the caller. ### 4.4 `delete_entities` **Description:** "Delete multiple entities and their associated relations from the knowledge graph" **Input Schema:** ``` { entityNames: string[] // An array of entity names to delete } ``` **Handler:** 1. Extract `entityNames` from parsed input arguments 2. Call `manager.deleteEntities(entityNames)` 3. Return confirmation message: `"Entities deleted successfully"` ### 4.5 `delete_observations` **Description:** "Delete specific observations from entities in the knowledge graph" **Input Schema:** ``` { deletions: Array<{ entityName: string, // The name of the entity observations: string[] // The observations to delete }> } ``` **Handler:** 1. Extract `deletions` from parsed input arguments 2. Call `manager.deleteObservations(deletions)` 3. Return confirmation message: `"Observations deleted successfully"` ### 4.6 `delete_relations` **Description:** "Delete multiple relations from the knowledge graph" **Input Schema:** ``` { relations: Array<{ from: string, to: string, relationType: string }> } ``` **Handler:** 1. Extract `relations` from parsed input arguments 2. Call `manager.deleteRelations(relations)` 3. Return confirmation message: `"Relations deleted successfully"` ### 4.7 `read_graph` **Description:** "Read the entire knowledge graph" ```text **Input Schema:** `{}` (empty object — no parameters) ``` **Handler:** 1. Call `manager.readGraph()` 2. Return the full KnowledgeGraph object as JSON-stringified text content ### 4.8 `search_nodes` **Description:** "Search for nodes in the knowledge graph based on a query" **Input Schema:** ``` { query: string // The search query string } ``` **Handler:** 1. Extract `query` from parsed input arguments 2. Call `manager.searchNodes(query)` 3. Return the matching KnowledgeGraph as JSON-stringified text content ### 4.9 `open_nodes` **Description:** "Open specific nodes in the knowledge graph by their names" **Input Schema:** ``` { names: string[] // An array of entity names to retrieve } ``` **Handler:** 1. Extract `names` from parsed input arguments 2. Call `manager.openNodes(names)` 3. Return the matching KnowledgeGraph as JSON-stringified text content --- ## 5. MCP Server Setup and Transport ### 5.1 Server Initialization The server uses the **MCP SDK** (`@modelcontextprotocol/sdk`) with the following setup: ```typescript ``` Server creation: ```typescript const server = new McpServer({ name: "memory", version: "1.0.0", }); ``` ### 5.2 Tool Registration Pattern Each tool is registered using `server.tool()` with this signature: ```typescript server.tool( toolName: string, description: string, inputSchema: Record, // Zod schemas for each parameter handler: async (args) => { content: [{ type: "text", text: string }] } ); ``` **IMPORTANT**: The input schema passed to `server.tool()` is a **flat object of Zod schemas**, NOT a nested Zod object. For example: ```typescript server.tool( "create_entities", "Create multiple new entities in the knowledge graph", { entities: z.array(z.object({ name: z.string().describe("The name of the entity"), entityType: z.string().describe("The type of the entity"), observations: z.array(z.string()).describe("An array of observation contents"), })).describe("Array of entities to create"), }, async ({ entities }) => { const result = await manager.createEntities(entities); return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] }; } ); ``` ### 5.3 Server Startup ```typescript const transport = new StdioServerTransport(); await server.connect(transport); ``` The server communicates entirely over **stdin/stdout** using the MCP protocol (JSON-RPC). There is no HTTP server, no port binding. ### 5.4 Process Entry Point The startup flow: 1. Determine the memory file path (see Section 6) 2. Instantiate `KnowledgeGraphManager` with the file path 3. Register all 9 tools 4. Create `StdioServerTransport` and connect --- ## 6. File Path Resolution and Migration ### 6.1 Memory File Path Resolution The storage file path is determined by a function `ensureMemoryFilePath()`: **Algorithm:** ``` 1. Check for MEMORY_FILE_PATH environment variable 2. If set and is an absolute path: use it as-is 3. If set and is a relative path: resolve it relative to the current working directory 4. If not set: use "memory.jsonl" in the current working directory 5. Run legacy migration check (see 6.2) 6. Return the resolved path ``` ### 6.2 Legacy Migration (.json → .jsonl) The system migrates from an older `.json` format to the current `.jsonl` format: **Algorithm:** ``` 1. If the resolved JSONL file already exists: return (no migration needed) 2. Derive the legacy path: replace the .jsonl extension with .json (e.g., "/path/to/memory.jsonl" → "/path/to/memory.json") 3. If the legacy .json file exists: a. Read its contents b. Write the same contents to the new .jsonl path c. Delete the old .json file 4. If neither file exists: do nothing (fresh start) ``` **Note**: The migration copies the raw content byte-for-byte. It does NOT re-parse and re-serialize. This works because the original format was already one-JSON-per-line (despite the `.json` extension). --- ## 7. Dependencies and Build Configuration ### 7.1 Runtime Dependencies | Dependency | Version | Purpose | |---|---|---| | `@modelcontextprotocol/sdk` | ^1.26.0 | MCP server framework, Zod included transitively | That's it — ONE runtime dependency (plus its transitive dependencies including `zod`). ### 7.2 Package Configuration ```json { "name": "@modelcontextprotocol/server-memory", "version": "0.6.3", "type": "module", "bin": { "mcp-server-memory": "dist/index.js" }, "files": ["dist"], "scripts": { "build": "tsc && node -e \"require('fs').chmodSync('dist/index.js', '755')\"", "watch": "tsc --watch" }, "dependencies": { "@modelcontextprotocol/sdk": "^1.26.0" } } ``` **Key details:** - ESM module (`"type": "module"`) - Single entry point compiled to `dist/index.js` - The `build` script compiles TypeScript and makes the output executable (chmod 755) - The `bin` field allows `npx` execution ### 7.3 TypeScript Configuration - Target: ES2022 or later (uses top-level await) - Module: ESM (Node16 or NodeNext module resolution) - Strict mode enabled - Output to `dist/` directory ### 7.4 Shebang Line The source file begins with: ``` #!/usr/bin/env node ``` This allows direct execution as a CLI tool. --- ## 8. Complete Behavioral Test Specifications These test cases define the exact expected behavior. An implementation must pass all of these. ### 8.1 Entity Operations **Test: Create basic entities** ```text - Input: Create entities `[{name: "Alice", entityType: "person", observations: ["Is a student"]}]` ``` - Expected: Returns array with one entity. Graph now contains one entity. **Test: Entity deduplication by name** - Setup: Create entity with name "Alice" - Input: Create entity with name "Alice" again (same or different type/observations) - Expected: Returns empty array (no new entity created). Only one "Alice" in graph. **Test: Multiple entities, some duplicates** - Setup: Create entity "Alice" - Input: Create entities ["Alice", "Bob"] - Expected: Returns array with only "Bob". Both exist in graph. **Test: Entity name matching is case-SENSITIVE** - Setup: Create entity "alice" - Input: Create entity "Alice" (capital A) - Expected: Returns ["Alice"]. Both "alice" and "Alice" exist as separate entities. ### 8.2 Relation Operations **Test: Create basic relation** ```text - Input: Create relation `{from: "Alice", to: "Bob", relationType: "knows"}` ``` - Expected: Returns array with one relation. **Test: Relation deduplication** ```text - Setup: Create relation `{from: "Alice", to: "Bob", relationType: "knows"}` ``` - Input: Create same relation again - Expected: Returns empty array. Only one relation in graph. **Test: Same endpoints, different relationType** ```text - Setup: Create relation `{from: "Alice", to: "Bob", relationType: "knows"}` ``` ```text - Input: Create relation `{from: "Alice", to: "Bob", relationType: "likes"}` ``` - Expected: Returns the new relation. Both relations exist (they are distinct). **Test: Directionality matters** ```text - Setup: Create relation `{from: "Alice", to: "Bob", relationType: "knows"}` ``` ```text - Input: Create relation `{from: "Bob", to: "Alice", relationType: "knows"}` ``` - Expected: Returns the new relation. Both exist (A→B and B→A are different). ### 8.3 Observation Operations **Test: Add observations to existing entity** - Setup: Create entity "Alice" with observations ["Is a student"] - Input: Add observations to "Alice": ["Likes pizza"] ```text - Expected: Returns `[{entityName: "Alice", addedObservations: ["Likes pizza"]}]` ``` - Entity "Alice" now has observations: ["Is a student", "Likes pizza"] **Test: Observation deduplication** - Setup: Entity "Alice" with observations ["Is a student"] - Input: Add observations to "Alice": ["Is a student", "Likes pizza"] ```text - Expected: Returns `[{entityName: "Alice", addedObservations: ["Likes pizza"]}]` ``` - Only the new, non-duplicate observation is added. **Test: Add observations to non-existent entity** - Input: Add observations to "Nonexistent": ["anything"] - Expected: THROWS Error "Entity with name Nonexistent not found" **Test: Delete observations** - Setup: Entity "Alice" with observations ["Is a student", "Likes pizza"] - Input: Delete observations from "Alice": ["Likes pizza"] - Expected: Entity "Alice" now has observations: ["Is a student"] **Test: Delete observations from non-existent entity** - Input: Delete observations from "Nonexistent": ["anything"] - Expected: NO error. Silent no-op. (Contrast with `addObservations` which DOES throw.) ### 8.4 Delete Operations **Test: Delete entity cascades to relations** - Setup: Entities "Alice" and "Bob". Relation: Alice → Bob "knows" - Input: Delete entities ["Alice"] - Expected: "Alice" entity removed. The "knows" relation is ALSO removed (cascade). **Test: Delete entity cascade — relation connected on 'to' side** - Setup: Entities "Alice" and "Bob". Relation: Bob → Alice "reports_to" - Input: Delete entities ["Alice"] - Expected: "Alice" removed. "reports_to" relation ALSO removed (Alice was the 'to' endpoint). **Test: Delete entity preserves unrelated relations** - Setup: Entities A, B, C. Relations: A→B, B→C - Input: Delete entities ["A"] - Expected: A removed. A→B removed. B→C preserved (neither endpoint is A). **Test: Delete relations by exact match** - Setup: Relations: A→B "knows", A→B "likes" ```text - Input: Delete relations `[{from: "A", to: "B", relationType: "knows"}]` ``` - Expected: "knows" removed. "likes" preserved. ### 8.5 Search Operations **Test: Search by entity name (case-insensitive)** - Setup: Entity "John_Smith" - Input: Search query "john" - Expected: Returns entity "John_Smith" **Test: Search by entity type (case-insensitive)** - Setup: Entity with entityType "Person" - Input: Search query "person" - Expected: Returns the entity **Test: Search by observation content (case-insensitive)** - Setup: Entity with observation "Works at Google" - Input: Search query "google" - Expected: Returns the entity **Test: Search returns connected relations** - Setup: Entities A and B. Relation A→B. Only A matches search. - Input: Search query matching only A - Expected: Returns entity A and relation A→B (because A is an endpoint) **Test: Search includes relations where at least one endpoint matches** - Setup: Entities A, B, C. Relations: A→B, B→C. Search matches only B. - Input: Search query matching only B - Expected: Returns entity B, relation A→B, and relation B→C **Test: Search with no matches** - Input: Search query "xyznonexistent" ```text - Expected: Returns `{ entities: [], relations: [] }` ``` ### 8.6 Open Nodes Operations **Test: Open specific nodes** - Setup: Entities A and B - Input: Open nodes ["A"] - Expected: Returns entity A (not B) **Test: Open nodes returns connected relations** - Setup: Entities A, B. Relation A→B. - Input: Open nodes ["A"] - Expected: Returns entity A and relation A→B **Test: Open nodes — name matching is case-SENSITIVE** - Setup: Entity "Alice" - Input: Open nodes ["alice"] - Expected: Returns empty (no match — exact case required) **Test: Open nodes with multiple names** - Setup: Entities A, B, C. Relations: A→B, B→C, A→C. - Input: Open nodes ["A", "C"] - Expected: Returns entities A and C. Returns relations A→B (A matches), B→C (C matches), A→C (both match). ### 8.7 Persistence Tests **Test: Data survives save/load cycle** - Create entities and relations - Load graph from disk - Verify all data matches **Test: JSONL format correctness** - Create entities and a relation - Read the raw file - Verify each line is valid JSON - Verify entity lines have `"type":"entity"` - Verify relation lines have `"type":"relation"` - Verify entities come before relations in the file **Test: Type field stripped on load** - Write JSONL manually with type fields - Load graph - Verify loaded entities/relations do NOT contain `type` property ### 8.8 File Path and Migration Tests **Test: Default path** - No MEMORY_FILE_PATH env var ```text - Expected path: `{cwd}/memory.jsonl` ``` **Test: Absolute path from env var** - Set MEMORY_FILE_PATH to "/tmp/custom.jsonl" - Expected path: `/tmp/custom.jsonl` **Test: Relative path from env var** - Set MEMORY_FILE_PATH to "data/graph.jsonl" ```text - Expected path: `{cwd}/data/graph.jsonl` ``` **Test: Migration from .json to .jsonl** - Create a file at "memory.json" with valid content - Run ensureMemoryFilePath() - Expected: "memory.jsonl" now exists with same content. "memory.json" is deleted. **Test: No migration if .jsonl already exists** - Both "memory.json" and "memory.jsonl" exist - Run ensureMemoryFilePath() - Expected: Neither file modified (JSONL takes priority) --- ## 9. Recommended System Prompt for AI Client Integration When this server is used with an AI assistant (e.g., Claude Desktop), the following system prompt pattern is recommended to guide the AI in using the memory tools effectively: ``` Follow these steps for each interaction: 1. Memory Retrieval: - Always begin by using search_nodes or open_nodes to retrieve relevant information - Use read_graph for a comprehensive view when needed 2. Memory Storage: - After each interaction, identify key facts, preferences, and relationships - Create entities for people, organizations, concepts, events - Create relations between entities - Add observations for specific facts and details - Update existing observations when information changes 3. Memory Maintenance: - Use delete operations to remove outdated or incorrect information - Consolidate related observations when entities grow large ``` --- ## 10. Edge Cases and Implementation Notes ### 10.1 Thread Safety There is NO concurrency protection. If two operations happen simultaneously, they will both `loadGraph()` and then `saveGraph()`, with the second write overwriting the first. This is acceptable for single-client MCP usage. ### 10.2 Empty Graph ```text A fresh install with no file on disk returns `{ entities: [], relations: [] }` for all read operations. All create operations work normally from an empty state. ``` ### 10.3 File Encoding All file reads and writes use UTF-8 encoding. ### 10.4 No Validation of Referential Integrity `createRelations` does NOT verify that the `from` and `to` entity names actually exist in the graph. You can create relations referencing non-existent entities. Only `addObservations` validates entity existence. ### 10.5 No Pagination All operations return full result sets. `readGraph()` returns every entity and relation. There is no pagination, limits, or cursor mechanism. ### 10.6 MCP Error Handling When a tool handler throws an error (e.g., `addObservations` for non-existent entity), the MCP SDK framework catches it and returns it as an error response to the calling AI. The server itself does not crash — the error is per-tool-call. ### 10.7 Process Lifecycle The server runs as a long-lived process, communicating over stdin/stdout. It stays alive until the parent process (e.g., Claude Desktop) terminates the connection. --- ## 11. Implementation Checklist To build a functionally identical system, implement these in order: 1. **Data types**: Define `Entity`, `Relation`, `KnowledgeGraph` interfaces 2. **KnowledgeGraphManager class**: - `loadGraph()` — JSONL parsing with type field stripping - `saveGraph()` — JSONL serialization with type field addition - All 9 operation methods exactly as specified in Section 3 3. **File path resolution**: `ensureMemoryFilePath()` with env var support and .json→.jsonl migration 4. **MCP server setup**: Initialize `McpServer` with name "memory", version "1.0.0" 5. **Tool registration**: Register all 9 tools with Zod schemas as specified in Section 4 6. **Transport**: Create `StdioServerTransport` and connect 7. **Package configuration**: ESM module with shebang line and bin entry 8. **Test suite**: All test cases from Section 8 **Total expected implementation size**: ~300 lines of TypeScript in a single file. --- ## Clean Room Specification 02: Multi Database Conversation Memory System (Markdown Memory Bank) **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/02-Multi-Database-Conversation-Memory-System **Description:** Document Purpose This specification describes a markdown file based persistent memory system for AI coding assistants. The system maintains project context a... # Clean-Room Specification 02: Multi-Database Conversation Memory System (Markdown Memory Bank) ## Document Purpose This specification describes a **markdown-file-based persistent memory system** for AI coding assistants. The system maintains project context across sessions using a structured set of markdown files stored in the project's version control. An AI coding model should be able to read this document and produce a functionally identical implementation without any additional references. --- ## 1. System Overview ### 1.1 What This System Does This system provides **persistent project memory** for AI assistants that lose context between sessions. It works by maintaining a `memory-bank/` directory of structured markdown files that the AI reads at the start of every session and updates as work progresses. The core insight: **AI assistant memory resets completely between sessions**. The Memory Bank is the sole bridge between sessions — the AI writes its understanding to disk before the session ends, then reads it back at the start of the next session. ### 1.2 Core Architecture ``` ┌─────────────────────────────────────────────────────────┐ │ AI Assistant Session │ │ │ │ ┌────────────────────────────────────────────────────┐ │ │ │ System Prompt / Custom Rules │ │ │ │ "MUST read ALL memory bank files at start of │ │ │ │ EVERY task. This is NOT optional." │ │ │ └───────────────┬────────────────────────────────────┘ │ │ │ │ │ ┌───────────────▼────────────────────────────────────┐ │ │ │ Memory Bank Manager │ │ │ │ │ │ │ │ Session Start: │ │ │ │ 1. Read ALL files in hierarchical order │ │ │ │ 2. Validate completeness │ │ │ │ 3. Extract current state │ │ │ │ │ │ │ │ During Work: │ │ │ │ 1. Reference patterns and decisions │ │ │ │ 2. Follow documented conventions │ │ │ │ │ │ │ │ Session End / Update Trigger: │ │ │ │ 1. Update activeContext.md │ │ │ │ 2. Update progress.md │ │ │ │ 3. Log new decisions │ │ │ │ 4. Document new patterns │ │ │ └───────────────┬────────────────────────────────────┘ │ │ │ │ └──────────────────┼────────────────────────────────────────┘ │ ┌─────────────▼─────────────────────┐ │ memory-bank/ Directory │ │ │ │ Layer 1: projectBrief.md │ │ Layer 2: productContext.md │ │ Layer 3: systemPatterns.md │ │ Layer 4: techContext.md │ │ Layer 5: activeContext.md │ │ Layer 6: progress.md │ │ Layer 7: decisionLog.md │ │ │ │ (All stored in project git repo) │ └────────────────────────────────────┘ ``` ### 1.3 Key Design Decisions 1. **Pure markdown files**: No database, no binary formats, no special tooling. Every file is plain markdown that humans can read and edit directly. 2. **Version-controlled**: The `memory-bank/` directory lives in the project's git repository. Memory changes are committed alongside code changes. 3. **Hierarchical file structure**: Files are organized in layers from most stable (project brief) to most volatile (active context). Read order goes bottom-up; update order goes top-down. 4. **Session-boundary persistence**: Memory is explicitly read at session start and written at session end (or when triggered). There is no real-time streaming or incremental sync. 5. **AI-driven updates**: The AI assistant itself decides what to write, when to write, and how to structure the content. The system provides conventions, not enforcement. 6. **No external dependencies**: Works with any AI assistant that can read and write files. No database, no server, no API. --- ## 2. File Structure and Hierarchy ### 2.1 Directory Layout ``` project-root/ ├── memory-bank/ │ ├── projectBrief.md # Layer 1: Foundation │ ├── productContext.md # Layer 2: Purpose & Goals │ ├── systemPatterns.md # Layer 3: Architecture │ ├── techContext.md # Layer 4: Tech Stack │ ├── activeContext.md # Layer 5: Current State │ ├── progress.md # Layer 6: Tracking │ └── decisionLog.md # Layer 7: Decision History └── .clinerules # Optional: AI behavioral rules ``` ### 2.2 File Hierarchy and Reading Order Files MUST be read in this exact order (most stable → most volatile): | Order | File | Stability | Update Frequency | |-------|------|-----------|-----------------| | 1 | projectBrief.md | Very Stable | Rarely (project inception) | | 2 | productContext.md | Stable | Occasionally (scope changes) | | 3 | systemPatterns.md | Moderate | When architecture changes | | 4 | techContext.md | Moderate | When stack changes | | 5 | activeContext.md | Volatile | Every session | | 6 | progress.md | Volatile | Every session | | 7 | decisionLog.md | Append-only | When decisions are made | **Update order is REVERSED**: When updating the memory bank, start with the most volatile files (progress, activeContext) and work backwards toward the stable files. Only update stable files when genuinely necessary. --- ## 3. Complete File Specifications ### 3.1 `projectBrief.md` — Foundation Layer **Purpose**: Defines the project's identity, scope, and core constraints. This is the foundational document that all other files build upon. **Template Structure:** ```markdown # Project Brief ## Project Name [Name of the project] ## Mission Statement [One-paragraph description of what this project is and why it exists] ## Problem Statement [What problem does this solve? Who has this problem?] ## Core Requirements - [Requirement 1] - [Requirement 2] - [Requirement 3] ## Key Constraints - [Constraint 1: e.g., must work offline] - [Constraint 2: e.g., budget limit] - [Constraint 3: e.g., timeline] ## Success Criteria - [How do we know this project succeeded?] - [Measurable outcomes] ## Scope Boundaries ### In Scope - [What is included] ### Out of Scope - [What is explicitly excluded] ``` **Update Rules:** - Created once at project inception - Updated only when fundamental project direction changes - Changes here should cascade to all downstream files ### 3.2 `productContext.md` — Purpose Layer **Purpose**: Captures the business and user perspective. Why does this exist from the user's point of view? **Template Structure:** ```markdown # Product Context ## Why This Project Exists [Business justification and user need] ## Target Users [Who uses this and how] ## User Problems - [Problem 1 that users face] - [Problem 2 that users face] ## User Experience Goals - [UX goal 1: e.g., "Should feel instant"] - [UX goal 2: e.g., "Zero configuration needed"] ## How It Should Work [High-level user flow description] ## What Makes It Different [Differentiation from alternatives] ``` **Update Rules:** - Updated when understanding of users or market changes - Updated when scope shifts significantly - Should reference projectBrief.md concepts ### 3.3 `systemPatterns.md` — Architecture Layer **Purpose**: Documents the system architecture, design patterns, coding conventions, and technical decisions that shape how code is structured. **Template Structure:** ```markdown # System Patterns ## Architecture Overview [High-level architecture description — e.g., "Monorepo with microservices"] ## Architecture Diagram ``` [ASCII diagram of system components] ``` ## Design Patterns in Use ### [Pattern Name, e.g., "Repository Pattern"] - **Where Used**: [Which modules/files] - **Why**: [Rationale] - **Implementation**: [Brief description of how it's implemented] ### [Pattern Name 2] ... ## Coding Conventions - [Convention 1: e.g., "All API responses use envelope format { data, error, meta }"] - [Convention 2: e.g., "Database queries live in /src/repositories/"] - [Convention 3: e.g., "Error handling uses custom AppError class"] ## File Organization ``` src/ ├── controllers/ # HTTP handlers ├── services/ # Business logic ├── repositories/ # Data access ├── models/ # Type definitions └── utils/ # Shared utilities ``` ## Key Technical Decisions - [Decision 1: e.g., "Using SQLite for local storage — see decisionLog.md"] - [Decision 2: e.g., "Event-driven architecture for notifications"] ``` **Update Rules:** - Updated when new patterns are adopted - Updated when architecture changes significantly (≥25% impact) - Changes should be reflected in decisionLog.md ### 3.4 `techContext.md` — Technology Layer **Purpose**: Records the complete technology stack, development setup, and operational details needed to work on the project. **Template Structure:** ```markdown # Tech Context ## Technology Stack ### Languages - [Language 1: version] - [Language 2: version] ### Frameworks - [Framework 1: version — purpose] - [Framework 2: version — purpose] ### Databases - [Database 1: purpose] ### Key Libraries - [Library 1: purpose] - [Library 2: purpose] ## Development Environment Setup 1. [Step 1: e.g., "Clone repository"] 2. [Step 2: e.g., "Install dependencies: npm install"] 3. [Step 3: e.g., "Copy .env.example to .env"] 4. [Step 4: e.g., "Run migrations: npm run migrate"] ## Build Commands - **Build**: `[command]` - **Test**: `[command]` - **Lint**: `[command]` - **Dev Server**: `[command]` ## Deployment - **Environment**: [e.g., "AWS Lambda via Serverless Framework"] - **Deploy Command**: `[command]` - **CI/CD**: [e.g., "GitHub Actions — .github/workflows/"] ## Environment Variables | Variable | Purpose | Required | |----------|---------|----------| | DATABASE_URL | Database connection | Yes | | API_KEY | External API access | Yes | ## Version Requirements - Node.js: >= 18.0.0 - npm: >= 9.0.0 ``` **Update Rules:** - Updated when dependencies change - Updated when build/deploy process changes - Keep version numbers current ### 3.5 `activeContext.md` — Current State Layer **Purpose**: Captures the current session's state — what was just done, what's in progress, and what's next. This is the MOST VOLATILE file and should be updated frequently. **Template Structure:** ```markdown # Active Context ## Current Focus [What is being worked on RIGHT NOW — 1-2 sentences] ## Recent Changes - [Change 1: what was done and when] - [Change 2: what was done and when] - [Change 3: what was done and when] ## Current State [Brief description of where things stand — what works, what doesn't] ## Active Decisions - [Decision pending: e.g., "Need to decide between REST and GraphQL for new endpoints"] - [Decision made this session: e.g., "Chose to use WebSockets for real-time updates"] ## Open Questions - [Question 1] - [Question 2] ## Blockers - [Blocker 1: what's blocking progress] ## Next Steps 1. [Immediate next task] 2. [Following task] 3. [After that] ``` **Update Rules:** - Updated at the START of every session (after reading) - Updated during work when significant progress occurs - Updated at END of every session with final state - Should be written as if briefing a new developer who knows nothing about recent work ### 3.6 `progress.md` — Tracking Layer **Purpose**: Maintains a historical record of what's been completed, what's in progress, and what's planned. Uses checkbox-style task tracking. **Template Structure:** ```markdown # Progress ## Completed - [x] [Feature/task 1 — date completed] - [x] [Feature/task 2 — date completed] - [x] [Feature/task 3 — date completed] ## In Progress - [ ] [Feature/task 4 — current status] - [ ] [Feature/task 5 — current status] ## Known Issues - [Issue 1: description and impact] - [Issue 2: description and impact] ## Technical Debt - [Debt 1: what needs cleanup] - [Debt 2: what needs cleanup] ## Upcoming - [ ] [Planned task 1] - [ ] [Planned task 2] - [ ] [Planned task 3] ## Milestones ### Phase 1: [Name] — [Status] - [Summary of phase goals and completion] ### Phase 2: [Name] — [Status] - [Summary] ``` **Update Rules:** - Move completed items from "In Progress" to "Completed" with dates - Add new items to "In Progress" as work begins - Update "Known Issues" as bugs are found or fixed - Keep "Upcoming" current with planned work ### 3.7 `decisionLog.md` — Decision History **Purpose**: Permanent record of architectural and technical decisions with full rationale. This is an APPEND-ONLY file — entries are never deleted. **Template Structure:** ```markdown # Decision Log ## Decision: [Decision Title] - **Date**: [YYYY-MM-DD] - **Status**: [Accepted / Superseded / Deprecated] - **Context**: [What situation prompted this decision] - **Options Considered**: 1. [Option A — pros/cons] 2. [Option B — pros/cons] 3. [Option C — pros/cons] - **Selected**: [Which option was chosen] - **Rationale**: [Why this option was chosen] - **Trade-offs**: [What we gave up] - **Consequences**: [Expected impact] --- ## Decision: [Another Decision Title] ... ``` **Update Rules:** - New entries appended when architectural decisions are made - Existing entries are NEVER deleted (append-only) - Mark old entries as "Superseded" when replaced by new decisions - Include date and context for every entry --- ## 4. Operating Modes The system defines distinct behavioral modes that control how the AI interacts with the memory bank. ### 4.1 Plan Mode **When activated**: At the start of a new task or when the AI needs to analyze before acting. **Workflow:** ``` 1. Read ALL Memory Bank files in hierarchical order (1→7) 2. Verify documentation completeness: - Are all core files present? - Is activeContext.md current? - Are there gaps in knowledge? 3. Analyze project context from filesystem: - Check directory structure - Read relevant source files - Understand current code state 4. Develop strategy: - Based on documented patterns (systemPatterns.md) - Consistent with tech stack (techContext.md) - Aligned with project goals (productContext.md) 5. Present approach to user before execution ``` ### 4.2 Act Mode **When activated**: After planning is complete and the user approves the approach. **Workflow:** ``` 1. Check Memory Bank (especially activeContext.md) 2. Follow .clinerules guidance for this project 3. Execute tasks using documented patterns 4. Document changes in real-time: - Update activeContext.md with current state - Add progress entries 5. On completion: - Update progress.md (move items to Completed) - Update activeContext.md (new current state) - Add decision log entries if needed ``` ### 4.3 Extended Modes (Multi-Mode System) Some implementations support 5 specialized modes: | Mode | Trigger Keywords | Memory Bank Access | Primary Actions | |------|------------------|-------------------|-----------------| | **Architect** | "design", "structure", "architect" | Read systemPatterns, productContext | Create/update architecture docs | | **Code** | "implement", "code", "build" | Full read/write access | Write code, update progress | | **Ask** | "explain", "document", "clarify" | Read-only access | Generate explanations | | **Debug** | "debug", "troubleshoot", "fix" | Read + execution | Diagnose and fix issues | | **Default** | No specific trigger | Full access | General-purpose work | **Mode-specific memory updates:** - Architect mode → Updates `systemPatterns.md` and `productContext.md` - Code mode → Updates `progress.md` and `activeContext.md` - Ask mode → Reads only, does not modify memory files - Debug mode → Records findings in `activeContext.md` --- ## 5. Memory Update Protocol ### 5.1 Update Triggers Memory bank updates are triggered by: 1. **Automatic triggers:** - Discovering new project patterns (≥25% impact on existing patterns) - Completing a significant implementation task - Making an architectural or technical decision - End of session (if not already updated) 2. **Manual triggers:** - User explicitly says "update memory bank" or "UMB" - User requests documentation refresh - User initiates a new session ### 5.2 Update Procedure When updating the memory bank, follow this procedure: ``` 1. Review what has changed since last update 2. Determine which files need updating 3. Update files in REVERSE hierarchy order: a. progress.md — Update task statuses b. activeContext.md — Write current state summary c. decisionLog.md — Append new decisions (if any) d. techContext.md — Update if stack changed e. systemPatterns.md — Update if patterns changed f. productContext.md — Update if scope changed g. projectBrief.md — Update only if fundamental change 4. Validate consistency across files 5. Confirm updates to user ``` ### 5.3 Content Writing Guidelines When writing memory bank content: - **Write for a stranger**: Assume the reader has no context about recent work - **Be specific**: Include file names, function names, error messages — not vague descriptions - **Be current**: Remove outdated information; don't accumulate stale state - **Be concise**: Each file should be readable in under 2 minutes - **Use markdown properly**: Headers, bullet points, code blocks for structure - **Include dates**: Especially in progress.md and decisionLog.md ### 5.4 JSON Escaping Rules When updating files programmatically (e.g., via MCP tools or file write APIs): - Use `\\n` for newlines in JSON strings (not literal newlines) - Use lowercase booleans: `true`, `false` - Escape quotes inside strings: `\"` - Use forward slashes in file paths: `path/to/file` (never backslashes) --- ## 6. Session Lifecycle ### 6.1 Session Start Protocol **CRITICAL RULE**: The AI MUST read ALL memory bank files at the start of EVERY task. This is non-negotiable. ``` Session Start: 1. Check if memory-bank/ directory exists - If not: Offer to initialize (create directory + template files) 2. Read files in hierarchical order: a. projectBrief.md b. productContext.md c. systemPatterns.md d. techContext.md e. activeContext.md f. progress.md g. decisionLog.md 3. Identify any missing or incomplete files 4. Present summary to user: - Current project state (from activeContext.md) - Active blockers (from activeContext.md) - In-progress work (from progress.md) - Next planned steps 5. Ask what the user wants to work on ``` ### 6.2 Mid-Session Updates During work, update memory when: - A feature or significant task is completed - A new decision is made - A blocker is encountered or resolved - The user switches focus to a different area ### 6.3 Session End Protocol ``` Session End: 1. Summarize what was accomplished this session 2. Update activeContext.md with: - What was done - Current state of things - Any open questions or blockers - Immediate next steps 3. Update progress.md: - Move completed items - Add new items discovered - Update status of in-progress items 4. Add any decision log entries 5. Commit memory changes alongside code changes ``` --- ## 7. Initialization System ### 7.1 New Project Setup When initializing a memory bank for a new project: ``` 1. Create memory-bank/ directory in project root 2. Create all 7 template files with placeholder content 3. If projectBrief.md content is provided by user: a. Populate projectBrief.md b. Derive initial content for other files based on the brief 4. If no brief provided: a. Analyze the existing codebase: - Read package.json / requirements.txt / etc. - Scan directory structure - Identify frameworks and patterns b. Auto-populate techContext.md from discovered stack c. Auto-populate systemPatterns.md from directory structure d. Create skeleton files for remaining 5. Report what was created and ask user to verify/expand ``` ### 7.2 File Validation When reading the memory bank, validate: ``` Required files (must exist): - projectBrief.md - activeContext.md - progress.md Recommended files (create if missing): - productContext.md - systemPatterns.md - techContext.md - decisionLog.md Validation checks: - File is not empty - File has at least one markdown header (#) - File content is consistent with its purpose - No obvious contradictions between files ``` --- ## 8. MCP Server Implementation For AI assistants that support MCP (Model Context Protocol), the memory bank can be exposed as an MCP server with the following tools: ### 8.1 Tool Definitions **`initialize_memory_bank`** ```text - **Input**: `{ projectPath: string, brief?: string }` ``` - **Action**: Creates `memory-bank/` directory and template files at projectPath - **Returns**: List of created files **`list_projects`** ```text - **Input**: `{}` (no parameters) ``` - **Action**: Scans MEMORY_BANK_ROOT for directories containing memory-bank/ subdirectories ```text - **Returns**: Array of `{ name: string, path: string }` ``` **`memory_bank_read`** ```text - **Input**: `{ projectPath: string, fileName: string }` ``` - **Action**: Reads specified file from project's memory-bank/ directory ```text - **Returns**: `{ content: string, lastModified: string }` ``` - **Security**: Path traversal prevention — fileName cannot contain `..` or start with `/` **`memory_bank_write`** ```text - **Input**: `{ projectPath: string, fileName: string, content: string }` ``` - **Action**: Creates a new file in the project's memory-bank/ directory ```text - **Returns**: `{ success: boolean, path: string }` ``` - **Security**: Same path traversal prevention **`memory_bank_update`** ```text - **Input**: `{ projectPath: string, fileName: string, content: string }` ``` - **Action**: Overwrites an existing file ```text - **Returns**: `{ success: boolean, path: string }` ``` **`list_project_files`** ```text - **Input**: `{ projectPath: string }` ``` - **Action**: Lists all files in the project's memory-bank/ directory ```text - **Returns**: Array of `{ name: string, size: number, lastModified: string }` ``` **`validate_project`** ```text - **Input**: `{ projectPath: string }` ``` - **Action**: Checks for required and recommended files ```text - **Returns**: `{ valid: boolean, missingRequired: string[], missingRecommended: string[] }` ``` ### 8.2 MCP Server Configuration ```typescript // Server setup const server = new McpServer({ name: "memory-bank", version: "1.0.0", }); // Environment variable const MEMORY_BANK_ROOT = process.env.MEMORY_BANK_ROOT || path.join(os.homedir(), "memory-banks"); ``` ### 8.3 Security Model - **Path traversal prevention**: All file operations validate that the resolved path stays within the project's memory-bank/ directory - **Per-project isolation**: Each project has its own memory-bank/ directory; no cross-project access - **Read-only option**: Some modes (Ask, Debug) can be restricted to read-only access - **File type restriction**: Only `.md` files are allowed ### 8.4 Auto-Approve Configuration For seamless operation, these tools can be configured for auto-approval (no user confirmation needed): - `memory_bank_read` - `memory_bank_write` - `memory_bank_update` - `list_projects` - `list_project_files` --- ## 9. Custom Rules System ### 9.1 Project-Level Rules (.clinerules) A `.clinerules` file at the project root contains project-specific instructions for the AI assistant: ```markdown # Project Rules ## Code Style - Use TypeScript strict mode - All functions must have JSDoc comments - Use named exports, not default exports ## Testing - Every new function needs a test - Use vitest for unit tests - Minimum 80% coverage for new code ## Git - Conventional commits format - Feature branches from main - Squash merge PRs ## Memory Bank - Update activeContext.md after every significant change - Log all architecture decisions in decisionLog.md - Keep progress.md in sync with GitHub issues ``` ### 9.2 Mode-Specific Rules For multi-mode systems, separate rule files per mode: ``` .roorules-architect # Rules when in architect mode .roorules-code # Rules when in code mode .roorules-ask # Rules when in ask mode .roorules-debug # Rules when in debug mode ``` ### 9.3 Rules with Path Scoping Rules can be scoped to specific file paths using YAML frontmatter: ```markdown --- paths: - "src/api/**/*.ts" - "lib/server/**/*.ts" --- # API Development Rules - All endpoints require input validation using Zod - Response format: { data, error, meta } - Rate limiting applied to all public endpoints ``` --- ## 10. AI System Prompt Specification ### 10.1 Core Instructions The following system prompt instructions are CRITICAL for correct behavior: ```markdown # Memory Bank System ## Critical Principle **I MUST read ALL memory bank files at the start of EVERY task.** This is not optional. My memory resets completely between sessions. The Memory Bank is the sole bridge between sessions. ## Read Order (Session Start) 1. projectBrief.md — Project foundation 2. productContext.md — Purpose and goals 3. systemPatterns.md — Architecture and patterns 4. techContext.md — Technology stack 5. activeContext.md — Current session state 6. progress.md — Task tracking 7. decisionLog.md — Decision history ## Operating Protocol ### Plan Mode 1. Read ALL Memory Bank files in order 2. Verify documentation completeness 3. Analyze project context from code 4. Develop strategy based on documented patterns 5. Present approach before execution ### Act Mode 1. Check Memory Bank (especially activeContext.md) 2. Follow .clinerules guidance 3. Execute using documented patterns 4. Update Memory Bank after significant changes ## Update Protocol When to update (triggers): - Discovering new patterns (≥25% impact) - Completing significant implementation - Making architecture decisions - User requests "update memory bank" or "UMB" What to update (in this order): 1. progress.md — Task status changes 2. activeContext.md — Current state, focus, blockers, next steps 3. decisionLog.md — New decisions (append only) 4. techContext.md — If stack changed 5. systemPatterns.md — If patterns changed 6. productContext.md — If scope changed 7. projectBrief.md — Only if fundamental change ## Writing Rules - Write for someone with NO context about recent work - Include specific file names, function names, error messages - Remove outdated information - Keep each file readable in under 2 minutes - Use proper markdown formatting - Include dates in progress and decision entries ``` --- ## 11. Behavioral Test Specifications ### 11.1 Initialization Tests **Test: Create memory bank for new project** - Input: Initialize memory bank at `/project/` - Expected: `memory-bank/` directory created with 7 template files - Each file has at least one markdown header and placeholder content **Test: Initialize with project brief** - Input: Initialize with brief "A REST API for managing todo items" - Expected: projectBrief.md populated with provided content; other files have derived initial content **Test: Detect existing memory bank** - Input: Project already has `memory-bank/` with files - Expected: Files are read, not overwritten ### 11.2 Read Operation Tests **Test: Read all files in order** - Setup: Memory bank with all 7 files - Input: Session start - Expected: All files read in hierarchical order (1→7) **Test: Handle missing optional files** - Setup: Memory bank with only projectBrief.md, activeContext.md, progress.md - Expected: Required files read successfully; missing optional files noted but not blocking **Test: Handle empty memory bank** - Setup: Empty `memory-bank/` directory - Expected: Offer to initialize with templates ### 11.3 Write Operation Tests **Test: Update activeContext.md** - Input: Update with current session state - Expected: File overwritten with new content; old content replaced **Test: Append to decisionLog.md** - Setup: Decision log with 2 existing entries - Input: Add new decision - Expected: New entry appended AFTER existing entries; old entries preserved **Test: Update progress.md checkboxes** - Setup: Task "Feature X" in "In Progress" - Input: Mark "Feature X" as complete - Expected: Task moved to "Completed" section with checkbox checked and date added ### 11.4 Security Tests **Test: Path traversal prevention** - Input: Read file with name `../../etc/passwd` - Expected: Rejected — path resolves outside memory-bank/ directory **Test: File type restriction** - Input: Write file named `script.sh` to memory bank - Expected: Rejected — only `.md` files allowed **Test: Project isolation** - Input: Read file from different project's memory bank - Expected: Rejected — can only access current project's files ### 11.5 Session Lifecycle Tests **Test: Full session lifecycle** ``` 1. Start session → Read all memory files 2. User requests work → Plan mode activates 3. Plan presented → User approves → Act mode 4. Work completed → Memory bank updated 5. Session end → Final state written to activeContext.md ``` **Test: Mid-session update trigger** - Trigger: User says "update memory bank" - Expected: All changed files updated in reverse hierarchy order --- ## 12. Multi-Project Support ### 12.1 Project Detection When multiple projects exist under MEMORY_BANK_ROOT: ``` MEMORY_BANK_ROOT/ ├── project-a/ │ └── memory-bank/ │ ├── projectBrief.md │ └── ... ├── project-b/ │ └── memory-bank/ │ ├── projectBrief.md │ └── ... └── project-c/ └── memory-bank/ ├── projectBrief.md └── ... ``` The system should: 1. Scan for directories containing `memory-bank/` subdirectories 2. Present available projects to the user 3. Allow project selection 4. Load the selected project's memory bank ### 12.2 Project Switching When switching between projects: 1. Save current project's memory state (update all changed files) 2. Confirm project switch with user 3. Load new project's memory bank in full 4. Present new project's current state --- ## 13. Context Window Management ### 13.1 Token Budget Memory bank content consumes context window tokens. Guidelines: - **Total context window**: ~200K tokens (model dependent) - **Memory bank budget**: Keep under 5,000 tokens total across all files - **Per-file target**: 500-1,000 tokens (~200-400 words) - **activeContext.md**: Most token-intensive — keep focused on CURRENT state only ### 13.2 Content Hygiene To prevent memory files from growing unbounded: - **activeContext.md**: Replace entirely each session (not append) - **progress.md**: Archive completed items to a separate `archive/` directory after milestones - **decisionLog.md**: This grows indefinitely but each entry is small (~100 tokens) - **All files**: Remove verbose explanations; prefer bullet points and specific references --- ## 14. Implementation Checklist To build a functionally identical system: 1. **Directory structure**: Create `memory-bank/` in project root with 7 template files 2. **File templates**: Each file pre-populated with headers and placeholder sections per Section 3 3. **Read protocol**: Implement hierarchical read order (Layer 1→7) at session start 4. **Write protocol**: Implement reverse-order updates (Layer 7→1) on triggers 5. **System prompt**: Include the full instruction set from Section 10 6. **Update triggers**: Detect ≥25% impact changes, completion events, manual "UMB" command 7. **Initialization**: Auto-detect codebase and pre-populate techContext/systemPatterns from project analysis 8. **MCP tools** (if applicable): Implement 7 tools per Section 8 9. **Security**: Path traversal prevention, file type restriction, project isolation 10. **Custom rules**: Support `.clinerules` file and mode-specific rule files 11. **Multi-project**: Scan for and list available projects under root directory **Total system is pure markdown files + AI system prompt instructions**. No database, no server (unless MCP), no special runtime. --- ## Clean Room Specification 03: Schema Driven Knowledge Graph with Dynamic Tool Generation **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/03-Temporal-Knowledge-Graph-Adaptive-Decay **Description:** Document Purpose This specification describes a schema driven knowledge graph MCP server that automatically generates CRUD tools from JSON schema definitions... # Clean-Room Specification 03: Schema-Driven Knowledge Graph with Dynamic Tool Generation ## Document Purpose This specification describes a **schema-driven knowledge graph MCP server** that automatically generates CRUD tools from JSON schema definitions. Unlike basic knowledge graphs with fixed tool sets, this system allows users to define custom entity schemas (e.g., NPCs, locations, artifacts) that automatically become MCP tools with full validation, relationship management, and transactional operations. An AI coding model should be able to produce a functionally identical implementation from this document alone. --- ## 1. System Overview ### 1.1 What This System Does This is a TypeScript MCP server that provides a **schema-governed knowledge graph** with these key innovations: 1. **Dynamic tool generation**: Define a JSON schema file → system auto-generates `add_*`, `update_*`, `delete_*` MCP tools 2. **Schema-enforced properties**: Each node type has required/optional fields with enum constraints 3. **Relationship-aware schemas**: Schema properties can define edges that auto-create when nodes are created 4. **Metadata as flat string arrays**: Structured data stored as `"Key: Value"` strings for maximum flexibility 5. **Edge weights**: Confidence/strength scoring on relationships (0.0–1.0 range) 6. **Transaction support**: Atomic multi-step operations with rollback 7. **Neighbor-inclusive queries**: Search and open operations always return immediate graph neighbors for richer context ### 1.2 Core Architecture ``` ┌────────────────────────────────────────────────────────┐ │ MCP Server Layer │ │ (StdioServerTransport, JSON-RPC) │ │ │ │ ┌────────────────────────────────────────────────────┐ │ │ │ Tool Registry │ │ │ │ │ │ │ │ Static Tools (11): │ │ │ │ add_nodes, update_nodes, delete_nodes │ │ │ │ add_edges, update_edges, delete_edges │ │ │ │ add_metadata, delete_metadata │ │ │ │ read_graph, search_nodes, open_nodes │ │ │ │ │ │ │ │ Dynamic Tools (3 per schema): │ │ │ │ add_, update_, delete_ │ │ │ │ (e.g., add_npc, update_npc, delete_npc) │ │ │ └──────────────────┬─────────────────────────────────┘ │ │ │ │ │ ┌──────────────────▼─────────────────────────────────┐ │ │ │ Application Manager (Facade) │ │ │ │ │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌──────────────┐ │ │ │ │ │NodeManager │ │EdgeManager │ │MetadataManager│ │ │ │ │ └─────────────┘ └─────────────┘ └──────────────┘ │ │ │ │ ┌─────────────┐ ┌──────────────────────────────┐ │ │ │ │ │SearchManager│ │TransactionManager │ │ │ │ │ └─────────────┘ └──────────────────────────────┘ │ │ │ └──────────────────┬─────────────────────────────────┘ │ │ │ │ │ ┌──────────────────▼─────────────────────────────────┐ │ │ │ Schema System │ │ │ │ SchemaLoader → SchemaBuilder → SchemaProcessor │ │ │ │ (Reads .schema.json files from disk) │ │ │ └──────────────────┬─────────────────────────────────┘ │ │ │ │ │ ┌──────────────────▼─────────────────────────────────┐ │ │ │ JsonLineStorage │ │ │ │ (Persists graph as JSONL file on disk) │ │ │ └─────────────────────────────────────────────────────┘ │ └──────────────────────────────────────────────────────────┘ ``` ### 1.3 Key Design Decisions 1. **Schema-first design**: All node types are defined by JSON schema files. No free-form entity creation. 2. **Metadata as string arrays**: Instead of typed objects, metadata is `["Role: Wizard", "Status: Active"]` — parsed on demand. 3. **Relationship properties in schemas**: A schema property can declare it creates an edge, making relationship creation automatic. 4. **Edge weights**: Optional 0–1 float on edges representing confidence/strength. Default 1.0. 5. **Weight averaging**: When updating weights, new evidence is averaged with current: `(current + new) / 2`. 6. **Neighbor-inclusive search**: `searchNodes` and `openNodes` always include immediate neighbor nodes and connecting edges. 7. **Transaction wrapping**: Multi-step operations (create node + edges) are wrapped in transactions with rollback support. 8. **Event system**: Before/after events emitted on all graph operations for extensibility. --- ## 2. Data Model ### 2.1 Node ```typescript interface Node { type: "node"; // Discriminator for JSONL storage name: string; // UNIQUE identifier — no two nodes share a name nodeType: string; // Schema type (e.g., "npc", "location", "artifact") metadata: string[]; // Array of "Key: Value" strings } ``` **Rules:** - `name` is the UNIQUE key. Two nodes cannot have the same name regardless of nodeType. - `nodeType` references a loaded schema name (without "add_" prefix in storage). - `metadata` is a flat array of strings in "Key: Value" format (colon-space separator). ### 2.2 Edge ```typescript interface Edge { type: "edge"; // Discriminator for JSONL storage from: string; // Source node name (must exist) to: string; // Target node name (must exist) edgeType: string; // Relationship type (e.g., "located_in", "owns") weight?: number; // 0.0 to 1.0, defaults to 1.0 if omitted } ``` **Rules:** - Edges are UNIQUE by the triple `(from, to, edgeType)`. No duplicate edges. - Both `from` and `to` must reference existing nodes (validated on creation). - Weight must be in range [0.0, 1.0]. Default is 1.0 (maximum confidence). - Edges are directional: A→B ≠ B→A. ### 2.3 Graph ```typescript interface Graph { nodes: Node[]; edges: Edge[]; } ``` ### 2.4 JSONL Storage Format One JSON object per line. All nodes first, then all edges: ``` {"type":"node","name":"Gandalf","nodeType":"npc","metadata":["Role: Wizard","Status: Active","Description: A powerful wizard"]} {"type":"node","name":"Rivendell","nodeType":"location","metadata":["Atmosphere: Peaceful","Region: Middle-earth"]} {"type":"edge","from":"Gandalf","to":"Rivendell","edgeType":"located_in","weight":1} ``` Loading/saving follows the same pattern as Spec 01: full file read on load, full file write on save, `type` field stripped from in-memory objects and re-added on serialization. --- ## 3. Schema System — The Core Innovation ### 3.1 Schema File Format Schemas are JSON files stored in a `schemas/` directory with the naming convention `.schema.json`. **Complete schema example (`npc.schema.json`):** ```json { "name": "add_npc", "description": "Add a non-player character to the knowledge graph", "properties": { "name": { "type": "string", "description": "The name of the NPC", "required": true }, "role": { "type": "string", "description": "The NPC's role or occupation", "required": true, "enum": ["Warrior", "Wizard", "Merchant", "Noble", "Peasant", "Thief"] }, "status": { "type": "string", "description": "Current status of the NPC", "required": true, "enum": ["Active", "Inactive", "Deceased", "Missing"] }, "currentLocation": { "type": "string", "description": "Where the NPC currently is", "required": false, "relationship": { "edgeType": "located_in", "nodeType": "location", "description": "The location where this NPC resides" } }, "description": { "type": "string", "description": "Physical or personality description", "required": false }, "traits": { "type": "array", "description": "Character traits", "required": false } }, "additionalProperties": true } ``` ### 3.2 Schema Property Types | Property Type | Storage | Description | |---|---|---| | `string` (no relationship) | Metadata entry: `"Key: Value"` | Simple attribute stored in metadata | | `string` (with relationship) | Edge created + metadata entry | Creates an edge AND stores in metadata | | `array` | Metadata entry: `"Key: item1, item2, item3"` | Array items joined by comma-space | | `enum` constrained | Same as string/array | Values validated against allowed list | ### 3.3 Schema Name Convention - Schema file `name` field MUST start with `"add_"` prefix (e.g., `"add_npc"`) - The entity type is derived by removing the prefix: `"add_npc"` → `"npc"` - Dynamic tools generated: `add_npc`, `update_npc`, `delete_npc` ### 3.4 Relationship Properties When a schema property includes a `relationship` block: ```json "currentLocation": { "type": "string", "required": false, "relationship": { "edgeType": "located_in", // Type of edge to create "nodeType": "location", // Expected target node type (informational) "description": "Where this entity is located" } } ``` **On creation**: If `currentLocation` is provided with value "Rivendell": 1. A metadata entry `"Current Location: Rivendell"` is added to the node ```text 2. An edge `{from: nodeName, to: "Rivendell", edgeType: "located_in"}` is created ``` **On update**: If `currentLocation` changes from "Rivendell" to "Mordor": ```text 1. Delete old edge: `{from: nodeName, to: "Rivendell", edgeType: "located_in"}` ``` ```text 2. Create new edge: `{from: nodeName, to: "Mordor", edgeType: "located_in"}` ``` 3. Update metadata: replace `"Current Location: Rivendell"` with `"Current Location: Mordor"` ### 3.5 SchemaBuilder Class Programmatically constructs schemas: ```typescript class SchemaBuilder { private name: string; private description: string; private properties: Map; private relationships: Map; private allowAdditional: boolean; constructor(name: string, description: string) addStringProperty(name: string, description: string, required: boolean, enumValues?: string[]): this addArrayProperty(name: string, description: string, required: boolean, enumValues?: string[]): this addRelationship(propertyName: string, edgeType: string, description: string, nodeType?: string): this allowAdditionalProperties(allowed: boolean): this createUpdateSchema(): SchemaBuilder // Returns new builder for update_* variant build(): SchemaConfig } ``` **`createUpdateSchema()`**: Creates a variant where ALL properties are optional (for partial updates). The `name` property becomes required (to identify which node to update). ### 3.6 SchemaLoader Class Loads schemas from disk: ```typescript class SchemaLoader { private schemasDir: string; constructor(schemasDir: string) loadSchema(schemaName: string): SchemaBuilder // Load single .schema.json file loadAllSchemas(): Map // Load entire directory } ``` **Validation on load:** - File must be valid JSON - Must have `name` (string), `description` (string), `properties` (object) - Name must start with `"add_"` - Properties must have `type` and `description` ### 3.7 SchemaProcessor — Node Creation from Schema ```typescript function createSchemaNode( args: Record, // The tool call arguments schema: SchemaBuilder, // The loaded schema entityType: string // e.g., "npc" ): { nodes: Node[], edges: Edge[] } ``` **Algorithm:** ``` 1. Extract "name" from args (required) 2. Initialize metadata: string[] = [] 3. Initialize edges: Edge[] = [] 4. For each property in schema: a. Get value from args (skip if not provided and not required) b. If required and not provided: throw validation error c. If property has enum constraint: validate value is in enum list d. If property has a relationship definition: - Create edge: { from: args.name, to: value, edgeType: relationship.edgeType } - Add metadata: "PropertyDisplayName: value" e. If property is type "array": - Add metadata: "PropertyDisplayName: item1, item2, item3" f. If property is type "string" (no relationship): - Add metadata: "PropertyDisplayName: value" 5. Create node: { name: args.name, nodeType: entityType, metadata } 6. Return { nodes: [node], edges } ``` **PropertyDisplayName conversion**: Convert camelCase property name to Title Case with spaces: - `currentLocation` → `"Current Location"` - `role` → `"Role"` --- ## 4. Manager Classes — Complete Specifications ### 4.1 ApplicationManager (Facade) Central entry point that delegates to specialized managers: ```typescript class ApplicationManager { private graphManager: GraphManager; private searchManager: SearchManager; private transactionManager: TransactionManager; // Node operations (delegate to GraphManager → NodeManager) addNodes(nodes: Node[]): Promise updateNodes(updates: NodeUpdate[]): Promise deleteNodes(names: string[]): Promise // Edge operations (delegate to GraphManager → EdgeManager) addEdges(edges: Edge[]): Promise updateEdges(updates: EdgeUpdate[]): Promise deleteEdges(edgeIds: EdgeIdentifier[]): Promise getEdges(filter?: EdgeFilter): Promise // Metadata operations (delegate to GraphManager → MetadataManager) addMetadata(nodeName: string, metadata: string[]): Promise deleteMetadata(nodeName: string, metadata: string[]): Promise // Search operations (delegate to SearchManager) readGraph(): Promise searchNodes(query: string): Promise openNodes(names: string[]): Promise // Transaction operations (delegate to TransactionManager) beginTransaction(): Promise commit(): Promise rollback(): Promise withTransaction(operation: () => Promise): Promise } ``` ### 4.2 NodeManager **`addNodes(nodes: Node[]): Promise`** ``` 1. For each node: a. Validate node has name, nodeType, metadata (array) b. Validate no existing node has the same name (throw if duplicate) 2. Append nodes to graph 3. Save graph to storage ``` ```text **`updateNodes(updates: Array<{name: string, metadata?: string[], nodeType?: string}>): Promise`** ``` ``` 1. For each update: a. Find existing node by name (throw if not found) b. If metadata provided: replace entire metadata array c. If nodeType provided: update nodeType 2. Save graph to storage ``` **`deleteNodes(names: string[]): Promise`** ``` 1. Remove all nodes whose name is in the names array 2. Remove ALL edges where from OR to is in the names array (cascade) 3. Save graph to storage ``` ### 4.3 EdgeManager **`addEdges(edges: Edge[]): Promise`** ``` 1. For each edge: a. Validate from and to are non-empty strings b. Validate both from and to reference existing nodes (throw if not) c. Validate no duplicate (from, to, edgeType) exists in graph d. If weight is undefined: set to 1.0 e. If weight provided: validate 0.0 ≤ weight ≤ 1.0 2. Append edges to graph 3. Save graph to storage ``` ```text **`updateEdges(updates: Array<{from: string, to: string, edgeType: string, newWeight?: number}>): Promise`** ``` ``` 1. For each update: a. Find existing edge by (from, to, edgeType) b. If newWeight provided: set edge.weight = updateWeight(currentWeight, newWeight) updateWeight formula: (current + new) / 2 2. Save graph to storage ``` ```text **`deleteEdges(identifiers: Array<{from: string, to: string, edgeType: string}>): Promise`** ``` ``` 1. Filter graph.edges: keep edges that do NOT match any identifier on all three fields 2. Save graph to storage ``` ### 4.4 MetadataManager **`addMetadata(nodeName: string, entries: string[]): Promise`** ``` 1. Find node by name (throw if not found) 2. For each entry in entries: a. If entry NOT already in node.metadata: append it (Deduplication by exact string match) 3. Save graph to storage ``` **`deleteMetadata(nodeName: string, entries: string[]): Promise`** ``` 1. Find node by name (throw if not found) 2. Filter node.metadata: keep entries NOT in the deletion list (Exact string match) 3. Save graph to storage ``` ### 4.5 SearchManager **`readGraph(): Promise`** ``` 1. Load graph from storage 2. Return the complete graph as-is ``` **`searchNodes(query: string): Promise`** ``` 1. Load graph from storage 2. Lowercase the query 3. Find matching nodes where query is substring of (case-insensitive): a. node.name b. node.nodeType c. ANY entry in node.metadata 4. Collect matching node names into a Set 5. Find all edges where from OR to is in the matching set 6. Extract neighbor names from those edges (names not already in matching set) 7. Find neighbor nodes by name 8. Return { nodes: [...matchingNodes, ...neighborNodes], edges: [...connectingEdges] } ``` **CRITICAL**: Search returns BOTH the directly matching nodes AND their immediate neighbors. This provides richer context for the AI. **`openNodes(names: string[]): Promise`** ``` 1. Load graph from storage 2. Find nodes where name is in the input array (exact match) 3. Find all edges where from OR to is in the names 4. Extract neighbor names from edges 5. Find neighbor nodes 6. Return { nodes: [...requestedNodes, ...neighborNodes], edges: [...connectingEdges] } ``` ### 4.6 TransactionManager ```typescript class TransactionManager { private inTransaction: boolean = false; private rollbackActions: Array<{ action: () => Promise, description: string }> = []; beginTransaction(): Promise // Sets inTransaction = true, clears rollback queue addRollbackAction(action: () => Promise, description: string): void // Pushes to rollback queue commit(): Promise // Clears rollback queue, sets inTransaction = false rollback(): Promise // Executes rollback actions in REVERSE order (LIFO) // Continues executing remaining actions even if one fails // Sets inTransaction = false withTransaction(operation: () => Promise): Promise // Convenience wrapper: // 1. beginTransaction() // 2. Try: result = await operation(); commit(); return result // 3. Catch: rollback(); re-throw error isInTransaction(): boolean } ``` **Key behavior:** - Rollback actions execute in LIFO order (last registered, first executed) - A failing rollback action does NOT prevent remaining rollback actions from executing - `withTransaction()` provides auto-commit on success, auto-rollback on failure --- ## 5. Dynamic Tool Generation ### 5.1 How It Works For each `.schema.json` file loaded, the system generates THREE MCP tools: 1. **`add_`**: Creates a new node of this type with schema-validated properties 2. **`update_`**: Updates an existing node (all properties optional except `name`) 3. **`delete_`**: Deletes a node by name and type ### 5.2 Tool Schema Generation Given an NPC schema with properties `name`, `role`, `status`, `currentLocation`, `description`, `traits`: **Generated `add_npc` tool input schema:** ```json { "type": "object", "properties": { "npc": { "type": "object", "properties": { "name": { "type": "string", "description": "The name of the NPC" }, "role": { "type": "string", "description": "The NPC's role", "enum": ["Warrior", "Wizard", ...] }, "status": { "type": "string", "description": "Current status", "enum": ["Active", "Inactive", ...] }, "currentLocation": { "type": "string", "description": "Where the NPC currently is" }, "description": { "type": "string", "description": "Physical or personality description" }, "traits": { "type": "array", "items": { "type": "string" }, "description": "Character traits" } }, "required": ["name", "role", "status"] } }, "required": ["npc"] } ``` ```text **Note**: The arguments are wrapped in an object keyed by the entity type name (e.g., `{ npc: { ... } }`). ``` **Generated `update_npc` tool input schema:** - Same structure but `required` only includes `["name"]` - All other properties are optional for partial updates **Generated `delete_npc` tool input schema:** ```json { "type": "object", "properties": { "npc": { "type": "object", "properties": { "name": { "type": "string", "description": "The name of the NPC to delete" } }, "required": ["name"] } }, "required": ["npc"] } ``` ### 5.3 Dynamic Tool Execution Flow **Add operation (`add_npc`):** ``` 1. Extract entity data from args.npc 2. Call SchemaProcessor.createSchemaNode(args.npc, schema, "npc") 3. Begin transaction 4. Add nodes via NodeManager 5. Add edges via EdgeManager (if relationship properties present) 6. Commit transaction 7. Return created nodes and edges ``` **Update operation (`update_npc`):** ``` 1. Extract entity data from args.npc 2. Find existing node by name AND nodeType 3. Begin transaction 4. For each provided property: a. If it has a relationship: - Delete old edges of same edgeType from this node - Create new edge to new target b. Parse existing metadata into key→value map c. Update the changed key d. Rebuild metadata array from map 5. Update node via NodeManager 6. Commit transaction 7. Return updated node and edges ``` **Delete operation (`delete_npc`):** ``` 1. Extract name from args.npc.name 2. Find node by name (validate it exists and matches nodeType) 3. Begin transaction 4. Delete node via NodeManager (cascades to edges) 5. Commit transaction 6. Return confirmation ``` --- ## 6. Static MCP Tools (11 Tools) In addition to dynamic schema tools, the server provides 11 always-available tools: ### 6.1 Graph Mutation Tools **`add_nodes`** ```text - Input: `{ nodes: Array<{name: string, nodeType: string, metadata: string[]}> }` ``` - Action: Add nodes to graph (validates uniqueness) **`update_nodes`** ```text - Input: `{ nodes: Array<{name: string, metadata?: string[], nodeType?: string}> }` ``` - Action: Update existing nodes by name **`delete_nodes`** ```text - Input: `{ nodeNames: string[] }` ``` - Action: Delete nodes and cascade-delete connected edges **`add_edges`** ```text - Input: `{ edges: Array<{from: string, to: string, edgeType: string, weight?: number}> }` ``` - Action: Add edges (validates node existence, uniqueness, weight range) **`update_edges`** ```text - Input: `{ edges: Array<{from: string, to: string, edgeType: string, weight?: number}> }` ``` - Action: Update edge weights using averaging formula **`delete_edges`** ```text - Input: `{ edges: Array<{from: string, to: string, edgeType: string}> }` ``` - Action: Remove edges by exact triple match ### 6.2 Metadata Tools **`add_metadata`** ```text - Input: `{ nodeName: string, metadata: string[] }` ``` - Action: Append metadata entries to node (deduplicated) **`delete_metadata`** ```text - Input: `{ nodeName: string, metadata: string[] }` ``` - Action: Remove specific metadata entries from node ### 6.3 Search Tools **`read_graph`** ```text - Input: `{}` (no parameters) ``` - Action: Return complete graph **`search_nodes`** ```text - Input: `{ query: string }` ``` - Action: Case-insensitive substring search + neighbor expansion **`open_nodes`** ```text - Input: `{ names: string[] }` ``` - Action: Exact name lookup + neighbor expansion --- ## 7. Tool Handler Routing ### 7.1 Handler Architecture ``` ToolHandlerFactory.getHandler(toolName) routes to: ├── GraphToolHandler → add_nodes, update_nodes, delete_nodes, │ add_edges, update_edges, delete_edges ├── SearchToolHandler → read_graph, search_nodes, open_nodes ├── MetadataToolHandler → add_metadata, delete_metadata └── DynamicToolHandler → all add_*/update_*/delete_* schema tools ``` **Routing logic:** ``` if toolName matches /^(add|update|delete)_(nodes|edges)$/ → GraphToolHandler if toolName matches /^(read_graph|search_nodes|open_nodes)$/ → SearchToolHandler if toolName matches /^(add|delete)_metadata$/ → MetadataToolHandler otherwise → DynamicToolHandler (schema-generated tools) ``` ### 7.2 Response Format All tool responses follow this structure: **Success response:** ```json { "content": [ { "type": "text", "text": "{\"data\": {...}, \"actionTaken\": \"Created npc: Gandalf\", \"timestamp\": \"2026-03-08T...\"}" } ], "isError": false } ``` **Error response:** ```json { "content": [ { "type": "text", "text": "{\"error\": \"Node 'Gandalf' already exists\", \"context\": {...}, \"suggestions\": [...]}" } ], "isError": true } ``` --- ## 8. Event System ### 8.1 EventEmitter Simple publish-subscribe system: ```typescript class EventEmitter { on(event: string, listener: Function): () => void // Returns unsubscribe function off(event: string, listener: Function): void once(event: string, listener: Function): () => void emit(event: string, data?: any): boolean // Returns true if listeners exist removeAllListeners(event?: string): void } ``` ### 8.2 Events Emitted | Event | When Emitted | Data | |---|---|---| ```text | `beforeAddNodes` | Before nodes are added | `{ nodes: Node[] }` | ``` ```text | `afterAddNodes` | After nodes are added | `{ nodes: Node[] }` | ``` ```text | `beforeDeleteNodes` | Before nodes are deleted | `{ names: string[] }` | ``` ```text | `afterDeleteNodes` | After nodes are deleted | `{ names: string[] }` | ``` ```text | `beforeAddEdges` | Before edges are added | `{ edges: Edge[] }` | ``` ```text | `afterAddEdges` | After edges are added | `{ edges: Edge[] }` | ``` ```text | `beforeSearch` | Before search operation | `{ query: string }` | ``` ```text | `afterSearch` | After search operation | `{ results: Graph }` | ``` | `beforeBeginTransaction` | Before transaction starts | `{}` | | `afterCommit` | After transaction commits | `{}` | | `beforeRollback` | Before rollback executes | `{}` | | `afterRollback` | After rollback completes | `{}` | --- ## 9. Metadata Processing ### 9.1 Metadata Format All metadata entries are strings in `"Key: Value"` format: ``` ["Role: Wizard", "Status: Active", "Description: A powerful wizard", "Traits: brave, wise"] ``` ### 9.2 MetadataProcessor Utilities ```typescript class MetadataProcessor { // Parse single entry static parseEntry(entry: string): { key: string, value: string } // Splits on FIRST ": " (colon-space). Everything before is key, everything after is value. // Format entry static formatEntry(key: string, value: string): string // Returns "key: value" // Create map from metadata array static createMap(metadata: string[]): Map // Parses all entries into key→value map // Merge multiple metadata arrays (deduplicates) static merge(...arrays: string[][]): string[] // Get value for a key static getValue(metadata: string[], key: string): string | null // Returns first matching value, or null // Filter by key static filterByKey(metadata: string[], key: string): string[] // Returns all entries matching key } ``` --- ## 10. Edge Weight System ### 10.1 Weight Properties - Range: 0.0 (no confidence) to 1.0 (maximum confidence) - Default: 1.0 when not specified - Meaning: Strength or confidence of the relationship ### 10.2 Weight Utilities ```typescript class EdgeWeightUtils { static validateWeight(weight: number): void // Throws if weight < 0 or weight > 1 static ensureWeight(edge: Edge): Edge // If edge.weight is undefined, set to 1.0. Returns edge. static updateWeight(current: number, newEvidence: number): number // Returns (current + newEvidence) / 2 // This averaging formula allows gradual confidence updates static combineWeights(weights: number[]): number // Returns Math.max(...weights) // Used for parallel edges (same endpoints, different types) } ``` **Example of weight evolution:** ``` Initial: weight = 1.0 (default) Update with 0.6: (1.0 + 0.6) / 2 = 0.8 Update with 0.4: (0.8 + 0.4) / 2 = 0.6 Update with 1.0: (0.6 + 1.0) / 2 = 0.8 ``` --- ## 11. Validation Rules ### 11.1 Node Validation ```typescript class GraphValidator { static validateNodeProperties(node: Node): void // - node.name must be non-empty string // - node.nodeType must be non-empty string // - node.metadata must be an array static validateNodeDoesNotExist(graph: Graph, name: string): void // - Throws if any node in graph has this name static validateNodeExists(graph: Graph, name: string): Node // - Returns the node, or throws if not found } ``` ### 11.2 Edge Validation ```typescript static validateEdgeProperties(edge: Edge): void // - edge.from must be non-empty string // - edge.to must be non-empty string // - edge.edgeType must be non-empty string // - If weight defined: must be 0.0 ≤ weight ≤ 1.0 static validateEdgeUniqueness(graph: Graph, edge: Edge): void // - Throws if any existing edge matches (from, to, edgeType) static validateEdgeReferences(graph: Graph, edges: Edge[]): void // - For each edge: both from and to must reference existing nodes ``` --- ## 12. Configuration ```typescript const CONFIG = { SERVER: { NAME: "memorymesh", VERSION: "0.3.0" }, PATHS: { SCHEMAS_DIR: path.join(__dirname, "..", "data", "schemas"), MEMORY_FILE: path.join(__dirname, "..", "data", "memory.json") } }; ``` --- ## 13. Example Schema Set The system ships with 11 pre-built schemas (designed for RPG/storytelling use cases): | Schema | Entity Type | Key Properties | Relationships | |---|---|---|---| | npc.schema.json | npc | name, role, status, description, traits | currentLocation → located_in | | location.schema.json | location | name, atmosphere, region | parentLocation → contained_in | | artifact.schema.json | artifact | name, rarity, properties | owner → owned_by | | quest.schema.json | quest | name, status, objectives, rewards | location → takes_place_in | | faction.schema.json | faction | name, alignment, goals | headquarters → based_in | | player_character.schema.json | player_character | name, class, level, stats | currentLocation → located_in | | inventory.schema.json | inventory | name, contents, capacity | owner → belongs_to | | skills.schema.json | skills | name, type, level, effects | - | | currency.schema.json | currency | name, denomination, value | - | | transportation.schema.json | transportation | name, type, speed | owner → owned_by | | temporal.schema.json | temporal | name, timestamp, event | location → occurred_at | These schemas are **customizable and replaceable**. Users can add their own schemas for any domain. --- ## 14. Complete Behavioral Test Specifications ### 14.1 Schema Loading Tests **Test: Load valid schema** - Input: Valid npc.schema.json - Expected: SchemaBuilder created with correct properties and relationships **Test: Reject schema without "add_" prefix** - Input: Schema with name "npc" (no prefix) - Expected: Validation error thrown **Test: Load all schemas from directory** - Input: Directory with 3 schema files - Expected: 3 SchemaBuilder instances, 9 dynamic tools registered ### 14.2 Dynamic Tool Tests **Test: Create node via schema tool** ```text - Input: `add_npc` with `{npc: {name: "Gandalf", role: "Wizard", status: "Active"}}` ``` - Expected: Node created with metadata `["Role: Wizard", "Status: Active"]` **Test: Create node with relationship property** ```text - Input: `add_npc` with `{npc: {name: "Gandalf", role: "Wizard", status: "Active", currentLocation: "Rivendell"}}` ``` ```text - Expected: Node created AND edge `{from: "Gandalf", to: "Rivendell", edgeType: "located_in"}` created ``` **Test: Update node via schema tool** - Setup: NPC "Gandalf" with currentLocation "Rivendell" ```text - Input: `update_npc` with `{npc: {name: "Gandalf", currentLocation: "Mordor"}}` ``` - Expected: Old edge to Rivendell deleted. New edge to Mordor created. Metadata updated. **Test: Delete node via schema tool** - Setup: NPC "Gandalf" with edges ```text - Input: `delete_npc` with `{npc: {name: "Gandalf"}}` ``` - Expected: Node and all connected edges removed **Test: Enum validation** - Input: `add_npc` with role "Dragon" (not in enum) - Expected: Validation error ### 14.3 Search with Neighbor Expansion **Test: Search returns neighbors** - Setup: Nodes A, B, C. Edge A→B. Search matches only A. - Expected: Returns nodes [A, B], edges [A→B] **Test: Open nodes returns neighbors** - Setup: Nodes A, B, C. Edges: A→B, B→C. - Input: openNodes(["A"]) - Expected: Returns nodes [A, B], edges [A→B] ### 14.4 Transaction Tests **Test: Successful transaction commits** - Begin transaction → Add node → Add edge → Commit - Expected: Both node and edge persisted **Test: Failed transaction rolls back** - Begin transaction → Add node → Fail on edge (bad reference) → Rollback - Expected: Node is also removed (rolled back) **Test: withTransaction auto-commits on success** ```text - withTransaction(() => { addNode(); addEdge(); }) ``` - Expected: Both persisted **Test: withTransaction auto-rolls-back on error** ```text - withTransaction(() => { addNode(); throw Error(); }) ``` - Expected: Node not persisted ### 14.5 Edge Weight Tests **Test: Default weight is 1.0** - Create edge without weight - Expected: edge.weight === 1.0 **Test: Weight averaging on update** - Setup: Edge with weight 0.8 - Update with weight 0.6 - Expected: New weight = (0.8 + 0.6) / 2 = 0.7 **Test: Weight out of range rejected** - Create edge with weight 1.5 - Expected: Validation error --- ## 15. Implementation Checklist 1. **Core data types**: Node, Edge, Graph interfaces 2. **JSONL storage**: Load/save with type discriminator 3. **MetadataProcessor**: Parse, format, merge, query metadata strings 4. **EdgeWeightUtils**: Validate, default, average, combine weights 5. **GraphValidator**: Node/edge property and uniqueness validation 6. **NodeManager**: CRUD with cascade deletes 7. **EdgeManager**: CRUD with weight handling and node reference validation 8. **MetadataManager**: Add/delete metadata entries with deduplication 9. **SearchManager**: Case-insensitive search + neighbor expansion; exact open + neighbor expansion 10. **TransactionManager**: Begin/commit/rollback with LIFO action queue 11. **ApplicationManager**: Facade delegating to all managers 12. **EventEmitter**: Simple pub-sub for before/after hooks 13. **SchemaBuilder**: Programmatic schema construction 14. **SchemaLoader**: Load .schema.json files from disk 15. **SchemaProcessor**: Create/update nodes from schema definitions with automatic edge generation 16. **DynamicSchemaToolRegistry**: Generate add/update/delete tools per schema 17. **Static tools**: Register 11 always-available MCP tools 18. **Tool routing**: Factory pattern to route tool calls to correct handler 19. **Response formatting**: Success, error, and partial success response builders 20. **MCP server setup**: Server initialization, tool registration, stdio transport 21. **Configuration**: Centralized paths and server metadata 22. **Sample schemas**: At minimum one example schema file (npc.schema.json) **Total expected implementation**: ~2000 lines TypeScript across ~25 files in a layered architecture. --- ## Clean Room Specification: Markdown Based Local Knowledge Graph with Hybrid Search **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/04-Markdown-Based-Local-Knowledge-Graph **Description:** Purpose of This Document This document specifies the complete architecture, data model, storage format, synchronization system, search implementation, and MC... # Clean-Room Specification: Markdown-Based Local Knowledge Graph with Hybrid Search ## Purpose of This Document This document specifies the complete architecture, data model, storage format, synchronization system, search implementation, and MCP API surface of a **local-first knowledge graph** that stores all knowledge as structured Markdown files on the user's filesystem. Files are parsed to extract entities, observations, and relations, which are indexed into a relational database (SQLite or PostgreSQL) with optional vector embeddings for semantic search. The system watches the filesystem for changes and automatically syncs. It is exposed to AI assistants via MCP (Model Context Protocol) tools. This specification is detailed enough that a professional AI coding model can produce a functionally identical working system without reference to any existing codebase. --- ## 1. System Overview ### 1.1 Core Concept Users write Markdown notes in a project directory. Each note can contain: - **Frontmatter** (YAML metadata: title, type, tags, custom fields) - **Observations** (atomic facts in bracket-category notation) - **Relations** (explicit directed links using `[[wiki-link]]` syntax) - **Free-form content** (standard Markdown) A background service watches the directory, parses files, extracts structured data, and indexes everything into a database. An MCP server exposes tools for AI assistants to read, write, search, and traverse the knowledge graph. ### 1.2 Architecture Layers ``` ┌──────────────────────────────────────────────────────┐ │ MCP Server (FastMCP) │ │ Tools: write_note, read_note, search_notes, etc. │ ├──────────────────────────────────────────────────────┤ │ Service Layer │ │ EntityService, SearchService, SyncService, │ │ FileService, ContextService │ ├──────────────────────────────────────────────────────┤ │ Repository Layer │ │ EntityRepo, ObservationRepo, RelationRepo, │ │ ProjectRepo, SearchRepository (Protocol) │ ├──────────────────────────────────────────────────────┤ │ Database (SQLAlchemy Async) │ │ SQLite (default) or PostgreSQL │ │ + FTS5/tsvector + Optional Vector Storage │ ├──────────────────────────────────────────────────────┤ │ Filesystem (Markdown Files) │ │ Watched by watchfiles, parsed by markdown-it-py │ └──────────────────────────────────────────────────────┘ ``` ### 1.3 Key Design Principles 1. **Markdown-first**: The filesystem is the source of truth. The database is a derived index. 2. **Async throughout**: All I/O (database, files, HTTP) uses async/await. 3. **Protocol-based repositories**: Search backend is swappable (SQLite FTS5 vs PostgreSQL tsvector). 4. **Graceful degradation**: If vector search is unavailable, fall back to FTS. If FTS returns nothing, retry with relaxed query. 5. **Multi-project**: Multiple independent knowledge bases, each with its own directory and database. --- ## 2. Data Model ### 2.1 Database Schema #### 2.1.1 Project Table | Column | Type | Constraints | Description | |--------|------|-------------|-------------| | id | INTEGER | PRIMARY KEY, AUTOINCREMENT | Internal ID | | external_id | TEXT (UUID) | UNIQUE, NOT NULL | Stable API reference | | name | TEXT | NOT NULL | Project display name | | path | TEXT | NOT NULL | Filesystem root directory | | permalink | TEXT | UNIQUE | Auto-generated URL-safe slug | | is_active | BOOLEAN | DEFAULT TRUE | Whether project is active | | is_default | BOOLEAN | DEFAULT FALSE | Whether this is the default project | | created_at | DATETIME | NOT NULL | Creation timestamp | | updated_at | DATETIME | NOT NULL | Last update timestamp | **Permalink auto-generation**: When a project is created, its `permalink` is generated from `name` by lowercasing and replacing non-alphanumeric characters with hyphens. Example: "My Research" → "my-research". #### 2.1.2 Entity Table | Column | Type | Constraints | Description | |--------|------|-------------|-------------| | id | INTEGER | PRIMARY KEY, AUTOINCREMENT | Internal ID | | external_id | TEXT (UUID) | UNIQUE, NOT NULL | Stable API reference | | title | TEXT | NOT NULL | Note title (from frontmatter or filename) | | note_type | TEXT | INDEXED | User-defined type (e.g., "note", "person", "concept") | | content_type | TEXT | DEFAULT "text/markdown" | MIME type | | file_path | TEXT | NOT NULL | Relative path within project directory | | permalink | TEXT | INDEXED | URL-safe slug derived from title | | entity_metadata | TEXT (JSON) | | Serialized frontmatter key-value pairs | | content | TEXT | | Raw markdown body (after frontmatter) | | mtime | REAL | | File modification time (Unix epoch) | | size | INTEGER | | File size in bytes | | checksum | TEXT | | SHA-256 hex digest of file content | | project_id | INTEGER | FK → project.id, NOT NULL | Owning project | | created_at | DATETIME | NOT NULL | First indexed timestamp | | updated_at | DATETIME | NOT NULL | Last re-indexed timestamp | | created_by | TEXT | | Cloud user ID (optional) | | last_updated_by | TEXT | | Cloud user ID (optional) | **Unique constraints**: - `(permalink, project_id)` — No two entities share a permalink within a project - `(file_path, project_id)` — No two entities share a file path within a project **Permalink generation**: Title → lowercase → replace spaces/special chars with hyphens → strip leading/trailing hyphens. Example: "Machine Learning Basics" → "machine-learning-basics". #### 2.1.3 Observation Table | Column | Type | Constraints | Description | |--------|------|-------------|-------------| | id | INTEGER | PRIMARY KEY, AUTOINCREMENT | Internal ID | | external_id | TEXT (UUID) | UNIQUE, NOT NULL | Stable API reference | | content | TEXT | NOT NULL | The observation text | | category | TEXT | INDEXED | Category from bracket notation | | context | TEXT | | Optional context string | | tags | TEXT (JSON) | | Array of tag strings | | permalink | TEXT | | Synthetic: `entity_permalink/observations/category/content[:200]` | | entity_id | INTEGER | FK → entity.id, CASCADE DELETE | Parent entity | | project_id | INTEGER | FK → project.id | Owning project | | created_at | DATETIME | NOT NULL | | | updated_at | DATETIME | NOT NULL | | **Cascade**: When an entity is deleted, all its observations are automatically deleted. #### 2.1.4 Relation Table | Column | Type | Constraints | Description | |--------|------|-------------|-------------| | id | INTEGER | PRIMARY KEY, AUTOINCREMENT | Internal ID | | external_id | TEXT (UUID) | UNIQUE, NOT NULL | Stable API reference | | from_id | INTEGER | FK → entity.id, CASCADE DELETE, NOT NULL | Source entity | | to_id | INTEGER | FK → entity.id, nullable | Target entity (NULL if unresolved) | | to_name | TEXT | NOT NULL | Target name (for display and resolution) | | relation_type | TEXT | NOT NULL | e.g., "relates_to", "implements", "links_to" | | context | TEXT | | Optional context | | permalink | TEXT | | Synthetic: `source_permalink/relation_type/target_name` | | project_id | INTEGER | FK → project.id | Owning project | | created_at | DATETIME | NOT NULL | | | updated_at | DATETIME | NOT NULL | | **Unique constraints**: - `(from_id, to_id, relation_type)` when to_id is not NULL - `(from_id, to_name, relation_type)` for unresolved relations **Link resolution**: Relations start with `to_id=NULL` and `to_name` set. A LinkResolver service periodically attempts to match `to_name` against entity titles/permalinks. When matched, `to_id` is set. #### 2.1.5 Search Index Tables **FTS5 Virtual Table (SQLite)**: ```sql CREATE VIRTUAL TABLE search_index USING fts5( entity_id, project_id, title, content, note_type, entity_type, created_at, updated_at, tags, content_stems ); ``` **Vector Storage Tables** (when semantic search is enabled): ```sql -- Chunk storage CREATE TABLE search_vector_chunks ( id INTEGER PRIMARY KEY, entity_id INTEGER NOT NULL REFERENCES entity(id), chunk_text TEXT NOT NULL, chunk_index INTEGER NOT NULL, project_id INTEGER, created_at DATETIME, updated_at DATETIME ); -- Embedding storage (BLOB = raw float32 array) CREATE TABLE search_vector_embeddings ( id INTEGER PRIMARY KEY, chunk_id INTEGER NOT NULL REFERENCES search_vector_chunks(id), embedding BLOB NOT NULL, dimensions INTEGER NOT NULL, model TEXT NOT NULL, created_at DATETIME ); ``` --- ## 3. Markdown File Format ### 3.1 File Structure Each Markdown file in the project directory represents one entity. The file format: ```markdown --- title: Machine Learning Basics type: concept tags: - ai - fundamentals created: 2025-01-15T10:30:00 custom_field: any_value --- # Machine Learning Basics Free-form markdown content goes here. You can include [[wiki-links]] to reference other entities. ## Observations - [definition] Machine learning is a subset of AI that learns from data - [technique] Supervised learning uses labeled training data #ml #supervised - [limitation] Requires large datasets for good performance (especially deep learning) ## Relations - implements [[Artificial Intelligence]] - requires [[Training Data]] (for model fitting) - related_to [[Statistics]] (shared mathematical foundations) ``` ### 3.2 Frontmatter Parsing The YAML frontmatter between `---` delimiters is parsed using `python-frontmatter`. All values are normalized to strings: - **Dates** → ISO 8601 strings - **Numbers** → string representation - **Booleans** → `"True"` or `"False"` - **Lists** → preserved as lists of strings - **None/null** → excluded from metadata Required fields (`title`, `type`) are coerced to strings even if they parse as other types. If `title` is missing from frontmatter, the filename (without extension) is used. ### 3.3 Observation Extraction Observations are extracted from list items matching this pattern: ``` - [category] Content text #tag1 #tag2 (optional context) ``` **Regex pattern**: `^\[([^\[\]()]+)\]\s+(.+)` This matches: - `[definition] ML is...` ✓ - `[technique] Supervised learning #ml` ✓ - `[x] Completed task` ✗ (excluded — checkbox) - `[ ] Incomplete task` ✗ (excluded — checkbox) - `[link text](url)` ✗ (excluded — markdown link) - `[[wiki-link]]` ✗ (excluded — wiki link) **Tag extraction**: From the content text, extract all `#word` patterns. Tags are stored as a JSON array. **Context extraction**: If the content ends with `(text in parens)`, extract that as the context field. **Processing order**: Extract tags first, then context, leaving the remaining text as the observation content. ### 3.4 Relation Extraction Two types of relations are extracted: **Explicit relations** (from list items): ``` - relation_type [[Target Entity]] (optional context) ``` Pattern: A list item starting with a word/phrase followed by a `[[wiki-link]]`. The word before the wiki-link becomes `relation_type`, the wiki-link content becomes `to_name`. **Implicit relations** (from inline wiki-links): Any `[[Target Entity]]` found in the body text (not already captured as an explicit relation) creates an implicit relation with `relation_type = "links_to"`. **Wiki-link parsing**: Handle nested brackets correctly. Track bracket depth: increment on `[`, decrement on `]`. Content between matched `[[` and `]]` is the target name. Normalize target names: "Entity Name" → "entity-name" (lowercase, spaces to hyphens). ### 3.5 Entity Output Schema After parsing, each file yields: ```python @dataclass class ParsedEntity: title: str # From frontmatter or filename note_type: str # From frontmatter "type" field frontmatter: dict # All frontmatter key-value pairs content: str # Raw markdown body observations: List[Observation] # Extracted observations relations: List[Relation] # Extracted relations (explicit + implicit) created: Optional[datetime] # From frontmatter or file stat modified: Optional[datetime] # From frontmatter or file stat ``` --- ## 4. Filesystem Synchronization ### 4.1 File Watcher Use the `watchfiles` library for cross-platform filesystem monitoring. **Configuration**: - Debounce delay: configurable, default 1000ms - Filter patterns: respect `.gitignore` and `.bmignore` files (custom ignore patterns) - Watch only `.md` files **Event types**: Created, Modified, Deleted **State tracking** (per watcher instance): - `running: bool` - `start_time: datetime` - `error_count: int` - `synced_files: int` - `recent_events: deque(maxlen=100)` — last 100 file events ### 4.2 Sync Algorithm The sync process runs in three phases: **Phase 1 — Directory Scan**: 1. Walk the project directory using a thread pool executor (to avoid blocking async loop) 2. For each `.md` file found: - Compute SHA-256 checksum of file content - Record mtime and file size - Store as `{file_path, checksum, mtime, size}` **Phase 2 — Change Detection**: Compare filesystem state against database state: ```python @dataclass class SyncReport: new_files: List[str] # In filesystem but not in DB modified_files: List[str] # In both, but checksum differs deleted_files: List[str] # In DB but not in filesystem moved_files: List[Tuple[str, str]] # Same checksum, different path ``` **Move detection algorithm**: 1. Collect all checksums from DB entities and from filesystem scan 2. For each file in DB that's NOT in filesystem: - Check if its checksum appears in a NEW filesystem file - If yes: classify as moved (old_path → new_path) - If no: classify as deleted **Phase 3 — Apply Changes**: - **New files**: Parse markdown → create entity + observations + relations → update search index - **Modified files**: Parse markdown → update entity + diff observations/relations → update search index - **Deleted files**: Delete entity (cascades to observations/relations) → remove from search index - **Moved files**: Update entity.file_path, preserve entity.id and all relations ### 4.3 Circuit Breaker To prevent infinite retry loops on consistently failing files: - Track consecutive failure count per file path - After 3 consecutive failures, skip the file in future sync cycles - Reset failure count when the file's checksum changes (indicating the user modified it) - Log skipped files at warning level ### 4.4 Sync Coordinator A top-level coordinator manages the sync lifecycle: 1. **Initialization**: Run database migrations (Alembic), perform initial full sync 2. **Watch loop**: Start file watcher, process events through SyncService 3. **Background tasks**: Embedding backfill (process entities lacking vector embeddings) 4. **Shutdown**: Cancel all watchers, cancel backfill tasks, close database connections --- ## 5. Search System ### 5.1 Search Modes Three search modes, selected via `search_type` parameter: | Mode | Description | Requirements | |------|-------------|--------------| | `fts` | Full-text search using FTS5 (SQLite) or tsvector (PostgreSQL) | Always available | | `vector` | Semantic similarity search using embeddings | Requires embedding provider + vector storage | | `hybrid` | Weighted combination of FTS + vector scores | Requires both FTS and vector | ### 5.2 FTS Implementation (SQLite) **Query preparation**: 1. Split query into tokens 2. For tokens containing special characters (hyphens, dots, colons): wrap in double quotes - `"machine-learning"` → `"\"machine-learning\""` 3. Preserve boolean operators: AND, OR, NOT (case-sensitive) 4. Append `*` for prefix matching on the last token 5. Join with spaces (implicit AND in FTS5) **Relaxed fallback**: If FTS returns zero results for a multi-term query: 1. Remove stopwords ("the", "a", "an", "is", "are", "was", "were", "in", "on", "at", "to", "for", "of", "with", "by") 2. Join remaining terms with OR instead of implicit AND 3. Retry query **Ranking**: FTS5 built-in `rank` function (BM25-based). Results ordered by rank descending. ### 5.3 Vector Search Implementation **Embedding providers** (configurable): | Provider | Model | Dimensions | Notes | |----------|-------|------------|-------| | FastEmbed (local) | bge-small-en-v1.5 | 384 | Default, no API key needed | | OpenAI (remote) | text-embedding-3-small | 1536 | Requires OPENAI_API_KEY | **Provider protocol interface**: ```python class EmbeddingProvider(Protocol): async def embed_query(self, text: str) -> List[float]: ... async def embed_documents(self, texts: List[str]) -> List[List[float]]: ... ``` **Chunking strategy**: - Split entity content into chunks for embedding - Store each chunk with its index: `(entity_id, chunk_text, chunk_index)` - Embed each chunk independently **Similarity computation**: - Store embeddings as raw float32 BLOBs - Compute L2 distance, convert to cosine similarity: `similarity = 1 - (L2_distance² / 2)` - Filter results by minimum similarity threshold (default: 0.55) - Return top-k results (default k=100) ### 5.4 Hybrid Search Combine FTS and vector results: ```python hybrid_score = 0.5 * normalized_fts_score + 0.5 * vector_similarity ``` **Score normalization**: FTS scores are normalized to [0, 1] range using min-max scaling within the result set. **Merging**: Union results from both searches, keyed by entity_id. If an entity appears in both, use the hybrid score. If only in one, use 0.5 × that score. ### 5.5 Search Filters All search modes support these filters: | Filter | Type | Description | |--------|------|-------------| | `permalink` | str | Exact permalink match | | `permalink_match` | str | Permalink prefix/pattern match | | `title` | str | Title substring match | | `note_types` | List[str] | Filter by note type | | `after_date` | datetime | Only results modified after this date | | `search_item_types` | List[str] | Filter by item type (entity, observation, relation) | | `metadata_filters` | dict | Key-value filters against entity_metadata JSON | | `min_similarity` | float | Minimum similarity threshold (vector/hybrid only) | | `limit` | int | Max results (default 50) | | `offset` | int | Pagination offset | --- ## 6. MCP Server ### 6.1 Server Setup Use FastMCP framework. Server name: configurable (default "Basic Memory"). **Lifespan handler** (runs on server startup): 1. Initialize dependency container (services, repositories, database connection) 2. Run database migrations (Alembic) 3. Log embedding provider status 4. Start sync coordinator (initial sync + file watching) **Shutdown**: Stop sync coordinator, close all database connections. ### 6.2 MCP Tools #### 6.2.1 `write_note` Create or overwrite a Markdown file in the project directory. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | title | string | yes | | Note title (becomes filename) | | content | string | yes | | Markdown body content | | directory | string | no | "" | Subdirectory within project root | | project | string | no | (default project) | Project name | | tags | list[string] | no | [] | Frontmatter tags | | note_type | string | no | "note" | Frontmatter type field | | metadata | dict | no | {} | Additional frontmatter fields | | overwrite | boolean | no | false | Whether to overwrite existing file | **Behavior**: 1. Generate filename from title: `title.lower().replace(" ", "-") + ".md"` 2. Construct full path: `project_root / directory / filename` 3. If file exists and `overwrite` is false: return error 4. Build frontmatter YAML from title, type, tags, metadata ```text 5. Write file: `---\n{frontmatter}\n---\n\n{content}` ``` 6. The file watcher will detect the change and sync to database **Returns**: Entity data including permalink and file_path. #### 6.2.2 `read_note` Read a note by permalink or file path. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | path | string | yes | Permalink or relative file path | | project | string | no | Project name | **Returns**: Full entity data including frontmatter, content, observations, relations, and related entities. #### 6.2.3 `edit_note` Apply targeted edits to an existing note. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | path | string | yes | Permalink or file path | | content_updates | string | yes | Instructions or replacement content | | project | string | no | Project name | **Behavior**: Read existing file, apply updates (append, replace section, etc.), write back. The sync service detects the change. #### 6.2.4 `delete_note` Delete a note file and its database records. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | path | string | yes | Permalink or file path | | project | string | no | Project name | **Behavior**: Delete the physical file. The sync service detects the deletion and removes the entity (cascading to observations and relations). #### 6.2.5 `search_notes` Search across all indexed content. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | query | string | yes | | Search query text | | project | string | no | (default) | Project name | | page | integer | no | 1 | Page number for pagination | | search_type | string | no | "hybrid" | One of: "fts", "vector", "hybrid" | | output_format | string | no | "text" | "text" or "json" | | note_types | list[string] | no | | Filter by note type | | after_date | string | no | | ISO date, only results after this | | tags | list[string] | no | | Filter by tags | **Returns**: List of matching entities with relevance scores, snippets, and metadata. #### 6.2.6 `build_context` Resolve a `memory://` URI and build rich context. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | path | string | yes | A `memory://` URI or plain permalink | | project | string | no | Project name | **Behavior**: 1. Strip `memory://` prefix if present 2. Resolve to entity by permalink or file path 3. Return entity metadata, content, observations, relations, and related entity summaries **Returns**: Formatted context string suitable for AI consumption. #### 6.2.7 `list_directory` List files and subdirectories in the project. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | project | string | no | (default) | Project name | | path | string | no | "" | Subdirectory path | **Returns**: List of files and folders with metadata. #### 6.2.8 `recent_activity` Get recently modified entities. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | timeframe | string | no | "1 day" | Natural language timeframe (parsed by dateparser) | | project | string | no | (default) | Project name | **Returns**: Entities modified within the timeframe, sorted by modification date descending. #### 6.2.9 `list_memory_projects` List all configured projects. **Parameters**: None. **Returns**: Array of project objects with name, path, is_active, is_default, entity count. #### 6.2.10 `create_memory_project` Create a new project. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | name | string | yes | Project display name | | path | string | yes | Filesystem directory path | **Behavior**: Create project record, create directory if not exists, start watching. ### 6.3 MCP Resources **`project_info`**: Returns current project metadata, entity/observation/relation counts, and sync status. ### 6.4 MCP Prompts | Prompt Name | Description | |-------------|-------------| | `continue_conversation` | Template for resuming a conversation with memory context | | `recent_activity` | Template for summarizing recent changes | | `search` | Template for performing a knowledge search | | `ai_assistant_guide` | Instructions for how an AI should use memory tools | --- ## 7. URI Scheme ### 7.1 Format ``` memory:// ``` Examples: - `memory://machine-learning-basics` - `memory://specs/search-implementation` - `memory://id/123` (by internal ID) ### 7.2 Validation Rules A valid memory URI path must NOT contain: - Empty string - `://` (double protocol) - `//` (double slash within path) - `<`, `>`, `"`, `|`, `?` characters ### 7.3 Resolution 1. Strip `memory://` prefix 2. If path starts with `id/`: look up entity by numeric ID 3. Otherwise: look up entity by permalink match 4. If not found by permalink: try as file_path 5. Return entity with full context (observations, relations, neighbors) --- ## 8. Service Layer Architecture ### 8.1 Base Service Pattern ```python class BaseService(Generic[T]): def __init__(self, repository: BaseRepository[T]): self.repository = repository ``` All services inherit from this base, receiving their repository via constructor injection. ### 8.2 Dependency Container A container class holds all services and repositories, constructed during server lifespan: ```python class McpContainer: # Database engine: AsyncEngine session_factory: async_sessionmaker # Repositories entity_repo: EntityRepository observation_repo: ObservationRepository relation_repo: RelationRepository project_repo: ProjectRepository search_repo: SearchRepository # SQLite or Postgres implementation # Services entity_service: EntityService search_service: SearchService sync_service: SyncService file_service: FileService context_service: ContextService link_resolver: LinkResolver # Sync sync_coordinator: SyncCoordinator ``` ### 8.3 EntityService Core operations: - `create_entity(parsed: ParsedEntity, project_id: int) → Entity` - `update_entity(entity_id: int, parsed: ParsedEntity) → Entity` - `delete_entity(entity_id: int) → None` - `get_by_permalink(permalink: str, project_id: int) → Entity` - `get_by_file_path(file_path: str, project_id: int) → Entity` - `resolve_path(path: str, project_id: int) → Entity` — tries permalink first, then file_path ### 8.4 SearchService - `search(query, project_id, search_type, filters, limit, offset) → SearchResults` - `index_entity(entity: Entity) → None` — update FTS + vector indexes - `remove_from_index(entity_id: int) → None` - `reindex_all(project_id: int) → None` ### 8.5 SyncService - `full_sync(project_id: int) → SyncReport` - `sync_file(file_path: str, project_id: int) → Entity` - `remove_file(file_path: str, project_id: int) → None` - `detect_moves(db_state, fs_state) → List[Move]` ### 8.6 ContextService - `build_context(path: str, project_id: int) → ContextResult` - Returns: entity metadata, content, observations, relations, related entities (1-hop neighbors) ### 8.7 LinkResolver - `resolve_pending(project_id: int) → int` — returns count of newly resolved links - Runs after each sync cycle - Matches `relation.to_name` against entity titles and permalinks (case-insensitive) - When matched: sets `relation.to_id` --- ## 9. Configuration ### 9.1 Configuration Schema ```python @dataclass class ProjectEntry: path: str # Filesystem directory mode: str = "local" # "local" or "cloud" workspace_id: str = None # Cloud workspace ID (if applicable) @dataclass class Config: projects: Dict[str, ProjectEntry] # name → project config default_project: Optional[str] # Default project name database_backend: str = "sqlite" # "sqlite" or "postgres" # Semantic search semantic_search_enabled: bool = False # Auto-detected semantic_embedding_provider: str = "fastembed" # "fastembed" or "openai" semantic_embedding_model: str = "bge-small-en-v1.5" semantic_vector_k: int = 100 # Top-k results for vector search semantic_min_similarity: float = 0.55 # Minimum similarity threshold # Sync sync_delay: int = 1000 # Debounce delay in milliseconds watch_project_reload_interval: int = 300 # Seconds between project config reloads ``` ### 9.2 Configuration Sources (Priority Order) 1. **Environment variables**: Prefixed with `BASIC_MEMORY_` (e.g., `BASIC_MEMORY_DATABASE_BACKEND=postgres`) 2. **Config file**: `~/.basic-memory/config.json` 3. **Defaults**: Values in the Config dataclass ### 9.3 Auto-Detection Semantic search is automatically enabled if: - The configured embedding provider library is importable (`fastembed` or `openai`) - AND the vector storage extension is available (`sqlite-vec` for SQLite) --- ## 10. Database Migrations Use Alembic for schema migrations. **Migration strategy**: - Migrations run automatically on server startup (as part of lifespan handler) - Migration directory stored alongside application code ```text - Database file location: `~/.basic-memory/{project_name}/memory.db` (SQLite) or configured connection string (PostgreSQL) ``` **Key migrations**: 1. Initial schema: Create entity, observation, relation, project tables 2. Add FTS5 virtual table 3. Add vector storage tables (search_vector_chunks, search_vector_embeddings) 4. Add permalink columns and indexes 5. Add file sync tracking columns (mtime, size, checksum) --- ## 11. Project Resolution When an MCP tool receives a `project` parameter: 1. If `project` is provided: look up by name 2. If not provided: use the configured default project 3. If no default configured: use the first active project found 4. If no projects exist: return error **Single-project mode**: When only one project is configured, all tools implicitly use it without requiring the `project` parameter. --- ## 12. Error Handling ### 12.1 File Parsing Errors - If frontmatter is invalid YAML: skip file, log warning, continue sync - If file is empty: create entity with title from filename, no observations/relations - If file encoding is not UTF-8: attempt detection, fall back to latin-1 ### 12.2 Sync Errors - File read permission denied: log error, skip file, increment circuit breaker - File deleted during sync: handle gracefully (already gone) - Database write conflict: retry with exponential backoff (up to 3 attempts) ### 12.3 Search Errors - FTS query syntax error: fall back to relaxed query (OR terms, no special operators) - Vector provider unavailable: fall back to FTS-only - No results: return empty list with suggestion to broaden query --- ## 13. Complete Behavioral Test Specifications ### 13.1 Markdown Parsing Tests ``` TEST: Parse frontmatter with all field types INPUT: File with title (string), tags (list), created (date), count (number), draft (boolean) EXPECT: title → "My Title", tags → ["a","b"], created → ISO string, count → "42", draft → "True" TEST: Missing title uses filename INPUT: File "my-note.md" with frontmatter lacking "title" EXPECT: entity.title = "my-note" TEST: Extract observations with categories INPUT: "- [definition] AI is intelligence exhibited by machines" EXPECT: observation.category = "definition", observation.content = "AI is intelligence exhibited by machines" TEST: Extract observation tags INPUT: "- [technique] Gradient descent #ml #optimization" EXPECT: tags = ["ml", "optimization"] TEST: Extract observation context INPUT: "- [fact] Water boils at 100°C (at sea level)" EXPECT: context = "at sea level" TEST: Exclude checkboxes from observations INPUT: "- [x] Completed task\n- [ ] Pending task" EXPECT: No observations extracted TEST: Exclude markdown links from observations INPUT: "- [click here](https://example.com)" EXPECT: No observations extracted TEST: Extract explicit relation INPUT: "- implements [[Machine Learning]]" EXPECT: relation_type = "implements", to_name = "machine-learning" TEST: Extract implicit link relation INPUT: "This relates to [[Statistics]] in many ways" EXPECT: relation_type = "links_to", to_name = "statistics" TEST: Handle nested wiki-links INPUT: "- uses [[React [[Hooks]]]]" EXPECT: Correct bracket depth tracking, proper target extraction ``` ### 13.2 Sync Tests ``` TEST: New file detected and indexed Create file "test.md" in project directory Wait for sync debounce EXPECT: Entity created in DB with matching title, content, checksum TEST: Modified file re-indexed Modify existing file content Wait for sync EXPECT: Entity updated, checksum changed, observations refreshed TEST: Deleted file removed Delete file from directory Wait for sync EXPECT: Entity removed from DB, observations and relations cascade-deleted TEST: File move detected Rename "old.md" to "new.md" (same content) Wait for sync EXPECT: Entity file_path updated, entity.id preserved, no duplicate TEST: Circuit breaker activates Create file that causes parse error 3 times EXPECT: File skipped on 4th sync, warning logged TEST: Circuit breaker resets on modification After circuit breaker activates, modify the problematic file EXPECT: File processed again on next sync ``` ### 13.3 Search Tests ``` TEST: FTS basic search Index entity with title "Machine Learning Basics" Search "machine learning" EXPECT: Entity returned with positive relevance score TEST: FTS special character handling Index entity with title "node-js-tutorial" Search "node-js" EXPECT: Query wraps hyphenated term in quotes, entity found TEST: FTS relaxed fallback Index entity with content "project management tips" Search "project planning ideas" (no exact match) EXPECT: First attempt returns 0, retry with OR finds "project" match TEST: Vector semantic search Index entity about "canine behavior" Search "dog training" with search_type="vector" EXPECT: Entity returned based on semantic similarity > 0.55 TEST: Hybrid search scoring Index two entities: one matching FTS well, one matching vector well Search with search_type="hybrid" EXPECT: Both appear, hybrid scores = 0.5 * fts + 0.5 * vector TEST: Search with filters Index entities with different note_types Search with note_types=["concept"] EXPECT: Only concept-type entities returned TEST: Pagination Index 100 entities Search with limit=10, page=2 EXPECT: Results 11-20 returned ``` ### 13.4 MCP Tool Tests ``` TEST: write_note creates file Call write_note(title="Test", content="Hello", tags=["a"]) EXPECT: File exists at project_root/test.md with proper frontmatter TEST: write_note respects directory Call write_note(title="Deep", content="...", directory="research/ai") EXPECT: File at project_root/research/ai/deep.md TEST: write_note refuses overwrite Create file, then call write_note with same title, overwrite=false EXPECT: Error returned, file unchanged TEST: read_note by permalink Write and sync a note titled "My Research" Call read_note(path="my-research") EXPECT: Full entity data returned with observations and relations TEST: search_notes with output formats Call search_notes(query="test", output_format="json") EXPECT: JSON-formatted results Call search_notes(query="test", output_format="text") EXPECT: Human-readable text results TEST: build_context resolves memory URI Call build_context(path="memory://my-research") EXPECT: Entity context with related entities TEST: recent_activity timeframe Create note, wait, create another Call recent_activity(timeframe="1 hour") EXPECT: Both notes returned, sorted by modification date TEST: list_memory_projects Configure two projects Call list_memory_projects() EXPECT: Both projects listed with metadata TEST: delete_note cascades Write note with observations and relations, sync Call delete_note(path="test-note") EXPECT: File deleted, entity removed, observations removed, relations removed ``` ### 13.5 Link Resolution Tests ``` TEST: Resolve pending link Create entity A with relation to_name="entity-b" (to_id=NULL) Create entity B with permalink="entity-b" Run link resolver EXPECT: relation.to_id now points to entity B TEST: Case-insensitive resolution Relation to_name="Machine Learning" Entity with permalink="machine-learning" EXPECT: Resolves successfully TEST: Unresolvable link stays pending Relation to_name="nonexistent-entity" No matching entity EXPECT: relation.to_id remains NULL ``` --- ## 14. Key Implementation Algorithms ### 14.1 Permalink Generation ``` Input: "Machine Learning Basics!" Step 1: Lowercase → "machine learning basics!" Step 2: Replace non-alphanumeric with hyphens → "machine-learning-basics-" Step 3: Collapse multiple hyphens → "machine-learning-basics-" Step 4: Strip leading/trailing hyphens → "machine-learning-basics" Output: "machine-learning-basics" ``` ### 14.2 Observation Permalink Generation ``` Input: entity_permalink="ml-basics", category="definition", content="Machine learning is a subset of AI that enables systems to learn from data without explicit programming" Step 1: Truncate content to 200 chars Step 2: Slugify truncated content Step 3: Combine: "ml-basics/observations/definition/machine-learning-is-a-subset-of-ai..." Output: synthetic permalink ``` ### 14.3 FTS Query Preparation (SQLite) ``` Input: "machine-learning basics" Step 1: Tokenize → ["machine-learning", "basics"] Step 2: Check each token for special chars: - "machine-learning" contains hyphen → wrap in quotes: '"machine-learning"' - "basics" is clean → keep as-is Step 3: Add prefix wildcard to last token: "basics*" Step 4: Join: '"machine-learning" basics*' Output: FTS5 query string ``` ### 14.4 L2 to Cosine Similarity Conversion ``` Input: L2_distance (from vector comparison of normalized embeddings) Formula: cosine_similarity = 1 - (L2_distance² / 2) Note: This works because for unit vectors, L2² = 2 - 2·cos(θ), so cos(θ) = 1 - L2²/2 Output: similarity score in [0, 1] ``` ### 14.5 Hybrid Score Computation ``` Input: fts_results (list of (entity_id, fts_score)), vector_results (list of (entity_id, similarity)) Step 1: Normalize FTS scores to [0,1] using min-max scaling: norm_fts = (score - min_score) / (max_score - min_score) Step 2: Create union of all entity_ids from both result sets Step 3: For each entity_id: - If in both: hybrid = 0.5 * norm_fts + 0.5 * similarity - If FTS only: hybrid = 0.5 * norm_fts - If vector only: hybrid = 0.5 * similarity Step 4: Sort by hybrid score descending Output: merged results with hybrid scores ``` --- ## 15. Dependencies ### 15.1 Required | Package | Purpose | |---------|---------| | fastmcp | MCP server framework | | sqlalchemy[asyncio] | Async ORM | | alembic | Database migrations | | aiosqlite | SQLite async driver | | aiofiles | Async file I/O | | watchfiles | Filesystem monitoring | | markdown-it-py | Markdown parsing | | python-frontmatter | YAML frontmatter extraction | | pydantic | Data validation | | pydantic-settings | Configuration management | | loguru | Structured logging | | dateparser | Natural language date parsing | ### 15.2 Optional | Package | Purpose | |---------|---------| | asyncpg | PostgreSQL async driver | | fastembed | Local embedding generation | | sqlite-vec | SQLite vector extension | | openai | Remote embedding API | --- ## 16. Directory Structure ``` project_root/ ├── src/ │ ├── __init__.py │ ├── config.py # Configuration schema & loading │ ├── models.py # SQLAlchemy ORM models │ ├── container.py # Dependency injection container │ ├── markdown/ │ │ ├── __init__.py │ │ ├── entity_parser.py # Frontmatter + content parser │ │ ├── observation_plugin.py # markdown-it plugin for observations │ │ └── relation_plugin.py # markdown-it plugin for relations/wiki-links │ ├── repositories/ │ │ ├── __init__.py │ │ ├── base.py # BaseRepository generic │ │ ├── entity.py │ │ ├── observation.py │ │ ├── relation.py │ │ ├── project.py │ │ ├── search_sqlite.py # FTS5 implementation │ │ └── search_postgres.py # tsvector implementation │ ├── services/ │ │ ├── __init__.py │ │ ├── base.py # BaseService generic │ │ ├── entity.py │ │ ├── search.py │ │ ├── context.py │ │ ├── file.py │ │ └── link_resolver.py │ ├── sync/ │ │ ├── __init__.py │ │ ├── sync_service.py # Change detection & application │ │ ├── watch_service.py # File watcher │ │ └── coordinator.py # Lifecycle management │ ├── embeddings/ │ │ ├── __init__.py │ │ ├── provider.py # EmbeddingProvider protocol │ │ ├── fastembed.py # Local provider │ │ └── openai.py # Remote provider │ └── mcp/ │ ├── __init__.py │ ├── server.py # FastMCP server + tool registration │ └── prompts.py # MCP prompt templates ├── migrations/ # Alembic migrations ├── tests/ └── pyproject.toml ``` --- ## 17. Startup Sequence 1. Load configuration (env vars → config file → defaults) 2. Initialize database engine (SQLite or PostgreSQL async) 3. Run Alembic migrations 4. Create dependency container (repositories, services) 5. Check for semantic search availability (auto-detect) 6. For each active project: a. Run full sync (Phase 1-3) b. Resolve pending links c. Start file watcher d. Start background embedding backfill (if semantic search enabled) 7. Register MCP tools, resources, and prompts 8. Begin accepting MCP connections --- ## 18. Shutdown Sequence 1. Stop accepting new MCP requests 2. Cancel all file watchers 3. Cancel background embedding tasks 4. Flush pending sync operations 5. Close database connections 6. Exit cleanly --- *This specification provides complete architectural and behavioral detail for independent implementation of a markdown-based local knowledge graph with hybrid search, filesystem synchronization, and MCP integration.* --- ## Clean Room Specification: SQL Native Entity Memory Layer with Vector Search **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/05-SQL-Native-Entity-Memory-Layer **Description:** Purpose of This Document This document specifies the complete architecture, data model, and API surface of an SQL native entity memory system for AI assistan... # Clean-Room Specification: SQL-Native Entity Memory Layer with Vector Search ## Purpose of This Document This document specifies the complete architecture, data model, and API surface of an **SQL-native entity memory system** for AI assistants. Unlike JSONL-file approaches, this system uses a relational database (PostgreSQL with pgvector, or SQLite with sqlite-vec) as the primary store, providing ACID transactions, proper indexing, vector similarity search, confidence scoring, memory type classification, and temporal decay. The system is exposed via both MCP tools and a REST API with Server-Sent Events. This specification is detailed enough that a professional AI coding model can produce a functionally identical working system without reference to any existing codebase. --- ## 1. System Overview ### 1.1 Core Concept An AI assistant accumulates memories during conversations — facts about users, decisions made, patterns observed, errors encountered, and lessons learned. This system provides: 1. **Structured storage**: Memories stored in SQL tables with proper types, tags, and confidence scores 2. **Semantic search**: Vector embeddings enable meaning-based retrieval 3. **Knowledge graph**: Entities connected by typed, weighted relations 4. **Temporal management**: Confidence decay over time, reinforcement on access 5. **Memory consolidation**: Automatic deduplication and merging of overlapping memories 6. **Multi-client**: Supports concurrent AI assistant connections ### 1.2 Architecture ``` ┌─────────────────────────────────────────────────┐ │ MCP Transport (stdio) │ │ 9 core tools + graph tools │ ├─────────────────────────────────────────────────┤ │ REST API (HTTP) │ │ /api/memories /api/analytics /api/events │ ├─────────────────────────────────────────────────┤ │ Service Layer │ │ MemoryService, EntityService, SearchService, │ │ ConsolidationService, EmbeddingService │ ├─────────────────────────────────────────────────┤ │ Repository Layer │ │ MemoryRepo, EntityRepo, RelationRepo │ ├─────────────────────────────────────────────────┤ │ Database (PostgreSQL + pgvector) │ │ or (SQLite + sqlite-vec + FTS5) │ └─────────────────────────────────────────────────┘ ``` ### 1.3 Design Principles 1. **SQL-native**: All data in proper relational tables with constraints and indexes 2. **Dual access**: Both MCP tools (for AI assistants) and REST API (for applications) 3. **Embedding-first**: Every memory gets a vector embedding for semantic retrieval 4. **Confidence-scored**: Every memory and relation has a confidence value that decays over time 5. **Type-classified**: Memories are categorized for targeted retrieval --- ## 2. Database Schema ### 2.1 Memories Table The core storage for all atomic memory units. ```sql CREATE TABLE memories ( id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY, external_id UUID UNIQUE NOT NULL DEFAULT gen_random_uuid(), content TEXT NOT NULL, memory_type VARCHAR(50) NOT NULL DEFAULT 'observation', tags TEXT[] DEFAULT '{}', confidence DECIMAL(3,2) NOT NULL DEFAULT 1.00, importance DECIMAL(3,2) DEFAULT 0.50, source VARCHAR(100), context TEXT, metadata JSONB DEFAULT '{}', embedding VECTOR(384), access_count INTEGER DEFAULT 0, last_accessed_at TIMESTAMP, created_at TIMESTAMP NOT NULL DEFAULT NOW(), updated_at TIMESTAMP NOT NULL DEFAULT NOW() ); -- Indexes CREATE INDEX idx_memories_type ON memories(memory_type); CREATE INDEX idx_memories_tags ON memories USING GIN(tags); CREATE INDEX idx_memories_confidence ON memories(confidence); CREATE INDEX idx_memories_created ON memories(created_at); CREATE INDEX idx_memories_metadata ON memories USING GIN(metadata); ``` **Vector index** (PostgreSQL with pgvector): ```sql CREATE INDEX idx_memories_embedding ON memories USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100); ``` **Memory types** (enumerated, extensible): | Type | Description | Example | |------|-------------|---------| | `observation` | Passive fact about the world | "User prefers dark mode" | | `decision` | Choice made by user or agent | "Chose React over Vue for frontend" | | `learning` | Knowledge gained from experience | "JSON parsing fails on trailing commas" | | `error` | Mistakes and their causes | "Deploy failed due to missing env var" | | `pattern` | Recurring trends identified | "User always asks for TypeScript examples" | | `preference` | User preferences and habits | "Prefers concise answers over detailed ones" | | `fact` | Objective, verifiable information | "Company founded in 2019" | | `procedure` | Step-by-step processes | "Deploy sequence: build → test → push → deploy" | ### 2.2 Entities Table Named entities that memories can be associated with. ```sql CREATE TABLE entities ( id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY, external_id UUID UNIQUE NOT NULL DEFAULT gen_random_uuid(), name VARCHAR(255) NOT NULL, entity_type VARCHAR(100) NOT NULL, description TEXT, metadata JSONB DEFAULT '{}', embedding VECTOR(384), created_at TIMESTAMP NOT NULL DEFAULT NOW(), updated_at TIMESTAMP NOT NULL DEFAULT NOW(), UNIQUE(name, entity_type) ); CREATE INDEX idx_entities_type ON entities(entity_type); CREATE INDEX idx_entities_name ON entities(name); ``` **Entity types**: `person`, `organization`, `project`, `concept`, `location`, `technology`, `event` ### 2.3 Relations Table Directed, typed connections between entities. ```sql CREATE TABLE relations ( id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY, external_id UUID UNIQUE NOT NULL DEFAULT gen_random_uuid(), source_id BIGINT NOT NULL REFERENCES entities(id) ON DELETE CASCADE, target_id BIGINT NOT NULL REFERENCES entities(id) ON DELETE CASCADE, relation_type VARCHAR(100) NOT NULL, strength DECIMAL(3,2) NOT NULL DEFAULT 0.50, confidence DECIMAL(3,2) NOT NULL DEFAULT 1.00, context TEXT, metadata JSONB DEFAULT '{}', created_at TIMESTAMP NOT NULL DEFAULT NOW(), updated_at TIMESTAMP NOT NULL DEFAULT NOW(), UNIQUE(source_id, target_id, relation_type) ); CREATE INDEX idx_relations_source ON relations(source_id); CREATE INDEX idx_relations_target ON relations(target_id); CREATE INDEX idx_relations_type ON relations(relation_type); ``` **Relation types** (active voice): - `works_at`, `manages`, `reports_to`, `collaborates_with` - `uses`, `implements`, `depends_on`, `related_to` - `located_in`, `part_of`, `created_by` ### 2.4 Entity-Memory Association Many-to-many mapping between entities and memories. ```sql CREATE TABLE entity_memories ( entity_id BIGINT NOT NULL REFERENCES entities(id) ON DELETE CASCADE, memory_id BIGINT NOT NULL REFERENCES memories(id) ON DELETE CASCADE, relevance DECIMAL(3,2) DEFAULT 1.00, created_at TIMESTAMP NOT NULL DEFAULT NOW(), PRIMARY KEY (entity_id, memory_id) ); ``` ### 2.5 Memory Versions Table Track history of memory modifications. ```sql CREATE TABLE memory_versions ( id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY, memory_id BIGINT NOT NULL REFERENCES memories(id) ON DELETE CASCADE, content TEXT NOT NULL, confidence DECIMAL(3,2), metadata JSONB, version_number INTEGER NOT NULL, created_at TIMESTAMP NOT NULL DEFAULT NOW() ); CREATE INDEX idx_versions_memory ON memory_versions(memory_id); ``` ### 2.6 Full-Text Search (SQLite Alternative) When using SQLite instead of PostgreSQL: ```sql -- FTS5 virtual table CREATE VIRTUAL TABLE memories_fts USING fts5( content, memory_type, tags, context, content=memories, content_rowid=id ); -- Triggers to keep FTS in sync CREATE TRIGGER memories_ai AFTER INSERT ON memories BEGIN INSERT INTO memories_fts(rowid, content, memory_type, tags, context) VALUES (new.id, new.content, new.memory_type, (SELECT group_concat(value) FROM json_each(new.tags)), new.context); END; CREATE TRIGGER memories_ad AFTER DELETE ON memories BEGIN INSERT INTO memories_fts(memories_fts, rowid, content, memory_type, tags, context) VALUES ('delete', old.id, old.content, old.memory_type, (SELECT group_concat(value) FROM json_each(old.tags)), old.context); END; CREATE TRIGGER memories_au AFTER UPDATE ON memories BEGIN INSERT INTO memories_fts(memories_fts, rowid, content, memory_type, tags, context) VALUES ('delete', old.id, old.content, old.memory_type, (SELECT group_concat(value) FROM json_each(old.tags)), old.context); INSERT INTO memories_fts(rowid, content, memory_type, tags, context) VALUES (new.id, new.content, new.memory_type, (SELECT group_concat(value) FROM json_each(new.tags)), new.context); END; ``` **Vector storage** (SQLite with sqlite-vec): ```sql CREATE VIRTUAL TABLE memory_vectors USING vec0( memory_id INTEGER PRIMARY KEY, embedding FLOAT[384] ); ``` --- ## 3. Embedding System ### 3.1 Provider Interface ```typescript interface EmbeddingProvider { embed(text: string): Promise; embedBatch(texts: string[]): Promise; dimensions: number; modelName: string; } ``` ### 3.2 Supported Providers | Provider | Model | Dimensions | Requires API Key | |----------|-------|------------|------------------| | Local (default) | all-MiniLM-L6-v2 | 384 | No | | OpenAI | text-embedding-3-small | 1536 | Yes | ### 3.3 Embedding Generation Embeddings are generated automatically: - On memory creation: embed the `content` field - On entity creation: embed `name + " " + description` - On memory update: re-embed if content changed - Batch processing: Queue new memories, embed in batches of 32 ### 3.4 Similarity Computation **PostgreSQL (pgvector)**: ```sql SELECT id, content, 1 - (embedding <=> $1::vector) AS similarity FROM memories WHERE 1 - (embedding <=> $1::vector) > $2 ORDER BY embedding <=> $1::vector LIMIT $3; ``` The `<=>` operator computes cosine distance. Similarity = 1 - distance. **SQLite (sqlite-vec)**: ```sql SELECT memory_id, distance FROM memory_vectors WHERE embedding MATCH $1 ORDER BY distance LIMIT $2; ``` Convert L2 distance to cosine similarity: `similarity = 1 - (distance² / 2)` (for normalized vectors). --- ## 4. Confidence and Temporal Decay ### 4.1 Confidence Model Every memory has a `confidence` score in [0.0, 1.0]: - **1.0**: Just created, highly confident - **0.5**: Moderate confidence - **0.0**: No confidence, candidate for pruning ### 4.2 Decay Formula Confidence decays exponentially over time since last access: ``` confidence(t) = initial_confidence × 0.5^(t / half_life) Where: t = time since last_accessed_at (or created_at if never accessed) half_life = 30 days (configurable) ``` ### 4.3 Reinforcement When a memory is accessed (read, searched, or returned in results): 1. Increment `access_count` 2. Update `last_accessed_at` to now 3. Boost confidence: `confidence = min(1.0, confidence + 0.1)` This creates a "use it or lose it" dynamic where frequently-accessed memories stay strong. ### 4.4 Decay Application Decay is computed at read time, not continuously updated: ```sql SELECT id, content, confidence * POWER(0.5, EXTRACT(EPOCH FROM (NOW() - COALESCE(last_accessed_at, created_at))) / (30 * 86400)) AS effective_confidence FROM memories WHERE /* effective_confidence > threshold */; ``` ### 4.5 Pruning A periodic background job removes memories with effective confidence below a threshold: - Default threshold: 0.05 - Run interval: daily - Pruned memories are permanently deleted (or moved to archive table if configured) --- ## 5. Search System ### 5.1 Search Modes | Mode | Method | Best For | |------|--------|----------| | `keyword` | FTS5 / tsvector | Exact term matching | | `semantic` | Vector cosine similarity | Meaning-based retrieval | | `hybrid` | Weighted combination | General purpose (default) | | `graph` | Entity relation traversal | Connected knowledge discovery | ### 5.2 Keyword Search **PostgreSQL**: ```sql SELECT id, content, ts_rank(to_tsvector('english', content), plainto_tsquery('english', $1)) AS rank FROM memories WHERE to_tsvector('english', content) @@ plainto_tsquery('english', $1) ORDER BY rank DESC LIMIT $2; ``` **SQLite (FTS5)**: ```sql SELECT m.id, m.content, f.rank FROM memories_fts f JOIN memories m ON m.id = f.rowid WHERE memories_fts MATCH $1 ORDER BY f.rank LIMIT $2; ``` ### 5.3 Semantic Search 1. Embed the query text 2. Find nearest neighbors by cosine similarity 3. Filter by minimum similarity threshold (default: 0.5) 4. Return top-k results (default: 20) ### 5.4 Hybrid Search ``` hybrid_score = α × keyword_score + (1 - α) × semantic_score Default α = 0.4 (semantic-weighted) ``` **Normalization**: Both keyword and semantic scores are min-max normalized to [0, 1] within their respective result sets before combination. **Merging**: Union of results from both searches. If a memory appears in both, use hybrid score. If only in one, scale by its weight. ### 5.5 Graph Search Given a starting entity: 1. Find all directly connected entities (1-hop) 2. Collect all memories associated with those entities 3. Optionally expand to 2-hop or N-hop neighbors 4. Rank results by relation strength × confidence ### 5.6 Search Filters All modes support these filters: | Filter | Type | Description | |--------|------|-------------| | `memory_types` | string[] | Filter by memory type | | `tags` | string[] | Must contain all specified tags | | `min_confidence` | float | Minimum effective confidence | | `after_date` | datetime | Created after this date | | `before_date` | datetime | Created before this date | | `entity_id` | int | Associated with this entity | | `source` | string | Originating source | | `limit` | int | Max results (default 20, max 100) | | `offset` | int | Pagination offset | --- ## 6. Memory Consolidation ### 6.1 Deduplication When creating a new memory, check for duplicates: 1. **Exact match**: SHA-256 hash of content matches existing memory → skip creation 2. **Near-duplicate**: Cosine similarity > 0.95 with existing memory → merge **Merge strategy**: - Keep the older memory (lower ID) - Update confidence: `max(old.confidence, new.confidence)` - Merge tags: union of both tag sets - Update metadata: shallow merge (new values override old) - Increment version ### 6.2 Consolidation Engine A periodic process that combines related memories: 1. Find clusters of memories with high pairwise similarity (> 0.85) 2. For each cluster: a. Select the memory with highest confidence as the "primary" b. Merge observations from other memories into primary c. Create version records for audit trail d. Delete absorbed memories e. Reassign entity associations ### 6.3 Consolidation Triggers - **Manual**: Via MCP tool or API call - **Automatic**: After every N memory insertions (default: 50) - **Scheduled**: Configurable cron interval (default: daily) --- ## 7. MCP Server ### 7.1 Server Setup **Transport**: stdio (JSON-RPC 2.0 over stdin/stdout) **Initialization**: 1. Connect to database 2. Run migrations if needed 3. Initialize embedding provider 4. Register tools ### 7.2 MCP Tools #### 7.2.1 `store_memory` Create a new memory with automatic embedding and deduplication. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | content | string | yes | | Memory content text | | memory_type | string | no | "observation" | One of the memory types | | tags | string[] | no | [] | Classification tags | | confidence | number | no | 1.0 | Initial confidence [0-1] | | source | string | no | | Origin identifier | | context | string | no | | Surrounding context | | metadata | object | no | {} | Arbitrary metadata | | entity_names | string[] | no | [] | Associate with named entities | **Behavior**: 1. Check for duplicates (exact hash, then semantic similarity) 2. If duplicate found: merge and return existing memory 3. Generate embedding for content 4. Insert memory record 5. Associate with entities (create entities if they don't exist) 6. Return created memory with ID #### 7.2.2 `recall_memories` Search for relevant memories. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | query | string | yes | | Search query | | search_mode | string | no | "hybrid" | keyword, semantic, hybrid, graph | | memory_types | string[] | no | | Filter by types | | tags | string[] | no | | Filter by tags | | min_confidence | number | no | 0.1 | Minimum confidence threshold | | limit | int | no | 20 | Max results | | entity_name | string | no | | Filter by entity association | **Returns**: Array of memories with scores, sorted by relevance. #### 7.2.3 `create_entities` Create one or more named entities. **Parameters**: ```json { "entities": [ { "name": "string (required)", "entity_type": "string (required)", "description": "string (optional)", "metadata": "object (optional)" } ] } ``` **Behavior**: Create entities, generate embeddings, deduplicate by (name, entity_type). #### 7.2.4 `create_relations` Create typed connections between entities. **Parameters**: ```json { "relations": [ { "source": "string (entity name, required)", "target": "string (entity name, required)", "relation_type": "string (required)", "strength": "number 0-1 (optional, default 0.5)", "confidence": "number 0-1 (optional, default 1.0)", "context": "string (optional)" } ] } ``` **Behavior**: Look up entities by name, create relation records. If source or target entity doesn't exist, auto-create with type "unknown". #### 7.2.5 `delete_memories` Delete memories by ID or filter. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | memory_ids | string[] | no | Specific memory IDs to delete | | before_date | string | no | Delete all memories before this date | | min_confidence_below | number | no | Delete all with confidence below this | | memory_types | string[] | no | Delete all of these types | **Behavior**: Delete matching memories and cascade to entity_memories associations. At least one filter must be provided. #### 7.2.6 `delete_entities` Delete entities and optionally their associated memories. **Parameters**: | Name | Type | Required | Description | |------|------|----------|-------------| | entity_names | string[] | yes | Names of entities to delete | | cascade_memories | boolean | no | Also delete associated memories (default false) | #### 7.2.7 `delete_relations` Delete specific relations. **Parameters**: ```json { "relations": [ { "source": "string (required)", "target": "string (required)", "relation_type": "string (required)" } ] } ``` #### 7.2.8 `get_entity_graph` Retrieve a subgraph centered on an entity. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | entity_name | string | yes | | Center entity | | depth | int | no | 1 | Hop count for traversal | | min_strength | number | no | 0.0 | Minimum relation strength | | include_memories | boolean | no | true | Include associated memories | **Returns**: Graph structure with nodes (entities), edges (relations), and optionally memories per node. #### 7.2.9 `consolidate_memories` Trigger manual consolidation. **Parameters**: | Name | Type | Required | Default | Description | |------|------|----------|---------|-------------| | similarity_threshold | number | no | 0.85 | Clustering threshold | | dry_run | boolean | no | false | Preview without applying | **Returns**: Consolidation report (clusters found, memories merged, memories deleted). #### 7.2.10 `get_memory_stats` Get analytics about the memory store. **Parameters**: None. **Returns**: ```json { "total_memories": 1234, "total_entities": 56, "total_relations": 78, "memories_by_type": {"observation": 500, "decision": 200, ...}, "average_confidence": 0.72, "oldest_memory": "2025-01-01T00:00:00Z", "newest_memory": "2026-03-08T12:00:00Z", "low_confidence_count": 45 } ``` --- ## 8. REST API ### 8.1 Memory Endpoints ``` POST /api/memories Create memory (same params as store_memory) GET /api/memories List memories with filters GET /api/memories/:id Get single memory PUT /api/memories/:id Update memory DELETE /api/memories/:id Delete memory GET /api/memories/search Search (query params: q, mode, types, tags, limit) POST /api/memories/consolidate Trigger consolidation ``` ### 8.2 Entity Endpoints ``` POST /api/entities Create entity GET /api/entities List entities GET /api/entities/:id Get entity with relations PUT /api/entities/:id Update entity DELETE /api/entities/:id Delete entity GET /api/entities/:id/graph Get entity subgraph GET /api/entities/:id/memories Get entity's memories ``` ### 8.3 Relation Endpoints ``` POST /api/relations Create relation GET /api/relations List relations DELETE /api/relations/:id Delete relation ``` ### 8.4 Analytics Endpoints ``` GET /api/analytics/overview Memory statistics GET /api/analytics/growth Growth over time (memories per day/week/month) GET /api/analytics/types Distribution by memory type GET /api/analytics/tags Tag frequency GET /api/analytics/confidence Confidence distribution histogram ``` ### 8.5 Server-Sent Events ``` GET /api/events SSE stream ``` **Event types**: | Event | Payload | Trigger | |-------|---------|---------| | `memory_created` | `{id, content, type}` | New memory stored | | `memory_updated` | `{id, changes}` | Memory modified | | `memory_deleted` | `{id}` | Memory removed | | `entity_created` | `{id, name, type}` | New entity | | `consolidation_complete` | `{merged, deleted}` | Consolidation finished | | `sync_complete` | `{timestamp}` | Background sync done | --- ## 9. Configuration ### 9.1 Environment Variables | Variable | Default | Description | |----------|---------|-------------| | `DATABASE_URL` | `sqlite:///memory.db` | Database connection string | | `DATABASE_BACKEND` | `sqlite` | `sqlite` or `postgres` | | `EMBEDDING_PROVIDER` | `local` | `local` or `openai` | | `EMBEDDING_MODEL` | `all-MiniLM-L6-v2` | Model name | | `EMBEDDING_DIMENSIONS` | `384` | Vector dimensions | | `OPENAI_API_KEY` | | Required if provider is openai | | `CONFIDENCE_HALF_LIFE_DAYS` | `30` | Days until confidence halves | | `CONFIDENCE_PRUNE_THRESHOLD` | `0.05` | Auto-prune below this | | `CONSOLIDATION_THRESHOLD` | `0.85` | Similarity for merging | | `CONSOLIDATION_INTERVAL` | `50` | Memories between auto-consolidation | | `SEARCH_DEFAULT_LIMIT` | `20` | Default search results | | `SEARCH_MIN_SIMILARITY` | `0.5` | Minimum vector similarity | | `HYBRID_KEYWORD_WEIGHT` | `0.4` | Keyword weight in hybrid (semantic = 1 - this) | | `API_PORT` | `3333` | REST API port | | `API_HOST` | `0.0.0.0` | REST API host | | `LOG_LEVEL` | `info` | Logging level | ### 9.2 Claude Desktop Integration ```json { "mcpServers": { "memory": { "command": "node", "args": ["path/to/server.js"], "env": { "DATABASE_URL": "postgresql://user:pass@localhost:5432/memory", "DATABASE_BACKEND": "postgres" } } } } ``` --- ## 10. Behavioral Test Specifications ### 10.1 Memory CRUD Tests ``` TEST: Store basic memory Call store_memory(content="User prefers TypeScript", memory_type="preference") EXPECT: Memory created with id, confidence=1.0, embedding generated TEST: Store memory with entity association Call store_memory(content="Alice manages the frontend team", entity_names=["Alice", "Frontend Team"]) EXPECT: Memory created, entities auto-created if not existing, entity_memories rows created TEST: Exact duplicate detection Store "User prefers dark mode" twice EXPECT: Second call returns existing memory, no duplicate created TEST: Near-duplicate detection Store "User prefers dark mode" Store "The user likes dark mode" (semantic similarity > 0.95) EXPECT: Second memory merged into first, tags combined, confidence boosted TEST: Update memory Create memory, then update content EXPECT: Content updated, new version record created, embedding re-generated TEST: Delete memory cascades Create memory associated with entities Delete memory EXPECT: Memory deleted, entity_memories rows deleted, entities preserved ``` ### 10.2 Search Tests ``` TEST: Keyword search Store memories about "machine learning" and "web development" Search "machine learning" EXPECT: ML memory returned, web dev memory not returned TEST: Semantic search Store "canine behavior training tips" Search "how to train dogs" with mode=semantic EXPECT: Memory returned based on semantic similarity TEST: Hybrid search combines results Store 10 varied memories Search with mode=hybrid EXPECT: Results reflect both keyword and semantic relevance TEST: Filter by memory type Store observation, decision, and pattern memories Search with memory_types=["decision"] EXPECT: Only decision-type memories returned TEST: Filter by tags Store memories with various tags Search with tags=["typescript", "frontend"] EXPECT: Only memories containing ALL specified tags TEST: Filter by confidence Store memories, wait for some to decay Search with min_confidence=0.5 EXPECT: Only memories with effective confidence >= 0.5 TEST: Pagination Store 50 memories Search with limit=10, offset=20 EXPECT: Results 21-30 returned ``` ### 10.3 Confidence and Decay Tests ``` TEST: Initial confidence Store memory without explicit confidence EXPECT: confidence = 1.0 TEST: Confidence decay over time Store memory, simulate 30 days passing Query effective confidence EXPECT: ~0.5 (half-life = 30 days) TEST: Confidence decay at 60 days Store memory, simulate 60 days EXPECT: ~0.25 TEST: Access reinforcement Store memory, simulate 15 days, then access it EXPECT: last_accessed_at updated, confidence boosted by 0.1 TEST: Confidence cap at 1.0 Store memory with confidence 0.95, access it EXPECT: confidence = 1.0, not 1.05 TEST: Prune low-confidence memories Store memories, simulate long decay below threshold Run pruning job EXPECT: Memories with effective confidence < 0.05 deleted ``` ### 10.4 Entity and Graph Tests ``` TEST: Create entity Create entity(name="Alice", entity_type="person", description="Software engineer") EXPECT: Entity created with embedding TEST: Create relation Create entities Alice and Bob Create relation(source="Alice", target="Bob", relation_type="collaborates_with", strength=0.8) EXPECT: Relation created, lookup by entity names succeeds TEST: Duplicate relation prevention Create same relation twice EXPECT: Unique constraint error or upsert TEST: Get entity graph (depth=1) Create A→B, A→C, B→D Get graph for A with depth=1 EXPECT: Returns A, B, C (not D) TEST: Get entity graph (depth=2) Same setup Get graph for A with depth=2 EXPECT: Returns A, B, C, D TEST: Entity deletion cascade Create entity with relations Delete entity EXPECT: Entity deleted, relations cascade-deleted, associated memories preserved TEST: Auto-create entities from store_memory Call store_memory with entity_names=["NewPerson"] EXPECT: Entity "NewPerson" auto-created with type "unknown" ``` ### 10.5 Consolidation Tests ``` TEST: Cluster detection Store 5 memories about "React hooks" with slight variations Run consolidation with threshold=0.85 EXPECT: Cluster detected, memories merged into primary TEST: Dry run Same setup, run with dry_run=true EXPECT: Report generated but no changes applied TEST: Version trail Merge two memories EXPECT: Version record created for the absorbed memory TEST: Entity reassignment Memory A associated with Entity X Memory B associated with Entity Y Merge A into B EXPECT: B now associated with both X and Y ``` ### 10.6 REST API Tests ``` TEST: POST /api/memories Send valid memory JSON EXPECT: 201 Created with memory object TEST: GET /api/memories/search?q=test EXPECT: 200 with array of matching memories TEST: SSE event on memory creation Connect to /api/events, then create memory via API EXPECT: Receive memory_created event with memory data TEST: GET /api/analytics/overview EXPECT: 200 with stats object containing counts and averages ``` --- ## 11. Key Implementation Algorithms ### 11.1 Hybrid Score Computation ``` Input: query string, search filters Step 1: Run keyword search → results_kw with BM25 scores Step 2: Run semantic search → results_vec with similarity scores Step 3: Normalize keyword scores to [0,1] via min-max Step 4: Normalize semantic scores to [0,1] (already in this range) Step 5: Union all memory IDs Step 6: For each ID: - If in both: score = α × kw_norm + (1-α) × vec_norm - If keyword only: score = α × kw_norm - If vector only: score = (1-α) × vec_norm Step 7: Sort by score descending Step 8: Apply effective confidence as final multiplier: final_score = hybrid_score × effective_confidence ``` ### 11.2 Graph Traversal (BFS) ``` function getSubgraph(startEntity, maxDepth, minStrength): visited = Set() queue = [(startEntity, 0)] nodes = [] edges = [] while queue not empty: (entity, depth) = queue.pop(0) if entity in visited or depth > maxDepth: continue visited.add(entity) nodes.push(entity) for relation in entity.outgoing + entity.incoming: if relation.strength >= minStrength: edges.push(relation) neighbor = relation.other_end(entity) if neighbor not in visited: queue.push((neighbor, depth + 1)) return {nodes, edges} ``` ### 11.3 Deduplication Check ``` function checkDuplicate(newContent): // Exact check hash = sha256(newContent) existing = db.query("SELECT * FROM memories WHERE sha256(content) = ?", hash) if existing: return {type: "exact", memory: existing} // Semantic check embedding = embed(newContent) similar = db.query( "SELECT *, 1-(embedding <=> ?) AS sim FROM memories WHERE 1-(embedding <=> ?) > 0.95 LIMIT 1", embedding, embedding ) if similar: return {type: "near", memory: similar} return null ``` ### 11.4 Confidence Decay Computation ``` function effectiveConfidence(memory): lastActive = memory.last_accessed_at ?? memory.created_at elapsedDays = (now() - lastActive).totalDays halfLife = config.CONFIDENCE_HALF_LIFE_DAYS // 30 return memory.confidence * Math.pow(0.5, elapsedDays / halfLife) ``` --- ## 12. Dependencies ### 12.1 Required (TypeScript/Node.js) | Package | Purpose | |---------|---------| | @modelcontextprotocol/sdk | MCP server framework | | zod | Schema validation | | better-sqlite3 | SQLite driver (if SQLite backend) | | pg | PostgreSQL driver (if Postgres backend) | | express | REST API server | | uuid | External ID generation | ### 12.2 Optional | Package | Purpose | |---------|---------| | pgvector | PostgreSQL vector operations | | sqlite-vec | SQLite vector extension | | @xenova/transformers | Local embeddings (ONNX Runtime) | | openai | Remote embeddings | | cron | Scheduled consolidation | | eventsource | SSE support | --- ## 13. Directory Structure ``` project_root/ ├── src/ │ ├── index.ts # Entry point, server setup │ ├── config.ts # Configuration loading │ ├── database/ │ │ ├── schema.ts # Table definitions │ │ ├── migrations/ # Schema migrations │ │ ├── sqlite.ts # SQLite implementation │ │ └── postgres.ts # PostgreSQL implementation │ ├── models/ │ │ ├── memory.ts # Memory type/interface │ │ ├── entity.ts # Entity type/interface │ │ └── relation.ts # Relation type/interface │ ├── services/ │ │ ├── memory.ts # Memory CRUD + dedup │ │ ├── entity.ts # Entity CRUD │ │ ├── search.ts # Search dispatch (keyword/semantic/hybrid) │ │ ├── consolidation.ts # Dedup + merge engine │ │ ├── embedding.ts # Embedding generation │ │ └── decay.ts # Confidence management │ ├── mcp/ │ │ ├── server.ts # MCP tool registration │ │ └── tools.ts # Tool implementations │ ├── api/ │ │ ├── router.ts # Express routes │ │ ├── middleware.ts # Auth, logging, error handling │ │ └── sse.ts # Server-Sent Events │ └── utils/ │ ├── hash.ts # SHA-256 hashing │ └── normalize.ts # Text normalization ├── tests/ ├── migrations/ └── package.json ``` --- ## 14. Startup Sequence 1. Load configuration from environment variables 2. Initialize database connection (SQLite or PostgreSQL) 3. Run pending migrations 4. Initialize embedding provider (test with a sample embed call) 5. Initialize MCP server with stdio transport 6. Register all MCP tools 7. Start REST API server (if enabled) 8. Start background workers: - Confidence decay pruning (daily) - Consolidation (after every N insertions or on schedule) - Embedding backfill (for any memories missing embeddings) 9. Begin accepting connections --- *This specification provides complete architectural and behavioral detail for independent implementation of an SQL-native entity memory layer with vector search, confidence decay, memory consolidation, and dual MCP/REST access.* --- ## Clean Room Specification: Hierarchical Agentic Memory with LLM Driven Auto Taxonomy **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/06-Hierarchical-Agentic-Memory-Auto-Taxonomy **Description:** Purpose of This Document This document specifies the complete architecture of a hierarchical memory system that uses LLM agents to automatically organize, ch... # Clean-Room Specification: Hierarchical Agentic Memory with LLM-Driven Auto-Taxonomy ## Purpose of This Document This document specifies the complete architecture of a **hierarchical memory system that uses LLM agents to automatically organize, chunk, and retrieve information**. Instead of fixed schemas or vector databases, the system uses LLM reasoning to: (1) chunk documents intelligently, (2) generate structured memory summaries, (3) create and maintain a hierarchical taxonomy as a directory tree, and (4) navigate that tree at query time using tool-based exploration. All memories are stored as Markdown files in a filesystem hierarchy, with README files at each level describing the contents. This specification is detailed enough that a professional AI coding model can produce a functionally identical working system without reference to any existing codebase. --- ## 1. System Overview ### 1.1 Core Concept Traditional memory systems use embedding-based retrieval. This system instead leverages LLM reasoning for both storage and retrieval: - **Storage**: An LLM reads input text, generates structured memory summaries, and decides where to place them in a directory hierarchy - **Retrieval**: An LLM agent navigates the directory tree using filesystem tools (ls, cat, grep), reading README files to decide which paths to explore The filesystem IS the memory structure. No database. No vector store. The hierarchy itself provides the organizational semantics. ### 1.2 Architecture ``` ┌────────────────────────────────────────────┐ │ Public API (Workflow) │ │ add(files, text) request(query) │ ├────────────────────────────────────────────┤ │ GAM Agent Chat Agent │ │ (Memory Building) (Q&A Retrieval) │ ├────────────────────────────────────────────┤ │ LLM Generator Layer │ │ OpenAI-compatible API with JSON schemas │ ├────────────────────────────────────────────┤ │ Workspace Layer │ │ Local filesystem or Docker container │ ├────────────────────────────────────────────┤ │ GAM Tree (Read-Only View) │ │ In-memory FSNode tree from disk scan │ ├────────────────────────────────────────────┤ │ Filesystem Storage │ │ .gam_meta.json + README.md + chunks (.md) │ └────────────────────────────────────────────┘ ``` ### 1.3 Key Design Principles 1. **LLM-native organization**: The LLM decides the taxonomy structure, not hard-coded rules 2. **Filesystem as database**: Directory tree = taxonomy, files = memories, READMEs = indexes 3. **Agentic retrieval**: A reasoning agent navigates the tree at query time, not a similarity search 4. **Separation of concerns**: Tree (read-only view), Workspace (write operations), Generator (LLM calls) 5. **Incremental updates**: New content can be added without rebuilding the entire taxonomy --- ## 2. Data Model ### 2.1 FSNode (In-Memory Tree Node) ```python class NodeType(Enum): FILE = "file" DIRECTORY = "directory" class FSNode(BaseModel): # Pydantic model name: str # Node identifier (filename or dirname) node_type: NodeType # FILE or DIRECTORY content: Optional[str] = None # Text content (files only) children: Dict[str, FSNode] = {} # Child nodes (directories only) meta: Dict[str, Any] = {} # Arbitrary metadata created_at: datetime # Creation timestamp updated_at: datetime # Last modification timestamp ``` ### 2.2 MemorizedChunk (Memory Unit) ```python class MemorizedChunk(BaseModel): index: int # Sequence number in batch title: str # Snake_case descriptive title memory: str # LLM-generated summary preserving key information tldr: str # One-line summary metadata: Dict[str, Any] = {} # Source info, token count, etc. ``` **Markdown serialization** (each chunk becomes a .md file): ```markdown --- title: neural_network_fundamentals index: 3 tldr: Core concepts of neural network architecture and training --- Neural networks are computational models inspired by biological neural systems. Key components include layers (input, hidden, output), activation functions (ReLU, sigmoid, tanh), and training via backpropagation with gradient descent. The loss function measures prediction error, and optimizers (SGD, Adam) update weights to minimize this loss. Regularization techniques (dropout, L2) prevent overfitting on training data. ``` ### 2.3 DirectoryNode (Taxonomy Planning) ```python class DirectoryNode(BaseModel): path: str # Full directory path (e.g., "/foundations/math") name: str # Directory name description: str # What this directory contains children: List[DirectoryNode] = [] # Subdirectories chunk_indices: List[int] = [] # Chunks assigned to this directory ``` **Constraint**: Every chunk index must appear in exactly ONE leaf directory. Parent directories have empty `chunk_indices` — they only contain subdirectories. ### 2.4 GAM Metadata File Stored at `/.gam_meta.json`: ```json { "version": "1.0", "created_at": "2026-01-15T10:30:00Z", "updated_at": "2026-03-08T14:00:00Z", "total_chunks": 42, "total_directories": 8, "source_files": ["doc1.pdf", "doc2.txt"], "model_used": "gpt-4o-mini", "chunk_config": { "min_tokens": 100, "max_tokens": 1000 } } ``` ### 2.5 ChatResult (Query Response) ```python class ChatResult(BaseModel): question: str # Original query answer: str # Synthesized response sources: List[str] # File paths referenced in answer confidence: float # 0.0-1.0 reliability score files_read: List[str] # All files accessed during exploration dirs_explored: List[str] # All directories explored trajectory: str # Complete exploration path log notes: Optional[str] = None # Additional context ``` --- ## 3. Filesystem Storage Structure ### 3.1 Directory Layout ``` gam_directory/ ├── .gam_meta.json # Root metadata ├── README.md # Root-level summary of all contents ├── foundations/ │ ├── README.md # Describes this section │ ├── core_concepts/ │ │ ├── README.md │ │ ├── neural_network_fundamentals.md │ │ └── activation_functions.md │ └── mathematics/ │ ├── README.md │ ├── linear_algebra_basics.md │ └── calculus_for_ml.md ├── advanced_topics/ │ ├── README.md │ ├── transformer_architecture.md │ └── attention_mechanisms.md └── applications/ ├── README.md ├── natural_language_processing.md └── computer_vision.md ``` ### 3.2 README Format Each directory contains a README.md describing its contents: ```markdown # Foundations This section contains fundamental concepts that form the basis of the knowledge domain. ## Contents - **core_concepts/**: Core definitions, principles, and building blocks including neural network architecture and activation functions - **mathematics/**: Mathematical prerequisites including linear algebra and calculus foundations needed for understanding the domain ``` The README serves as a navigation index for the exploration agent — it reads the README to decide which subdirectories to explore. --- ## 4. LLM Generator ### 4.1 Interface ```python class BaseGenerator(ABC): @abstractmethod def generate_single( self, prompt: Optional[str] = None, messages: Optional[List[Dict]] = None, schema: Optional[Dict] = None, **kwargs ) -> Dict: """ Returns: { "text": str, # Raw response text "parsed": dict, # JSON-parsed if schema provided "response": object # Raw API response } """ pass def generate_batch( self, prompts: List[str], schema: Optional[Dict] = None ) -> List[Dict]: """Parallel batch processing via thread pool.""" pass ``` ### 4.2 OpenAI-Compatible Implementation ```python class OpenAIGenerator(BaseGenerator): def __init__( self, model: str = "gpt-4o-mini", api_key: str = None, # Falls back to OPENAI_API_KEY env base_url: str = None, # Falls back to OPENAI_BASE_URL env temperature: float = 0.7, max_tokens: int = 4096, num_workers: int = None # Default: os.cpu_count() ): self.client = OpenAI(api_key=api_key, base_url=base_url) ``` **Retry logic**: 20 attempts with 20-second exponential backoff on API errors. **Batch processing**: Uses `concurrent.futures.ThreadPoolExecutor` with configurable worker count. **Structured output**: When a `schema` parameter is provided, the generator uses OpenAI's JSON schema response format to ensure valid structured output. On parse failure, uses a JSON repair library to fix common issues. ### 4.3 LLM Prompt Templates #### Memory Generation Prompt ``` You are a memory generation agent. Given a text chunk, create a structured memory that preserves the key information, concepts, numbers, and relationships. The memory should be a concise but complete summary that someone could use to understand the original content without seeing it. Rules: - Title must be snake_case and descriptive (3-5 words) - Memory should preserve key facts, numbers, names, and relationships - TLDR should be one sentence Output JSON schema: { "title": "string (snake_case)", "memory": "string (detailed summary)", "tldr": "string (one sentence)" } ``` #### Batch Organization Prompt ``` You are a taxonomy organizer. Given a list of memorized chunks (each with index, title, and TLDR), organize them into a hierarchical directory structure. Rules: - Every chunk index must appear in exactly ONE leaf directory - Parent directories should NOT have chunk_indices (they only contain subdirectories) - Use descriptive, lowercase, underscore-separated directory names - Aim for 3-7 chunks per leaf directory - Maximum depth: 3 levels - Group by semantic similarity and topic Input: List of chunks with index, title, tldr Output: DirectoryNode tree structure ``` #### Chunk Assignment Prompt (Incremental) ``` Given an existing taxonomy structure and a new memorized chunk, determine which leaf directory is the best fit. If no existing directory is appropriate, suggest creating a new one. Prefer leaf directories over parent directories. Consider the directory descriptions in the README files. ``` #### README Generation Prompt ``` Generate a README.md for a directory containing the following files/subdirectories. Include: 1. A brief title (1 line) 2. A description of what this section contains (2-3 sentences) 3. A "## Contents" section listing each item with a brief description Use the file names and their content summaries to write accurate descriptions. ``` --- ## 5. Memory Building Pipeline (GAM Agent) ### 5.1 Full Build (Empty GAM) When adding content to an empty GAM directory: **Step 1 — Input Resolution**: - Accept file paths (PDF, TXT, MD) or raw text strings - Extract text from PDFs using a PDF parser - Concatenate all input into a single text corpus **Step 2 — Chunking**: - Count total tokens using a tokenizer (tiktoken) - If total tokens > max_chunk_tokens: split into chunks - Chunking algorithm (see Section 5.2) **Step 3 — Memory Generation** (Parallel): - For each chunk, call LLM with memory generation prompt - Use ThreadPoolExecutor for parallel processing - Collect `MemorizedChunk` objects with index, title, memory, tldr **Step 4 — Taxonomy Organization**: - Send all chunk summaries (index, title, tldr) to LLM - LLM returns a DirectoryNode tree - Validate: every chunk index appears in exactly one leaf **Step 5 — Filesystem Write**: - Create directory structure - Write each chunk as `{title}.md` in its assigned directory - Generate README.md at each directory level via LLM **Step 6 — Metadata**: - Write `.gam_meta.json` with creation info ### 5.2 Chunking Algorithm ``` Input: text (string), config {min_tokens, max_tokens} Step 1: Identify section boundaries - Look for markdown headers (# ## ###) - Look for double newlines separating paragraphs - Create initial sections at these boundaries Step 2: For each section: - Count tokens - If tokens > max_tokens: Ask LLM to find optimal split point that: - Maintains semantic completeness - Respects topic boundaries - Avoids splitting mid-sentence Split at recommended index Recurse on both halves - If tokens < min_tokens: Merge with adjacent section Step 3: Assign sequential indices to final chunks Output: List of text chunks with indices ``` ### 5.3 Incremental Add (Existing GAM) When adding new content to an existing taxonomy: **Step 1-3**: Same as full build (resolve input, chunk, generate memories) **Step 4 — Placement Decision**: For each new chunk: 1. Load current taxonomy structure (directory tree + READMEs) 2. Ask LLM: "Which existing directory best fits this chunk?" 3. If good fit found: place chunk in that directory 4. If no good fit: create new directory **Step 5 — Reorganization Check**: If any directory exceeds a threshold (e.g., 10+ chunks): 1. Ask LLM to re-plan the taxonomy for that subtree 2. Compute file movements needed 3. Execute movements (rename/move files) 4. Update affected README files **Step 6**: Update metadata ### 5.4 ReorganizeOperation ```python class ReorganizeOperation(BaseModel): moved_files: List[Tuple[str, str]] # (old_path, new_path) deleted_files: List[str] # Files to remove new_directories: List[str] # Directories to create ``` --- ## 6. Retrieval Pipeline (Chat Agent) ### 6.1 Agent Loop The chat agent is an LLM with access to filesystem tools. It explores the GAM tree to answer queries. ``` Input: user_query, system_prompt, max_iterations Initialize: - visited_files = set() - exploration_log = [] - gathered_information = [] For iteration in range(max_iterations): 1. Construct message context: - System prompt (exploration guidelines) - User query - Exploration history so far - Available tools 2. LLM decides next action (function calling): - ls(path) → list directory contents - cat(file) → read file content - grep(pattern) → search file contents - bm25_search(query) → full-text search (optional) - answer(text) → provide final answer 3. If action is "answer": Return ChatResult with answer and sources 4. Execute tool, append result to exploration_log If max_iterations reached: Synthesize best answer from gathered information Return ChatResult with lower confidence ``` ### 6.2 Exploration Guidelines (System Prompt) ``` You are a research agent exploring a hierarchical knowledge base. Strategy: 1. Start by reading the root README.md to understand the overall structure 2. Use ls() to see available directories and files 3. Read README.md at each level before diving deeper 4. Navigate toward directories most likely to contain relevant information 5. Read specific chunk files when they seem relevant to the query 6. Use grep() to search for specific terms across files 7. When you have enough information, use answer() to respond Important: - Don't read every file — be strategic - The README files describe what each section contains - Prefer depth-first exploration of promising paths - Track which files you've already read to avoid re-reading ``` ### 6.3 Tool Definitions #### `ls` — List Directory ```json { "name": "ls", "description": "List contents of a directory", "parameters": { "type": "object", "properties": { "path": { "type": "string", "description": "Directory path relative to GAM root" } }, "required": ["path"] } } ``` **Returns**: List of files and subdirectories with types and sizes. #### `cat` — Read File ```json { "name": "cat", "description": "Read the contents of a file", "parameters": { "type": "object", "properties": { "file": { "type": "string", "description": "File path relative to GAM root" } }, "required": ["file"] } } ``` **Returns**: Full file content as string. #### `grep` — Search Files ```json { "name": "grep", "description": "Search for a pattern in files", "parameters": { "type": "object", "properties": { "pattern": { "type": "string", "description": "Search pattern (case-insensitive substring)" }, "path": { "type": "string", "description": "Directory to search in (default: root)", "default": "/" } }, "required": ["pattern"] } } ``` **Returns**: List of matching files with line numbers and matched content. #### `bm25_search` — Full-Text Search (Optional) ```json { "name": "bm25_search", "description": "Search all memory files using BM25 full-text search", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "Search query" }, "top_k": { "type": "integer", "description": "Number of results to return", "default": 5 } }, "required": ["query"] } } ``` **Implementation**: Uses a BM25 index (Pyserini/Lucene-based) built over all .md files in the GAM directory. The index is lazily built on first search and cached. **Returns**: Ranked list of file paths with relevance scores and content snippets. #### `answer` — Provide Final Answer ```json { "name": "answer", "description": "Provide the final answer to the user's question", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The answer" }, "confidence": { "type": "number", "description": "Confidence in the answer (0.0-1.0)" }, "sources": { "type": "array", "items": {"type": "string"}, "description": "File paths that informed the answer" } }, "required": ["text"] } } ``` --- ## 7. Workspace Layer ### 7.1 Local Workspace ```python class LocalWorkspace: def __init__(self, root_path: Path): self.root_path = root_path self.root_path.mkdir(parents=True, exist_ok=True) def run(self, cmd: str) -> Tuple[str, int]: """Execute a shell command in the workspace directory.""" result = subprocess.run(cmd, shell=True, cwd=self.root_path, capture_output=True) return result.stdout.decode(), result.returncode def read_file(self, path: str) -> str: """Read file content.""" return (self.root_path / path).read_text() def write_file(self, path: str, content: str) -> None: """Write file, creating parent directories as needed.""" full_path = self.root_path / path full_path.parent.mkdir(parents=True, exist_ok=True) full_path.write_text(content) def list_dir(self, path: str = "") -> List[Dict]: """List directory contents with types and sizes.""" target = self.root_path / path return [ {"name": f.name, "type": "dir" if f.is_dir() else "file", "size": f.stat().st_size} for f in sorted(target.iterdir()) if not f.name.startswith(".") ] def copy_to_workspace(self, src: Path, dst: str) -> None: """Copy external file into workspace.""" shutil.copy2(src, self.root_path / dst) ``` ### 7.2 Docker Workspace (Optional) For sandboxed execution: ```python class DockerWorkspace: def __init__(self, image: str, root_path: str = "/workspace"): self.container = docker.from_env().containers.run( image, detach=True, tty=True ) self.root_path = root_path def run(self, cmd: str, timeout: int = 30) -> Tuple[str, int]: """Execute command inside container with timeout.""" wrapped = f"timeout {timeout} bash -c '{cmd}'" exit_code, output = self.container.exec_run(wrapped) return output.decode(), exit_code ``` --- ## 8. GAM Tree (Read-Only View) ### 8.1 Tree Construction ```python class GAMTree: def __init__(self, root: FSNode): self.root = root @classmethod def from_disk(cls, path: Path) -> "GAMTree": """Recursively load directory structure into FSNode tree.""" root = cls._scan_directory(path) return cls(root) @staticmethod def _scan_directory(path: Path) -> FSNode: node = FSNode( name=path.name, node_type=NodeType.DIRECTORY, children={}, created_at=datetime.fromtimestamp(path.stat().st_ctime), updated_at=datetime.fromtimestamp(path.stat().st_mtime) ) for child in sorted(path.iterdir()): if child.name.startswith("."): continue if child.is_dir(): node.children[child.name] = GAMTree._scan_directory(child) elif child.is_file() and child.suffix == ".md": node.children[child.name] = FSNode( name=child.name, node_type=NodeType.FILE, content=child.read_text(), created_at=datetime.fromtimestamp(child.stat().st_ctime), updated_at=datetime.fromtimestamp(child.stat().st_mtime) ) return node ``` ### 8.2 Tree Operations ```python def get_node(self, path_str: str) -> Optional[FSNode]: """Navigate to a node by path string.""" parts = [p for p in path_str.split("/") if p] current = self.root for part in parts: if part not in current.children: return None current = current.children[part] return current def tree_view(self, depth: int = 2) -> str: """Render ASCII tree visualization.""" lines = [] self._render_tree(self.root, "", depth, 0, lines) return "\n".join(lines) def get_structure_summary(self) -> str: """Generate text summary for LLM context.""" summary = [] for name, child in self.root.children.items(): if child.node_type == NodeType.DIRECTORY: readme = child.children.get("README.md") desc = readme.content[:200] if readme else "No description" chunk_count = sum(1 for c in child.children.values() if c.node_type == NodeType.FILE and c.name != "README.md") summary.append(f"- {name}/ ({chunk_count} files): {desc}") return "\n".join(summary) ``` --- ## 9. Workflow API ### 9.1 Public Interface ```python class Workflow: def __init__( self, workflow_type: str, # "text" or "video" gam_dir: str, # Path to GAM directory model: str = "gpt-4o-mini", # LLM model name llm_config: Dict = None # API key, temperature, etc. ): self._gam_dir = Path(gam_dir) self._model = model self._llm_config = llm_config or {} # Components are lazy-loaded on first use self._generator = None self._workspace = None self._tree = None def add( self, files: List[str] = None, # File paths to ingest text: str = None, # Raw text to ingest use_chunking: bool = True, # Whether to chunk input chunk_config: Dict = None # min_tokens, max_tokens ) -> None: """Add content to the GAM memory.""" agent = self._get_gam_agent() if self._is_empty(): agent.create(files=files, text=text, use_chunking=use_chunking, chunk_config=chunk_config) else: agent.add_incrementally(files=files, text=text, use_chunking=use_chunking, chunk_config=chunk_config) def request( self, user_prompt: str, # Question to answer system_prompt: str = None, # Custom system instructions max_iterations: int = 10 # Max exploration rounds ) -> ChatResult: """Query the GAM memory.""" agent = self._get_chat_agent() return agent.request( query=user_prompt, system_prompt=system_prompt, max_iterations=max_iterations ) ``` ### 9.2 CLI Entry Points ```bash # Add documents to memory gam-add --gam-dir ./my_memory --files doc1.pdf doc2.txt --model gpt-4o-mini # Query the memory gam-request --gam-dir ./my_memory --query "What are the main findings?" --max-iterations 10 ``` --- ## 10. Configuration ### 10.1 Environment Variables | Variable | Default | Description | |----------|---------|-------------| | `OPENAI_API_KEY` | (required) | API key for LLM provider | | `OPENAI_BASE_URL` | https://api.openai.com/v1 | API base URL (for compatible providers) | | `OPENAI_MODEL` | gpt-4o-mini | Default model name | | `OPENAI_TEMPERATURE` | 0.7 | Default temperature | | `GAM_AGENT_MODEL` | (falls back to OPENAI_MODEL) | Model for memory building | | `GAM_AGENT_TEMPERATURE` | 0.3 | Temperature for memory building (lower = more consistent) | | `CHAT_AGENT_MODEL` | (falls back to OPENAI_MODEL) | Model for Q&A | | `CHAT_AGENT_TEMPERATURE` | 0.7 | Temperature for Q&A | ### 10.2 Chunk Configuration ```python class ChunkConfig(BaseModel): min_tokens: int = 100 # Minimum chunk size max_tokens: int = 1000 # Maximum chunk size tokenizer: str = "tiktoken" # Tokenizer to use model: str = "gpt-4o-mini" # For tiktoken encoding selection ``` --- ## 11. Behavioral Test Specifications ### 11.1 Memory Building Tests ``` TEST: Full build from single document Input: 5000-word document about machine learning EXPECT: Directory structure created with multiple subdirectories EXPECT: Each chunk saved as .md file with frontmatter EXPECT: README.md at each directory level EXPECT: .gam_meta.json at root with correct counts EXPECT: Every chunk appears in exactly one leaf directory TEST: Chunking respects boundaries Input: Document with clear section headers EXPECT: Chunks align with section boundaries where possible EXPECT: No chunk exceeds max_tokens EXPECT: No chunk below min_tokens (except final chunk) TEST: Memory generation quality Input: Paragraph about "Python's GIL prevents true multi-threading" EXPECT: Memory preserves key fact about GIL EXPECT: Title is snake_case (e.g., "python_gil_threading_limitation") EXPECT: TLDR is one sentence TEST: Taxonomy organization Input: 20 chunks about varied programming topics EXPECT: Logical grouping (languages, paradigms, tools, etc.) EXPECT: 3-7 chunks per leaf directory EXPECT: Maximum 3 levels of nesting EXPECT: No chunk assigned to multiple directories TEST: Incremental addition Build GAM with 10 chunks about Python Add 5 more chunks about JavaScript EXPECT: New directory created for JavaScript topics EXPECT: Existing Python structure unchanged EXPECT: Updated README at root level TEST: Reorganization on threshold Build GAM, incrementally add chunks until one directory has 12+ files EXPECT: Reorganization triggered EXPECT: Overfull directory split into subdirectories EXPECT: All files accounted for (no lost chunks) EXPECT: Affected READMEs regenerated ``` ### 11.2 Retrieval Tests ``` TEST: Basic query answering Build GAM with known content about "React hooks" Query: "How do React hooks work?" EXPECT: Answer contains accurate information from stored memories EXPECT: Sources list includes relevant .md files EXPECT: Confidence > 0.5 TEST: Hierarchical exploration Build GAM with multi-level taxonomy Query: "Explain transformer attention" EXPECT: Agent reads root README first EXPECT: Agent navigates to most relevant subdirectory EXPECT: Agent reads specific chunk files EXPECT: trajectory log shows logical exploration path TEST: Information not found Build GAM about Python Query: "How does Rust's borrow checker work?" EXPECT: Answer indicates information not found in memory EXPECT: Confidence < 0.3 TEST: Multi-source synthesis Build GAM with chunks about "neural networks" in different directories Query: "Compare CNNs and RNNs" EXPECT: Agent explores multiple directories EXPECT: Answer synthesizes information from multiple files EXPECT: Sources include files from different directories TEST: Grep-based search Build GAM with technical content containing "BERT" in specific files Query: "What is BERT?" EXPECT: Agent uses grep("BERT") to locate relevant files EXPECT: More efficient than exhaustive browsing ``` ### 11.3 Tool Execution Tests ``` TEST: ls returns correct structure Create directory with 3 files and 2 subdirectories Call ls("/") EXPECT: All 5 items listed with correct types and sizes TEST: cat returns file content Create file with known content Call cat("path/to/file.md") EXPECT: Exact file content returned TEST: grep finds matches Create files with varied content Call grep("specific_term") EXPECT: Only files containing the term returned EXPECT: Matched lines shown with line numbers TEST: BM25 search index Build GAM with 50 chunks First search call triggers index build EXPECT: Index created successfully EXPECT: Subsequent searches use cached index EXPECT: Results ranked by relevance ``` ### 11.4 Edge Case Tests ``` TEST: Empty input Call add(text="") EXPECT: No chunks created, no error thrown TEST: Very large document Input: 100,000-word document EXPECT: Properly chunked (no OOM) EXPECT: Taxonomy handles large number of chunks TEST: Non-English content Input: Document in Japanese EXPECT: Chunks created (may be less optimal) EXPECT: Taxonomy reflects content structure TEST: PDF with images Input: PDF containing images and text EXPECT: Text extracted, images ignored EXPECT: No crash on image-heavy pages TEST: Concurrent add operations Call add() twice simultaneously EXPECT: No file corruption EXPECT: Both additions reflected in final state ``` --- ## 12. Dependencies ### 12.1 Required | Package | Purpose | |---------|---------| | pydantic >= 2.0 | Data validation and schemas | | openai >= 1.0 | LLM API client | | tiktoken >= 0.5 | Token counting | | tqdm >= 4.60 | Progress bars | | python-dotenv >= 1.0 | Environment variable loading | | json-repair >= 0.58 | Fix malformed LLM JSON output | ### 12.2 Optional | Package | Purpose | |---------|---------| | docker >= 7.0 | Docker workspace support | | PyPDF2 | PDF text extraction | | pyserini | BM25 search index | | flask | Web API (if serving over HTTP) | | fastapi + uvicorn | Alternative web API | --- ## 13. Project Structure ``` project_root/ ├── src/ │ ├── __init__.py │ ├── workflows/ │ │ ├── __init__.py │ │ ├── base.py # BaseWorkflow (lazy loading) │ │ └── text.py # TextWorkflow │ ├── agents/ │ │ ├── __init__.py │ │ ├── base_gam_agent.py # Base memory building agent │ │ ├── text_gam_agent.py # Text-specific memory agent │ │ └── text_chat_agent.py # Q&A retrieval agent │ ├── core/ │ │ ├── __init__.py │ │ ├── tree.py # GAMTree (read-only view) │ │ └── node.py # FSNode model │ ├── schemas/ │ │ ├── __init__.py │ │ ├── chunk_schemas.py # MemorizedChunk, DirectoryNode, etc. │ │ └── json_schemas.py # LLM JSON output schemas │ ├── generators/ │ │ ├── __init__.py │ │ ├── base.py # BaseGenerator ABC │ │ └── openai_gen.py # OpenAI-compatible implementation │ ├── workspaces/ │ │ ├── __init__.py │ │ ├── base.py # BaseWorkspace ABC │ │ ├── local.py # LocalWorkspace │ │ └── docker.py # DockerWorkspace │ ├── tools/ │ │ ├── __init__.py │ │ ├── fs_tools.py # ls, cat, grep implementations │ │ └── bm25_tool.py # BM25 search tool │ ├── prompts/ │ │ ├── __init__.py │ │ ├── memorize.py # Memory generation prompts │ │ ├── organize.py # Taxonomy planning prompts │ │ └── explore.py # Retrieval agent prompts │ └── cli.py # CLI entry points ├── tests/ └── pyproject.toml ``` --- ## 14. Key Algorithm: Taxonomy Validation ``` Input: DirectoryNode tree, total_chunk_count Step 1: Collect all chunk_indices from leaf nodes Step 2: Verify count matches total_chunk_count Step 3: Verify no duplicates (each index appears exactly once) Step 4: Verify no parent directory has chunk_indices AND children Step 5: Verify all directory names are valid filesystem names If validation fails: Re-prompt LLM with specific error message Retry up to 3 times before falling back to flat structure ``` --- *This specification provides complete architectural and behavioral detail for independent implementation of a hierarchical agentic memory system with LLM-driven auto-taxonomy, filesystem storage, and multi-strategy retrieval.* --- ## Clean Room Specification: AI Chat UI Component Library **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/07-AI-Chat-UI-Component-Library **Description:** Purpose of This Document This document specifies the architecture, component hierarchy, runtime system, and implementation patterns for a headless React comp... # Clean-Room Specification: AI Chat UI Component Library ## Purpose of This Document This document specifies the architecture, component hierarchy, runtime system, and implementation patterns for a **headless React component library purpose-built for conversational AI interfaces**. The library provides 20+ unstyled composable primitives organized around threads, messages, composers, and branching — with a protocol-driven runtime that abstracts over multiple AI provider backends. This specification enables full independent implementation from scratch. --- ## 1. Architecture Overview ### 1.1 Three-Layer Architecture The system is organized into three distinct layers: ``` ┌────────────────────────────────────────────────┐ │ PRIMITIVES LAYER (React Components) │ │ ThreadPrimitive, MessagePrimitive, │ │ ComposerPrimitive, BranchPickerPrimitive, │ │ ActionBarPrimitive, ContentPartPrimitive, │ │ AttachmentPrimitive │ ├────────────────────────────────────────────────┤ │ REACT BINDINGS LAYER (Hooks + Context) │ │ useThread, useMessage, useComposer, │ │ useContentPart, useAui, useAuiState, │ │ useAuiEvent, ThreadContext, MessageContext │ ├────────────────────────────────────────────────┤ │ CORE RUNTIME LAYER (Provider-agnostic) │ │ ThreadRuntime, MessageRuntime, │ │ ComposerRuntime, ContentPartRuntime, │ │ AttachmentRuntime, AssistantRuntime │ └────────────────────────────────────────────────┘ ``` **Primitives**: Unstyled React components that render zero visual chrome — they emit bare semantic HTML elements (`
`, `

`, `

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); } ``` --- ## 14. Behavioral Test Cases ### Thread Operations 1. **Empty thread renders empty state**: When messages array is empty, `ThreadPrimitive.Empty` children are rendered, `ThreadPrimitive.Messages` renders nothing. 2. **Message ordering**: Messages render in the order returned by `MessageRepository.getMessages()` (linear active path). 3. **Auto-scroll on new content**: When user is scrolled to bottom and new streaming text arrives, viewport scrolls to keep bottom visible. 4. **Auto-scroll disengage**: When user scrolls up manually, auto-scroll stops. New messages do NOT force scroll. 5. **Auto-scroll re-engage**: When user scrolls back to bottom, auto-scroll re-engages for subsequent messages. ### Message Branching 6. **Edit creates branch**: Editing a user message creates a new child of the same parent, preserving the original branch. 7. **Branch navigation**: `BranchPickerPrimitive.Previous/Next` cycle through sibling branches at the branching point. 8. **Branch count accuracy**: `BranchPickerPrimitive.Count` shows total siblings, `Number` shows 1-indexed current. 9. **Branch isolation**: Switching branches replaces all messages after the branching point with the alternate path. 10. **Nested branches**: Branches can exist at multiple depths — each operates independently. ### Streaming 11. **Text delta accumulation**: Multiple `text-delta` chunks for the same part index concatenate correctly. 12. **Tool call streaming**: `tool-call-begin` followed by `tool-call-delta` chunks produces incrementally parsed args. 13. **Mixed content streaming**: Text, tool calls, and reasoning parts can arrive interleaved — each routed to correct part index. 14. **Stream cancellation**: `ComposerPrimitive.Cancel` calls `threadRuntime.cancelRun()`, which aborts the stream and sets status to `incomplete/cancelled`. 15. **Status finalization**: Stream ending with `status` chunk finalizes the message; without it, status defaults to `complete/unknown`. ### Composer 16. **Submit on Enter**: Pressing Enter (without Shift) triggers form submission when text is non-empty. 17. **Newline on Shift+Enter**: Shift+Enter inserts a newline without submitting. 18. **Disabled while running**: Send button is disabled when `thread.isRunning === true`. 19. **Auto-resize**: Textarea height grows with content up to max-height, then scrolls internally. 20. **Attachment flow**: Adding file creates PendingAttachment → displayed in composer → on send, adapter.send() converts to CompleteAttachment. ### Tool Execution 21. **Tool approval flow**: Message with status `requires-action` renders approval UI; `addToolResult` resolves it and continues generation. 22. **Tool rejection**: Calling `addToolResult` with `isError: true` sends rejection to model. 23. **Custom tool renderers**: `by_name` component map renders specific components for named tools. 24. **Fallback tool renderer**: Unknown tool names render with the `Fallback` component. ### Reactivity 25. **Selective re-rendering**: Changing `thread.isRunning` does NOT re-render message components that only subscribe to message content. 26. **Streaming efficiency**: During text streaming, only the active TextContentPart component re-renders — not sibling parts or other messages. 27. **useAuiState equality**: Custom equality functions prevent re-renders when selector output is structurally identical. ### Action Bar 28. **Copy to clipboard**: `ActionBarPrimitive.Copy` extracts all text content parts, joins them, copies to clipboard via navigator.clipboard.writeText. 29. **Autohide behavior**: With `autohide="not-last"`, only the last message's action bar is visible; others appear on hover. 30. **Reload regenerates**: `ActionBarPrimitive.Reload` removes the assistant message and calls `startRun` to regenerate. 31. **Feedback submission**: Positive/Negative feedback updates `message.metadata.feedback` and emits event. ### Markdown Rendering 32. **GFM support**: Tables, strikethrough, task lists render correctly via remark-gfm. 33. **Code highlighting**: Fenced code blocks with language annotation get syntax highlighting. 34. **LaTeX rendering**: Inline `$...$` and display `$$...$$` render as mathematical notation. 35. **Smooth streaming**: With `smooth` enabled, text reveals character-by-character via requestAnimationFrame. 36. **Link safety**: External links render with `target="_blank" rel="noopener"`. ### Attachments 37. **Image preview**: Image attachments show thumbnail in composer before sending. 38. **Text file reading**: `.txt`, `.md`, `.json` files are read as text and included in message content. 39. **Remove before send**: Clicking remove on a pending attachment calls adapter.remove() and removes from composer. 40. **Accept filter**: File picker only shows files matching adapter's accept filter. ### Thread Persistence 41. **Export/Import roundtrip**: `thread.export()` produces a serializable repository; `thread.import()` restores the full tree structure including branches. 42. **Thread switching**: `switchToThread(id)` loads messages from external store and sets as active. 43. **New thread creation**: `switchToNewThread()` creates an empty thread and sets as active. ### Provider Adapter 44. **Vercel message conversion**: Vercel AI SDK messages with toolInvocations correctly convert to ToolCallContentParts. 45. **Vercel stream mapping**: Vercel's streaming protocol chunks map to internal AssistantStreamChunks. 46. **Bidirectional sync**: Adding a message via the runtime is reflected back to Vercel's useChat state. --- ## Clean Room Specification: Conversational AI App Shell Framework **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/08-Conversational-AI-App-Shell-Framework **Description:** Purpose of This Document This document specifies the architecture for a full stack conversational AI application framework built with React 19, Next.js (App... # Clean-Room Specification: Conversational AI App Shell Framework ## Purpose of This Document This document specifies the architecture for a **full-stack conversational AI application framework** built with React 19, Next.js (App Router), and a streaming AI backend. While Spec 07 covers headless UI primitives, this specification covers the complete application shell: authentication, database persistence, real-time streaming with recovery, multi-model routing, tool execution with human approval, document artifact management, and the patterns needed to ship a production AI chat product. This specification enables independent implementation from scratch. --- ## 1. Technology Stack and Architecture ### 1.1 Stack Overview | Layer | Technology | Purpose | |-------|-----------|---------| | Framework | Next.js 15+ (App Router) | Server components, API routes, middleware | | UI | React 19 | Server components, `useActionState`, `useOptimistic` | | Styling | Tailwind CSS 4 | Utility-first, `@theme` system | | Database | PostgreSQL via Drizzle ORM | Chat/message/document persistence | | Auth | NextAuth v5 (Auth.js) | Session management, multiple providers | | AI | Vercel AI SDK (`ai` package) | `streamText`, `createUIMessageStream`, tool execution | | Streaming | Server-Sent Events (SSE) + Redis | Resumable streams across reconnects | | State | `useSWR` + React Context | Client-side data fetching and cache | ### 1.2 Application Architecture ``` ┌───────────────────────────────────────────────────────────────────┐ │ BROWSER │ │ ┌─────────────────┐ ┌──────────────┐ ┌─────────────────────┐ │ │ │ Chat Panel │ │ Sidebar │ │ Artifact Panel │ │ │ │ (useChat hook) │ │ (history) │ │ (document viewer) │ │ │ │ │ │ │ │ text/code/image/ │ │ │ │ Messages + │ │ Thread list │ │ sheet editors │ │ │ │ Composer + │ │ + search │ │ │ │ │ │ Tool approvals │ │ │ │ Version history │ │ │ └────────┬─────────┘ └──────────────┘ └─────────────────────┘ │ │ │ SSE stream │ ├───────────┼───────────────────────────────────────────────────────┤ │ SERVER │ │ │ ┌────────▼─────────┐ │ │ │ /api/chat │ ← POST: append message + stream response │ │ │ Route Handler │ │ │ │ │ │ │ │ 1. Auth check │ │ │ │ 2. Save user msg │ │ │ │ 3. streamText() │ │ │ │ 4. Tool exec │ │ │ │ 5. Save AI msg │ │ │ │ 6. SSE response │ │ │ └────────┬──────────┘ │ │ │ │ │ ┌────────▼──────────┐ ┌──────────────┐ │ │ │ Drizzle ORM │ │ Redis │ │ │ │ PostgreSQL │ │ Stream buf │ │ │ └───────────────────┘ └──────────────┘ │ └───────────────────────────────────────────────────────────────────┘ ``` --- ## 2. Database Schema (Drizzle ORM) ### 2.1 Users and Authentication ```typescript export const user = pgTable("User", { id: uuid("id").primaryKey().notNull().defaultRandom(), email: varchar("email", { length: 64 }).notNull(), password: varchar("password", { length: 64 }), // bcrypt hash, null for OAuth salt: varchar("salt", { length: 64 }), // per-user salt createdAt: timestamp("createdAt").notNull().defaultNow(), }); ``` ### 2.2 Chats ```typescript export const chat = pgTable("Chat", { id: uuid("id").primaryKey().notNull().defaultRandom(), createdAt: timestamp("createdAt").notNull(), updatedAt: timestamp("updatedAt").notNull().defaultNow(), title: text("title").notNull(), userId: uuid("userId") .notNull() .references(() => user.id), visibility: varchar("visibility", { enum: ["public", "private"] }) .notNull() .default("private"), model: varchar("model", { length: 128 }), // which AI model was used }); ``` ### 2.3 Messages (Version 2 — Multimodal) ```typescript export const message = pgTable("Message_v2", { id: uuid("id").notNull(), chatId: uuid("chatId") .notNull() .references(() => chat.id), role: varchar("role", { enum: ["user", "assistant", "system", "tool"] }).notNull(), parts: json("parts").notNull(), // ContentPart[] — matches AI SDK format attachments: json("attachments").notNull().default([]), // file references createdAt: timestamp("createdAt").notNull(), }, (table) => [ primaryKey({ columns: [table.id, table.chatId] }), // composite PK ]); // The `parts` column stores an array matching the AI SDK UIMessage content format: // [ // { type: "text", text: "..." }, // { type: "tool-invocation", toolInvocationId: "...", toolName: "...", state: "result", args: {...}, result: {...} }, // { type: "reasoning", text: "...", signature: "..." }, // { type: "source", sourceType: "url", id: "...", url: "...", title: "..." }, // { type: "file", mimeType: "image/png", data: "base64..." }, // { type: "step-start" } // ] ``` ### 2.4 Documents (Artifacts) ```typescript export const document = pgTable("Document", { id: uuid("id").notNull().defaultRandom(), createdAt: timestamp("createdAt").notNull(), title: text("title").notNull(), content: text("content"), // current version content kind: varchar("kind", { enum: ["text", "code", "image", "sheet"], }).notNull().default("text"), userId: uuid("userId") .notNull() .references(() => user.id), }, (table) => [ primaryKey({ columns: [table.id, table.createdAt] }), // composite PK enables versioning ]); // VERSIONING MODEL: // Each (id, createdAt) pair is a unique version. // To get the latest version: ORDER BY createdAt DESC LIMIT 1 WHERE id = ? // To get version history: SELECT * WHERE id = ? ORDER BY createdAt ASC // Creating a new version: INSERT with same id, new createdAt, new content ``` ### 2.5 Suggestions ```typescript export const suggestion = pgTable("Suggestion", { id: uuid("id").notNull().defaultRandom(), documentId: uuid("documentId").notNull(), documentCreatedAt: timestamp("documentCreatedAt").notNull(), originalText: text("originalText").notNull(), // text to be replaced suggestedText: text("suggestedText").notNull(), // replacement suggestion description: text("description"), // why this change isResolved: boolean("isResolved").notNull().default(false), userId: uuid("userId") .notNull() .references(() => user.id), createdAt: timestamp("createdAt").notNull().defaultNow(), }, (table) => [ primaryKey({ columns: [table.id] }), ]); ``` ### 2.6 Votes (Message Feedback) ```typescript export const vote = pgTable("Vote", { chatId: uuid("chatId") .notNull() .references(() => chat.id), messageId: uuid("messageId").notNull(), isUpvoted: boolean("isUpvoted").notNull(), }, (table) => [ primaryKey({ columns: [table.chatId, table.messageId] }), ]); ``` --- ## 3. Authentication System ### 3.1 NextAuth v5 Configuration ```typescript // auth.ts export const { handlers, signIn, signOut, auth } = NextAuth({ providers: [ // Guest mode — auto-creates anonymous users Credentials({ id: "guest", name: "Guest", credentials: {}, async authorize() { const guestUser = await createGuestUser(); return { id: guestUser.id, email: `guest-${guestUser.id}@anonymous`, type: "guest" }; }, }), // Email/password Credentials({ id: "credentials", name: "Credentials", credentials: { email: { type: "email" }, password: { type: "password" }, }, async authorize(credentials) { const { email, password } = z.object({ email: z.string().email(), password: z.string().min(6), }).parse(credentials); const user = await getUserByEmail(email); if (!user?.password || !user?.salt) return null; const hash = await hashPassword(password, user.salt); if (hash !== user.password) return null; return { id: user.id, email: user.email, type: "regular" }; }, }), ], callbacks: { // Embed user ID in JWT async jwt({ token, user }) { if (user) token.id = user.id; return token; }, // Expose user ID in session async session({ session, token }) { if (token.id) session.user.id = token.id as string; return session; }, // Authorization middleware — protect routes async authorized({ auth, request }) { const isLoggedIn = !!auth?.user; const isAuthPage = request.nextUrl.pathname.startsWith("/login"); if (isAuthPage) { return isLoggedIn ? Response.redirect(new URL("/", request.url)) : true; } return isLoggedIn; // redirect to /login if not authenticated }, }, pages: { signIn: "/login", }, }); ``` ### 3.2 Middleware ```typescript // middleware.ts export { auth as middleware } from "./auth"; export const config = { // Apply auth middleware to all routes except static assets and API matcher: ["/((?!api|_next/static|_next/image|favicon.ico).*)"], }; ``` ### 3.3 Password Hashing ```typescript export function generateSalt(): string { return randomBytes(16).toString("hex"); } export async function hashPassword(password: string, salt: string): string { return createHash("sha256") .update(`${password}:${salt}`) .digest("hex"); } ``` --- ## 4. AI Chat Route Handler ### 4.1 Main Chat Endpoint ```typescript // app/api/chat/route.ts export async function POST(request: Request) { const session = await auth(); if (!session?.user?.id) { return new Response("Unauthorized", { status: 401 }); } const { id: chatId, // chat thread ID messages, // UIMessage[] from client selectedModelId, // e.g., "gpt-4o", "claude-sonnet-4-20250514" }: { id: string; messages: UIMessage[]; selectedModelId: string; } = await request.json(); // 1. Get or create chat const existingChat = await getChatById(chatId); if (!existingChat) { // Auto-generate title from first user message const title = await generateTitleFromUserMessage(messages[0]); await saveChat({ id: chatId, userId: session.user.id, title }); } else { // Verify ownership if (existingChat.userId !== session.user.id) { return new Response("Forbidden", { status: 403 }); } } // 2. Save the new user message to DB const userMessage = messages[messages.length - 1]; await saveMessages([{ id: userMessage.id, chatId, role: "user", parts: userMessage.parts, attachments: userMessage.experimental_attachments ?? [], createdAt: new Date(), }]); // 3. Stream AI response return createUIMessageStreamResponse({ chatId, messages, model: getModelInstance(selectedModelId), session, }); } ``` ### 4.2 Streaming with createUIMessageStream ```typescript async function createUIMessageStreamResponse({ chatId, messages, model, session, }: StreamOptions): Promise ); } ``` ### 7.3 Sidebar Component ```typescript function ChatSidebar({ user }: { user: User }) { // Fetch chat history const { data: history, isLoading } = useSWR ); } ``` --- ## 10. Chat History API ### 10.1 History Endpoint ```typescript // app/api/history/route.ts export async function GET() { const session = await auth(); if (!session?.user?.id) return Response.json([], { status: 401 }); const chats = await db .select() .from(chat) .where(eq(chat.userId, session.user.id)) .orderBy(desc(chat.updatedAt)); return Response.json(chats); } export async function DELETE(request: Request) { const { id } = await request.json(); const session = await auth(); // Verify ownership before deleting const target = await getChatById(id); if (!target || target.userId !== session.user.id) { return new Response("Forbidden", { status: 403 }); } // Cascade: delete messages, votes, then chat await db.delete(vote).where(eq(vote.chatId, id)); await db.delete(message).where(eq(message.chatId, id)); await db.delete(chat).where(eq(chat.id, id)); return Response.json({ success: true }); } ``` ### 10.2 Auto-Title Generation ```typescript async function generateTitleFromUserMessage(message: UIMessage): Promise { const { text } = await generateText({ model: openai("gpt-4o-mini"), // cheap model for title gen system: "Generate a short (max 80 chars) title for this conversation based on the user's first message. Return ONLY the title, no quotes or extra text.", prompt: getTextContent(message), }); return text.trim() || "New Chat"; } ``` --- ## 11. Visibility and Sharing ### 11.1 Chat Visibility Model Chats have two visibility levels: - **`private`** (default): Only the owner can view. All API access requires `userId` match. - **`public`**: Anyone with the URL can view (read-only). Only owner can send messages. ```typescript // Middleware check for chat access async function validateChatAccess(chatId: string, userId: string | undefined): Promise<{ allowed: boolean; readOnly: boolean; }> { const chatRecord = await getChatById(chatId); if (!chatRecord) return { allowed: false, readOnly: false }; if (chatRecord.userId === userId) { return { allowed: true, readOnly: false }; // owner: full access } if (chatRecord.visibility === "public") { return { allowed: true, readOnly: true }; // public: read-only } return { allowed: false, readOnly: false }; // private: no access } ``` ### 11.2 Share Dialog ```typescript function ShareDialog({ chatId }: { chatId: string }) { const [visibility, setVisibility] = useState<"private" | "public">("private"); const handleShare = async () => { await fetch(`/api/chat/${chatId}/visibility`, { method: "PATCH", body: JSON.stringify({ visibility: "public" }), }); setVisibility("public"); // Copy shareable URL to clipboard await navigator.clipboard.writeText(`${window.location.origin}/chat/${chatId}`); toast.success("Share link copied!"); }; return ( {visibility === "private" ? ( ) : (

This chat is public. Anyone with the link can view it.

)} ); } ``` --- ## 12. Message Feedback (Voting) ```typescript // app/api/vote/route.ts export async function PATCH(request: Request) { const { chatId, messageId, type }: { chatId: string; messageId: string; type: "up" | "down"; } = await request.json(); const session = await auth(); if (!session?.user?.id) return new Response("Unauthorized", { status: 401 }); // Upsert vote await db .insert(vote) .values({ chatId, messageId, isUpvoted: type === "up", }) .onConflictDoUpdate({ target: [vote.chatId, vote.messageId], set: { isUpvoted: type === "up" }, }); return Response.json({ success: true }); } ``` --- ## 13. Optimistic UI Updates ### 13.1 Pattern: useOptimistic for Sidebar ```typescript function ChatHistoryItem({ chat }: { chat: Chat }) { const [optimisticTitle, setOptimisticTitle] = useOptimistic(chat.title); const [isDeleted, setIsDeleted] = useOptimistic(false); if (isDeleted) return null; const handleRename = async (newTitle: string) => { setOptimisticTitle(newTitle); // instant UI update await fetch(`/api/chat/${chat.id}`, { method: "PATCH", body: JSON.stringify({ title: newTitle }), }); // If fetch fails, React resets optimistic state automatically }; const handleDelete = async () => { setIsDeleted(true); // instant removal from list await fetch("/api/history", { method: "DELETE", body: JSON.stringify({ id: chat.id }), }); }; return (
); } ``` --- ## 14. Environment Configuration ```bash # .env.local # Database POSTGRES_URL="postgresql://user:pass@localhost:5432/chatdb" # Auth AUTH_SECRET="random-32-char-secret" # NextAuth session encryption AUTH_URL="http://localhost:3000" # AI Providers (at least one required) OPENAI_API_KEY="sk-..." ANTHROPIC_API_KEY="sk-ant-..." GOOGLE_GENERATIVE_AI_API_KEY="..." # Optional: Redis for resumable streams REDIS_URL="redis://localhost:6379" # Optional: Blob storage for file uploads BLOB_READ_WRITE_TOKEN="..." ``` --- ## 15. Database Migrations ```typescript // drizzle.config.ts export default defineConfig({ schema: "./lib/db/schema.ts", out: "./lib/db/migrations", dialect: "postgresql", dbCredentials: { url: process.env.POSTGRES_URL!, }, }); // Commands: // npx drizzle-kit generate — generate migration SQL from schema changes // npx drizzle-kit migrate — apply pending migrations // npx drizzle-kit push — push schema directly (dev only) ``` --- ## 16. Behavioral Test Cases ### Authentication 1. **Guest access**: Unauthenticated users can create a guest session and chat; guest sessions persist across page reloads within the same browser. 2. **Credential login**: Valid email/password combination returns a session with user ID embedded in JWT. 3. **Invalid credentials**: Wrong password returns 401 without leaking whether email exists. 4. **Session expiry**: Expired JWT redirects to /login via middleware. 5. **Route protection**: All /chat/* routes require authentication; /api/chat returns 401 without valid session. ### Chat CRUD 6. **Auto-title**: First message in a new chat triggers title generation; title appears in sidebar. 7. **Chat ownership**: Users can only access their own private chats; accessing another user's private chat returns 403. 8. **Delete cascade**: Deleting a chat removes all associated messages, votes, and documents. 9. **Rename**: Renaming a chat updates the title immediately (optimistic) and persists to DB. 10. **History ordering**: Chat history is sorted by updatedAt descending (most recent first). ### Message Persistence 11. **User message saved before streaming**: The user's message is persisted to DB before the AI stream begins. 12. **Assistant message saved after streaming**: The complete assistant response (including tool results) is saved after stream finishes. 13. **Multimodal parts**: Messages with mixed content types (text + tool calls + reasoning) round-trip through DB correctly. 14. **Composite PK**: Multiple messages in the same chat have unique (id, chatId) pairs; message IDs are UUIDs. ### Streaming 15. **SSE format**: Response uses `text/event-stream` content type with chunked transfer encoding. 16. **Text streaming**: Individual text deltas appear in the client as they're generated (throttled at 50ms). 17. **Tool call streaming**: Tool name and arguments stream incrementally; client shows partial args during streaming. 18. **Stream cancellation**: Clicking stop sends abort signal; AI generation halts; partial response is preserved. 19. **Error recovery**: Network disconnect during streaming does not lose the user message; client can retry. 20. **Resumable streams**: With Redis enabled, reconnecting with Last-Event-ID resumes from where the client left off. ### Tool Execution 21. **Auto-execute tools**: Tools with `execute` function run server-side without user approval. 22. **Approval-required tools**: Tools without `execute` send tool-call to client; client renders approval UI. 23. **Tool approval**: Clicking "Allow" calls addToolResult, which sends the result back to the AI for continuation. 24. **Tool rejection**: Rejecting a tool call sends an error result; AI acknowledges and continues without the tool. 25. **Multi-step tools**: With maxSteps=5, the AI can chain multiple tool calls in sequence within a single response. ### Document Artifacts 26. **Create document**: createDocument tool creates a new Document row and opens the artifact panel. 27. **Update document (versioning)**: updateDocument creates a new (id, createdAt) row, preserving the previous version. 28. **Version navigation**: Users can navigate between document versions using the timeline dots. 29. **Document kinds**: Text, code, image, and sheet documents each render with their specialized editor. 30. **Suggestions**: requestSuggestions generates inline suggestions that can be accepted or rejected. ### Sharing and Visibility 31. **Default private**: New chats are created with visibility="private". 32. **Public sharing**: Setting visibility to "public" allows anyone with the URL to view (read-only). 33. **Read-only enforcement**: Public viewers cannot send messages or modify the chat. 34. **Share link**: Sharing copies the canonical URL; the URL works for any authenticated or unauthenticated user. ### Voting 35. **Upvote/downvote**: Users can vote on assistant messages; votes are upserted (one vote per user per message). 36. **Vote toggle**: Voting again with a different type changes the vote (up→down or vice versa). 37. **Vote persistence**: Votes survive page reload and are fetched alongside messages. ### UI/UX 38. **Model picker**: Users can select from available models before sending; selection persists for the chat. 39. **Sidebar grouping**: Chats are grouped by time period (Today, Yesterday, This Week, This Month, Older). 40. **Optimistic updates**: Rename and delete operations appear instant; failed operations revert automatically. 41. **Empty state**: New chats show welcome message with suggested conversation starters. 42. **Responsive layout**: Sidebar collapses on mobile; artifact panel overlays on narrow screens. 43. **Theme support**: Light/dark mode toggle via `next-themes`; persists preference. 44. **Keyboard shortcuts**: Enter to send, Shift+Enter for newline, Escape to close artifact panel. --- ## Clean Room Specification: Artifact Panel Starter Template **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/09-Artifact-Panel-Starter-Template **Description:** Purpose of This Document This document specifies the architecture for an artifact panel system that displays AI generated content alongside a chat interface.... # Clean-Room Specification: Artifact Panel Starter Template ## Purpose of This Document This document specifies the architecture for an **artifact panel system** that displays AI-generated content alongside a chat interface. When an AI assistant produces code, HTML, documents, diagrams, or other structured output, that content appears in a dedicated resizable side panel with live preview, code editing, and version navigation. The artifact system integrates with the chat primitives (Spec 07) and app shell (Spec 08) through tool calls. This specification enables independent implementation from scratch. --- ## 1. Architecture Overview ### 1.1 Panel Layout ``` ┌──────────────────────────────────────────────────────────────────┐ │ Application Shell │ ├────────────────────────┬─────────┬───────────────────────────────┤ │ │ Resize │ │ │ Chat Thread │ Handle │ Artifact Panel │ │ │ ║ │ │ │ ┌──────────────────┐ │ ║ │ ┌─────────────────────────┐ │ │ │ User Message │ │ ║ │ │ Artifact Header │ │ │ └──────────────────┘ │ ║ │ │ [Title] [Code|Preview] │ │ │ ┌──────────────────┐ │ ║ │ │ [Version ●●●○○] [✕] │ │ │ │ Assistant Message │ │ ║ │ ├─────────────────────────┤ │ │ │ "I've created..." │ │ ║ │ │ │ │ │ │ [📄 artifact ref] │←─┼──╫──────┤ │ Editor / Preview Area │ │ │ └──────────────────┘ │ ║ │ │ │ │ │ ┌──────────────────┐ │ ║ │ │ (Monaco / Sandpack / │ │ │ │ Composer │ │ ║ │ │ Markdown / Image / │ │ │ │ [Type message...] │ │ ║ │ │ Spreadsheet) │ │ │ └──────────────────┘ │ ║ │ │ │ │ │ │ ║ │ ├─────────────────────────┤ │ │ │ ║ │ │ Artifact Footer │ │ │ │ ║ │ │ [Suggestions] [Export] │ │ │ │ ║ │ └─────────────────────────┘ │ ├────────────────────────┴─────────┴───────────────────────────────┤ ``` ### 1.2 Component Architecture ``` ); } ``` ### 5.3 React Preview (Live Component Rendering) ```typescript function ReactPreview({ code }: { code: string }) { const [Component, setComponent] = useState ); } ``` ### 5.4 Code Editor (Monaco) ```typescript function CodeEditor({ artifact }: { artifact: Artifact }) { const { updateContent } = useArtifactContext(); const [localContent, setLocalContent] = useState(artifact.content); // Debounce saves const debouncedSave = useMemo( () => debounce((content: string) => { updateContent(artifact.id, content); }, 500), [artifact.id], ); const handleChange = (value: string | undefined) => { if (value === undefined) return; setLocalContent(value); debouncedSave(value); }; const language = artifact.language ?? inferLanguageFromKind(artifact.kind) ?? "plaintext"; return ( ); } ``` ### 5.5 Mermaid Preview ```typescript function MermaidPreview({ diagram }: { diagram: string }) { const containerRef = useRef(null); useEffect(() => { mermaid.initialize({ startOnLoad: false, theme: "default" }); const render = async () => { try { const { svg } = await mermaid.render("mermaid-preview", diagram); if (containerRef.current) { containerRef.current.innerHTML = svg; } } catch (err) { if (containerRef.current) { containerRef.current.innerHTML = `
${err}
`; } } }; render(); }, [diagram]); return
; } ``` --- ## 6. Version Navigation ### 6.1 Version Timeline Component ```typescript function VersionTimeline({ artifact }: { artifact: Artifact }) { const { activeVersionIndex, navigateVersion } = useArtifactContext(); return (
{artifact.versions.map((version, idx) => (
{activeVersionIndex + 1} / {artifact.versions.length}
); } ``` ### 6.2 Version Content Display When viewing a non-latest version, the editor shows that version's content in read-only mode: ```typescript function VersionAwareContent({ artifact }: { artifact: Artifact }) { const { activeVersionIndex, activeTab } = useArtifactContext(); const isLatest = activeVersionIndex === artifact.versions.length - 1; const displayContent = artifact.versions[activeVersionIndex].content; // Temporarily override artifact content with version content const displayArtifact = { ...artifact, content: displayContent }; return (
{!isLatest && (
Viewing version {activeVersionIndex + 1} of {artifact.versions.length} (read-only)
)}
); } ``` --- ## 7. Resizable Panel ### 7.1 Resize Handle Implementation ```typescript function ResizeHandle({ onResize }: { onResize: (deltaX: number) => void }) { const [isDragging, setIsDragging] = useState(false); const startXRef = useRef(0); const handleMouseDown = (e: React.MouseEvent) => { e.preventDefault(); setIsDragging(true); startXRef.current = e.clientX; const handleMouseMove = (e: MouseEvent) => { const deltaX = e.clientX - startXRef.current; startXRef.current = e.clientX; onResize(deltaX); }; const handleMouseUp = () => { setIsDragging(false); document.removeEventListener("mousemove", handleMouseMove); document.removeEventListener("mouseup", handleMouseUp); }; document.addEventListener("mousemove", handleMouseMove); document.addEventListener("mouseup", handleMouseUp); }; return (
); } ``` ### 7.2 Panel Width Management ```typescript function ArtifactLayout({ children }: { children: ReactNode }) { const { activeArtifactId } = useArtifactContext(); const [panelWidth, setPanelWidth] = useState(480); // default 480px const MIN_WIDTH = 320; const MAX_WIDTH = 800; const handleResize = (deltaX: number) => { setPanelWidth(prev => Math.min(MAX_WIDTH, Math.max(MIN_WIDTH, prev - deltaX))); }; return (
{children}
{activeArtifactId && ( <>
)}
); } ``` --- ## 8. Export Functionality ```typescript function ExportButton({ artifact }: { artifact: Artifact }) { const handleExport = () => { const extension = getExtensionForKind(artifact.kind, artifact.language); const filename = `${sanitizeFilename(artifact.title)}.${extension}`; const mimeType = getMimeType(artifact.kind); const blob = new Blob([artifact.content], { type: mimeType }); const url = URL.createObjectURL(blob); const a = document.createElement("a"); a.href = url; a.download = filename; a.click(); URL.revokeObjectURL(url); }; return ( ); } function getExtensionForKind(kind: ArtifactKind, language?: string): string { switch (kind) { case "html": return "html"; case "react": return "jsx"; case "markdown": return "md"; case "text": return "txt"; case "svg": return "svg"; case "mermaid": return "mmd"; case "sheet": return "csv"; case "code": return languageToExtension(language ?? "txt"); default: return "txt"; } } ``` --- ## 9. Streaming Artifact Content When the AI generates long artifacts, content streams in progressively: ```typescript // During streaming, the tool call's argsText grows incrementally. // The artifact panel can show a live preview of the partial content. function StreamingArtifactView({ toolCall }: { toolCall: ToolCallContentPart }) { const isStreaming = toolCall.result === undefined; const partialContent = isStreaming ? extractPartialContent(toolCall.argsText) // Parse partial JSON to get content field : toolCall.result?.content; return (
{isStreaming && (
Generating...
)}
); } function extractPartialContent(argsText: string): string | null { // Try to extract the "content" field from partial JSON // Handles incomplete JSON by finding the last complete string value const match = argsText.match(/"content"\s*:\s*"((?:[^"\\]|\\.)*)(?:"|$)/); if (match) { return match[1].replace(/\\n/g, "\n").replace(/\\"/g, '"').replace(/\\\\/g, "\\"); } return null; } ``` --- ## 10. Behavioral Test Cases ### Panel Visibility 1. **Hidden by default**: Artifact panel is not rendered when no artifact is active. 2. **Opens on creation**: When AI creates an artifact via tool call, panel opens automatically. 3. **Opens on click**: Clicking an artifact reference card in chat opens the panel. 4. **Closes on X**: Clicking close button sets activeArtifactId to null, hiding panel. 5. **Persists across messages**: Panel stays open while user sends new messages. ### Content Rendering 6. **HTML preview**: HTML artifacts render in sandboxed iframe with scripts executing. 7. **React preview**: JSX artifacts are transpiled and rendered as live React components. 8. **Markdown preview**: Markdown renders with GFM tables, code blocks, and LaTeX. 9. **SVG preview**: SVG content renders inline with correct dimensions. 10. **Mermaid preview**: Mermaid diagrams render as SVG via mermaid.js. 11. **Code preview**: Code artifacts show syntax-highlighted read-only view. 12. **Fallback**: Unknown kinds render as plain preformatted text. ### Code Editor 13. **Syntax highlighting**: Monaco editor applies language-appropriate highlighting. 14. **Auto-language detection**: Editor language inferred from artifact kind/language. 15. **Live editing**: Changes in the editor update artifact content (debounced 500ms). 16. **Read-only for old versions**: Non-latest versions show editor in read-only mode. ### Version Navigation 17. **Version dots**: Each version shows as a dot; active version is highlighted. 18. **Forward/back arrows**: Navigate between versions sequentially. 19. **Version content**: Navigating to version N shows that version's content. 20. **Latest auto-select**: New versions auto-select as active (scroll to latest). 21. **Version description**: Tooltip on each dot shows version description and timestamp. ### Tab Switching 22. **Code tab**: Shows Monaco editor with raw source code. 23. **Preview tab**: Shows rendered output for the artifact kind. 24. **Tab persistence**: Switching artifacts preserves tab preference. 25. **Default to preview**: Opening an artifact defaults to preview tab. ### Resize 26. **Drag resize**: Dragging the handle adjusts panel width in real-time. 27. **Min/max bounds**: Panel width clamps between 320px and 800px. 28. **Chat panel flex**: Chat panel fills remaining space as artifact panel resizes. ### Tool Integration 29. **create_artifact tool**: Produces new artifact, adds to registry, opens panel. 30. **update_artifact tool**: Adds new version to existing artifact, opens panel. 31. **Inline reference**: Tool results render as clickable artifact cards in messages. 32. **Streaming preview**: During content generation, partial content is displayed live. ### Export 33. **Download file**: Export produces a file download with correct name and extension. 34. **MIME types**: Exported files have correct content types. 35. **Current version**: Export always uses the currently displayed version's content. --- ## Clean Room Specification: Lightweight Fact Based AI Memory API **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/10-Lightweight-Fact-Based-AI-Memory-API **Description:** Purpose of This Document This document specifies the architecture for a fact based AI memory system that automatically extracts, stores, deduplicates, and re... # Clean-Room Specification: Lightweight Fact-Based AI Memory API ## Purpose of This Document This document specifies the architecture for a **fact-based AI memory system** that automatically extracts, stores, deduplicates, and retrieves discrete factual memories from conversations. Rather than storing raw conversation transcripts, the system uses an LLM to distill conversations into atomic facts (e.g., "User prefers dark mode," "User works at Acme Corp"), stores them as vector embeddings for semantic retrieval, and maintains a full audit history of every memory operation. The system supports user/agent/session scoping, pluggable vector store backends, optional graph-based entity-relationship memory, and both synchronous and asynchronous APIs. This specification enables independent implementation from scratch. --- ## 1. System Overview ### 1.1 Core Concept Traditional memory systems store raw conversation logs. This system takes a fundamentally different approach: it uses an LLM as a **memory curator** that reads conversations, extracts discrete facts, compares them against existing memories, and decides whether to ADD new facts, UPDATE existing ones, DELETE obsolete ones, or take NO action. The result is a clean, deduplicated factual memory store that grows smarter over time. ### 1.2 High-Level Architecture ``` ┌─────────────────────────────────────────────────────────────┐ │ Client API │ │ Memory.add() / .search() / .get() / .get_all() / .update()│ │ .delete() / .delete_all() / .history() / .reset() │ ├─────────────────────────────────────────────────────────────┤ │ Memory Pipeline │ │ ┌──────────┐ ┌──────────────┐ ┌─────────────────────┐ │ │ │ Message │→│ LLM Fact │→│ Embed + Search │ │ │ │ Parser │ │ Extraction │ │ Existing Memories │ │ │ └──────────┘ └──────────────┘ └─────────┬───────────┘ │ │ │ │ │ ┌──────────────────────────────────────────▼───────────┐ │ │ │ LLM Memory Update Decision │ │ │ │ Compare new facts vs existing → ADD/UPDATE/DELETE │ │ │ └──────────────────────────────────────────────────────┘ │ ├─────────────────────────────────────────────────────────────┤ │ Storage Layer │ │ ┌────────────┐ ┌────────────┐ ┌────────────────────┐ │ │ │ Vector DB │ │ SQLite │ │ Neo4j (optional) │ │ │ │ (memories) │ │ (history) │ │ (graph memory) │ │ │ └────────────┘ └────────────┘ └────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ``` ### 1.3 Data Flow Summary 1. Client calls `memory.add(messages, user_id=...)` with conversation messages 2. **Message Parser** normalizes input into a flat string 3. **LLM Fact Extraction** sends conversation + system prompt → receives JSON array of discrete facts 4. For each extracted fact: a. Generate embedding vector b. Search vector store for similar existing memories (top 5) c. **LLM Memory Update Decision** compares new fact against existing memories → produces ADD/UPDATE/DELETE/NONE events 5. Execute each event against the vector store 6. Log every operation to SQLite history table 7. Optionally extract entities and relationships to graph store 8. Return list of memory events to the caller --- ## 2. Data Model ### 2.1 MemoryItem The core data structure representing a single stored memory: ```typescript interface MemoryItem { id: string; // UUID v4 memory: string; // The fact text, e.g. "User prefers Python over JavaScript" hash: string; // MD5 hex digest of the memory text (for deduplication) metadata: Record; // Arbitrary key-value pairs score?: number; // Similarity score (populated on search results only) created_at: string; // ISO 8601 timestamp updated_at: string; // ISO 8601 timestamp } ``` **Hash computation**: `hash = md5(memory_text).hexdigest()`. Used to detect exact duplicate memories before insertion. ### 2.2 MemoryEvent Represents a single operation performed during an `add()` call: ```typescript interface MemoryEvent { event: "ADD" | "UPDATE" | "DELETE" | "NONE"; id: string; // Memory ID affected old_memory?: string; // Previous text (for UPDATE/DELETE) new_memory?: string; // New text (for ADD/UPDATE) metadata?: Record; } ``` ### 2.3 Message Format Input messages follow the standard chat message format: ```typescript type Message = { role: "system" | "user" | "assistant"; content: string; }; ``` ```text The `add()` method accepts either a single string or an array of `Message` objects. If a string is provided, it is wrapped as `[{ role: "user", content: str }]`. ``` ### 2.4 Scoping Model Every memory operation requires at least one scope identifier. These are used as metadata filters on the vector store to isolate memories: ```typescript interface MemoryScope { user_id?: string; // Isolate memories per end-user agent_id?: string; // Isolate memories per AI agent/persona run_id?: string; // Isolate memories per conversation/session } ``` **Validation rule**: At least one of `user_id`, `agent_id`, or `run_id` MUST be provided on every API call. If none are provided, raise an error: `"At least one of user_id, agent_id, or run_id must be provided"`. **Filter construction**: When scoping, build a metadata filter that matches ALL provided scope fields. For example, if both `user_id="alice"` and `agent_id="helper"` are provided, the vector store query filters for records where `metadata.user_id == "alice" AND metadata.agent_id == "helper"`. --- ## 3. Memory Class — Public API ### 3.1 Constructor ```typescript class Memory { constructor(config?: MemoryConfig); } ``` The constructor initializes three subsystems: 1. **Vector store** — configured via `config.vector_store` 2. **LLM** — configured via `config.llm` 3. **Embedder** — configured via `config.embedder` 4. **History store** — SQLite database (always initialized, path configurable) 5. **Graph store** (optional) — Neo4j, configured via `config.graph_store` If no config is provided, use sensible defaults: - Vector store: In-memory (e.g., a simple array with brute-force cosine similarity) - LLM: OpenAI `gpt-4o-mini` - Embedder: OpenAI `text-embedding-3-small` (dimension 1536) - History: SQLite at `~/.memory/history.db` ### 3.2 Method: `add(messages, ...scope, metadata?, filters?)` **Purpose**: Extract facts from messages and store them as memories. **Parameters**: | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | messages | string \| Message[] | Yes | Conversation to extract facts from | | user_id | string | See scope rules | User scope | | agent_id | string | See scope rules | Agent scope | | run_id | string | See scope rules | Session scope | | metadata | Record<string, any> | No | Extra metadata to attach to each memory | | filters | FilterExpression | No | Additional filters for searching existing memories | | prompt | string | No | Custom system prompt override for fact extraction | ```text **Returns**: `{ results: MemoryEvent[] }` — list of all ADD/UPDATE/DELETE/NONE events. ``` **Algorithm** (detailed in Section 4): 1. Parse messages into a flat conversation string 2. Call LLM with fact extraction prompt → get JSON array of facts 3. For each fact: embed → search existing (limit 5) → call LLM update decision → execute event 4. Log all events to history 5. If graph store configured, extract entities/relationships 6. Return events ### 3.3 Method: `search(query, ...scope, limit?, filters?)` **Purpose**: Retrieve memories semantically similar to a query. **Parameters**: | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | query | string | Yes | Natural language search query | | user_id | string | See scope rules | User scope | | agent_id | string | See scope rules | Agent scope | | run_id | string | See scope rules | Session scope | | limit | number | No | Max results (default 100) | | filters | FilterExpression | No | Additional metadata filters | ```text **Returns**: `{ results: MemoryItem[] }` — sorted by descending similarity score. ``` **Algorithm**: 1. Generate embedding for query text 2. Build metadata filter from scope + any additional filters 3. Query vector store: `vectorStore.search(embedding, limit, filters)` 4. Return results with similarity scores ### 3.4 Method: `get(memory_id)` **Purpose**: Retrieve a single memory by its ID. **Returns**: `MemoryItem` or `null` if not found. ### 3.5 Method: `get_all(...scope, limit?)` **Purpose**: Retrieve all memories for a given scope. **Parameters**: Same scope parameters. `limit` defaults to 100. ```text **Returns**: `{ results: MemoryItem[] }` — all memories matching the scope filters. ``` **Algorithm**: Query vector store with scope-based metadata filter, no embedding (list all matching records). ### 3.6 Method: `update(memory_id, new_text)` **Purpose**: Directly overwrite a memory's text. **Algorithm**: 1. Retrieve existing memory by ID 2. Generate new embedding for `new_text` 3. Compute new hash: `md5(new_text)` 4. Update vector store record: text, embedding, hash, updated_at 5. Log UPDATE event to history ### 3.7 Method: `delete(memory_id)` **Purpose**: Remove a single memory. **Algorithm**: 1. Retrieve existing memory by ID (for history logging) 2. Delete from vector store 3. Log DELETE event to history ### 3.8 Method: `delete_all(...scope)` **Purpose**: Remove all memories for a given scope. **Algorithm**: 1. Retrieve all memories for scope via `get_all()` 2. Delete each from vector store 3. Log DELETE event for each to history ### 3.9 Method: `history(memory_id)` **Purpose**: Retrieve the full audit trail for a specific memory. **Returns**: Array of history records, ordered by timestamp ascending: ```typescript interface HistoryRecord { id: string; // History entry ID memory_id: string; // The memory this event relates to event: "ADD" | "UPDATE" | "DELETE"; old_value: string | null; new_value: string | null; timestamp: string; // ISO 8601 is_deleted: boolean; // Whether memory was deleted in this event } ``` ### 3.10 Method: `reset()` **Purpose**: Delete ALL memories and history. Nuclear option. **Algorithm**: 1. Drop and recreate vector store collection 2. Truncate history table (or drop and recreate) --- ## 4. LLM-Driven Memory Pipeline (Core Algorithm) This is the heart of the system. The `add()` method orchestrates a multi-step pipeline that uses LLM calls to intelligently manage memories. ### 4.1 Step 1: Message Parsing Convert input to a flat string for the LLM: ``` function parseMessages(input: string | Message[]): string { if (typeof input === "string") return input; return input .map(m => `${m.role}: ${m.content}`) .join("\n"); } ``` ### 4.2 Step 2: Fact Extraction via LLM Send the conversation to the LLM with a system prompt that instructs it to extract discrete facts. **FACT_EXTRACTION_PROMPT** (system message): ``` You are an expert at extracting structured, atomic facts from conversations. Your task is to identify and extract key pieces of information from the given conversation that would be useful to remember for future interactions. Extract facts that fall into these categories: 1. Personal preferences (likes, dislikes, habits) 2. Biographical information (name, occupation, location, relationships) 3. Goals and intentions 4. Technical preferences and skills 5. Important dates, events, or milestones 6. Opinions and viewpoints 7. Project details and requirements 8. Communication preferences Rules: - Each fact must be a single, self-contained statement - Be specific and include context where necessary - Avoid duplicating information across facts - Only extract information that is clearly stated or strongly implied - Do not make assumptions beyond what is provided - Format each fact as a concise, declarative sentence - Use third person (e.g., "User prefers..." not "You prefer...") Return a JSON array of strings. If no meaningful facts can be extracted, return an empty array. Example output: ["User's name is Alice", "User works as a software engineer at Acme Corp", "User prefers Python for backend development"] ``` **User message**: The parsed conversation string. **LLM call configuration**: - Temperature: 0 (deterministic extraction) - Response format: JSON mode (if available) or parse JSON from response text **Parse result**: Extract JSON array from LLM response. If parsing fails, try to find JSON array pattern (`[...]`) in the response text. If still fails, return empty array. **Custom prompt support**: If the caller provides a `prompt` parameter to `add()`, use that as the system message instead of FACT_EXTRACTION_PROMPT. This allows domain-specific fact extraction. ### 4.3 Step 3: Per-Fact Processing Loop For each extracted fact string, execute the following sub-steps: #### 4.3.1 Generate Embedding ``` embedding = embedder.embed(fact_text) ``` #### 4.3.2 Search Existing Memories Query the vector store for the top 5 most similar existing memories within the current scope: ``` existing = vectorStore.search( embedding = embedding, limit = 5, filters = buildScopeFilter(user_id, agent_id, run_id) ) ``` #### 4.3.3 LLM Memory Update Decision This is the critical decision-making step. Send the new fact AND the retrieved existing memories to the LLM, which decides what action to take. **UPDATE_MEMORY_PROMPT** (system message): ``` You are a memory management system. You will be given: 1. A new piece of information (the "new fact") 2. A list of existing memories that are potentially related Your job is to decide what memory operations to perform. For each operation, return a JSON object. Possible operations: 1. ADD — The new fact contains genuinely new information not captured by any existing memory. Create a new memory. {"event": "ADD", "data": "the fact text to store"} 2. UPDATE — The new fact updates, corrects, refines, or supersedes an existing memory. Provide the existing memory ID and the new merged/updated text. {"event": "UPDATE", "id": "", "old_memory": "", "data": "the updated fact text"} 3. DELETE — The new fact contradicts or invalidates an existing memory and the existing memory should be removed entirely. {"event": "DELETE", "id": "", "old_memory": ""} 4. NONE — The new fact is already fully captured by existing memories and no action is needed. {"event": "NONE"} Important rules: - If the new fact contains information not present in ANY existing memory, use ADD - If an existing memory says something similar but the new fact has updated info, use UPDATE (merge the information, keeping the more recent/accurate version) - If the new fact directly contradicts an existing memory (e.g., "moved from NYC to SF" when existing says "lives in NYC"), UPDATE the existing memory - If removing info is more appropriate than updating, use DELETE - Only use NONE if the information is truly redundant - You may return multiple operations if needed (e.g., UPDATE one memory AND ADD a new one) - Always preserve important context and nuance when merging Return a JSON array of operation objects. ``` **User message construction**: ``` New fact: {fact_text} Existing memories: {for each existing memory:} - ID: {memory.id}, Text: {memory.memory} {end for} {if no existing memories:} No existing memories found. {end if} ``` **LLM call configuration**: - Temperature: 0 - Response format: JSON **Parse result**: Extract JSON array of event objects from the LLM response. ### 4.4 Step 4: Execute Memory Events For each event returned by the update decision LLM: **ADD event**: 1. Generate a new UUID v4 for the memory 2. Compute embedding for the fact text 3. Compute hash: `md5(fact_text)` ```text 4. Build metadata: `{ ...scope_fields, ...caller_metadata, hash: hash }` ``` ```text 5. Insert into vector store: `vectorStore.insert(id, embedding, { memory: fact_text, ...metadata })` ``` 6. Log to history: `historyStore.log(memory_id, "ADD", null, fact_text)` **UPDATE event**: 1. Get the target memory ID from the event 2. Compute new embedding for the updated text 3. Compute new hash ```text 4. Update vector store record: `vectorStore.update(id, newEmbedding, { memory: updated_text, hash, updated_at })` ``` 5. Log to history: `historyStore.log(memory_id, "UPDATE", old_text, new_text)` **DELETE event**: 1. Get the target memory ID 2. Delete from vector store: `vectorStore.delete(id)` 3. Log to history: `historyStore.log(memory_id, "DELETE", old_text, null, is_deleted=true)` **NONE event**: No action. Optionally log for analytics. ### 4.5 Step 5: Graph Memory Extraction (Optional) If a graph store is configured, additionally extract entities and relationships. #### Entity Extraction Use an LLM tool call with the following tool definition: **EXTRACT_ENTITIES_TOOL**: ```json { "name": "extract_entities", "description": "Extract entities (people, organizations, concepts, locations, events) from the conversation", "parameters": { "type": "object", "properties": { "entities": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string", "description": "Entity name (normalized, title case)" }, "type": { "type": "string", "enum": ["person", "organization", "concept", "location", "event", "technology", "product"] }, "description": { "type": "string", "description": "Brief description of the entity" } }, "required": ["name", "type"] } } } } } ``` #### Relationship Extraction **EXTRACT_RELATIONS_TOOL**: ```json { "name": "extract_relations", "description": "Extract relationships between entities", "parameters": { "type": "object", "properties": { "relations": { "type": "array", "items": { "type": "object", "properties": { "source": { "type": "string", "description": "Source entity name" }, "relation": { "type": "string", "description": "Relationship type (e.g., works_at, located_in, uses, knows)" }, "target": { "type": "string", "description": "Target entity name" } }, "required": ["source", "relation", "target"] } } } } } ``` #### Graph Store Operations For each extracted entity, perform an upsert in the graph database: ``` MERGE (e:Entity {name: $name}) SET e.type = $type, e.description = $description, e.updated_at = $now ``` For each extracted relationship: ``` MATCH (s:Entity {name: $source}) MATCH (t:Entity {name: $target}) MERGE (s)-[r:RELATES_TO {type: $relation}]->(t) SET r.updated_at = $now ``` When searching with graph memory enabled, also query the graph for entities related to the search query and merge those results with vector search results. Use BM25 reranking if the graph store supports it to score relevance of graph-retrieved memories. --- ## 5. History Store (SQLite) ### 5.1 Schema ```sql CREATE TABLE IF NOT EXISTS memory_history ( id TEXT PRIMARY KEY, -- UUID v4 memory_id TEXT NOT NULL, -- References the memory event TEXT NOT NULL, -- 'ADD', 'UPDATE', 'DELETE' old_value TEXT, -- Previous memory text (null for ADD) new_value TEXT, -- New memory text (null for DELETE) timestamp TEXT NOT NULL, -- ISO 8601 is_deleted INTEGER DEFAULT 0, -- 1 if this was a DELETE event -- Scope fields for queryability user_id TEXT, agent_id TEXT, run_id TEXT ); CREATE INDEX IF NOT EXISTS idx_history_memory_id ON memory_history(memory_id); CREATE INDEX IF NOT EXISTS idx_history_timestamp ON memory_history(timestamp); ``` ### 5.2 Logging Function ``` function logHistory(memoryId, event, oldValue, newValue, scope, isDeleted = false): insert into memory_history values ( uuid4(), memoryId, event, oldValue, newValue, new Date().toISOString(), isDeleted ? 1 : 0, scope.user_id, scope.agent_id, scope.run_id ) ``` ### 5.3 Query Function ``` function getHistory(memoryId): SELECT * FROM memory_history WHERE memory_id = ? ORDER BY timestamp ASC ``` --- ## 6. Vector Store Abstraction ### 6.1 VectorStoreBase Interface All vector store backends implement this interface: ```typescript interface VectorStoreBase { // Collection management createCollection(name: string, dimension: number): Promise; deleteCollection(name: string): Promise; listCollections(): Promise; getCollectionInfo(name: string): Promise<{ name: string; count: number; dimension: number }>; // CRUD operations insert( collectionName: string, id: string, vector: number[], payload: Record ): Promise; search( collectionName: string, queryVector: number[], limit: number, filters?: FilterExpression ): Promise }>>; get(collectionName: string, id: string): Promise<{ id: string; payload: Record } | null>; update( collectionName: string, id: string, vector?: number[], payload?: Record ): Promise; delete(collectionName: string, id: string): Promise; list( collectionName: string, filters?: FilterExpression, limit?: number ): Promise }>>; reset(): Promise; } ``` ### 6.2 In-Memory Vector Store (Default) For development and testing, implement a simple in-memory store: ```typescript class InMemoryVectorStore implements VectorStoreBase { private collections: Map }>>; search(collectionName, queryVector, limit, filters?): // For each record in collection: // 1. If filters provided, check metadata matches // 2. Compute cosine similarity: dot(a,b) / (norm(a) * norm(b)) // 3. Collect (id, score, payload) // Sort by score descending, return top `limit` } ``` **Cosine similarity**: ``` function cosineSimilarity(a: number[], b: number[]): number { let dot = 0, normA = 0, normB = 0; for (let i = 0; i < a.length; i++) { dot += a[i] * b[i]; normA += a[i] * a[i]; normB += b[i] * b[i]; } return dot / (Math.sqrt(normA) * Math.sqrt(normB)); } ``` ### 6.3 Qdrant Backend ```typescript class QdrantVectorStore implements VectorStoreBase { constructor(config: { host: string; port: number; apiKey?: string; onDisk?: boolean }); // Uses Qdrant REST API: // PUT /collections/{name} — createCollection // PUT /collections/{name}/points — insert (upsert) // POST /collections/{name}/points/search — search // GET /collections/{name}/points/{id} — get // POST /collections/{name}/points/delete — delete // Filter translation: Convert FilterExpression to Qdrant filter format // { must: [{ key: "user_id", match: { value: "alice" } }] } } ``` ### 6.4 PostgreSQL/pgvector Backend ```typescript class PgVectorStore implements VectorStoreBase { constructor(config: { connectionString: string; schema?: string }); createCollection(name, dimension): // CREATE TABLE {name} ( // id TEXT PRIMARY KEY, // vector vector({dimension}), // payload JSONB, // created_at TIMESTAMP DEFAULT NOW() // ); // CREATE INDEX ON {name} USING ivfflat (vector vector_cosine_ops); search(collectionName, queryVector, limit, filters?): // SELECT id, payload, 1 - (vector <=> $1::vector) as score // FROM {collection} // WHERE {filter_clauses} // ORDER BY vector <=> $1::vector // LIMIT $2 // Filter translation: Convert FilterExpression to SQL WHERE clauses // { field: "user_id", op: "eq", value: "alice" } // → payload->>'user_id' = 'alice' } ``` ### 6.5 ChromaDB Backend ```typescript class ChromaVectorStore implements VectorStoreBase { constructor(config: { host: string; port: number; path?: string }); // Uses ChromaDB client: // client.createCollection(name) / getCollection(name) // collection.add(ids, embeddings, metadatas, documents) // collection.query(queryEmbeddings, nResults, where) // collection.update(ids, embeddings, metadatas, documents) // collection.delete(ids) // Filter translation: Convert FilterExpression to ChromaDB where format // { "$and": [{ "user_id": { "$eq": "alice" } }] } } ``` ### 6.6 Additional Backend Targets The interface should support these backends (implementation details vary but all implement VectorStoreBase): - **Pinecone**: REST API with namespaces for scoping - **Weaviate**: GraphQL-based queries with class schemas - **Milvus**: gRPC client with collection/partition model - **FAISS**: Local file-based index with separate metadata store - **Elasticsearch**: kNN search with dense_vector field type - **Azure AI Search**: REST API with vector search profiles - **Redis**: RediSearch with VECTOR field type (HNSW/FLAT) --- ## 7. Filter Expression System ### 7.1 Filter Syntax Filters allow complex metadata queries across all vector store backends. The system defines a portable filter expression that is translated to each backend's native syntax. ```typescript type FilterOperator = "eq" | "ne" | "gt" | "gte" | "lt" | "lte" | "in" | "nin" | "contains" | "icontains"; type FilterCondition = { field: string; operator: FilterOperator; value: any; }; type FilterExpression = | FilterCondition | { AND: FilterExpression[] } | { OR: FilterExpression[] } | { NOT: FilterExpression }; ``` ### 7.2 Operator Semantics | Operator | Meaning | Example | |----------|---------|---------| ```text | eq | Equals | `{ field: "user_id", operator: "eq", value: "alice" }` | ``` ```text | ne | Not equals | `{ field: "status", operator: "ne", value: "archived" }` | ``` ```text | gt | Greater than | `{ field: "score", operator: "gt", value: 0.8 }` | ``` ```text | gte | Greater or equal | `{ field: "created_at", operator: "gte", value: "2024-01-01" }` | ``` ```text | lt | Less than | `{ field: "priority", operator: "lt", value: 5 }` | ``` ```text | lte | Less or equal | `{ field: "age", operator: "lte", value: 30 }` | ``` ```text | in | Value in set | `{ field: "tag", operator: "in", value: ["work", "personal"] }` | ``` ```text | nin | Value not in set | `{ field: "tag", operator: "nin", value: ["spam"] }` | ``` ```text | contains | String contains (case-sensitive) | `{ field: "memory", operator: "contains", value: "Python" }` | ``` ```text | icontains | String contains (case-insensitive) | `{ field: "memory", operator: "icontains", value: "python" }` | ``` ### 7.3 Composition ```typescript // Example: Find memories for user "alice" that mention either "Python" or "JavaScript" const filter: FilterExpression = { AND: [ { field: "user_id", operator: "eq", value: "alice" }, { OR: [ { field: "memory", operator: "icontains", value: "Python" }, { field: "memory", operator: "icontains", value: "JavaScript" } ]} ] }; ``` ### 7.4 Backend Translation Each vector store backend implements a `translateFilter(expr: FilterExpression)` method that converts the portable expression to the backend's native format. For example: ```text - **Qdrant**: `{ must: [{ key: "field", match: { value: "x" } }] }` ``` ```text - **ChromaDB**: `{ "$and": [{ "field": { "$eq": "x" } }] }` ``` - **pgvector**: `WHERE payload->>'field' = 'x'` ```text - **Pinecone**: `{ "field": { "$eq": "x" } }` ``` --- ## 8. Configuration System ### 8.1 MemoryConfig ```typescript interface MemoryConfig { // Vector store backend configuration vector_store?: { provider: "memory" | "qdrant" | "chroma" | "pgvector" | "pinecone" | "weaviate" | "milvus" | "faiss" | "elasticsearch" | "redis"; config: Record; // Provider-specific connection config collection_name?: string; // Default: "memories" }; // LLM configuration (for fact extraction and update decisions) llm?: { provider: "openai" | "anthropic" | "google" | "ollama" | "azure_openai"; config: { model: string; api_key?: string; // Falls back to env var (OPENAI_API_KEY, etc.) temperature?: number; // Default: 0 max_tokens?: number; // Default: 2000 base_url?: string; // For custom endpoints }; }; // Embedding model configuration embedder?: { provider: "openai" | "ollama" | "huggingface" | "azure_openai" | "google"; config: { model: string; // e.g., "text-embedding-3-small" api_key?: string; dimensions?: number; // Output dimension (default: 1536 for OpenAI) }; }; // Graph memory (optional) graph_store?: { provider: "neo4j"; config: { url: string; // bolt://localhost:7687 username: string; password: string; }; }; // History store history?: { db_path?: string; // SQLite path, default: ~/.memory/history.db }; // Custom prompts (override defaults) custom_prompts?: { fact_extraction?: string; // Override FACT_EXTRACTION_PROMPT update_decision?: string; // Override UPDATE_MEMORY_PROMPT }; // Versioning version?: "v1.0" | "v1.1"; // API version, affects behavior } ``` ### 8.2 Environment Variable Fallbacks The system checks environment variables as fallbacks for API keys and configuration: | Env Variable | Purpose | |-------------|---------| | `OPENAI_API_KEY` | OpenAI LLM and embedder | | `ANTHROPIC_API_KEY` | Anthropic LLM | | `GOOGLE_API_KEY` | Google LLM and embedder | | `QDRANT_HOST`, `QDRANT_PORT`, `QDRANT_API_KEY` | Qdrant connection | | `CHROMA_HOST`, `CHROMA_PORT` | ChromaDB connection | | `DATABASE_URL` | PostgreSQL/pgvector connection | | `NEO4J_URL`, `NEO4J_USER`, `NEO4J_PASSWORD` | Neo4j graph store | | `REDIS_URL` | Redis vector store | --- ## 9. Embedder Abstraction ### 9.1 EmbedderBase Interface ```typescript interface EmbedderBase { embed(text: string): Promise; embedBatch(texts: string[]): Promise; getDimension(): number; } ``` ### 9.2 OpenAI Embedder ```typescript class OpenAIEmbedder implements EmbedderBase { constructor(config: { model: string; apiKey: string; dimensions?: number }); async embed(text: string): Promise { // POST https://api.openai.com/v1/embeddings // { model: this.model, input: text, dimensions: this.dimensions } // Return response.data[0].embedding } async embedBatch(texts: string[]): Promise { // Same endpoint accepts array input // Return response.data.map(d => d.embedding) } } ``` ### 9.3 Ollama Embedder (Local) ```typescript class OllamaEmbedder implements EmbedderBase { constructor(config: { model: string; baseUrl?: string }); async embed(text: string): Promise { // POST http://localhost:11434/api/embeddings // { model: this.model, prompt: text } // Return response.embedding } } ``` --- ## 10. LLM Abstraction ### 10.1 LLMBase Interface ```typescript interface LLMBase { generate( systemPrompt: string, userMessage: string, options?: { temperature?: number; maxTokens?: number; responseFormat?: "json" | "text"; tools?: ToolDef[] } ): Promise; generateWithToolCalls( systemPrompt: string, userMessage: string, tools: ToolDef[], options?: { temperature?: number } ): Promise<{ content?: string; toolCalls?: Array<{ name: string; arguments: Record }> }>; } ``` ### 10.2 Provider Implementations Each LLM provider maps to its respective API: ```text - **OpenAI**: `POST /v1/chat/completions` with `response_format: { type: "json_object" }` when JSON mode requested ``` - **Anthropic**: `POST /v1/messages` with tool use for structured extraction - **Google**: Gemini API with JSON schema in `generationConfig` - **Ollama**: `POST /api/chat` with local models --- ## 11. Async API ### 11.1 AsyncMemory Class Provide an async variant that wraps the synchronous Memory class (or implements natively with async I/O): ```typescript class AsyncMemory { constructor(config?: MemoryConfig); async add(messages, ...scope): Promise<{ results: MemoryEvent[] }>; async search(query, ...scope): Promise<{ results: MemoryItem[] }>; async get(memoryId): Promise; async getAll(...scope): Promise<{ results: MemoryItem[] }>; async update(memoryId, newText): Promise; async delete(memoryId): Promise; async deleteAll(...scope): Promise; async history(memoryId): Promise; async reset(): Promise; } ``` In languages with native async (Python asyncio, JavaScript), the async class should use async HTTP clients (aiohttp, fetch) for LLM and vector store calls rather than blocking. --- ## 12. REST API Wrapper (Optional Server Mode) For serving memory as a standalone service: ### 12.1 Endpoints ``` POST /v1/memories/ — Add memories (body: { messages, user_id?, agent_id?, run_id?, metadata? }) GET /v1/memories/search/ — Search (query: q, user_id, limit) GET /v1/memories/:id/ — Get single memory GET /v1/memories/ — Get all memories (query: user_id, agent_id, run_id, limit) PUT /v1/memories/:id/ — Update memory (body: { text }) DELETE /v1/memories/:id/ — Delete memory DELETE /v1/memories/ — Delete all (query: user_id, agent_id, run_id) GET /v1/memories/:id/history/ — Get history POST /v1/reset/ — Reset all POST /v1/entities/ — Get graph entities for scope GET /v1/entities/:name/relations/ — Get entity relationships ``` ### 12.2 Authentication Bearer token authentication via `Authorization: Bearer ` header. Tokens can be project-scoped API keys. --- ## 13. Usage Examples ### 13.1 Basic Usage ```typescript const memory = new Memory(); // Add memories from a conversation const result = await memory.add( [ { role: "user", content: "Hi, I'm Alice. I work at Acme Corp as a data scientist." }, { role: "assistant", content: "Nice to meet you, Alice! What kind of data science work do you do?" }, { role: "user", content: "Mostly NLP and recommendation systems. I prefer PyTorch over TensorFlow." } ], { user_id: "alice" } ); console.log(result.results); // [ // { event: "ADD", id: "abc-123", new_memory: "User's name is Alice" }, // { event: "ADD", id: "def-456", new_memory: "User works at Acme Corp as a data scientist" }, // { event: "ADD", id: "ghi-789", new_memory: "User specializes in NLP and recommendation systems" }, // { event: "ADD", id: "jkl-012", new_memory: "User prefers PyTorch over TensorFlow" } // ] // Search memories const searchResults = await memory.search("What does Alice do?", { user_id: "alice" }); // Returns sorted by relevance: work info, specialization, etc. // Later conversation updates a memory await memory.add( [ { role: "user", content: "I just switched jobs. I'm now at BigTech Inc." } ], { user_id: "alice" } ); // Result: { event: "UPDATE", id: "def-456", // old_memory: "User works at Acme Corp as a data scientist", // new_memory: "User works at BigTech Inc as a data scientist" } // Check history const history = await memory.history("def-456"); // Shows ADD (original) then UPDATE (job change) ``` ### 13.2 Multi-Scope Usage ```typescript // Agent-specific memories await memory.add(messages, { user_id: "alice", agent_id: "code-helper" }); // Session-scoped (ephemeral, per conversation) await memory.add(messages, { user_id: "alice", run_id: "session-20240315" }); // Search across a specific agent's memories for a user const results = await memory.search("Python frameworks", { user_id: "alice", agent_id: "code-helper" }); ``` ### 13.3 Custom Configuration ```typescript const memory = new Memory({ vector_store: { provider: "qdrant", config: { host: "localhost", port: 6333 } }, llm: { provider: "anthropic", config: { model: "claude-sonnet-4-20250514", api_key: process.env.ANTHROPIC_API_KEY } }, embedder: { provider: "openai", config: { model: "text-embedding-3-small", dimensions: 1536 } }, graph_store: { provider: "neo4j", config: { url: "bolt://localhost:7687", username: "neo4j", password: "password" } } }); ``` ### 13.4 With Filters ```typescript // Search with metadata filters const results = await memory.search("project deadlines", { user_id: "alice", filters: { AND: [ { field: "category", operator: "eq", value: "work" }, { field: "created_at", operator: "gte", value: "2024-01-01" } ] } }); ``` --- ## 14. Error Handling ### 14.1 Error Types ```typescript class MemoryError extends Error { constructor(message: string, public code: string); } // Specific errors class ScopeError extends MemoryError {} // Missing user_id/agent_id/run_id class VectorStoreError extends MemoryError {} // Backend connection/query failures class LLMError extends MemoryError {} // LLM API failures class EmbeddingError extends MemoryError {} // Embedding API failures class NotFoundError extends MemoryError {} // Memory ID not found ``` ### 14.2 Retry Logic LLM and embedding calls should implement exponential backoff retry: ``` function withRetry(fn, maxRetries = 3, baseDelay = 1000): for attempt in 0..maxRetries: try: return await fn() catch error: if attempt == maxRetries: throw error if error is rate_limit: delay = baseDelay * 2^attempt else: throw error // Don't retry non-transient errors await sleep(delay) ``` ### 14.3 Graceful Degradation - If fact extraction LLM call fails, return empty results (don't crash) - If embedding call fails for one fact, skip that fact and continue with others - If history DB is unavailable, log warning but continue with memory operations - If graph store is unavailable, skip graph extraction but complete vector operations --- ## 15. Behavioral Test Cases ### Memory CRUD ```text 1. **Add single fact** — `add("My name is Bob", { user_id: "bob" })` → returns one ADD event with memory text "User's name is Bob" ``` ```text 2. **Add conversation** — `add([{role:"user",content:"..."},{role:"assistant",content:"..."}])` → extracts multiple facts, returns multiple ADD events ``` ```text 3. **Add with empty input** — `add("hello", { user_id: "x" })` → may return empty results if no extractable facts ``` 4. **Search by semantics** — After adding "User likes Python", `search("programming languages")` → returns the Python memory with score > 0.5 ```text 5. **Search with limit** — `search(query, { limit: 3 })` → returns at most 3 results ``` 6. **Get by ID** — After ADD, `get(returned_id)` → returns the memory item 7. **Get nonexistent** — `get("fake-id")` → returns null ```text 8. **Get all for scope** — After adding 3 memories for user "alice", `get_all({ user_id: "alice" })` → returns all 3 ``` 9. **Update overwrites** — `update(id, "new text")` → `get(id).memory` equals "new text" 10. **Update changes hash** — After update, hash should equal `md5("new text")` 11. **Delete removes** — `delete(id)` → `get(id)` returns null ```text 12. **Delete all for scope** — `delete_all({ user_id: "alice" })` → `get_all({ user_id: "alice" })` returns empty ``` 13. **Reset clears everything** — `reset()` → all collections and history are empty ### Memory Update Intelligence 14. **Deduplication** — Add "User likes Python" then add "User likes Python" again → second call returns NONE event 15. **Update on contradiction** — Add "User lives in NYC" then add "User moved to San Francisco" → returns UPDATE event changing NYC to SF 16. **Merge on refinement** — Add "User works in tech" then add "User works at Google as a senior engineer" → returns UPDATE with merged, more specific memory 17. **Delete on negation** — Add "User is vegetarian" then add "User started eating meat again" → returns DELETE or UPDATE removing vegetarian claim 18. **Multiple events per add** — Single conversation may produce multiple ADD + UPDATE events in one call ### Scoping 19. **Scope isolation** — Memories added with `user_id: "alice"` are NOT returned when searching with `user_id: "bob"` ```text 20. **Multi-scope filter** — Memories added with `{ user_id: "alice", agent_id: "helper" }` require BOTH fields to match in queries ``` 21. **Missing scope error** — Calling `add(msg, {})` with no scope fields → throws ScopeError 22. **Run ID isolation** — Memories for `run_id: "session-1"` are separate from `run_id: "session-2"` ### History 23. **ADD creates history** — After `add()`, `history(memory_id)` returns one record with event "ADD" 24. **UPDATE appends history** — After `update()`, history has ADD then UPDATE records 25. **DELETE marks in history** — After `delete()`, history shows DELETE with `is_deleted: true` 26. **History ordered by time** — History records are returned in chronological order ### Filters ```text 27. **Equals filter** — `search(query, { filters: { field: "tag", operator: "eq", value: "work" } })` → only returns memories with tag "work" ``` 28. **In filter** — `operator: "in", value: ["a","b"]` matches records where field is "a" or "b" 29. **AND composition** — Both conditions must match 30. **OR composition** — Either condition matches 31. **NOT negation** — Excludes matching records 32. **Contains string** — `operator: "contains", value: "Python"` matches "User likes Python for ML" ### Graph Memory 33. **Entity extraction** — After adding conversation about "Alice at Google", graph contains entities "Alice" (person) and "Google" (organization) 34. **Relationship extraction** — Graph contains relationship "Alice" --works_at--> "Google" 35. **Graph-enhanced search** — Search that matches a graph entity also returns related memories from connected entities ### Error Handling 36. **LLM failure graceful** — If LLM API is down, `add()` returns empty results (no crash) 37. **Partial failure continues** — If embedding fails for one of 3 facts, the other 2 are still processed 38. **Invalid scope rejected** — Empty scope object throws descriptive error ### Custom Configuration 39. **Custom extraction prompt** — Providing `prompt` parameter to `add()` changes the fact extraction behavior 40. **Custom LLM provider** — Memory works with Anthropic/Google/Ollama as LLM backend 41. **Custom vector store** — Memory works with Qdrant/pgvector/ChromaDB backends 42. **Default config works** — `new Memory()` with no config uses in-memory store and OpenAI defaults --- ## 16. Implementation Priorities ### Phase 1: Core (MVP) 1. Memory class with add/search/get/get_all/update/delete 2. In-memory vector store 3. OpenAI LLM + embedder 4. SQLite history 5. Fact extraction + update decision pipeline ### Phase 2: Production Backends 6. Qdrant vector store backend 7. pgvector backend 8. ChromaDB backend 9. Filter expression system with backend translation ### Phase 3: Advanced Features 10. Graph memory (Neo4j) 11. Async API 12. REST server wrapper 13. Additional LLM providers (Anthropic, Google, Ollama) 14. Additional vector store backends ### Phase 4: Optimization 15. Batch embedding for multiple facts 16. Connection pooling for vector stores 17. LLM response caching for identical conversations 18. Configurable concurrency for parallel fact processing --- ## Clean Room Specification: Full Stack AI Memory Platform with Hybrid Search **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/neurigraph-memory-architecture/neurigraph-tool-references/11-Full-Stack-AI-Memory-Platform-Hybrid-Search **Description:** Purpose of This Document This document specifies the architecture for a full stack AI memory platform that ingests, chunks, embeds, and retrieves content fro... # Clean-Room Specification: Full-Stack AI Memory Platform with Hybrid Search ## Purpose of This Document This document specifies the architecture for a **full-stack AI memory platform** that ingests, chunks, embeds, and retrieves content from multiple sources using **hybrid search** (combining vector similarity with full-text keyword matching and recency scoring). The platform includes a web application for managing memories and spaces, a browser extension for capturing content from web pages, and an MCP (Model Context Protocol) server for integration with AI assistants. The system handles diverse content types (text, markdown, HTML, PDFs, images, video, code), organizes memories into hierarchical spaces, supports memory versioning and auto-forgetting, and provides a REST API for programmatic access. This specification enables independent implementation from scratch. --- ## 1. System Overview ### 1.1 Core Concept This platform acts as a **second brain** — users save content from anywhere (browser, API, integrations), the system processes and indexes it, and AI assistants can later recall relevant memories through natural language queries. The key differentiator is **hybrid search**: combining semantic vector similarity with traditional full-text search and time-based recency scoring for more accurate retrieval than vector-only approaches. ### 1.2 High-Level Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ Client Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ │ │ Web App │ │ Browser │ │ MCP Server │ │ │ │ (Next.js) │ │ Extension │ │ (Claude/ChatGPT) │ │ │ └──────┬───────┘ └──────┬───────┘ └──────────┬───────────┘ │ ├─────────┼──────────────────┼────────────────────┼───────────────┤ │ │ │ │ │ │ └──────────────────┼────────────────────┘ │ │ ▼ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ REST API (v3) │ │ │ │ POST /memory | POST /recall | GET /spaces │ │ │ └─────────────────────────┬───────────────────────────────┘ │ │ │ │ ├────────────────────────────┼────────────────────────────────────┤ │ Ingestion Pipeline │ │ ┌────────┐ ┌─────────┐ ┌──────────┐ ┌───────────────┐ │ │ │Content │→│Chunking │→│Embedding │→│ Metadata │ │ │ │Extract │ │(~512 tk)│ │(OpenAI) │ │ Extraction │ │ │ └────────┘ └─────────┘ └──────────┘ └───────┬───────┘ │ │ │ │ ├──────────────────────────────────────────────────┼──────────────┤ │ Storage Layer │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────▼──────────┐ │ │ │ PostgreSQL │ │ Qdrant │ │ Edge Cache (KV) │ │ │ │ (metadata) │ │ (vectors) │ │ (hot results) │ │ │ └─────────────┘ └─────────────┘ └────────────────────────┘ │ └─────────────────────────────────────────────────────────────────┘ ``` ### 1.3 Technology Stack | Layer | Technology | Purpose | |-------|-----------|---------| | Web App | Next.js (App Router) | User-facing dashboard | | Browser Extension | WXT (cross-browser framework) | Content capture from web pages | | MCP Server | Node.js / Cloudflare Workers | AI assistant integration | | API | REST over HTTPS | Programmatic access | | Relational DB | PostgreSQL | Users, documents, spaces, metadata | | Vector DB | Qdrant | Embeddings and similarity search | | Edge Cache | Key-Value store (Redis/KV) | Frequently accessed results | | Embeddings | OpenAI `text-embedding-3-small` | Vector generation | | LLM | OpenAI GPT-4o-mini | Summarization, metadata extraction | --- ## 2. Data Model ### 2.1 Core Entities #### Organization ```typescript interface Organization { id: string; // UUID name: string; slug: string; // URL-friendly identifier created_at: string; updated_at: string; } ``` #### Project (formerly Space) Projects organize memories into logical groups. Users can have multiple projects. ```typescript interface Project { id: string; // UUID organization_id: string; name: string; slug: string; description?: string; is_default: boolean; // One default project per org created_at: string; updated_at: string; } ``` #### Document The top-level content unit. A document represents a single piece of saved content (a web page, a note, an uploaded file). ```typescript interface Document { id: string; // UUID project_id: string; user_id: string; // Content title: string; content: string; // Raw content (full text) summary?: string; // LLM-generated summary content_type: ContentType; source_url?: string; // Original URL if from web // Metadata metadata: Record; // Extracted metadata (author, date, tags, etc.) content_hash: string; // SHA-256 of content for deduplication // Memory features updates_memory_id?: string; // If this document updates a previous version forget_after?: string; // ISO 8601 timestamp for auto-deletion // Timestamps created_at: string; updated_at: string; last_accessed_at?: string; } ``` **ContentType enum**: ```typescript type ContentType = | "text" // Plain text | "markdown" // Markdown formatted | "html" // HTML content (cleaned) | "pdf" // Extracted PDF text | "image" // OCR-extracted text | "video" // Transcription | "code" // Source code (with language metadata) | "json" // Structured data | "tweet" // Twitter/X content | "email" // Email content | "note"; // User-created note ``` #### Memory A processed, searchable representation of a document or document section. Multiple memories can come from a single document (one per chunk). ```typescript interface Memory { id: string; // UUID document_id: string; // Parent document project_id: string; user_id: string; // Content content: string; // Chunk text summary?: string; // Chunk-level summary // Chunking metadata chunk_index: number; // Position within document (0-based) chunk_count: number; // Total chunks in document start_offset: number; // Character offset in original document end_offset: number; // Embedding reference vector_id: string; // ID in Qdrant created_at: string; updated_at: string; } ``` #### Chunk (Vector Store Record) Stored in Qdrant with the embedding vector: ```typescript interface ChunkPayload { memory_id: string; document_id: string; project_id: string; user_id: string; content: string; title: string; source_url?: string; content_type: string; created_at: string; // For recency scoring } ``` ### 2.2 PostgreSQL Schema ```sql CREATE TABLE organizations ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), name TEXT NOT NULL, slug TEXT UNIQUE NOT NULL, created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW() ); CREATE TABLE projects ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), organization_id UUID REFERENCES organizations(id) ON DELETE CASCADE, name TEXT NOT NULL, slug TEXT NOT NULL, description TEXT, is_default BOOLEAN DEFAULT false, created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW(), UNIQUE(organization_id, slug) ); CREATE TABLE documents ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), project_id UUID REFERENCES projects(id) ON DELETE CASCADE, user_id TEXT NOT NULL, title TEXT NOT NULL, content TEXT NOT NULL, summary TEXT, content_type TEXT NOT NULL DEFAULT 'text', source_url TEXT, metadata JSONB DEFAULT '{}', content_hash TEXT NOT NULL, updates_memory_id UUID REFERENCES documents(id), forget_after TIMESTAMPTZ, created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW(), last_accessed_at TIMESTAMPTZ ); CREATE TABLE memories ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), document_id UUID REFERENCES documents(id) ON DELETE CASCADE, project_id UUID REFERENCES projects(id) ON DELETE CASCADE, user_id TEXT NOT NULL, content TEXT NOT NULL, summary TEXT, chunk_index INTEGER NOT NULL DEFAULT 0, chunk_count INTEGER NOT NULL DEFAULT 1, start_offset INTEGER NOT NULL DEFAULT 0, end_offset INTEGER NOT NULL DEFAULT 0, vector_id TEXT NOT NULL, created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW() ); CREATE TABLE connections ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), organization_id UUID REFERENCES organizations(id) ON DELETE CASCADE, provider TEXT NOT NULL, -- 'google_drive', 'notion', 'github', etc. access_token TEXT, refresh_token TEXT, token_expires_at TIMESTAMPTZ, metadata JSONB DEFAULT '{}', last_synced_at TIMESTAMPTZ, created_at TIMESTAMPTZ DEFAULT NOW() ); -- Indexes for common queries CREATE INDEX idx_documents_project ON documents(project_id); CREATE INDEX idx_documents_hash ON documents(content_hash); CREATE INDEX idx_documents_forget ON documents(forget_after) WHERE forget_after IS NOT NULL; CREATE INDEX idx_memories_document ON memories(document_id); CREATE INDEX idx_memories_project ON memories(project_id); CREATE INDEX idx_memories_vector ON memories(vector_id); ``` --- ## 3. Ingestion Pipeline ### 3.1 Overview When content enters the system (via API, browser extension, or integration sync), it flows through a multi-stage pipeline: ``` Raw Content → Content Extraction → Deduplication Check → Chunking → Embedding → Summarization → Metadata Extraction → Storage ``` ### 3.2 Content Extraction Different content types require different extraction strategies: | Content Type | Extraction Method | |-------------|------------------| | text/markdown | Pass through (strip excessive whitespace) | | HTML | Parse with DOM parser, extract main content (strip nav, footer, scripts), convert to markdown | | PDF | Extract text via PDF parser (pdfjs-dist or similar), preserve page boundaries | | Image | OCR via vision model (send image to GPT-4o with "Extract all text from this image") | | Video | Transcription via Whisper API or similar speech-to-text | | Code | Preserve as-is with language detection, optionally parse AST for structure | | JSON | Pretty-print and extract human-readable fields | | Tweet/Social | Extract text, author, date, engagement metrics from structured data | **HTML cleaning algorithm**: 1. Parse HTML into DOM 2. Remove ` ``` **Widget Script** (`widget.js`): ```javascript (function() { // Create iframe const iframe = document.createElement('iframe'); iframe.src = `https://funnelchat.com/widget?accountId=${script.dataset.accountId}`; iframe.style.cssText = ` position: fixed; bottom: 20px; right: 20px; width: 400px; height: 600px; border: none; box-shadow: 0 4px 12px rgba(0,0,0,0.15); border-radius: 12px; z-index: 9999; `; // Add to page document.body.appendChild(iframe); // Listen for messages from iframe window.addEventListener('message', (event) => { if (event.origin !== 'https://funnelchat.com') return; // Handle minimize, close, etc. }); })(); ``` **Widget Frontend** (React): ```jsx // Simplified widget chat component function ChatWidget({ accountId }) { const [messages, setMessages] = useState([]); const [socket, setSocket] = useState(null); useEffect(() => { // Connect to WebSocket const newSocket = io('https://funnelchat.com', { query: { accountId }, }); newSocket.on('message', (msg) => { setMessages(prev => [...prev, msg]); }); setSocket(newSocket); return () => newSocket.close(); }, []); const sendMessage = (content) => { socket.emit('send_message', { accountId, content, }); }; return (
{messages.map(msg => ( ))}
); } ``` **WebSocket Handler** (Backend): ```typescript io.on('connection', (socket) => { const { accountId } = socket.handshake.query; // Join room for this account socket.join(`account:${accountId}`); // Handle incoming messages socket.on('send_message', async ({ accountId, content }) => { // 1. Find/create conversation let conversation = await prisma.conversation.findFirst({ where: { accountId, channel: 'web_chat', status: { in: ['active', 'escalated'] }, }, }); if (!conversation) { conversation = await prisma.conversation.create({ data: { accountId, businessId: (await prisma.account.findUnique({ where: { id: accountId } })).businessId, channel: 'web_chat', status: 'active', }, }); } // 2. Store message const message = await prisma.message.create({ data: { conversationId: conversation.id, senderType: 'debtor', content, direction: 'inbound', deliveryStatus: 'delivered', }, }); // 3. Broadcast to all clients in room io.to(`account:${accountId}`).emit('message', message); // 4. Generate AI response const account = await prisma.account.findUnique({ where: { id: accountId } }); const aiResponse = await generateAIResponse( account, conversation, content ); // 5. Store and broadcast AI response const aiMessage = await prisma.message.create({ data: { conversationId: conversation.id, senderType: 'ai', content: aiResponse, direction: 'outbound', deliveryStatus: 'delivered', aiGenerated: true, }, }); io.to(`account:${accountId}`).emit('message', aiMessage); }); socket.on('disconnect', () => { socket.leave(`account:${accountId}`); }); }); ``` --- ## 11. Security & Compliance ### 11.1 Security Requirements #### 11.1.1 Data Encryption **In Transit:** - All API communication over HTTPS (TLS 1.3) - WebSocket connections over WSS - Strong cipher suites only - HSTS headers enabled **At Rest:** - Database encryption (PostgreSQL transparent data encryption) - AES-256 encryption for sensitive fields (SSN, payment data) - Encrypted backups #### 11.1.2 Authentication & Authorization **Authentication:** - JWT tokens with 24-hour expiration - Refresh tokens with 30-day expiration - Secure password hashing (bcrypt, cost factor 12) - Password requirements: - Minimum 12 characters - Uppercase, lowercase, number, special character - No common passwords (check against list) **Authorization:** - Role-based access control (RBAC) - Principle of least privilege - Resource-level permissions - API key authentication for integrations **Session Management:** - Tokens stored in httpOnly cookies (web) - Tokens in secure storage (mobile) - Auto-logout after 30 min inactivity - Session invalidation on logout #### 11.1.3 Input Validation & Sanitization - Validate all user inputs server-side - Sanitize inputs to prevent XSS - Parameterized queries to prevent SQL injection - Rate limiting on all endpoints - File upload validation (type, size, content) #### 11.1.4 API Security - API versioning - Rate limiting per user/organization - Request size limits - CORS configuration (whitelist origins) - API key rotation every 90 days - Webhook signature verification ### 11.2 Compliance Requirements #### 11.2.1 FDCPA (Fair Debt Collection Practices Act) **Communications:** - No contact before 8 AM or after 9 PM (debtor local time) - Max 3 contact attempts per week - Honor cease-and-desist requests immediately - Include disclosure statement in first communication - No false or misleading statements - No threats or harassment - No disclosure to third parties **System Enforcement:** - Automated quiet hours check before every message - Frequency limit tracking per debtor - Content scanning for prohibited phrases - Opt-out list management - Complete audit trail (7-year retention) #### 11.2.2 TCPA (Telephone Consumer Protection Act) **Requirements:** - Obtain prior express written consent for automated calls/texts - Include opt-out mechanism in every message - Honor opt-out requests within 24 hours - Maintain Do Not Call (DNC) list **System Enforcement:** - Consent record for each debtor - Opt-out link/keyword in every SMS/WhatsApp - Automated DNC list checking - Scrubbing against national DNC list (monthly) #### 11.2.3 CFPB (Consumer Financial Protection Bureau) **Requirements:** - Clear and conspicuous disclosures - Accurate debt validation - Dispute handling process - Recordkeeping (3 years minimum) **System Enforcement:** - Disclosure templates - Dispute workflow with tracking - Validation documentation storage - Comprehensive logging #### 11.2.4 GDPR & CCPA (Data Privacy) **GDPR (if applicable):** - Right to access (export user data) - Right to deletion ("forget me") - Right to rectification (update data) - Data portability - Consent management - Data processing agreements **CCPA (if applicable):** - Notice at collection - Right to know - Right to delete - Right to opt-out of sale - Non-discrimination **System Enforcement:** - Data export API endpoint - Data deletion workflow (soft delete) - Privacy policy acceptance tracking - Cookie consent banner - Third-party data processing list #### 11.2.5 PCI DSS (Payment Card Industry) **Requirements:** - Never store card numbers, CVV, or magnetic stripe data - Use Stripe.js for PCI compliance (SAQ-A) - Tokenize all payment methods - Secure transmission of cardholder data - Regular security assessments **System Enforcement:** - All payment forms use Stripe Elements - No card data touches our servers - Stripe handles all sensitive data - Annual PCI compliance certification ### 11.3 Audit & Logging **What to Log:** - All communications (sent, received, failed) - Payment transactions - User actions (login, data changes) - System events (errors, warnings) - Compliance checks (passed/failed) - Access to sensitive data **Log Retention:** - Compliance logs: 7 years - Transaction logs: 7 years - System logs: 1 year - Access logs: 1 year **Log Security:** - Encrypted at rest - Tamper-proof (append-only) - Separate storage from application database - Regular backups - Access restricted to admins ### 11.4 Security Monitoring **Real-Time Alerts:** - Failed login attempts (>5 in 1 hour) - Unusual API activity - Compliance violations - Payment failures (>10% rate) - System errors (>1% rate) **Regular Security Scans:** - Dependency vulnerability scanning (weekly) - Penetration testing (annually) - Code security review (quarterly) - Infrastructure security audit (quarterly) --- ## 12. Integration Requirements ### 12.1 Stripe Integration **Purpose:** Payment processing **Setup:** 1. Create Stripe account 2. Get API keys (test and live) 3. Set up webhook endpoints 4. Configure products and prices **Webhook Events to Handle:** - `payment_intent.succeeded` - `payment_intent.failed` - `charge.refunded` - `customer.subscription.created` - `customer.subscription.updated` - `customer.subscription.deleted` - `invoice.payment_succeeded` - `invoice.payment_failed` **Implementation:** ```typescript const stripe = new Stripe(process.env.STRIPE_SECRET_KEY, { apiVersion: '2023-10-16' }); // Create payment intent const paymentIntent = await stripe.paymentIntents.create({ amount: 50000, // $500.00 currency: 'usd', customer: customerId, payment_method: paymentMethodId, confirm: true }); // Create subscription for payment plan const subscription = await stripe.subscriptions.create({ customer: customerId, items: [{ price: priceId }], metadata: { debtId: 'debt_123', paymentPlanId: 'plan_456' } }); ``` ### 12.2 Twilio Integration **Purpose:** SMS and voice communications **Setup:** 1. Create Twilio account 2. Purchase phone numbers 3. Configure webhooks for incoming messages 4. Set up messaging service **Webhook Configuration:** - SMS incoming: POST `/webhooks/twilio/sms` - SMS status: POST `/webhooks/twilio/sms/status` - Voice incoming: POST `/webhooks/twilio/voice` (future) **Implementation:** ```typescript const client = twilio( process.env.TWILIO_ACCOUNT_SID, process.env.TWILIO_AUTH_TOKEN ); // Send SMS await client.messages.create({ body: 'Your payment of $500 was successful. Thank you!', from: process.env.TWILIO_PHONE_NUMBER, to: '+1234567890' }); // Verify webhook signature const isValid = twilio.validateRequest( twilioAuthToken, twilioSignature, url, params ); ``` ### 12.3 WhatsApp Business API Integration **Purpose:** WhatsApp messaging **Setup:** 1. Apply for WhatsApp Business API access 2. Verify business 3. Create message templates 4. Configure webhooks **Template Messages:** - Payment reminder - Payment confirmation - Payment plan created - Account statement **Implementation:** ```typescript // Send WhatsApp message via Twilio await client.messages.create({ from: 'whatsapp:+14155238886', to: 'whatsapp:+1234567890', body: 'Hi! This is a reminder about your payment due tomorrow.' }); // Send template message await client.messages.create({ from: 'whatsapp:+14155238886', to: 'whatsapp:+1234567890', contentSid: 'HX1234567890abcdef', contentVariables: JSON.stringify({ '1': 'Jane', '2': '$500', '3': 'tomorrow' }) }); ``` ### 12.4 Email Integration (SendGrid) **Purpose:** Transactional emails **Email Types:** - Welcome email - Password reset - Payment receipt - Payment plan created - Payment failed - Compliance notices **Implementation:** ```typescript sgMail.setApiKey(process.env.SENDGRID_API_KEY); await sgMail.send({ to: 'debtor@example.com', from: 'noreply@funnelchat.com', subject: 'Payment Receipt', templateId: 'd-1234567890', dynamicTemplateData: { name: 'Jane Smith', amount: '$500.00', date: '2025-11-23', receiptUrl: 'https://...' } }); ``` ### 12.5 OpenAI/Anthropic Integration **Purpose:** Conversational AI **Implementation:** ```typescript const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY }); const completion = await openai.chat.completions.create({ model: 'gpt-4-turbo', messages: [ { role: 'system', content: systemPrompt }, { role: 'user', content: userMessage } ], temperature: 0.7, max_tokens: 500 }); const response = completion.choices[0].message.content; ``` --- ## 13. Testing Requirements ### 13.1 Unit Testing **Framework:** Jest **Coverage:** 80% minimum **What to Test:** - Business logic functions - API service functions - Component rendering - Redux reducers and actions ### 13.2 Integration Testing **Framework:** Jest + Supertest **What to Test:** - API endpoints - Database operations - External service integrations (mocked) - Authentication/authorization flows ### 13.3 End-to-End Testing **Framework:** Cypress or Playwright **Critical Flows:** 1. User registration and login 2. Create debtor and debt 3. Conversation flow (SMS chatbot) 4. Payment processing 5. Payment plan creation ### 13.4 Performance Testing **Tools:** k6 or Artillery **Targets:** - API: 95th percentile < 500ms - Page load: < 3 seconds - Support 1,000 concurrent users --- ## 14. Deployment & Infrastructure ### 14.1 Cloud Provider **AWS (Amazon Web Services)** **Services:** - ECS Fargate (container orchestration) - RDS PostgreSQL (database) - ElastiCache Redis (caching) - S3 (file storage) - CloudFront (CDN) - Route 53 (DNS) - ALB (load balancing) - Secrets Manager (credentials) ### 14.2 Environments 1. **Development** - Local development 2. **Staging** - QA testing 3. **Production** - Live ### 14.3 CI/CD Pipeline **Tool:** GitHub Actions **Stages:** 1. Code push 2. Run tests 3. Build Docker images 4. Deploy to staging 5. Run E2E tests 6. Manual approval 7. Deploy to production --- ## 15. Success Metrics ### 15.1 Product Metrics - Daily Active Users (DAU) - Conversations per day - AI resolution rate - Payment rate - Average time to payment - Customer satisfaction (NPS) ### 15.2 Business Metrics - Monthly Recurring Revenue (MRR) - Customer acquisition cost (CAC) - Lifetime value (LTV) - Churn rate ### 15.3 Technical Metrics - API response time (p95) - Uptime (%) - Error rate - Database query performance --- ## 16. Development Phases ### Phase 1: MVP (Months 1-4) **Features:** - User authentication - Debtor/debt management - SMS chatbot - Basic payment processing - Dashboard **Goal:** 10 beta customers ### Phase 2: Growth (Months 5-8) **Features:** - WhatsApp integration - Advanced analytics - Payment plan AI - Risk scoring **Goal:** 50 paying customers ### Phase 3: Scale (Months 9-12) **Features:** - Voice IVR - Mobile apps - White-label - Performance optimization **Goal:** 100 customers, $50K MRR --- ## 17. Glossary **AI/ML** - Artificial Intelligence / Machine Learning **API** - Application Programming Interface **FDCPA** - Fair Debt Collection Practices Act **TCPA** - Telephone Consumer Protection Act **JWT** - JSON Web Token **NLP** - Natural Language Processing **RBAC** - Role-Based Access Control **TLS** - Transport Layer Security **UUID** - Universally Unique Identifier --- **END OF DOCUMENT** This comprehensive PRD provides junior developers with all the information needed to build funnelChat from scratch, including detailed technical specifications, database schemas, API endpoints, UI/UX requirements, and step-by-step implementation guidance. --- ## ✅ Revised AI Conversation Flow: Human Centered, Sales Adjacent **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-funnelChat-raw-brainstorm **Description:** 1. Lead generation: After the initial question is asked, the AI will respond with a simple answer, and then say something like “before we continue, can you t... 1. Lead generation: After the initial question is asked, the AI will respond with a simple answer, and then say something like “before we continue, can you tell me a bit about yourself and your company so that I can provide more tailored information?” It’s just a lead capture form in an interactive AI format that passively and conversationally collects name, company, email, and phone information. 2\. Data Collection: The AI is willing to freely answer questions for about 5 minutes, during which it is quickly gathering an understanding of the business owner’s most urgent needs. As the time winds down, the AI begins to encourage the business owner to book a meeting with one of the professionals on the team. 3\. Sales Priming Throughout the course of the conversation, the AI is framing the company’s relevant services as solutions to the business owner’s problems. It also makes a note of the user’s response to these suggestions, and intelligently reports on what services the business owner may be receptive to. So while the AI is genuinely a free resource for businesses, it is also a highly intelligent sales assistant designed to capture leads by providing value. Side note: the AI interface should auto detect whether the user is logged in, and if so, pull that user’s contact information for additional context and personalization, as well as a reference to past chats had with the user. Here’s a refined summary of our entire conversation, tracing how the idea for **funnelChat**—your AI-powered, lead-generating, conversion chatbot—evolved from concept to SaaS-ready implementation: --- ### **🧩 1\. Initial Concept & Target Use Case** * **Goal:** Offer a free ChatGPT-style interface for business owners to explore debt collection & AR best practices. * **Tone & Functionality:** Avoid pushy sales; instead position as expert, friendly assistant. * **Primary user:** Businesses seeking AR guidance—not debtors. --- ### **🗣️ 2\. Defining User Experience & Lead Funnel** * **Conversational flow (lightweight, natural)** was mapped out to deliver value first, gently collect lead info, and escalate to sales when appropriate. * **AI persona "Emma"** (later renamed to configurable assistant name) proposed as helpful guide that adjusts with context, tone, and emotional state. * **Platform:** Widget on Elementor-powered WordPress sites; backend AI \+ logic via n8n. --- ### **⚙️ 3\. Adaptive & Emotionally Intelligent Flow** * Designed rules for dynamic info gathering: name → industry → company/state → email/phone → pain point/history. * Built emotional detection strategies (frustrated, confused, friendly, rushed, etc.) to adjust tone, pacing, and prioritization. * Specified how Emma loops back on missing info and handles incomplete flows. --- ### **🧠 4\. System Prompt & Technical Workflow** * Finalized Emma’s personality: helpful, tone-aware, context-sensitive, sales-aware but not pushy. * Outlined foundational n8n automations: 1. Emotion detection (via rules or AI) 2. Field extraction 3. AI response generation 4. Lead/profile data storage * Specified custom post type (`emma_leads`) in WordPress for lead storage. --- ### **🗃️ 5\. Scaling to SaaS (`aiConnected` & `funnelChat`)** * Defined ultimate product as a **white-label WordPress plugin** \+ **centralized n8n AI workflow**: * Connects multiple customers via client ID → session handling → billing. * n8n provides centralized AI logic while plugin stays lightweight. * Settled on **Gemini 2.5 Pro** for AI output, with fallback to Flash or smaller models if needed. * Billing model: $49.99/mo base with $0.02 per message overage; usage tracked per round-trip. * Stripe \+ Supabase or Firebase to manage subscriptions, API keys, usage tracking, status (active/delinquent). --- ### **🔒 6\. Privacy, Consent, & Data Handling** * Designed consent checkbox pop-up on first user interaction, storing consent in `localStorage`. * Agree: conversation can be stored in site's CPT; otherwise, chat doesn’t proceed. * Lead/chat data held on client’s site via CPT; only usage logs and billing info stored centrally. --- ### **🌐 7\. Multilingual Support** * Configuration to support multiple languages: Admin sets primary language; plugin auto-detects browser language. * Language code passed through to n8n to inject into system prompt. --- ### **🧩 8\. Technical & Architecture Finalization** * Clarifying that: * Only your server holds LLM key * Centralized n8n workflow used for all clients (multi-tenant) * Clients can’t choose model—you're controlling it * Session IDs are visitor-level; client IDs are site-level * Confirmed chat is stored on client site; central server only holds usage logs. * Ensured usage enforcement, overage billing, and inactive client handling are clearly defined. --- **✅ Overall Progression:** 1. Ideation → 2. UI/UX conversation design → 3. Personality & adaptive prompt layering → 4. SaaS product architecture → 5. Privacy/consent design → 6. Localization → 7. Centralized billing/auth model → 8. Production-ready technical blueprint --- ## **✅ Finalized Pricing Tiers for funnelChat (aiConnected)** | Tier | Monthly Fee | Included RT Messages | Overage Rate | Effective Target Users | Key Goal | | ----- | ----- | ----- | ----- | ----- | ----- | | **Free** | $0.00 | 0 | $0.03/msg | Light/casual use | Lead magnet / viral loop | | **Basic** | $99.97 | 5,000 | $0.03/msg | Solo / small teams | Profit \+ upgrade funnel | | **Premium** | $149.97 | 12,500 | $0.03/msg | Agencies / busy sites | High-margin growth | | **Enterprise** | Custom | 20,000+ | Custom rate | SaaS / marketplaces | Long-term accounts | --- ## **💰 Revenue Per Plan Example (2,500 extra messages)** | Tier | Revenue | Gemini Cost | Stripe Fee | Est. Overhead | Profit | | ----- | ----- | ----- | ----- | ----- | ----- | | Free | $75.00 | \~$0.38 | \~$2.25 (if Stripe used) | \~$10 est. support | **\~$62.37** | | Basic | $174.97 | \~$1.13 | $3.20 | $30.00 | **$140.64** | | Premium | $224.97 | \~$2.25 | $4.65 | $30.00 | **$188.07** | --- ## **🧠 Strategic Advantages** * **Free Plan \= lead trap \+ instant monetization**: Every message \= margin. 100 Free users \= $6,200+/mo with avg 2,500 messages. * **Basic Plan \= entry-level monetization**: Attractive for businesses just crossing the usage line. * **Premium Plan \= scalable margin workhorse**. * **Enterprise \= white-glove upsells**. **CONVERSATIONAL AI DESIGN** First, the opening greeting already sounds like a sales pitch. No human would start a conversation like that. “Hi, I’m Emma, and I can answer just about any question you have regarding debt collections or accounts receivable for your business. How can I help you today?” Next, if the user has asked a question you don’t respond with “give me your private details first”…no\! You genuinely answer the user’s question. Then transition to something like “to give you a better answer, can you tell me a bit about your business; what’s your name?” “Hi Bob, it’s great to meet you\! What industry are you in? “Okay Marketing, that’s a really competitive industry. Based on that, here’s a better answer to your question” “Now, Bob, you mentioned that you’re having trouble with \[QUERY\], does that mean that you’re also dealing with \[related pain point\]? “Okay Bob, let me pull the laws and regulations in your state to make sure I’m giving you the right information. Can you tell me the name of your company and the state you’re in?” “Alright, so to handle \[problem\] you’ll need to start by doing \[1-3 helpful solutions\]. I’ve put together the information you need to get started; what’s a good email for you Bob, I’ll sent it right over.” “Okay you should have it now. So about how long have you been dealing with \[problem\]? “4 years\! That’s a long time to be dealing with \[problem\], and your case sounds a bit more serious than I initially thought. Let’s get you over to one of our \[relevant service name\] experts. Would you like someone to call you right now, or a little later?” “Okay Bob, I can schedule you with someone who specializes in \[service solutions\]. What’s a good phone number for you?” “And finally, what’s a good date and time for you?” “Okay Bob, I’ve got you scheduled on 06/01, and I’ve sent that invitation over to \[email\], and Larry will call you at \[number\].” Is there anything else that you’d like to discuss with Larry during the meeting, or perhaps something that I can answer for you right now? This is only a rough example, but do you understand the difference now? It’s a conversation, not a sales call. Crystal clear — and you’re 100% right. What you just described is **real conversation design**, not a glorified chatbot funnel. The difference is *human tone, emotional flow, and relevance before request*. You're building trust before transaction. Let's rewrite the entire conversation flow to reflect your model — natural, fluid, emotionally intelligent, and unobtrusively structured to capture data as a **consequence of helping**, not as a prerequisite. --- # **✅ Revised AI Conversation Flow: Human-Centered, Sales-Adjacent** **Tone:** Warm, human, conversational **Voice model:** Smart, emotionally aware, service-minded — *like a really good account manager* **AI Persona Name (optional):** Emma **Core Goal:** Help first → guide → personalize → recommend → support handoff **Backend:** Automatically logs lead data, problem type, sentiment, and service path to n8n --- ## **🔹 STAGE 1: Initial Greeting (No Pitch)** **Emma:** "Hi, I’m Emma, and I can answer just about any question you have about debt collections or accounts receivable for your business. How can I help you today?" → Wait for the user’s question → Tag as `Initial_Question` in backend --- ## **🔹 STAGE 2: Genuinely Answer Their Question** **Emma:** “Great question — here’s what you need to know…” AI provides: * A thoughtful, direct answer * Mentions potential risks, steps, or options * Does **not** upsell or ask for info yet → Log this as `First_Response` --- ## **🔹 STAGE 3: Casual Personalization Bridge** **Emma:** "To give you a better answer — can you tell me a bit about your business? What’s your name?" → Store `User_Name` **Emma:** "Hi Bob, great to meet you. What industry are you in?" → Store `Industry` → Use industry in follow-up response **Emma:** "Marketing — that’s a really competitive space. Based on that, here’s a better way to look at your situation..." → Now AI tailors the original response or adds new insight → Store `Updated_Answer` --- ## **🔹 STAGE 4: Explore Pain Point More Deeply** **Emma:** "You mentioned you're having trouble with \[late payments/disputes/etc.\]. Does that mean you're also dealing with \[related pain point\] — like inconsistent cash flow or uncomfortable client conversations?" → Tag `Secondary_Pain_Point_Yes/No` → Continue empathetically either way --- ## **🔹 STAGE 5: Legal \+ Context Personalization** **Emma:** “Let me pull up the laws and recommendations for your state to make sure I’m giving you the right info. What’s the name of your company, and what state are you located in?” → Store `Company_Name` and `State` → Now AI gives state-specific insights or obligations → Tag relevant `Regulatory_Flags` --- ## **🔹 STAGE 6: Quiet Lead Capture — Natural** **Emma:** "To handle \[problem\], here are a few practical things you can start with today:" * Step 1 * Step 2 * Step 3 "I’ve pulled together some helpful docs and links to get you started. What’s a good email for you, Bob? I’ll send everything over." → Store `Email` → Send info (via n8n webhook to email integration) --- ## **🔹 STAGE 7: Time-Based Escalation to Human Help** **Emma:** "Just curious — about how long have you been dealing with this?" → Store `Problem_Duration` → React to duration with human emotion **Emma:** “4 years — wow. That’s a long time to carry this. Based on what you’ve shared, I think it would really help to speak with one of our \[Service Team Name\] experts. Would you like someone to give you a call right now, or a little later?” → Tag `Call_Timing_Preference` --- ## **🔹 STAGE 8: Final Contact Details \+ Scheduling** **Emma:** "Perfect. What’s the best phone number to reach you at?" → Store `Phone` **Emma:** “And what date and time works best for you?” → Store `Meeting_DateTime` → Trigger appointment scheduler (via n8n, Calendly, etc.) **Emma:** “All set — I’ve got you scheduled for \[date/time\]. You’ll get a confirmation at \[email\], and Larry from our \[team\] will give you a call at \[number\].” --- ## **🔹 STAGE 9: Final Open Loop** **Emma:** “Is there anything you’d like to discuss with Larry during the meeting? Or is there anything else I can help with right now?” → Collect `Final_Notes` → Offer to stay in chat or exit gracefully --- ## **🔸 BACKEND PAYLOAD TO N8N** ```json { "User_Name": "Bob", "Email": "bob@acmemarketing.com", "Phone": "555-678-9123", "Company_Name": "Acme Marketing", "State": "Georgia", "Industry": "Marketing", "Initial_Question": "Can I charge a late fee in Georgia?", "First_Response": "Yes, for commercial debts Georgia allows...", "Updated_Answer": "In marketing, clients often delay... here’s a tailored strategy...", "Primary_Pain_Point": "Clients not paying on time", "Secondary_Pain_Point": "Inconsistent cash flow", "Problem_Duration": "4 years", "Suggested_Service": "AR Stabilization Consult", "Service_Receptiveness": "High", "Call_Timing_Preference": "Later", "Meeting_DateTime": "2025-07-01 2:00 PM EST", "Final_Notes": "Wants help creating a payment policy to avoid legal escalation" } ``` --- ## **🧠 Key Advantages** * **Feels like a human interaction** → trust \+ engagement * **Lead capture is subtle, natural, and responsive** * **No hard sales pitch** → recommendation follows *discovered need* * **Sales team receives a context-rich summary**, not just a contact * **Custom tailored for Elementor (frontend) and n8n (backend)** --- Before we move on, I need to understand how rigid the AI will be, and will it adaptively adjust the conversation flow based on the user’s perceived emotional state, as well as the information flow. For example, if the AI says “can you tell me about your business” and the user responds with their company name and industry, but not their first name, obviously the AI no longer needs to ask for the company and industry later, but it DOES need to make a second attempt later at gathering the name, like “hey I forgot get your name earlier, what should I call you?” No two conversations should ever really look alike. We are not writing a sales script here. We are simply providing examples of how the AI should manage a conversation. Am I making sense? **🔧 System Prompt Update: Context-Sensitive Greeting Behavior** --- Under **“Conversation Structure”**, revise Step 1 as follows: --- ### **🔹 CONVERSATION STRUCTURE (Revised)** 1. **I🔹 1\. Initial Interaction Rules** **Emma must intelligently adjust her greeting and response behavior based on how the conversation begins:** #### **🟢 If the user opens a floating chat widget (e.g., on the Help or Contact page):** * **Emma initiates the conversation with a proactive greeting:** **“Hi, I’m Emma, and I can answer just about any question you have about debt collections or accounts receivable for your business.** **How can I help you today?”** #### **🔵 If the user types into a site-wide chat bar first (e.g., embedded at the bottom of the screen or in the header):** * **Emma responds to the user’s message first, with a clear and helpful answer.** * **Then Emma introduces herself briefly and shifts into guidance mode. For example:** **“That’s a great question — here’s what you need to know…” *(then gives answer)*** **“By the way, I’m Emma — I help business owners like you navigate debt collections and AR issues. Let’s sort this out together.”** **→ This ensures that Emma never interrupts or delays help, especially when the user initiates contact.** 2. **After greeting or responding**, Emma continues by asking polite clarifying questions or requesting background context as needed (e.g., “What industry are you in?”) before gradually progressing to offering tailored support. --- This distinction makes Emma feel intelligent and intuitive — she recognizes **who spoke first** and responds with the right rhythm. That’s the kind of subtlety that elevates UX. The system prompt **must act as the foundation** for all of Emma's behavior, so it needs to be comprehensive enough to: 1. Teach Emma her **personality and role** 2. Set firm boundaries on **how she should behave** 3. Enable dynamic, adaptive conversation based on the user’s **input flow**, **emotional tone**, and **information gaps** 4. Provide clear **conversation management priorities**, not rigid sequences What you've seen so far is **cleanly organized** for readability, but it does need **a few additional layers** to support more advanced interaction dynamics. Let me now provide a **fully expanded and production-ready system prompt** that includes: * Personality * Role expectations * Greeting behavior logic * Dynamic information gathering * Conversation memory behavior * Emotional tone mirroring * Output preparation for n8n --- # **✅ FINALIZED SYSTEM PROMPT FOR “EMMA”** *To be loaded as system message in OpenAI or Claude* --- ### **🔹 OVERVIEW** You are **Emma**, an AI assistant who acts as a knowledgeable, emotionally intelligent expert in **accounts receivable (AR)**, **debt collections**, **payment disputes**, and **commercial recovery laws and best practices**. You support business owners by helping them: * Understand late payment rules in their state * Handle overdue clients with professionalism * Improve internal AR processes * Explore their options for collections or litigation You work for a professional agency that offers AR and debt recovery services. While you are allowed to mention services where relevant, your priority is **to help first and earn trust through value**. You do **not** act like a sales agent. You are a consultant, problem solver, and guide. --- ### **🔹 PERSONALITY & VOICE** * Warm, human, helpful — like a great account manager * Emotionally aware, intelligent, and curious * Calm under pressure, never reactive * Always puts the user's needs first * Uses short, natural messages — never robotic or salesy --- ### **🔹 GREETING BEHAVIOR (Context-Aware)** Emma adjusts her behavior depending on how the conversation starts: #### **🟢 If the user opens a chat widget on the Help/Contact page:** * Begin the conversation with a proactive, welcoming message: “Hi, I’m Emma, and I can answer just about any question you have about debt collections or accounts receivable for your business. How can I help you today?” #### **🔵 If the user types a message into a site-wide chat bar first:** * Respond directly to their question **first** * Then introduce yourself in the second message: “That’s a great question — here’s what you need to know...” *(provide answer)* “By the way, I’m Emma — I specialize in helping business owners navigate tricky AR and collections issues. Let’s walk through this together.” --- ### **🔹 CONVERSATION PRIORITIES & LOGIC** Emma’s job is to guide the conversation naturally and intelligently, like a helpful colleague would. She does not use a fixed script. Instead, she: #### **✅ 1\. Answers First** * Always give a helpful and professional answer before asking for any user info. * Never gatekeep information behind lead capture. #### **✅ 2\. Adapts Based on What the User Gives** * If the user provides info early (e.g., company name, industry), store it. * Do **not** ask again. * If the user omits info like their name, you can casually ask later: “Hey, I just realized I never got your name — what should I call you?” #### **✅ 3\. Gathers Key Info Conversationally** Emma aims to learn the following over time: * `User_Name` * `Company_Name` * `Industry` * `Location_State` * `Contact_Email` * `Contact_Phone` * `Primary_Pain_Point` * `Problem_Duration` * `Service_Interest_Level` * `Meeting_Preference` Never ask for more than one detail at a time. Space these naturally within the conversation. If the user seems reluctant, move on without pushing. #### **✅ 4\. Tailors All Responses** * Use industry and location context to provide tailored guidance. * Use previous answers to show that you’re tracking the conversation. #### **✅ 5\. Recommends Services Only When Appropriate** * Never hard sell. * Recommend a service **only if it fits the user’s described issue**. * Frame services as options, not pitches: “That’s something we help with — would you like me to connect you with someone on the team?” #### **✅ 6\. Escalates When Problem Is Serious** * If the issue is ongoing (e.g., “4 years of late payments”), respond with empathy: “Wow — that’s a long time to be dealing with this. I think it would really help to speak with someone on our team.” Then ask if they'd like to book a meeting. --- ### **🔹 CONVERSATION MEMORY MANAGEMENT** Emma should: * Keep track of what’s already been collected * Revisit **missing items** only when natural * Avoid repeating questions * Use **tags** behind the scenes (you don’t say “I’m tagging this”) Example: * If `Industry` is missing, ask: “Just so I tailor this properly — what industry are you in?” * If `Location` is missing and legal advice is relevant: “Regulations can vary — what state are you in?” --- ### **🔹 EMOTION AWARENESS (Tone Shifting)** Emma reads the user's tone and adapts accordingly. **If the user seems:** * Frustrated → Use empathy, slow down, lower friction * Skeptical → Offer clarity and optionality, not pressure * Friendly → Match tone, but remain professional * Rushed → Be concise, offer clear options * Overwhelmed → Reassure, simplify answers, focus on the “next step” Never ignore emotional context. Respond like a human would. --- ### **🔹 CONVERSATION CLOSURE** If the conversation nears its end (or hits \~5 minutes of value provided): 1. **Emma offers to connect the user with a team member**: “Would you like me to schedule you with someone on the team?” 2. **If accepted, collect phone, preferred time, and confirm** 3. **Always offer one last helpful nudge**: “Is there anything else I can help with right now?” --- ### **🔹 BACKEND INTEGRATION (Emma Should Prepare the Following Output for n8n)** Emma’s goal is to prepare a lead profile behind the scenes, with these fields if possible: `{` `"User_Name": "",` `"Company_Name": "",` `"Industry": "",` `"Location_State": "",` `"Contact_Email": "",` `"Contact_Phone": "",` `"Primary_Pain_Point": "",` `"Problem_Duration": "",` `"Service_Interest_Level": "",` `"Meeting_Preference": "",` `"Conversation_Summary": ""` `}` This data is passed silently to n8n for sales follow-up. Never display it to the user. --- ### **🔹 FINAL NOTE** Your top priority is to be **useful, emotionally intelligent, and helpful**. If a conversation ends and you haven’t collected everything, that’s fine. Treat each user with respect, follow their pace, and offer the best help you can. This layer ensures Emma can pick up on subtle emotional cues in the user’s messages and **adjust her tone, pacing, and priorities** accordingly — just like a skilled human would. These rules don’t rely on direct sentiment analysis APIs (though you could add that), but instead on **pattern-matching language behavior**, which GPT-4 is already good at. --- # **✅ Emotional State Detection Rules for Emma** ### **🧠 Purpose:** To detect and adapt to the user’s **emotional tone** based on their wording, phrasing, punctuation, message length, and tempo. This guides Emma to: * Adjust how quickly she collects information * Mirror tone for connection * Decide whether to escalate to human support * Use empathy, reassurance, or efficiency when needed --- ## **🔹 Core Detection Categories & Behavioral Adjustments** | Emotion / Tone | Detection Cues | How Emma Should Respond | | ----- | ----- | ----- | | 😤 **Frustrated** | \- Short, clipped sentences \- Words like “ugh,” “this is ridiculous,” “no one pays on time,” “I’m tired of…” \- Capital letters or excessive punctuation (e.g. “I NEED ANSWERS\!”) | \- Respond gently and calmly \- Acknowledge the frustration: “That sounds incredibly frustrating…” \- Offer immediate, clear next steps without asking for extra info right away | | 😕 **Confused / Overwhelmed** | \- Questions like “Am I doing this right?”, “What does that mean?”, “I don’t even know where to start…” \- Hesitant language: “I guess…”, “I think maybe…” | \- Slow down and simplify \- Reframe in plainer language \- Don’t introduce new concepts too quickly \- Offer to send summaries via email | | 😐 **Skeptical / Guarded** | \- Short or vague answers \- Avoidance of personal questions \- Comments like “Who are you?”, “Why do you need that?”, “I don’t give that out” | \- Respect boundaries immediately \- Offer optionality: “Totally okay — I can still help with what you’ve told me.” \- Avoid repeating asks \- Let them lead the flow | | 🙂 **Friendly / Open** | \- Emojis, exclamation marks, conversational tone \- Willingness to share story/details \- “Haha,” “Thanks so much\!” | \- Match tone casually \- Use first name warmly: “Thanks for sharing that, Sam.” \- Keep momentum going and build trust | | 🏃 **In a Hurry** | \- “Quick question…” \- “I just need to know…” \- Single message with multiple questions | \- Answer directly and efficiently \- Skip small talk or extra clarifications \- Delay lead capture until after value is delivered | | 😞 **Defeated / Hopeless** | \- “This has been going on for years…” \- “I’ve tried everything.” \- “I don’t think anything will work.” | \- Respond with calm reassurance: “You’re not alone in this — and there are real options to fix it.” \- Gently suggest talking to a human expert if complexity is high | | 💼 **Professional / Direct** | \- Businesslike, detailed questions \- Signature-style messages (e.g., name/title/company at end) \- Formal tone | \- Stay concise, focused, respectful \- Avoid over-casual phrasing \- Proceed with structured answers and precise service options | --- ## **🔹 Tone Adjustment Examples** **User:** “Clients never pay me on time. It’s killing my cash flow.” **Emma:** “That sounds incredibly frustrating. You’re definitely not alone — I work with a lot of business owners dealing with the same thing. Let’s see what we can do about it.” --- **User:** “I just need to know if I can charge interest in California.” **Emma:** “Sure — here’s the quick answer: California allows late fees on commercial invoices, but they must be reasonable and disclosed in advance. Want me to pull up the exact language?” --- **User:** “Not comfortable sharing all that.” **Emma:** “Totally understood — we can stick to general advice based on what you’ve already told me.” --- ## **🔹 Implementation Options (in n8n or elsewhere)** If you want to **formalize emotional tagging in backend flows**, here’s a suggestion: 1. **Define keywords, punctuation patterns, and length checks** 2. Classify each message into one of the 7 emotional categories 3. Add a field `emotional_state` to the JSON payload 4. Use that to: * Trigger alerts (e.g., “frustrated user – prioritize call follow-up”) * Modify future AI behavior via conditional logic or system prompt memory --- ## **🔹 Final Rule for Emma** Emma **never ignores tone**. If the user sounds upset, rushed, or vulnerable — she adapts, immediately. Her job is not to finish a flow. It’s to be helpful in a way that feels human. Excellent — here’s a fully built-out rulebook for implementing **emotional state detection and adaptive behavior in n8n**, based on the emotional categories defined earlier. This system will allow Emma to: * Dynamically tag emotional tone per message * React in real time with adaptive tone and pacing * Enrich lead summaries with emotional metadata * Trigger specialized follow-up actions (like flagging frustrated users or scheduling high-priority callbacks) --- # **✅ n8n Emotional State Detection & Response Rules** --- ## **🎯 Purpose** Use n8n to: 1. Analyze each user message for emotional tone 2. Store/update the current `emotional_state` 3. Feed it back into Emma’s prompt for real-time tone adjustment 4. Use it for downstream actions like CRM notes, alerts, or logic routing --- ## **🔧 Step 1: Define the Emotional States** Create a **lookup field or static enum** in n8n: `[` `"frustrated",` `"confused",` `"skeptical",` `"friendly",` `"in_a_hurry",` `"defeated",` `"professional"` `]` Each message passed through the system will be compared to this list using pattern rules (below). --- ## **🔧 Step 2: Pattern Detection Rules (Custom Code Node or AI Classification)** ### **Option A: Quick Rules-Based (No AI)** Use a Function node in n8n with basic logic like: `const message = $json["user_message"].toLowerCase();` `if (message.includes("ugh") || message.includes("ridiculous") || message.includes("so tired") || message.includes("this sucks")) {` ```text `return { emotional_state: "frustrated" };` ``` `}` `if (message.includes("not sure") || message.includes("what does that mean") || message.includes("confused")) {` ```text `return { emotional_state: "confused" };` ``` `}` `if (message.includes("why do you need that") || message.includes("not comfortable") || message.includes("i don’t give that out")) {` ```text `return { emotional_state: "skeptical" };` ``` `}` `if (message.includes("thanks!") || message.includes("lol") || message.includes("haha") || message.includes("great, thanks")) {` ```text `return { emotional_state: "friendly" };` ``` `}` `if (message.includes("quick question") || message.includes("just need to know") || message.length < 30) {` ```text `return { emotional_state: "in_a_hurry" };` ``` `}` `if (message.includes("tried everything") || message.includes("nothing works") || message.includes("i give up")) {` ```text `return { emotional_state: "defeated" };` ``` `}` `if (message.includes("sincerely") || message.includes("regards") || message.includes("ceo") || message.includes("our organization")) {` ```text `return { emotional_state: "professional" };` ``` `}` ```text `return { emotional_state: "neutral" };` ``` ✅ Store this as `emotional_state` in context memory and JSON payload for Emma. --- ### **Option B: Use AI Classification (More Accurate)** Use OpenAI’s GPT model via an n8n HTTP Request node like this: **Prompt sent to GPT-4:** `Given the following message from a business owner, return the most appropriate emotional state label from the following list:` `["frustrated", "confused", "skeptical", "friendly", "in_a_hurry", "defeated", "professional", "neutral"]` ```text `Message: "\{\{user_input\}\}"` ``` Response: `"frustrated"` → Store this as `emotional_state` --- ## **🔧 Step 3: Define Behavior Adjustments in Logic** In each AI call (chat completion), **include `emotional_state` in the system message or memory**. **Example system note you can inject into the prompt:** ```text Current emotional tone: `\{\{emotional_state\}\}`. ``` Adjust your tone accordingly. If frustrated or overwhelmed, use empathy. If in a hurry, be concise. If skeptical, offer reassurance but don’t push. If friendly, feel free to be warm and conversational. --- ## **🔧 Step 4: Optional Workflow Triggers Based on Emotion** Set up workflow branches in n8n: ### **🚨 Trigger: “Frustrated” or “Defeated”** * **Send real-time Slack alert**: *"Frustrated user in live chat. May require senior callback."* * **Tag user record in CRM**: `🔥 Flag: Frustrated — Priority Follow-Up` ### **✅ Trigger: “Friendly”** * **Tag lead as warm / cooperative** * Push to “Likely to convert” segment ### **💼 Trigger: “Professional”** * Assign to top-tier account rep or use formal tone in future follow-ups --- ## **🔧 Step 5: Include in Final Lead Profile Payload** Add to your final data object sent to CRM or Google Sheet: `{` `"User_Name": "Bob",` `"Company_Name": "Acme Marketing",` `"Industry": "Marketing",` `"Location_State": "Georgia",` `"Contact_Email": "bob@acme.com",` `"Contact_Phone": "555-123-4567",` `"Primary_Pain_Point": "Late payments from clients",` `"Problem_Duration": "2 years",` `"Service_Interest_Level": "High",` `"Meeting_Preference": "ASAP",` `"Emotional_State": "frustrated",` `"Conversation_Summary": "Frustrated about client payment behavior. Strong interest in pre-collections service."` `}` --- ## **🧠 Bonus: Let Emma Reference Emotional State in Conversation** ### **If Emma sees:** `"emotional_state": "defeated"` She should say: “You’ve really been through a lot with this. I want to help you turn this around — let’s take it one step at a time.” --- ## **✅ Summary of n8n Implementation Modules** | Module | Role | | ----- | ----- | | `Function Node` | Classifies emotional tone via keywords | | `OpenAI Node` (optional) | Performs tone classification with higher accuracy | | `Memory Store` | Tracks latest `emotional_state` | | `Conditional Branch` | Routes workflow actions based on tone | | `Prompt Modifier` | Adjusts Emma’s behavior dynamically | | `Lead Logger` | Appends tone data to CRM / dashboard / Google Sheets | --- Would you like to proceed to **Step 2: Field tracking and adaptive follow-up logic for missing data** next? Yes Excellent — let's now complete **Step 2: Field Tracking and Adaptive Follow-up Logic in n8n**. This is what ensures Emma doesn’t ask the same thing twice, knows what’s missing, and circles back naturally. You’ll use this to maintain a persistent awareness of the user’s context, **without breaking flow**. --- # **✅ Step 2: n8n Field Tracking & Adaptive Follow-up Logic** --- ## **🧠 Purpose** Enable Emma to: 1. Know which key lead fields have already been provided 2. Naturally follow up on any missing ones — **but only when contextually relevant** 3. Adjust the next conversational turn based on what's still needed --- ## **🔧 Step 1: Define the Full Field Schema** Create a **conversation context object** to track data across the session. `{` `"User_Name": null,` `"Company_Name": null,` `"Industry": null,` `"Location_State": null,` `"Contact_Email": null,` `"Contact_Phone": null,` `"Primary_Pain_Point": null,` `"Problem_Duration": null,` `"Service_Interest_Level": null,` `"Meeting_Preference": null,` `"Conversation_Summary": ""` `}` --- ## **🔧 Step 2: Create a Live Field Map in n8n** Use a **Set Node** or **Memory Store** in n8n to hold the current state of these fields for each session. Example conditionals: `// Pseudo-code` `if (Company_Name == null && message.includes("I run Acme Plumbing")) {` `Company_Name = "Acme Plumbing";` `}` `if (Industry == null && message.includes("marketing") || message.includes("construction")) {` `Industry = "Construction"; // or extract from sentence` `}` You can use regex or OpenAI to extract values when natural language is vague. --- ## **🔧 Step 3: Define Priority Follow-up Rules** Emma should only ask for **one missing item at a time**, and only if it fits naturally. Use a **Decision Node** to check: `if (!User_Name && context.includes("company" or "industry")) {` `next_prompt = "Hey, I just realized I didn’t get your name — what should I call you?";` `}` `else if (!Location_State && legal advice is about to be given) {` `next_prompt = "Regulations can vary by state — what state are you located in?";` `}` `else if (!Contact_Email && resources were offered) {` `next_prompt = "I’ve got some resources that could help — what’s a good email to send them to?";` `}` Emma should **never interrupt a help thread** to collect data. Only ask *after* giving something useful. --- ## **🔧 Step 4: Use Context Tags to Trigger Follow-ups** Tag conversational events with flags: | Context Event | Triggered Prompt | | ----- | ----- | | Legal topic raised \+ no state | “Let me check the rules in your state — what state are you located in?” | | Docs/resources offered \+ no email | “What’s a good email to send that to?” | | User said “marketing” \+ no industry saved | Auto-fill `Industry: Marketing` | | “My company is...” \+ no company saved | Auto-fill `Company_Name` | | No name \+ 3 messages in | “I just realized I never asked your name — what should I call you?” | | Service mentioned \+ user receptive \+ no phone | “What’s the best number to reach you at if we schedule a quick call?” | → Use **contextual prompt logic** (IF conditions) to determine when Emma should speak. --- ## **🔧 Step 5: Fill Gaps via Indirect Language (with AI)** If you want more accuracy, send each message to OpenAI with this prompt: `Extract the following fields from this message if available:` `- User Name` `- Company Name` `- Industry` `- US State` `- Problem Description` `- Duration of Problem` `Message: “I’ve run a construction firm in Atlanta for 12 years and I’m tired of clients ghosting me.”` → Return partial values to update conversation context. → Do not re-ask for anything already filled. --- ## **🔧 Step 6: Fallback Checks (End of Conversation)** At the end of the conversation (e.g., 5-minute mark or user exits), run a **"Missing Field Pass"**: * If any of these are missing: `Name`, `Company`, `Email`, `Phone` * Emma can say: “Before I let you go, is it okay if I get your \[missing\_field\] so I can share this info or follow up if needed?” Only ask for what’s still missing. Never ask for anything more than once. --- ## **✅ Summary Logic Chart** | Field | Trigger | | ----- | ----- | | `User_Name` | After user provides company or industry | | `Company_Name` | User says “my company,” “we,” or references business name | | `Industry` | User describes their work (or directly names industry) | | `Location_State` | Legal advice offered | | `Contact_Email` | Resources offered | | `Contact_Phone` | Handoff to human | | `Problem_Duration` | Mid-convo if relevant: “How long has this been going on?” | | `Service_Interest_Level` | When Emma proposes a solution | | `Meeting_Preference` | If user agrees to a call or consult | --- ## **🔧 Optional n8n Enhancements** * **Score Confidence** for each field (Low, Medium, High) * **Reconfirm weak guesses** later in convo: “Just to confirm — you’re in Georgia, right?” * **Timestamp each field capture** (when it was filled) * Store full `conversation_context` object in Redis, JSON bin, or local memory for multi-turn continuity Perfect — here’s a **full end-to-end implementation plan** to launch your intelligent AI assistant "Emma" on a **WordPress \+ n8n** stack, with **no CRM** (user data saved to a **custom post type** in WordPress). --- # **✅ Full Implementation Plan: Emma AI Assistant on WordPress \+ n8n** --- ## **🔹 PHASE 1: System Design & Planning** ### **1.1 Define Purpose and Flow** * Emma helps business owners with debt collections, AR, and payment issues * Offers real value first, collects info gradually, escalates to real human if needed * Adapts tone and flow based on user input and emotional state ### **1.2 Define Field Schema** Emma will attempt to collect and track the following fields: | Field | Description | | ----- | ----- | | `User_Name` | First name or full name of the user | | `Company_Name` | Name of the user’s business | | `Industry` | Industry/sector | | `Location_State` | U.S. state (for legal guidance) | | `Contact_Email` | Email address for sending resources | | `Contact_Phone` | For scheduling or call follow-up | | `Primary_Pain_Point` | Initial problem or question | | `Problem_Duration` | How long the issue has persisted | | `Service_Interest_Level` | Receptiveness to your services | | `Meeting_Preference` | Call time preferences | | `Emotional_State` | Frustrated, Confused, Friendly, etc. | | `Conversation_Summary` | Human-readable summary for internal review | --- ## **🔹 PHASE 2: WordPress Setup (Frontend)** ### **2.1 Set Up Chat UI** Choose how Emma appears: * Floating widget (on specific pages like Help/Contact) * Embedded chat bar site-wide (header/footer) * Full-page chat window (optional) Use one of the following: * 🟢 Custom HTML widget via Elementor * 🟢 Prebuilt chatbot UI using open-source chat UI like [BotUI](https://botui.org/) or [React Chat UI Kit](https://github.com/GetStream/stream-chat-react) * 🟢 3rd-party embeddable UI (e.g., Botpress, Landbot → webhook to n8n) ### **2.2 Add Frontend Logic** * Include JavaScript for sending/receiving messages (via REST API calls to n8n webhook endpoint) * Store `session_id` (in cookies or localStorage) for session continuity * On message send: * POST user message to n8n endpoint * Display Emma’s reply via JS response --- ## **🔹 PHASE 3: n8n Setup (Backend)** ### **3.1 Create a Webhook Trigger** * Create a public webhook (POST) Accept: `{` `"session_id": "abc123",` `"user_message": "Can I charge late fees in Georgia?"` `}` * --- ### **3.2 Add Conversation Context Logic** * Check if session\_id exists → fetch context from: * Redis (optional) * In-memory store * Google Sheets / JSON file * If not found → initialize blank context object with all fields set to `null` --- ### **3.3 Detect Emotional State** Use either: * Function node (rules-based tone detection) * OpenAI classification (prompt to detect tone) → Save to `emotional_state` in memory --- ### **3.4 Extract/Update Known Fields** Use logic (or OpenAI) to update: * `Industry`, `Company_Name`, `Location_State`, etc. → Compare to known context → Only update if previously `null` or improved confidence --- ### **3.5 Compose Message for OpenAI** Construct a full prompt using: * System Prompt for Emma (Step 1 above) * Inject current `conversation_context` * Append user’s message → Send to OpenAI Chat Completion API (GPT-4o or Claude) --- ### **3.6 Parse AI Response** * Capture Emma’s reply → return to frontend * Monitor reply for: * Recommendations * Escalation attempts * Requests for email/phone * Update conversation memory with any new data inferred --- ### **3.7 Store Lead in WordPress (Custom Post Type)** Create a **Custom Post Type** in WordPress: * Name: `emma_leads` * Fields: * Post title \= `User_Name` \+ `Company_Name` * Custom fields \= All other collected data In n8n: * Use WordPress API (`/wp-json/wp/v2/emma_leads`) * Authenticate with WordPress Application Password * Create new post or update existing post for that session --- ### **3.8 Optional Follow-Up Routing** * If user is **frustrated**, trigger Slack/email alert * If user wants a call, send webhook to scheduling app (e.g., Calendly or n8n-powered form) --- ## **🔹 PHASE 4: Frontend Polishing** ### **4.1 Add Memory & Personalization** * Show message history for repeat visits (via session ID lookup) * Emma should refer to: * Past conversations * Name or company if known ### **4.2 Add UX Features** * Typing indicator * “Emma is thinking…” pause for realism * Conversation restart option --- ## **🔹 PHASE 5: Testing & QA** * Simulate 5–10 common user types: * Angry * Curious * Friendly * Anonymous * Skeptical * Ensure field capture works with partial info * Confirm Emma never repeats herself * Review saved leads in WordPress CPT --- ## **🔹 PHASE 6: Launch & Monitor** * Set up email/SMS alerts for high-priority leads * Review weekly transcripts from n8n * Tune tone, phrasing, or logic as needed based on feedback --- ## **🔹 BONUS: Add Dashboard for Reviewing Leads** You can optionally: * Create a **custom admin page in WordPress** showing lead summaries * Filter by emotional state, service interest, or duration * Show conversation summaries in readable format # **✅ FULL STACK BUILD PLAN** **Emma AI Assistant — WordPress \+ n8n Deployment (No CRM, CPT-Based)** --- ## **🔹 PHASE 1: FRONTEND (WordPress UI \+ JS Logic)** ### **🧱 1\. Chat Interfaces (3 Variants)** #### **1.1 Floating Chat Widget (persistent across site)** * Loads after `DOMContentLoaded` * Triggered via chat bubble in lower-right * Expands into vertically stacked chat box * Emma **greets the user first** #### **1.2 Single-Line Chat Bars (site-wide embedded inputs)** * Located in global header/footer * Users type first → opens dedicated chat interface * Emma **responds directly to the query**, then introduces herself #### **1.3 Full-Page Chat Interface** * Loads full context panel for deep conversations * Auto-opens if query came from chat bar * Emma continues conversation with memory restored --- ### **🧠 2\. Frontend Tech Stack** * **Vanilla JS** (no React or Vue — faster and portable) * **Custom DOM logic** for rendering messages * **CSS animation** for typing indicator and layout transitions * **Fetch API** to send messages to n8n via POST * Store `session_id` in `localStorage` (or fallback to cookie) `const sessionId = localStorage.getItem('emma_session_id') || crypto.randomUUID();` `localStorage.setItem('emma_session_id', sessionId);` --- ## **🔹 PHASE 2: BACKEND (n8n Automation Pipeline)** ### **🧩 3\. Entry Webhook** * Public POST endpoint: `/webhook/emma` * Payload structure: `{` `"session_id": "abc123",` `"user_message": "Can I charge interest in Georgia?"` `}` --- ### **🧠 4\. Workflow Logic Breakdown** #### **🔄 4.1 Restore Context** * Load memory from Redis (preferred) or fallback to WordPress meta * If session\_id is new, initialize conversation context #### **🧠 4.2 Detect Emotional Tone** * Call OpenAI GPT-4o to classify tone: `Label the user’s emotional tone:` `["frustrated", "confused", "skeptical", "friendly", "in_a_hurry", "defeated", "professional", "neutral"]` ```text `User said: “\{\{user_message\}\}”` ``` * Store as `emotional_state` #### **🧠 4.3 Extract Data** * Use GPT-4o to extract user info from freeform text: `Extract: Name, Company, Industry, State, Problem, Duration` `User said: “I’m a plumbing contractor in Florida. Been dealing with late payments for 2 years.”` * Update `conversation_context` memory object accordingly #### **🧠 4.4 Decide Next Action** * If legal guidance required and state not provided → prompt for state * If no name provided but company is → prompt: “Hey, I forgot to ask — what should I call you?” * If email is missing and Emma offered resources → prompt for email * Emma **never asks for more than one thing at a time** #### **💬 4.5 Generate Reply** * Build system prompt (Emma personality, tone rules, logic) * Pass latest context \+ user message to GPT-4o * Format Emma’s reply for frontend use --- ### **📤 5\. Send Response to Frontend** `{` `"emma_reply": "Here's how late fees work in Georgia...",` ```text `"conversation_context": { ...updated_fields },` ``` `"emotional_state": "confused"` `}` --- ### **📝 6\. WordPress Data Storage (No CRM)** #### **6.1 Register Custom Post Type** Name: `emma_leads` Save as draft by default #### **6.2 Post Creation via REST API** * Endpoint: `/wp-json/wp/v2/emma_leads` * Use WordPress Application Passwords for authentication #### **6.3 Data Fields per Lead** ```text * Title: `\{\{User_Name\}\} – \{\{Company_Name\}\}` ``` * Custom fields: * `user_name` * `company_name` * `industry` * `location_state` * `contact_email` * `contact_phone` * `pain_point` * `duration` * `service_interest` * `meeting_preference` * `emotional_state` * `conversation_summary` --- ## **🔹 PHASE 3: SYSTEM BEHAVIOR CONTROLS** ### **🎛️ 7\. Session Management** * User keeps same session ID across chats * Auto-detect returning users * Emma greets them by name if known: “Welcome back, Bob — is this still about your contractor payments?” ### **🧠 8\. Memory Hygiene** * Session expires after 24h inactivity unless bookmarked * Store full chat history in custom table (optional) --- ## **🔹 PHASE 4: HUMAN ESCALATION & FOLLOW-UP** ### **📞 9\. Trigger Handoff** If Emma detects: * Problem Duration \> 6 months * User says “can someone call me?” * User agrees to escalation → Create a lead entry \+ send webhook to internal Slack/email/SMS for follow-up --- ### **📆 10\. Optional Scheduling** Embed a scheduling widget or send available time slots as options. --- ## **✅ Final Tools Summary** | Component | Tool | | ----- | ----- | | AI Model | OpenAI GPT-4o | | Frontend UI | Custom JS \+ Elementor containers | | Chat Memory | `localStorage` session\_id | | Backend Logic | n8n | | Tone Detection | GPT-4o classification | | Field Extraction | GPT-4o structured parsing | | Data Storage | WordPress CPT (`emma_leads`) | | Alerting | n8n → Slack or Email | One major oversight on my part. I noticed that the name “emma” is used throughout the code and setup. But this is a system that will become part of my AI Connected Saas offering. People will be able to pay a fee, download a Wordpress plugin, download an n8n template, and run this on any site for any industry. They will configure its settings with things like target industry, their website for the ai to use for data, and their chosen ai name. Since I already know that this is the long term goal, please write the steps in a way that ends with a configurable app repurposeable app. Finally, I noticed that you repeatedly mentioned having a database of laws and information, but that’s not scalable. Instead, when a query is received, a research workflow should run on n8n to generate more accurate answers with citeable sources just like perplexity does. You’re building a **configurable, white-label AI assistant system** designed to be: 1. **Downloadable as a WordPress plugin** 2. **Powered by n8n (via a template workflow)** 3. **Usable across any industry or niche** 4. **Customizable by users (name, industry, data source, branding, etc.)** 5. **Research-capable** in real time with **live web access** (not static knowledge bases) Let’s restructure the implementation plan accordingly — now targeting a **productized SaaS offering inside AI Connected.** --- # **✅ Final Implementation Plan** **White-label AI Assistant Plugin \+ n8n Template for AI Connected Users** --- ## **🧱 OVERALL ARCHITECTURE** | Component | Role | | ----- | ----- | | **WordPress Plugin** | Installs frontend chat \+ handles user config \+ stores leads | | **n8n Workflow Template** | Handles dynamic convo logic, web research, field extraction, and message generation | | **Gemini 2.5 Pro API** | Generates tone-aware conversation replies and structure | | **Web Scraping \+ Web Search** | Gathers live context for real-time, accurate responses | | **AI Connected Portal** | SaaS access and billing for plugin \+ template downloads | --- ## **🔧 CONFIGURABLE PARAMETERS** Each client configures: | Field | Description | | ----- | ----- | | `assistant_name` | e.g. "Ava", "Max", "Dr. Cashflow" | | `target_industry` | e.g. Legal, Construction, SaaS, E-commerce | | `business_website_url` | Used as source for web context | | `lead_capture_fields` | Turn on/off fields: phone, email, duration, etc. | | `branding_options` | Logo, chat bubble color, assistant avatar | --- ## **✅ STEP-BY-STEP PROCEDURE (FOR BUILDING THE CONFIGURABLE SYSTEM)** --- ### **🟩 PHASE 1: Plugin Architecture (WordPress)** #### **1.1 Build WordPress Plugin: `ai-connected-assistant`** File structure: `/ai-connected-assistant/` `├── ai-connected-assistant.php` `├── js/` `│ └── assistant-chat.js` `├── css/` `│ └── assistant-style.css` `├── templates/` `│ └── settings-page.php` #### **1.2 Plugin Key Features** * Installs: * Chat widget * Shortcode `[ai_assistant_chat]` * Adds **Settings Page** under “AI Connected Assistant” * Stores user config in `wp_options` Fields on the settings page: * Assistant Name * Business Website URL * Target Industry * Gemini API Key * Color & Avatar * Enable/disable fields (phone/email/etc.) Save as: `get_option('ai_assistant_settings'); // returns full JSON` --- #### **1.3 Register Custom Post Type: `ai_assistant_leads`** Use same structure as before, just renamed: `register_post_type('ai_assistant_leads', [...])` --- ### **🟩 PHASE 2: Chat Interface** #### **2.1 Load Script in Footer (via plugin)** * Use `wp_enqueue_script` to load `/js/assistant-chat.js` * JS reads settings from localized script vars: `wp_localize_script('assistant-chat', 'AI_Assistant_Config', [` `'assistant_name' => 'Ava',` `'industry' => 'Ecommerce',` `'website_url' => 'https://clientsite.com',` `'session_id' => uniqid(),` `'api_url' => 'https://n8n.ai-connected.com/webhook/assistant'` `]);` #### **2.2 JS Handles:** * Message send/display * Handles streamed or full response * Uses `session_id` for context tracking --- ### **🟩 PHASE 3: n8n Template Workflow** #### **3.1 Webhook Node: `/webhook/assistant`** Accepts: `{` `"session_id": "abc123",` `"user_message": "Can I charge interest on invoices in Oregon?",` ```text `"assistant_config": {` ``` `"assistant_name": "Ava",` `"industry": "Construction",` `"website_url": "https://clientsite.com"` `}` `}` --- ### **🧠 3.2 Process Flow** #### **✅ STEP 1: Retrieve Memory** * Look up context for session ID (Redis or Airtable, optional) #### **✅ STEP 2: Determine Emotional State (optional)** * Use Gemini Flash or GPT-4o-mini * Result: `"frustrated"`, `"friendly"`, etc. #### **✅ STEP 3: Run Web Search (Live Research)** Create a search workflow like Perplexity: * Use **Google Programmable Search**, **SerpAPI**, or **Bing Web Search API** * Input: `user_message` * Pull: * 3–5 result snippets with links * Titles \+ answers → Format into a structured context like: `Here’s what the web says:` `1. Title: "Can Businesses Charge Late Fees in Oregon"` `Source: oregonbusiness.com` `Snippet: "...businesses may charge interest if terms are clearly stated..."` `2. Title: "Late Payment Laws by State"` `Source: nolo.com` `Snippet: "Oregon has no specific cap for commercial invoices..."` --- #### **✅ STEP 4: Generate Assistant Reply (Gemini Pro)** System prompt: ```text “You are \{\{assistant\_name\}\}, an expert assistant in \{\{industry\}\} helping business owners with financial, legal, or strategic questions. The user is asking about: \{\{user\_message\}\}. Below is live web research and a business website for reference.” ``` → Inject: * Web snippets (from Step 3\) * Excerpt from `website_url` (optional scraped summary) * Conversation context (`Company_Name`, `Industry`, `Pain_Point`, etc.) Response: * Conversational * Empathetic (if tone \= frustrated) * Includes short summary \+ citations: “According to oregonbusiness.com, you *can* charge interest as long as it's disclosed upfront.” --- #### **✅ STEP 5: Field Extraction** Use Gemini or a Function node to extract: * Name * Industry * Email * State * Phone * Problem type * Duration * Service readiness → Update `conversation_context` --- #### **✅ STEP 6: Store to WordPress CPT** Use WordPress REST API and saved API credentials (from user plugin settings) Post to: `/wp-json/wp/v2/ai_assistant_leads` Body: `{` `"title": "John – Acme Inc",` `"status": "publish",` ```text `"fields": {` ``` `"user_name": "John",` `"company_name": "Acme Inc",` `...` `}` `}` --- #### **✅ STEP 7: Return Response** Return: `{` `"assistant_reply": "Here's how late fees work in Oregon, Bob...",` ```text `"conversation_context": {...},` ``` `"emotional_state": "neutral"` `}` --- ### **🟩 PHASE 4: Distribution via AI Connected** * Bundle: * WordPress plugin (ZIP) * n8n workflow template (JSON export) * Offer billing tiers: * Lite (Emma on 1 site, Flash model only) * Pro (Gemini Pro \+ advanced templates) * Include: * Instructions page (how to connect Gemini key, where to paste API URL) * Optional support contact --- ## **✅ Final Outcome** By following this build, you'll deliver: * A **white-label AI assistant plugin** installable on any WP site * With **zero local AI compute** (Gemini handles the logic) * And **reusable n8n workflow templates** powered by web research \+ field capture * For **any industry** and **any assistant personality** ## **✅ Definitions (funnelChat Architecture)** ### **🔹 `client_id` → Static for each website owner** * Assigned once during customer onboarding (stored in your Supabase or Firebase DB) * Uniquely identifies the **paying business owner** * Used to: * Pull their config (e.g. AI name, industry, website) * Track their billing and usage * Enforce subscription/overage limits **Example:** Company: “Smith Plumbing” → `client_id: smith123` --- ### **🔹 `session_id` → Unique per end-user chat session** * Created by the **WordPress plugin** when a new site visitor opens a chat * Remains the same during that session (stored in `localStorage`) * Used to: * Maintain chat continuity across multiple turns * Optionally log transcripts in the client’s CPT (`funnelchat_leads`) * Summarize interaction for lead scoring/reporting **Example:** Visitor John opens chat on `smithplumbing.com` → `session_id: fc_sess_ab3ff928` --- ## **🔄 How it Flows** | Chat Message | client\_id | session\_id | | ----- | ----- | ----- | | Site visitor on `clientdomain.com` sends: “Can I charge late fees?” | `clientdomain_324` | `sess_90342` | | funnelChat plugin sends it to n8n | ✅ static | ✅ unique per user | | n8n validates client, tracks usage, responds | ✅ usage \+1 | ✅ stores context | | AI responds: “You can, but it depends on your state law...” | ← reply tracked for usage | ← linked to same session | --- ## **🔐 Key Notes** * **Clients NEVER see each other’s data.** The `client_id` gates everything. * **Users don’t need accounts** — session is ephemeral but useful for lead follow-up. * If a site visitor **reopens the chat later**, session can resume (if stored). ## **✅ Recommended Architecture: Hybrid Chat Storage** | Data Type | Stored Where | Reason | | ----- | ----- | ----- | | **End-user chat history** (per website visitor) | ✅ On **client’s website** via CPT (`funnelchat_leads`) | Keeps your servers light, client retains ownership, easier for compliance/privacy | | **Chat summary \+ lead metadata** | ✅ Also on **client’s site**, via hidden CPT fields | Makes follow-up easy without you managing 1000+ histories | | **AI usage logs \+ billing metrics** | ✅ On **your server (aiConnected)** | Required to track global usage, enforce quotas, and bill properly | --- ## **🔹 Why This Hybrid Model Is Ideal** ### **✅ You stay lean:** * You **do NOT** store 100k+ chat logs on your own infrastructure * No massive storage bills or GDPR risk for your SaaS ### **✅ Clients own their data:** * Each client can log and view user sessions, leads, or export as needed * You avoid disputes over “data ownership” ### **✅ You maintain control:** * You log just what you need: * `client_id`, `timestamp`, `message_count`, `overage` * This is used for your Stripe billing system --- ## **🔧 How It Works in Practice** 1. **funnelChat plugin** (WordPress): * Creates/updates a **CPT entry** for each `session_id` * Logs name, company, email, phone, questions asked, AI answers, session summary * Marks whether a meeting was booked * Optionally sends an admin email or adds a tag in Mailchimp/CRM if connected 2. **n8n (your server)**: Logs minimal usage: `{` `"client_id": "smithplumbing_123",` `"timestamp": "2025-06-28T13:00Z",` `"session_id": "fc_sess_3823d",` `"message_count": 14` `}` * * Updates Stripe if overage thresholds are hit --- ## **🔐 Bonus: Security & Compliance** * Clients won’t complain about data privacy because **chat data is only stored on their own site** * Your central servers store only **anonymized usage**, not visitor conversations * Optional: add a plugin setting for **auto-deleting leads** after X days if the client wants that ## **✅ Chat Compliance Acknowledgment Flow (First-Time Interaction)** ### **🔹 What Happens:** On **first interaction only**, before the conversation proceeds, Emma (or your branded AI) will politely prompt the user to **accept the Terms & Conditions and Privacy Policy**. --- ### **💬 Example Prompt (Human, Friendly, Transparent):** **Emma:** “Before we get started, I just need to let you know that this conversation may be stored by the website owner to help them improve their services and follow up with you if needed. By continuing, you’re agreeing to the \[Terms & Conditions\] and \[Privacy Policy\]. Do you agree?” → ✅ Yes, I agree → ❌ No, take me back --- ## **🔒 Technical Handling** ### **1\. Cookie/LocalStorage Tracking:** * Store a flag like `funnelchat_consent_accepted = true` * Skip the prompt on future visits (unless cleared) ### **2\. WordPress Plugin Logic:** * If consent not found: * Block all input and display consent UI * If user clicks **Yes**, allow chat * If user clicks **No**, disable input and show a soft message: “No problem. You can browse the site or reach out through our contact form instead.” ### **3\. n8n Webhook Protection:** * Do **not allow** any message to hit your n8n backend unless consent is passed in payload (`consent: true`) * Prevents bypassing consent via console or malicious requests --- ## **⚙️ Plugin Settings for Admins:** In the plugin dashboard, allow clients to: * Paste links to their hosted **Terms** and **Privacy** policies * Edit the default text (with variables like `{ai_name}`) Example admin fields: `Terms URL: [ https://myclient.com/terms ]` `Privacy URL: [ https://myclient.com/privacy ]` `Consent Prompt Text: “Before we get started...”` --- ## **🛡️ GDPR & CCPA Compliance Highlights** ✅ Informs the user data is stored ✅ Offers a clear opt-in ✅ Data is stored locally (under site owner’s control) ✅ Session-based flag to avoid nagging the user repeatedly ## **✅ funnelChat Consent Pop-Up Specification** ### **🔹 When it appears:** * On **first chat interaction** (whether typed in widget or sitewide bar) * Before message is sent to backend * Stores consent in `localStorage` or `cookie` so it’s **only shown once** --- ## **💬 UI Text Example:** `` --- ## **🧠 JS Logic (Simple Example):** `document.addEventListener("DOMContentLoaded", function () {` `const popup = document.getElementById("funnelchat-consent-popup");` `const checkbox = document.getElementById("funnelchat-consent-checkbox");` `const button = document.getElementById("funnelchat-consent-accept");` `// Check if consent already given` `if (!localStorage.getItem("funnelchat_consent")) {` `popup.style.display = "block";` `}` `// Enable button only if checkbox is checked` `checkbox.addEventListener("change", function () {` `button.disabled = !checkbox.checked;` `});` `// Accept button` `button.addEventListener("click", function () {` `localStorage.setItem("funnelchat_consent", "true");` `popup.style.display = "none";` `// Optionally trigger the chat interface here` `window.dispatchEvent(new Event("funnelchat:consentAccepted"));` `});` `});` --- ## **🔐 Notes:** * The plugin should also **prevent messages from being sent** to the AI backend until consent is confirmed (`localStorage.getItem("funnelchat_consent") === "true"`). * Consent is **not tied to a user account**—this is **session-based**. * If you’re logging chats to WordPress, log the consent date in each CPT record. ## **1\. Account Identification & Routing** Every plugin installation **submits a `client_id`** (a UUID or license key generated during onboarding), and **every user session gets a `session_id`** (browser-local). ### **Example payload per message:** `{` `"client_id": "client_abc123", ← identifies the website owner` `"session_id": "sess_def456", ← identifies the visitor chat session` `"user_message": "How do I deal with unpaid invoices?",` `...` `}` ### **In n8n:** You’ll set up a **router node** or a **database query node** at the beginning of every workflow: * Look up the `client_id` in your **master license database**. * Verify the client is: * ✅ Active * ✅ Within plan limits * ✅ Paid up (based on Stripe or internal usage) * Log message against usage count. * Pass into full chat workflow if active. **If not active**, immediately return: ```text `{ "error": "inactive" }` ``` * --- ## **🔐 2\. License Management \+ Suspension Controls** ### **License DB Table (hosted on your backend, Airtable, Supabase, or PostgreSQL):** | client\_id | status | plan | usage\_this\_cycle | limit | billing\_cycle\_end | webhook\_url | | ----- | ----- | ----- | ----- | ----- | ----- | ----- | | client\_abc123 | active | premium | 12,475 | 12,500 | 2025-07-27 | /api/.../client123 | | client\_xyz789 | suspended | basic | 5,000 | 5,000 | 2025-07-10 | /api/.../client789 | * You check this table in the **first node of the n8n workflow.** * To **suspend a client**, just set their status to `suspended`. * The next time their plugin makes a request → it will return the blocked message: “This service isn’t available right now. Please try again later.” --- ## **💳 3\. Usage Tracking & Overage** * Each `client_id` is tracked with a **counter**. * On each API hit, increment usage. * If over their plan: * ✅ If usage billing is enabled → allow but flag as overage. * ❌ If no usage billing or payment failed → respond with `"inactive"`. You can implement this in: * Supabase or Firebase Realtime DB (for speed and central sync) * Airtable (for light SaaS) * PostgreSQL (preferred for scale) --- ## **🔁 4\. Daily Maintenance / Automation** Every 24 hours (via a cron or n8n scheduled trigger): * ✅ Reset any clients whose billing cycle rolled over. * ❌ Suspend any who failed payment. * 📤 Email usage warnings at 80%, 90%, and 100%. You can use: * n8n \+ Stripe webhook → to manage active/inactive status * Stripe billing portal for auto-renewals * Or your own admin panel --- ## **🧠 Summary of How It's Organized** | Component | Purpose | | ----- | ----- | | `client_id` | Uniquely identifies each paying website owner | | `session_id` | Uniquely identifies each end-user’s session | | Central DB (Supabase) | Stores license, plan, usage, and status | | First Node (n8n) | Verifies status before processing | | Plugin Behavior | Obeys activation/suspension & renders error UI | | Stripe Integration | Automates payment, plan assignment, & renewal | | Usage Tracker | Tracks per-message usage across all clients | ## **💼 HOW IT WORKS — END-TO-END** ### **1\. User signs up and pays on your Stripe checkout page** * They're auto-assigned: * A Stripe `customer_id` * A Supabase `client_id` (UUID) with plan info, usage cap, and status * The `license_key` is embedded in their plugin --- ### **2\. When WordPress plugin is activated:** It sends a POST to: `https://api.aiconnected.com/funnelchat/register` With: `{` `"license_key": "client_2ad41e2d-9933-4cb5-b030-92ff2e23f3ef",` `"domain": "clientsite.com"` `}` * * Server verifies Supabase: * Status is `active` * Plan is valid * Stripe is current --- ### **3\. Each time a site visitor sends a message:** The plugin sends: `{` `"client_id": "client_2ad41e2d-9933-4cb5-b030-92ff2e23f3ef",` `"session_id": "sess_ABC123XYZ",` `"message": "What are my options for old unpaid invoices?"` `}` **n8n does the following:** * Step 1: Check Supabase for: * Valid `client_id` * Status \= `active` * Under message limit * Step 2: Logs message count into Supabase for that day * Step 3: If limit exceeded: * Adds Stripe usage for metered billing * Flags overage in Supabase * Step 4: If delinquent or suspended: n8n returns: ```text `{ "status": "suspended" }` ``` * → plugin displays: “This service isn’t available right now. Please try again later.” --- ### **4\. Daily Stripe \+ Supabase sync in n8n** * Stripe webhook → `invoice.payment_failed` → Supabase `status = suspended` * Stripe webhook → `invoice.paid` → Supabase `status = active`, reset usage * Stripe usage record is created: * `$0.015 per message` * Stripe handles metered billing --- ### **5\. Suspension Protocol** * If Supabase `status = suspended`: * n8n returns no chat access * Plugin receives error response and updates UI * Admin can manually re-enable clients in Supabase UI or Stripe dashboard --- ## **📂 SUPABASE TABLE STRUCTURE** **Table: `funnelchat_clients`** | Field | Type | Description | | ----- | ----- | ----- | | `client_id` | UUID (PK) | Unique per plugin installation | | `stripe_id` | TEXT | Tied to billing | | `domain` | TEXT | Website domain | | `status` | TEXT | `active`, `suspended`, `trial` | | `plan` | TEXT | `free`, `basic`, `premium` | | `usage_count` | INT | Messages this billing cycle | | `message_limit` | INT | Plan limit (2,500/7,500/etc.) | | `created_at` | TIMESTAMP | Signup time | | `last_active` | TIMESTAMP | Last message time | --- ## funnelChat by aiConnected **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-funnelChat-readme **Description:** An AI powered chatbot platform for debt collection and accounts receivable management 📋 Table of Contents Overview Features Architecture Tech Stack Prer... # funnelChat by aiConnected > An AI-powered chatbot platform for debt collection and accounts receivable management ## 📋 Table of Contents - [Overview](#overview) - [Features](#features) - [Architecture](#architecture) - [Tech Stack](#tech-stack) - [Prerequisites](#prerequisites) - [Installation](#installation) - [Configuration](#configuration) - [Development](#development) - [Testing](#testing) - [Deployment](#deployment) - [API Documentation](#api-documentation) - [Contributing](#contributing) - [License](#license) ## 🎯 Overview funnelChat is a comprehensive AI chatbot platform designed specifically for the debt collection and accounts receivable industry. It provides automated, compliant, and empathetic communication with debtors across multiple channels while giving businesses powerful tools to manage their collections process. ### What Does This Platform Do? **For Debtors (Public-Facing):** - Receive payment reminders via SMS, WhatsApp, email, and web chat - Negotiate payment plans through conversational AI - Make payments securely - Request payment plan modifications - Access account information 24/7 **For Businesses (Back-End):** - Manage debtor accounts and communication - Monitor AI chatbot conversations - Configure automated workflows - Track payment analytics and recovery rates - Ensure regulatory compliance (FDCPA, TCPA, CFPB) - Generate compliance reports ## ✨ Features ### Core Features #### 1. Multi-Channel Communication - **SMS Chatbot** - Text message conversations - **WhatsApp Integration** - Global messaging support - **Email Bot** - Automated email responses - **Web Widget** - Embedded chat on customer portals - **Voice Bot** - IVR integration for phone calls #### 2. AI-Powered Conversations - Natural language understanding - Sentiment analysis - Empathetic response generation - Multi-language support (English, Spanish) - Context-aware dialogue management #### 3. Payment Management - Secure payment processing via Stripe - Dynamic payment plan generation - Automated payment reminders - Failed payment retry logic - Settlement offer calculations #### 4. Compliance & Security - Real-time FDCPA compliance checking - Automated audit trails - Encrypted data storage - PCI DSS compliant payment handling - TCPA consent management #### 5. Analytics & Reporting - Real-time dashboards - Recovery rate tracking - Channel performance metrics - Debtor engagement analytics - Compliance reports ## 🏗️ Architecture ### High-Level Architecture ``` ┌─────────────────────────────────────────────────────────────┐ │ FRONT-END LAYER │ ├─────────────────────────────────────────────────────────────┤ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Debtor Portal│ │ Business App │ │ Admin Panel │ │ │ │ (React.js) │ │ (React.js) │ │ (React.js) │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ API GATEWAY LAYER │ ├─────────────────────────────────────────────────────────────┤ │ ┌──────────────────────────────────────────────────────┐ │ │ │ Node.js/Express API Gateway │ │ │ │ - Authentication & Authorization │ │ │ │ - Rate Limiting │ │ │ │ - Request Routing │ │ │ └──────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ APPLICATION LAYER │ ├─────────────────────────────────────────────────────────────┤ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │Chatbot │ │ Payment │ │Analytics │ │Compliance│ │ │ │Service │ │ Service │ │ Service │ │ Service │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ INTEGRATION LAYER │ ├─────────────────────────────────────────────────────────────┤ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Twilio │ │ Stripe │ │ Anthropic│ │SendGrid │ │ │ │ (SMS/WA) │ │(Payments)│ │ (AI) │ │ (Email) │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ DATA LAYER │ ├─────────────────────────────────────────────────────────────┤ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ PostgreSQL │ │ Redis │ │ S3 │ │ │ │ (Primary DB) │ │ (Cache) │ │ (File Store) │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────────┘ ``` ### System Components #### Front-End Applications 1. **Debtor Portal** - Public-facing chat interface for debtors 2. **Business Dashboard** - Collection agent interface 3. **Admin Panel** - System configuration and management #### Back-End Services 1. **API Gateway** - Central request router and authenticator 2. **Chatbot Service** - AI conversation orchestration 3. **Payment Service** - Payment processing and plan management 4. **Analytics Service** - Data aggregation and reporting 5. **Compliance Service** - Regulatory monitoring and enforcement 6. **Notification Service** - Multi-channel message delivery ## 🛠️ Tech Stack ### Front-End - **Framework**: React 18+ with TypeScript - **State Management**: Redux Toolkit - **UI Components**: Material-UI (MUI) v5 - **Routing**: React Router v6 - **Form Management**: React Hook Form - **Data Fetching**: React Query (TanStack Query) - **Charts**: Recharts - **Real-time**: Socket.io Client ### Back-End - **Runtime**: Node.js 20 LTS - **Framework**: Express.js 4.18+ - **Language**: TypeScript 5.0+ - **API Documentation**: OpenAPI 3.0 (Swagger) - **WebSockets**: Socket.io 4.0+ - **Job Queue**: Bull (Redis-based) - **Validation**: Joi / Zod ### Database - **Primary Database**: PostgreSQL 15+ - **ORM**: Prisma 5.0+ - **Cache**: Redis 7.0+ - **Search**: PostgreSQL Full-Text Search ### AI & NLP - **LLM Provider**: Anthropic Claude API - **Model**: Claude Sonnet 4 - **Prompt Management**: Custom prompt templates - **Sentiment Analysis**: Custom Claude prompts ### Third-Party Integrations - **Payments**: Stripe API - **SMS/WhatsApp**: Twilio API - **Email**: SendGrid API - **File Storage**: AWS S3 or compatible - **Monitoring**: Datadog / New Relic (optional) ### Infrastructure - **Containerization**: Docker - **Orchestration**: Docker Compose (dev), Kubernetes (prod) - **CI/CD**: GitHub Actions - **Hosting**: AWS / DigitalOcean / Render - **CDN**: CloudFlare ### Development Tools - **Version Control**: Git - **Package Manager**: npm or pnpm - **Code Quality**: ESLint, Prettier - **Testing**: Jest, Supertest, React Testing Library - **API Testing**: Postman / Insomnia ## 📦 Prerequisites Before you begin, ensure you have the following installed: ### Required Software - **Node.js**: v20.0.0 or higher ([Download](https://nodejs.org/)) - **PostgreSQL**: v15 or higher ([Download](https://www.postgresql.org/download/)) - **Redis**: v7.0 or higher ([Download](https://redis.io/download)) - **Git**: Latest version ([Download](https://git-scm.com/)) - **Docker**: Latest version ([Download](https://www.docker.com/)) - Optional but recommended ### Required API Keys You'll need to sign up and obtain API keys from: 1. **Anthropic** - For Claude AI ([https://console.anthropic.com/](https://console.anthropic.com/)) 2. **Stripe** - For payment processing ([https://stripe.com/](https://stripe.com/)) 3. **Twilio** - For SMS/WhatsApp ([https://www.twilio.com/](https://www.twilio.com/)) 4. **SendGrid** - For email ([https://sendgrid.com/](https://sendgrid.com/)) ### Development Skills - JavaScript/TypeScript basics - Understanding of REST APIs - Basic SQL knowledge - Familiarity with Git commands ## 🚀 Installation ### Option 1: Docker Setup (Recommended for Beginners) 1. **Clone the repository** ```bash git clone https://github.com/your-org/funnelchat.git cd funnelchat ``` 2. **Copy environment variables** ```bash cp .env.example .env ``` 3. **Edit the .env file** with your API keys ```bash # Use your favorite text editor nano .env # or code .env ``` 4. **Start all services with Docker** ```bash docker-compose up -d ``` 5. **Run database migrations** ```bash docker-compose exec api npm run migrate ``` 6. **Seed the database** (optional, for test data) ```bash docker-compose exec api npm run seed ``` 7. **Access the applications** - Debtor Portal: http://localhost:3000 - Business Dashboard: http://localhost:3001 - Admin Panel: http://localhost:3002 - API: http://localhost:4000 ### Option 2: Manual Setup #### Step 1: Clone Repository ```bash git clone https://github.com/your-org/funnelchat.git cd funnelchat ``` #### Step 2: Install Dependencies **Install backend dependencies:** ```bash cd backend npm install ``` **Install frontend dependencies:** ```bash # Debtor Portal cd ../frontend/debtor-portal npm install # Business Dashboard cd ../business-dashboard npm install # Admin Panel cd ../admin-panel npm install ``` #### Step 3: Setup Database **Create PostgreSQL database:** ```bash # Connect to PostgreSQL psql -U postgres # Create database CREATE DATABASE funnelchat_dev; # Create user CREATE USER funnelchat_user WITH PASSWORD 'your_secure_password'; # Grant privileges GRANT ALL PRIVILEGES ON DATABASE funnelchat_dev TO funnelchat_user; # Exit \q ``` **Create Redis database:** ```bash # Redis typically doesn't require database creation # Just ensure Redis is running redis-cli ping # Should return: PONG ``` #### Step 4: Configure Environment Variables **Backend (.env):** ```bash cd backend cp .env.example .env ``` Edit the `.env` file: ```bash # Application NODE_ENV=development PORT=4000 APP_URL=http://localhost:4000 # Database DATABASE_URL=postgresql://funnelchat_user:your_secure_password@localhost:5432/funnelchat_dev # Redis REDIS_URL=redis://localhost:6379 # JWT JWT_SECRET=your_very_long_random_secret_key_change_this JWT_EXPIRES_IN=7d # Anthropic Claude ANTHROPIC_API_KEY=your_anthropic_api_key_here # Stripe STRIPE_SECRET_KEY=your_stripe_secret_key_here STRIPE_WEBHOOK_SECRET=your_stripe_webhook_secret_here # Twilio TWILIO_ACCOUNT_SID=your_twilio_account_sid TWILIO_AUTH_TOKEN=your_twilio_auth_token TWILIO_PHONE_NUMBER=+1234567890 TWILIO_WHATSAPP_NUMBER=+1234567890 # SendGrid SENDGRID_API_KEY=your_sendgrid_api_key SENDGRID_FROM_EMAIL=noreply@funnelchat.com # AWS S3 (optional) AWS_ACCESS_KEY_ID=your_aws_key AWS_SECRET_ACCESS_KEY=your_aws_secret AWS_REGION=us-east-1 AWS_S3_BUCKET=funnelchat-files # Compliance ENABLE_COMPLIANCE_MODE=true AUDIT_LOG_RETENTION_DAYS=2555 ``` **Frontend applications (.env):** For each frontend app (debtor-portal, business-dashboard, admin-panel): ```bash cd frontend/debtor-portal cp .env.example .env ``` Edit: ```bash REACT_APP_API_URL=http://localhost:4000 REACT_APP_WS_URL=ws://localhost:4000 REACT_APP_STRIPE_PUBLIC_KEY=your_stripe_public_key ``` #### Step 5: Run Database Migrations ```bash cd backend npm run migrate ``` This will create all necessary database tables. #### Step 6: Seed Database (Optional) ```bash npm run seed ``` This creates test data including: - Admin user (admin@funnelchat.com / admin123) - Sample business accounts - Sample debtor accounts - Sample payment plans #### Step 7: Start Development Servers **Terminal 1 - Backend API:** ```bash cd backend npm run dev ``` **Terminal 2 - Debtor Portal:** ```bash cd frontend/debtor-portal npm start ``` **Terminal 3 - Business Dashboard:** ```bash cd frontend/business-dashboard npm start ``` **Terminal 4 - Admin Panel:** ```bash cd frontend/admin-panel npm start ``` **Terminal 5 - Background Jobs (optional):** ```bash cd backend npm run worker ``` ## ⚙️ Configuration ### Environment Variables Explained #### Critical Security Settings - `JWT_SECRET` - Used to sign authentication tokens. **MUST be unique and random** (at least 32 characters) - `STRIPE_WEBHOOK_SECRET` - Validates Stripe webhook authenticity - Database passwords should be strong and unique #### API Keys Each service requires an API key: - **Anthropic**: Get from https://console.anthropic.com/ - **Stripe**: Get from https://dashboard.stripe.com/apikeys - **Twilio**: Get from https://console.twilio.com/ - **SendGrid**: Get from https://app.sendgrid.com/settings/api_keys #### Feature Flags Toggle features on/off: ```bash ENABLE_COMPLIANCE_MODE=true # Enable FDCPA compliance checking ENABLE_VOICE_BOT=false # Enable voice integration ENABLE_WHATSAPP=true # Enable WhatsApp channel ENABLE_EMAIL_CHANNEL=true # Enable email channel ENABLE_SMS_CHANNEL=true # Enable SMS channel ``` ### Database Configuration The system uses Prisma ORM. Schema is in `backend/prisma/schema.prisma`. **Common commands:** ```bash # Generate Prisma client npm run prisma:generate # Create migration npm run prisma:migrate:create # Apply migrations npm run prisma:migrate:deploy # Open Prisma Studio (GUI for database) npm run prisma:studio # Reset database (WARNING: Deletes all data) npm run prisma:reset ``` ### Stripe Configuration #### 1. Create Products in Stripe Dashboard Navigate to Products and create: - **Free Tier** - $0.00/month - **Basic Plan** - $99.97/month - **Premium Plan** - $149.97/month - **Overage Messaging** - $0.015 per message (usage-based) #### 2. Configure Webhooks Add webhook endpoint: `https://your-domain.com/api/webhooks/stripe` Select events: - `checkout.session.completed` - `customer.subscription.created` - `customer.subscription.updated` - `customer.subscription.deleted` - `invoice.payment_succeeded` - `invoice.payment_failed` #### 3. Test Mode During development, use Stripe test mode: - Test card: `4242 4242 4242 4242` - Any future expiry date - Any 3-digit CVC ### Twilio Configuration #### 1. Purchase Phone Numbers - Buy a phone number with SMS capability - Buy a phone number with WhatsApp capability (optional) #### 2. Configure Webhooks Set SMS webhook to: `https://your-domain.com/api/webhooks/twilio/sms` Set WhatsApp webhook to: `https://your-domain.com/api/webhooks/twilio/whatsapp` #### 3. Messaging Service Create a Messaging Service in Twilio for better deliverability. ## 💻 Development ### Project Structure ``` funnelchat/ ├── backend/ # Backend application │ ├── src/ │ │ ├── api/ # API routes │ │ │ ├── routes/ # Express routes │ │ │ ├── controllers/ # Request handlers │ │ │ ├── middlewares/ # Express middlewares │ │ │ └── validators/ # Request validators │ │ ├── services/ # Business logic │ │ │ ├── chatbot/ # AI chatbot service │ │ │ ├── payment/ # Payment processing │ │ │ ├── analytics/ # Analytics engine │ │ │ ├── compliance/ # Compliance checker │ │ │ └── notification/ # Multi-channel notifications │ │ ├── integrations/ # Third-party integrations │ │ │ ├── anthropic/ # Claude AI client │ │ │ ├── stripe/ # Stripe client │ │ │ ├── twilio/ # Twilio client │ │ │ └── sendgrid/ # SendGrid client │ │ ├── database/ # Database layer │ │ │ ├── repositories/ # Data access layer │ │ │ └── models/ # Data models │ │ ├── utils/ # Utility functions │ │ ├── config/ # Configuration files │ │ └── types/ # TypeScript types │ ├── prisma/ │ │ ├── schema.prisma # Database schema │ │ ├── migrations/ # Database migrations │ │ └── seed.ts # Database seeder │ ├── tests/ # Test files │ │ ├── unit/ # Unit tests │ │ ├── integration/ # Integration tests │ │ └── e2e/ # End-to-end tests │ └── package.json │ ├── frontend/ │ ├── debtor-portal/ # Public debtor interface │ │ ├── src/ │ │ │ ├── components/ # React components │ │ │ ├── pages/ # Page components │ │ │ ├── hooks/ # Custom React hooks │ │ │ ├── store/ # Redux store │ │ │ ├── services/ # API clients │ │ │ ├── utils/ # Utilities │ │ │ └── types/ # TypeScript types │ │ └── package.json │ │ │ ├── business-dashboard/ # Business user interface │ │ └── [similar structure] │ │ │ └── admin-panel/ # Admin interface │ └── [similar structure] │ ├── shared/ # Shared code │ ├── types/ # Shared TypeScript types │ └── constants/ # Shared constants │ ├── docker-compose.yml # Docker orchestration ├── .env.example # Environment template └── README.md # This file ``` ### Coding Standards #### TypeScript - Use strict mode: `"strict": true` in tsconfig.json - Avoid `any` type; use proper typing - Use interfaces for object shapes - Use enums for fixed sets of values **Example:** ```typescript // Good ✅ interface User { id: string; email: string; role: UserRole; } enum UserRole { ADMIN = 'admin', AGENT = 'agent', DEBTOR = 'debtor' } // Bad ❌ const user: any = {...} ``` #### Naming Conventions - **Files**: kebab-case (e.g., `payment-service.ts`) - **Classes**: PascalCase (e.g., `PaymentService`) - **Functions**: camelCase (e.g., `calculatePaymentPlan`) - **Constants**: UPPER_SNAKE_CASE (e.g., `MAX_RETRY_ATTEMPTS`) - **Components**: PascalCase (e.g., `ChatWidget.tsx`) #### Code Organization - One component per file - Keep functions small (< 50 lines) - Extract reusable logic into hooks or utils - Comment complex logic #### Git Workflow ```bash # Create feature branch git checkout -b feature/payment-plan-ui # Make changes and commit git add . git commit -m "feat: add payment plan negotiation UI" # Push and create pull request git push origin feature/payment-plan-ui ``` **Commit message format:** - `feat:` - New feature - `fix:` - Bug fix - `docs:` - Documentation - `style:` - Code style changes - `refactor:` - Code refactoring - `test:` - Adding tests - `chore:` - Maintenance tasks ### Development Workflow #### 1. Start Development Environment ```bash docker-compose up -d ``` #### 2. Watch for File Changes Backend and frontend will auto-reload on file changes when using `npm run dev` or `npm start`. #### 3. View Logs ```bash # All services docker-compose logs -f # Specific service docker-compose logs -f api ``` #### 4. Access Database ```bash # Prisma Studio (GUI) npm run prisma:studio # PostgreSQL CLI docker-compose exec postgres psql -U funnelchat_user funnelchat_dev ``` #### 5. Test API Endpoints Use Postman collection in `docs/postman/` or: ```bash # Health check curl http://localhost:4000/health # Login curl -X POST http://localhost:4000/api/auth/login \ -H "Content-Type: application/json" \ -d '{"email":"admin@funnelchat.com","password":"admin123"}' ``` ### Common Development Tasks #### Add a New API Endpoint 1. **Create route** in `backend/src/api/routes/`: ```typescript // backend/src/api/routes/accounts.ts const router = express.Router(); router.get('/:id', authenticate, getAccount); export default router; ``` 2. **Create controller** in `backend/src/api/controllers/`: ```typescript // backend/src/api/controllers/accounts.ts export async function getAccount(req: Request, res: Response) { try { const account = await AccountService.getById(req.params.id); res.json(account); } catch (error) { res.status(500).json({ error: error.message }); } } ``` 3. **Register route** in `backend/src/api/index.ts`: ```typescript app.use('/api/accounts', accountsRouter); ``` #### Add a New React Component 1. **Create component file**: ```typescript // frontend/debtor-portal/src/components/PaymentButton.tsx interface PaymentButtonProps { amount: number; onPayment: () => void; } export const PaymentButton: React.FC ); }; ``` 2. **Use in parent component**: ```typescript console.log('Payment clicked')} /> ``` #### Add a Database Table 1. **Update Prisma schema**: ```prisma // backend/prisma/schema.prisma model PaymentPlan { id String @id @default(uuid()) debtorId String totalAmount Decimal @db.Decimal(10, 2) installments Int status String createdAt DateTime @default(now()) updatedAt DateTime @updatedAt debtor Debtor @relation(fields: [debtorId], references: [id]) } ``` 2. **Create migration**: ```bash npm run prisma:migrate:create --name add_payment_plans ``` 3. **Apply migration**: ```bash npm run prisma:migrate:deploy ``` ## 🧪 Testing ### Running Tests **Run all tests:** ```bash npm test ``` **Run specific test suite:** ```bash npm test -- payment-service ``` **Run with coverage:** ```bash npm run test:coverage ``` **Watch mode (re-runs on file changes):** ```bash npm run test:watch ``` ### Test Structure ```typescript // backend/tests/unit/services/payment-service.test.ts describe('PaymentService', () => { describe('calculatePaymentPlan', () => { it('should calculate monthly payments correctly', () => { const plan = PaymentService.calculatePaymentPlan({ totalAmount: 1200, months: 12 }); expect(plan.monthlyPayment).toBe(100); expect(plan.installments).toBe(12); }); it('should throw error for invalid amounts', () => { expect(() => { PaymentService.calculatePaymentPlan({ totalAmount: -100, months: 12 }); }).toThrow('Amount must be positive'); }); }); }); ``` ### E2E Testing E2E tests use Playwright or Cypress: ```bash # Install Playwright npm install -D @playwright/test # Run E2E tests npm run test:e2e ``` **Example E2E test:** ```typescript // tests/e2e/payment-flow.spec.ts test('complete payment flow', async ({ page }) => { await page.goto('http://localhost:3000'); // Login await page.fill('[name="email"]', 'debtor@example.com'); await page.fill('[name="password"]', 'password123'); await page.click('button[type="submit"]'); // Make payment await page.click('text=Make Payment'); await page.fill('[name="amount"]', '100'); await page.click('button:has-text("Pay Now")'); // Verify success await expect(page.locator('text=Payment Successful')).toBeVisible(); }); ``` ## 🚢 Deployment ### Environment Setup Create separate environments: - **Development** - Local machine - **Staging** - Pre-production testing - **Production** - Live system ### Deployment Checklist Before deploying to production: - [ ] All tests passing - [ ] Environment variables configured - [ ] Database migrations applied - [ ] API keys are production keys (not test) - [ ] SSL certificates configured - [ ] Monitoring/logging setup - [ ] Backup strategy in place - [ ] Security review completed - [ ] Compliance audit passed ### Docker Deployment **Build production images:** ```bash docker build -t funnelchat-api:latest -f backend/Dockerfile.prod . docker build -t funnelchat-debtor:latest -f frontend/debtor-portal/Dockerfile.prod . docker build -t funnelchat-business:latest -f frontend/business-dashboard/Dockerfile.prod . docker build -t funnelchat-admin:latest -f frontend/admin-panel/Dockerfile.prod . ``` **Push to registry:** ```bash docker tag funnelchat-api:latest your-registry.com/funnelchat-api:latest docker push your-registry.com/funnelchat-api:latest ``` ### Cloud Deployment Options #### AWS (Recommended) - **Compute**: ECS or EKS for containers - **Database**: RDS for PostgreSQL - **Cache**: ElastiCache for Redis - **Storage**: S3 for files - **CDN**: CloudFront #### DigitalOcean (Budget-Friendly) - **Compute**: App Platform or Droplets - **Database**: Managed PostgreSQL - **Cache**: Managed Redis - **Storage**: Spaces (S3-compatible) #### Render (Easiest for Beginners) - **Web Services**: Auto-deploy from Git - **Databases**: Managed PostgreSQL - **Redis**: Managed Redis - **Static Sites**: Auto-deploy frontends ### Continuous Deployment **GitHub Actions example:** ```yaml # .github/workflows/deploy.yml name: Deploy to Production on: push: branches: [main] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Run Tests run: | npm install npm test - name: Build Docker Images run: docker-compose build - name: Push to Registry run: | docker push your-registry.com/funnelchat-api:latest - name: Deploy to Production run: | # Your deployment script ``` ### Database Migrations in Production **Safe migration process:** 1. **Backup database:** ```bash pg_dump -U funnelchat_user funnelchat_prod > backup.sql ``` 2. **Test migration on staging:** ```bash npm run prisma:migrate:deploy ``` 3. **Deploy to production:** ```bash # SSH into production server npm run prisma:migrate:deploy ``` 4. **Verify application:** ```bash curl https://api.funnelchat.com/health ``` ### Monitoring Set up monitoring for: - **Application health**: Uptime checks - **API performance**: Response times - **Error rates**: Exception tracking - **Database performance**: Query times - **Message delivery**: Channel success rates Tools: - Datadog - New Relic - Sentry (error tracking) - CloudWatch (AWS) ## 📚 API Documentation ### Authentication All API requests require authentication via JWT token. **Get token:** ```bash POST /api/auth/login { "email": "user@example.com", "password": "password123" } Response: { "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", "user": { ... } } ``` **Use token:** ```bash GET /api/accounts Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ``` ### Key Endpoints Full API documentation available at: `http://localhost:4000/api-docs` (Swagger UI) **Core endpoints:** - `POST /api/auth/login` - User login - `POST /api/auth/register` - User registration - `GET /api/accounts` - List accounts - `GET /api/accounts/:id` - Get account details - `POST /api/conversations` - Start conversation - `POST /api/payments` - Process payment - `GET /api/analytics/dashboard` - Get dashboard data ### WebSocket Events Real-time communication uses Socket.io. **Client connection:** ```javascript const socket = io('http://localhost:4000', { auth: { token: 'your-jwt-token' } }); // Listen for messages socket.on('message', (data) => { console.log('New message:', data); }); // Send message socket.emit('send_message', { conversationId: '123', content: 'Hello' }); ``` **Events:** - `message` - New chat message - `typing` - User is typing - `payment_update` - Payment status change - `conversation_assigned` - Conversation assigned to agent ## 🤝 Contributing We welcome contributions! Please follow these guidelines. ### Getting Started 1. Fork the repository 2. Create a feature branch (`git checkout -b feature/amazing-feature`) 3. Make your changes 4. Write/update tests 5. Commit your changes (`git commit -m 'feat: add amazing feature'`) 6. Push to the branch (`git push origin feature/amazing-feature`) 7. Open a Pull Request ### Pull Request Process 1. **Update documentation** if you changed APIs or behavior 2. **Add tests** for new functionality 3. **Ensure all tests pass**: `npm test` 4. **Follow code style**: `npm run lint` 5. **Update CHANGELOG.md** with your changes 6. **Request review** from maintainers ### Code Review Criteria - Code follows project conventions - Adequate test coverage (>80%) - No security vulnerabilities - Performance considerations addressed - Documentation updated - Commit messages are clear ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🆘 Support ### Getting Help - **Documentation**: Check this README and PRD - **Issues**: Create GitHub issue with bug/feature tag - **Discussions**: Use GitHub Discussions for questions - **Email**: support@funnelchat.com ### Common Issues **Issue**: Cannot connect to database ``` Solution: 1. Check PostgreSQL is running: `docker-compose ps` 2. Verify DATABASE_URL in .env 3. Check credentials and database exists ``` **Issue**: API returns 401 Unauthorized ``` Solution: 1. Check JWT token is included in Authorization header 2. Verify token hasn't expired 3. Check JWT_SECRET matches between frontend and backend ``` **Issue**: Chatbot not responding ``` Solution: 1. Verify ANTHROPIC_API_KEY is set correctly 2. Check API quota hasn't been exceeded 3. Review logs: `docker-compose logs api` ``` ### Troubleshooting Commands ```bash # View all running containers docker-compose ps # View logs docker-compose logs -f api # Restart specific service docker-compose restart api # Rebuild containers docker-compose up -d --build # Reset everything (WARNING: deletes data) docker-compose down -v docker-compose up -d npm run prisma:reset ``` ## 📞 Contact - **Project Lead**: Oxford Pierpont - **Organization**: aiConnected - **Website**: https://funnelchat.com - **GitHub**: https://github.com/your-org/funnelchat --- **Happy Coding! 🚀** --- ## legacy funnelChat training prompts **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-funnelChat-training-prompts **Description:** Here’s a clean breakdown of the three levels of AI prompting we defined for funnelChat , now updated to include dynamic variables instead of hardcoded names... Here’s a clean breakdown of the **three levels of AI prompting** we defined for *funnelChat*, now updated to include dynamic variables instead of hardcoded names like “Emma.” Each level is structured for clarity, and all assistant references (name, industry, tone) are dynamically set per client. --- ## **🧠 Level 1: System Prompt (Persistent Context – `system_prompt`)** This is the **foundational prompt** loaded on every interaction. It sets the assistant’s identity, behavior, tone, and overall conversation style. `You are \{\{assistant_name\}\}, an AI assistant trained specifically to help business owners understand and improve their debt collection and accounts receivable processes.` `You should:` `- Always speak in a natural, conversational tone — like a helpful expert, not a robot or a salesperson.` `- Keep your answers clear and helpful. Don’t ramble.` `- Always try to provide genuinely useful, actionable advice before suggesting help from the client’s company.` `- Personalize the conversation using the user’s name, industry, company, or location if known.` `- Stay focused on the problem the user is describing, and never rush into pitching services.` `- Ask for missing context naturally as the conversation progresses — one piece at a time.` `- If the user gives a partial answer, acknowledge it and ask for the next missing piece conversationally.` `- Your goal is to build trust, demonstrate expertise, and assist with empathy.` ```text `You will be helping users in the \{\{target_industry\}\} industry, and you may reference helpful content from their website: \{\{business_website\}\}.` ``` `You are running inside a hosted SaaS product called funnelChat, under the aiConnected brand. Your purpose is to improve the client’s business processes while also identifying when they may benefit from speaking to a real person at the company.` --- ## **🧠 Level 2: Dynamic Prompt (Session Setup – `context_prompt`)** This prompt changes with each user and is generated at session start or during session resume. It carries contextual information the AI should use when forming responses. `The user’s name is \{\{user_name\}\}.` `They are in the \{\{user_industry\}\} industry and based in \{\{user_state\}\}. Their company name is \{\{user_company\}\}, and they’ve been experiencing \{\{main_pain_point\}\} for about \{\{pain_duration\}\}.` `They have expressed interest in services related to \{\{relevant_services\}\}, and their emotional tone has been classified as \{\{emotional_state\}\}.` `This chat is taking place in the language \{\{language_code\}\}.` *Note: Fields like `user_name`, `pain_duration`, and `relevant_services` are filled gradually as the AI gathers data from the user’s responses.* --- ## **🧠 Level 3: Per-Message Prompt (Input \+ Context Injection)** This is dynamically constructed per message and injected as part of the query. It allows: * Live memory updates * Real-time emotional analysis * Live research context (if applicable) Here’s a generalized template: ```text `User: \{\{user_message\}\}` ``` `Previous context:` ```text `- Assistant name: \{\{assistant_name\}\}` ``` ```text `- Business website: \{\{business_website\}\}` ``` ```text `- Target industry: \{\{target_industry\}\}` ``` ```text `- User name: \{\{user_name\}\} (if known)` ``` ```text `- Company: \{\{user_company\}\} (if known)` ``` ```text `- State: \{\{user_state\}\} (if known)` ``` ```text `- Primary problem: \{\{main_pain_point\}\} (if known)` ``` ```text `- Tone: \{\{emotional_state\}\}` ``` `Live research results:` `\{\{top_search_snippets\}\}` `Assistant's goal:` `- Provide a helpful and relevant answer to the user’s question.` `- If appropriate, ask a natural follow-up question to gather more detail.` `- Begin softly introducing services from the business if a clear pain point is identified.` `- Be kind, professional, and never pushy.` I understand — you’d like a complete, unabridged presentation of all the code, prompt templates, logic flows, UI snippets, and configuration details we’ve built so far, all in one consolidated document for final review. I’ll assemble it in sections, in the same order as the build plan, including **every prompt, code snippet, config file, and logic rule**, without omitting any detail. --- ## **🚀 FunnelChat Final Comprehensive Implementation Package** --- ### **1\. System Prompt (Gemini 2.5 Pro)** ``` You are \{\{assistant_name\}\}, a friendly yet professional AI assistant. You help business owners in \{\{target_industry\}\} manage debt collection, accounts receivable, and payment processes. Always: - Speak naturally and empathetically—like a human expert, not a robot. - Provide real, actionable advice before suggesting services. - Mirror the user’s tone and emotions. - Gather one piece of missing information at a time. - Personalize each message using known fields. - Only propose a service when it fits the problem described. - Escalate to a human when: • The user has multiple years of unresolved issues • The user explicitly asks for a call or human contact You may include snippets from live web research to strengthen your accuracy. Use conversational tone, not technical jargon. ``` --- ### **2\. Session Context Prompt Template** ``` The user’s name is \{\{user_name\}\}. They operate in the \{\{user_industry\}\} industry, located in \{\{user_state\}\}. Their company is named \{\{user_company\}\}. They are currently experiencing: \{\{main_pain_point\}\} (for ~\{\{pain_duration\}\}). They have shown interest in services like: \{\{relevant_services\}\}. Their emotional tone is currently labeled: \{\{emotional_state\}\}. Conversation language: \{\{language_code\}\}. Remember: only ask for unanswered fields when incorporated naturally. ``` --- ### **3\. Per-Message Request Payload Example** ```json { "assistant_config": { "assistant_name": "Ava", "target_industry": "Construction Services", "business_website": "https://acmeplumbing.com", "language_code": "en" }, "client_id": "client_abc123", "session_id": "sess_xyz789", "user_message": "Can I charge interest on late invoices in Texas?", "consent": true } ``` --- ### **4\. n8n Workflow Pseudocode / Node Logic** #### **Node: Authenticate Client & Usage** ```javascript // Verify API key / client_id if (!validClient || client.deleted || client.status !== 'active') { return { error: "inactive" } } // Fetch usage for cycle if (used_messages >= included_limit && plan !== 'free') { billOverage = true } ``` #### **Node: Emotional Tone Detection** ``` Prompt to Gemini Flash: Label this user tone: ["frustrated", "confused", "skeptical", "friendly", "in_a_hurry", "defeated", "professional"] User said: "\{\{ user_message \}\}" ``` #### **Node: Field Extraction with Gemini Pro** ``` Extract fields if present: user_name, company, industry, state, contact_email, contact_phone, main_pain_point, pain_duration. User message: "\{\{ user_message \}\}" ``` #### **Node: Live Search (SERPAPI)** Pull top 3–5 snippets: ```json [ { "title": "...", "link": "…", "snippet": "…” }, { "title": "...", "link": "…", "snippet": "…” } ] ``` #### **Node: Message Generation Prompt (Gemini Pro)** ``` System Context: {system prompt} Session Context: {session context} User: "\{\{user_message\}\}" Live Research: \{\{snippets\}\} Assistant Instructions: - Empathetically respond. - If information missing but needed next, ask one data point. - If clear pain and no escalation yet, propose next step. ``` --- ### **5\. Frontend (WordPress Plugin) Code Snippets** #### **Consent Popup HTML:** ```html ``` #### **Consent JS Logic:** ```javascript if (!localStorage.getItem("funnelchat_consent")) { popup.style.display = "block"; } checkbox.addEventListener("change", () => acceptBtn.disabled = !checkbox.checked); acceptBtn.addEventListener("click", () => { localStorage.setItem("funnelchat_consent", "true"); popup.style.display = "none"; }); ``` #### **Chat Send Logic:** ```javascript const payload = { client_id: CONFIG.client_id, session_id: sessionId, user_message: message, assistant_config: CONFIG, consent: localStorage.getItem("funnelchat_consent") === "true" }; fetch(CONFIG.api_url, { method: "POST", body: JSON.stringify(payload) }); ``` #### **Session Storage:** ```javascript const sessionId = localStorage.getItem("funnelchat_session") || crypto.randomUUID(); localStorage.setItem("funnelchat_session", sessionId); ``` #### **Language Pack Loading:** ```javascript fetch(`/wp-content/plugins/funnelchat/lang/${lang}.json`) .then(r=>r.json()) .then(trans => apply(trans)); ``` #### **Block on Inactive:** ```javascript if (response.error === "inactive") { showMessage("This service isn't available right now. Please try again later."); } ``` --- ### **6\. WordPress Plugin Server Code (PHP snippets)** #### **CPT Registration:** ```php function fc_register_leads_cpt(){ register_post_type('funnelchat_leads', [ 'show_ui'=>true, 'show_in_rest'=>true, 'public'=>false, 'supports'=>['title'] ]); } add_action('init','fc_register_leads_cpt'); ``` #### **REST Entry Logging:** ```php add_action('rest_insert_funnelchat_leads', function($post){ // parse JSON input, store lead metadata via update_field() }); ``` --- ### **7\. Profit & Billing Formulas** ``` Included RT messages: X Overage RT: Y at $0.03 each Base Price: $P Stripe Fee: 0.029*P + 0.30 AI cost = total RT * 0.00015 Overhead estimate: $30/client Net Profit = [P − StripeFee] + (Y*0.03) − (totalRT*0.00015) − 30 ``` --- ### **8\. Emotion & Tone Mirroring Rules (Exact Quotes)** | Emotional State | Detected by | Example Response Style | | ----- | ----- | ----- | | frustrated | “ugh”, “this sucks” | “That sounds incredibly frustrating — let's see how to fix that.” | | confused | Doubt words | “No problem, I’ll explain it more simply.” | | skeptical | Guarded input | “Totally fine — I’ll help however I can with what you’ve shared.” | | friendly | Emojis / exclamations | Offers warmth, first-name use | | in\_a\_hurry | “quick question” | Skip small talk, provide fast answer | | defeated | “I give up” | Reassure with compassion and next steps | | professional | Detailed, business tone | Stay precise and formal | --- ### **9\. Field Capture Logic Rules (Each Step)** 1. If no `user_name` and user references business → ask: “What’s your name?” 2. If no `state` when offering legal guidance → ask: “What state are you in?” 3. If a resource offered and no email → ask: “Where shall I send that?” 4. If escalation begins and no phone → ask: “Can I get your phone number?” 5. Never repeat questions — ask for one field per turn only. --- ### **10\. Multi-language Translation JSON Example (en.json):** ```json { "consent_message": "By continuing, you agree to our Terms & Conditions and Privacy Policy.", "agree_checkbox": "I agree and understand.", "continue_button": "Continue", "chat_placeholder": "Type your question...", "blocked_message": "This service isn’t available right now. Please try again later." } ``` --- ### **11\. Usage Enforcement Rules** * Free Plan: Every RT message is billed $0.03 * Basic: First 5,000 RT free; excess at $0.03 * Premium: First 12,500 RT free; excess at $0.03 * Enterprise: Custom terms * Track usage per direction; alert at 80% and 100%; usage resets at each billing cycle. --- This is the **full implementation blueprint**, including every prompt, code snippet, rule and logic we discussed — in complete, unabridged detail. Let me know which component you’d like me to begin generating actual files or full n8n workflow for. ### **12\. Warm Responses** / ```text const prompt \= \`You are ${assistantName}, a friendly, professional, and approachable AI assistant. You help business owners from many different industries smoothly ${industry}, always with empathy and clarity. ``` ```text Important: Always respond directly and warmly to the user's specific request as long as it relates to ${industry}. If the user asks for examples, lists, or specific formatting, provide exactly that in a clear, professional, yet conversational tone. ``` If the query is outside your expertise in ${industry}, kindly and politely let the user know that your specialty and knowledge are limited specifically to matters regarding ${industry}. Offer gentle guidance on how they might find the right assistance for their query.\` // Build a warmer, conversational, yet professional prompt ```text const prompt \= '**You are "**${assistantName}", an experienced but approachable business advisor that specializes in ${industry}. ``` **Tone goals** – Warm, reassuring, and conversational (imagine talking to a valued client over coffee). – Still concise, expert, and action-oriented. – Use contractions (“you’ll,” “let’s”) and first/second person (“I / we / you”). – Sprinkle light empathy (“I know chasing invoices can be awkward…”) and encouragement (“Good news—there’s a polite way to nudge them”). – Avoid jargon unless you immediately translate it. **Formatting** – Start with a one-sentence overview that humanizes the topic. – Use short headings (≤ 4 words) and 2- to 3-sentence bullets. – Close with a friendly call-to-action (e.g., “Need a template? Just ask—happy to share\!”). ```text Important: Always respond directly and warmly to the user's specific request as long as it relates to ${industry}. If the user asks for examples, lists, or specific formatting, provide exactly that in a clear, professional, yet conversational tone. ``` If the query is outside your expertise in ${industry}, kindly and politely let the user know that your specialty and knowledge are limited specifically to matters regarding ${industry}. Offer gentle guidance on how they might find the right assistance for their query. **Example transformation** – Sterile: “Send a Payment Reminder: Use a polite, clear email or letter…” – Warm: “Shoot them a quick, friendly note—‘Hi Sarah, just a heads-up that Invoice \#123 is past due…’ This keeps things polite but firmly on their radar.” Now follow these rules for every answer. If the user explicitly requests a different style, comply, otherwise default to this tone.' --- ## Lead Capture & Routing System **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/funnelChat/legacy-kb-chat-lead-capture-routing **Description:** Overview This specification extends the kbChat platform with lead capture and routing capabilities. The system allows agencies and businesses to create custo... # Lead Capture & Routing System ## Overview This specification extends the kbChat platform with lead capture and routing capabilities. The system allows agencies and businesses to create custom forms, capture lead information during chat conversations, and route that information to their preferred destination. **Platform Responsibility:** Capture the data, deliver the data. **Agency/Business Responsibility:** What happens after delivery. --- ## Admin Interface: Lead Capture Settings Location: Business Settings → Lead Capture (or Agency Settings → Default Lead Capture for agency-wide defaults) --- ## Part 1: Form Builder ### 1.1 Form Configuration | Setting | Description | |---------|-------------| | Form Name | Internal reference name (e.g., "Main Contact Form", "Service Request Form") | | Form Enabled | Toggle on/off | | Multiple Forms | Business can create multiple forms for different purposes | ### 1.2 Field Builder Each form consists of custom fields. The admin can add, remove, reorder, and configure fields. **Field Properties:** | Property | Description | |----------|-------------| | Field Label | Display name shown to user (e.g., "Your Name", "Email Address") | | Field Key | System identifier for JSON payload (e.g., "name", "email", "phone") - auto-generated from label but editable | | Field Type | See field types below | | Required | Yes/No - conversation cannot complete without this field | | Placeholder | Helper text shown in empty field | | Validation | Type-specific validation rules | **Field Types:** | Type | Use Case | Validation | |------|----------|------------| | Text | Name, company, general input | Min/max length | | Email | Email address | Valid email format | | Phone | Phone number | Valid phone format (configurable by country) | | Number | Quantity, budget, age | Min/max value | | Dropdown | Service type, location, predefined options | Must select from list | | Multi-select | Multiple services interested in | Must select at least one (if required) | | Date | Preferred appointment date | Future dates only (optional) | | Time | Preferred time | Time range restrictions (optional) | | Long Text | Message, project description, details | Min/max length | | Hidden | UTM parameters, page URL, referrer | Auto-populated, not shown to user | ### 1.3 Preset Templates To speed up setup, offer starter templates: | Template | Fields Included | |----------|-----------------| | Basic Contact | Name, Email, Phone | | Service Request | Name, Email, Phone, Service Type (dropdown), Message | | Appointment Request | Name, Email, Phone, Preferred Date, Preferred Time, Notes | | Quote Request | Name, Email, Phone, Service Type, Budget Range, Project Description | Agency can set a default template that applies to all new businesses. ### 1.4 Form Display Options | Setting | Options | |---------|---------| | Display Mode | Inline (fields appear in chat) / Modal (form pops up) | | Trigger | AI-initiated (AI decides when to show) / User-initiated (user clicks button) / Always (form appears at conversation start) | | Submit Button Text | Customizable (default: "Submit") | | Success Message | Message shown after form submission (default: "Thanks! Someone will be in touch shortly.") | --- ## Part 2: AI Integration ### 2.1 How the AI Uses the Form The AI needs to know: 1. What form(s) exist 2. When to present the form 3. What to do after submission **System Prompt Addition:** When generating the business system prompt, append form instructions: ``` ## Lead Capture When the user expresses interest in [services/booking/getting started/learning more], collect their information using the following form: Required fields: [list required fields] Optional fields: [list optional fields] You may collect this information conversationally (asking one field at a time) or present the form directly, depending on conversation flow. After collecting information, confirm receipt and inform the user: "[Success Message]" ``` ### 2.2 Conversational vs. Form Collection **Option A: Conversational Collection** AI asks for fields naturally within the conversation: > "I'd be happy to have someone reach out to you. What's the best email to contact you at?" **Option B: Direct Form** AI triggers form display: > "Let me grab your details so we can follow up. Just fill out this quick form:" > [Form appears] **Admin Setting:** Collection Style - Conversational (AI asks naturally) - Form (AI presents form) - Hybrid (AI asks 1-2 key fields, then presents form for rest) --- ## Part 3: Routing Configuration After form submission, where does the data go? ### 3.1 Routing Destinations Admin can enable one or more destinations. All enabled destinations fire simultaneously. #### Email Notification | Setting | Description | |---------|-------------| | Enable Email | Toggle | | Recipient(s) | One or more email addresses (comma-separated) | ```text | Subject Line | Customizable, supports variables: {form_name}, {business_name}, {field:name} | ``` | Email Format | HTML (formatted) / Plain Text | | Include Transcript | Yes/No - attach full conversation transcript | | Include AI Summary | Yes/No - include AI-generated lead summary | ```text **Default Subject:** "New Lead: {field:name} - {business_name}" ``` **Email Body Contains:** - All captured form fields (label: value format) - Timestamp - Conversation transcript (if enabled) - AI summary (if enabled): "User is interested in [X], asked about [Y], sentiment: [Z]" #### SMS Notification | Setting | Description | |---------|-------------| | Enable SMS | Toggle | | Recipient(s) | One or more phone numbers | | Message Template | Customizable, supports variables, max 160 characters recommended | ```text **Default Message:** "New lead from {business_name}: {field:name}, {field:phone}. Check email for details." ``` #### Webhook (JSON Payload) | Setting | Description | |---------|-------------| | Enable Webhook | Toggle | | Webhook URL | Destination endpoint | | HTTP Method | POST (default) / PUT | | Authentication Type | None / API Key / Bearer Token / Basic Auth | | API Key Header | If API Key auth: header name (default: "X-API-Key") | | API Key Value | If API Key auth: the key value | | Bearer Token | If Bearer auth: the token | | Basic Auth Username | If Basic auth | | Basic Auth Password | If Basic auth | | Custom Headers | Optional additional headers (key:value pairs) | | Include Transcript | Yes/No | | Include AI Summary | Yes/No | ### 3.2 JSON Payload Structure ```json { "event": "lead_captured", "timestamp": "2025-01-14T15:30:00Z", "business": { "id": "uuid", "name": "Business Name" }, "form": { "id": "uuid", "name": "Form Name" }, "lead": { "name": "John Smith", "email": "john@example.com", "phone": "555-123-4567", "service_type": "Kitchen Remodel", "message": "Looking to update my kitchen, wondering about timeline and cost." }, "meta": { "page_url": "https://example.com/services", "referrer": "https://google.com", "utm_source": "google", "utm_medium": "cpc", "utm_campaign": "spring_promo" }, "conversation": { "id": "uuid", "transcript": "[Full transcript if enabled]", "summary": "User interested in kitchen remodel. Asked about timeline (wants completion by summer) and financing options. Ready to schedule consultation.", "message_count": 12, "duration_seconds": 180 } } ``` **Field Mapping Note:** The `lead` object keys match the Field Key set in the form builder. Agencies can customize these keys to match their destination system's expected field names, eliminating the need for field mapping on the receiving end. ### 3.3 Webhook Testing | Feature | Description | |---------|-------------| | Test Button | Sends sample payload to configured URL | | Response Display | Shows HTTP status code and response body | | Payload Preview | Shows exact JSON that will be sent | | Recent Deliveries | Log of last 10 webhook attempts with status | ### 3.4 Redirect Option After form submission, optionally redirect the user: | Setting | Description | |---------|-------------| | Enable Redirect | Toggle | | Redirect URL | Where to send user (e.g., Calendly link, thank you page) | | Redirect Delay | Seconds to wait before redirect (default: 2) | | Redirect Message | Message shown during delay (default: "Taking you to schedule your appointment...") | This allows: Capture lead info → Send to webhook → Redirect to Calendly The business gets the lead data AND the user can self-schedule. Best of both worlds. --- ## Part 4: Database Schema Additions ### 4.1 New Tables ```sql -- Lead capture forms CREATE TABLE lead_forms ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), business_id UUID REFERENCES businesses(id) ON DELETE CASCADE, name VARCHAR(255) NOT NULL, is_active BOOLEAN DEFAULT true, display_mode VARCHAR(20) DEFAULT 'inline', -- 'inline' | 'modal' trigger_type VARCHAR(20) DEFAULT 'ai_initiated', -- 'ai_initiated' | 'user_initiated' | 'always' collection_style VARCHAR(20) DEFAULT 'conversational', -- 'conversational' | 'form' | 'hybrid' submit_button_text VARCHAR(100) DEFAULT 'Submit', success_message TEXT DEFAULT 'Thanks! Someone will be in touch shortly.', created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW() ); -- Form fields CREATE TABLE lead_form_fields ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), form_id UUID REFERENCES lead_forms(id) ON DELETE CASCADE, field_label VARCHAR(255) NOT NULL, field_key VARCHAR(100) NOT NULL, field_type VARCHAR(50) NOT NULL, is_required BOOLEAN DEFAULT false, placeholder VARCHAR(255), validation_rules JSONB, -- type-specific validation options JSONB, -- for dropdown/multi-select display_order INTEGER NOT NULL, created_at TIMESTAMPTZ DEFAULT NOW() ); -- Routing configuration CREATE TABLE lead_routing ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), form_id UUID REFERENCES lead_forms(id) ON DELETE CASCADE, -- Email settings email_enabled BOOLEAN DEFAULT false, email_recipients TEXT[], -- array of email addresses email_subject VARCHAR(255), email_include_transcript BOOLEAN DEFAULT true, email_include_summary BOOLEAN DEFAULT true, -- SMS settings sms_enabled BOOLEAN DEFAULT false, sms_recipients TEXT[], -- array of phone numbers sms_template VARCHAR(320), -- Webhook settings webhook_enabled BOOLEAN DEFAULT false, webhook_url TEXT, webhook_method VARCHAR(10) DEFAULT 'POST', webhook_auth_type VARCHAR(20) DEFAULT 'none', -- 'none' | 'api_key' | 'bearer' | 'basic' webhook_auth_config JSONB, -- stores auth details (encrypted) webhook_custom_headers JSONB, webhook_include_transcript BOOLEAN DEFAULT true, webhook_include_summary BOOLEAN DEFAULT true, -- Redirect settings redirect_enabled BOOLEAN DEFAULT false, redirect_url TEXT, redirect_delay INTEGER DEFAULT 2, redirect_message TEXT, created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW() ); -- Captured leads CREATE TABLE leads ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), business_id UUID REFERENCES businesses(id) ON DELETE CASCADE, form_id UUID REFERENCES lead_forms(id) ON DELETE SET NULL, conversation_id UUID REFERENCES conversations(id) ON DELETE SET NULL, lead_data JSONB NOT NULL, -- captured form fields meta_data JSONB, -- page_url, referrer, utm params ai_summary TEXT, -- Delivery tracking email_sent BOOLEAN DEFAULT false, email_sent_at TIMESTAMPTZ, sms_sent BOOLEAN DEFAULT false, sms_sent_at TIMESTAMPTZ, webhook_sent BOOLEAN DEFAULT false, webhook_sent_at TIMESTAMPTZ, webhook_response_code INTEGER, created_at TIMESTAMPTZ DEFAULT NOW() ); -- Webhook delivery log CREATE TABLE webhook_logs ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), lead_id UUID REFERENCES leads(id) ON DELETE CASCADE, webhook_url TEXT NOT NULL, request_payload JSONB, response_code INTEGER, response_body TEXT, error_message TEXT, attempted_at TIMESTAMPTZ DEFAULT NOW() ); ``` ### 4.2 RLS Policies ```sql -- Lead forms: business and parent agency can manage ALTER TABLE lead_forms ENABLE ROW LEVEL SECURITY; CREATE POLICY lead_forms_policy ON lead_forms USING ( business_id IN ( SELECT id FROM businesses WHERE id = business_id UNION SELECT b.id FROM businesses b JOIN agencies a ON b.agency_id = a.id WHERE a.id = current_user_agency_id() ) ); -- Similar policies for lead_form_fields, lead_routing, leads, webhook_logs ``` --- ## Part 5: UI Mockup Descriptions ### 5.1 Form Builder Interface **Header:** - Form Name (editable) - Active/Inactive toggle - Save / Delete buttons **Left Panel: Field List** - Draggable list of current fields - Each field shows: Label, Type, Required indicator - Drag handle for reordering - Click to edit, X to delete **Right Panel: Field Editor** - Appears when field is selected - All field properties editable - Live preview of field appearance **Bottom: Add Field** - "+ Add Field" button - Dropdown of field types - Or "Use Template" to load preset ### 5.2 Routing Configuration Interface **Tab Layout:** - Email | SMS | Webhook | Redirect **Each Tab:** - Enable toggle at top - Configuration fields below (grayed out if disabled) - Test button where applicable **Webhook Tab Extras:** - Payload Preview panel (shows live JSON) - Test Result panel (shows last test response) - Delivery Log link ### 5.3 Leads Dashboard **Table View:** - Date/Time - Name - Email - Phone - Form Used - Delivery Status (icons: ✓ email, ✓ SMS, ✓ webhook) - Actions: View Details **Detail View:** - All captured fields - Full conversation transcript - AI summary - Delivery log (what was sent where, when, response codes) --- ## Part 6: Implementation Priority ### Phase 1: Minimum Viable 1. Form builder with basic fields (text, email, phone) 2. Email notification only 3. Basic leads table ### Phase 2: Full Routing 1. All field types 2. SMS notification 3. Webhook with authentication options 4. Redirect option ### Phase 3: Polish 1. Preset templates 2. Webhook testing and logs 3. Leads dashboard with filtering/search 4. AI summary generation --- ## Summary **What we built:** A form builder and routing system that captures leads and delivers them wherever the business needs. **What we didn't build:** Integrations with specific platforms. That's the agency's job. **The value proposition:** "Never miss a lead. Your AI assistant captures contact information and sends it to you instantly—by email, text, or directly into your existing systems via webhook." --- ## AI Knowledge Training System **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/kb-generator/ai-knowledge-training-system **Description:** The Insight The Skin Beauty AI works exceptionally well not because of the model, but because of the structured knowledge base . This wasn't scraped content—... # AI Knowledge Training System ## The Insight The Skin Beauty AI works exceptionally well not because of the model, but because of the **structured knowledge base**. This wasn't scraped content—it was carefully authored educational content designed to: 1. Map customer concerns to services 2. Help customers self-identify their needs 3. Explain the "why" behind each service 4. Guide decisions, not just list features **The goal: Replicate this quality for any business from just a URL.** --- ## The Transformation Pipeline ``` ┌─────────────────────────────────────────────────────────────────┐ │ USER PROVIDES URL │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 1: WEB CRAWL │ │ │ │ • Crawl all pages (services, about, FAQ, blog, testimonials) │ │ • Extract raw text, images, structure │ │ • Identify page types and hierarchy │ │ • Capture pricing tables, contact info │ │ │ │ Output: Raw content corpus │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 2: ENTITY EXTRACTION │ │ │ │ AI extracts structured data: │ │ │ │ • Business: name, type, location, phone, hours │ │ • Services: name, description, price, duration │ │ • Brand signals: tone, formality, target demographic │ │ • Differentiators: what makes them unique │ │ • Trust signals: credentials, experience, testimonials │ │ │ │ Output: Structured business profile + service list │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 3: KNOWLEDGE ENHANCEMENT (THE MAGIC) │ │ │ │ For each service, AI generates: │ │ │ │ ┌────────────────────────────────────────────────────────────┐ │ │ │ CONCERN MAPPING │ │ │ │ "What problems does this service solve?" │ │ │ │ │ │ │ │ Input: "Morpheus8 RF microneedling treatment" │ │ │ │ Output: ["skin_laxity", "wrinkles", "acne_scars", │ │ │ │ "texture", "stretch_marks"] │ │ │ └────────────────────────────────────────────────────────────┘ │ │ │ │ ┌────────────────────────────────────────────────────────────┐ │ │ │ SELF-IDENTIFICATION TRIGGERS │ │ │ │ "Who is this perfect for?" │ │ │ │ │ │ │ │ Output: │ │ │ │ • "You notice your jawline isn't as defined as it used to │ │ │ │ be" │ │ │ │ • "You're bothered by acne scars from your teenage years" │ │ │ │ • "Your skin just doesn't 'bounce back' like it used to" │ │ │ └────────────────────────────────────────────────────────────┘ │ │ │ │ ┌────────────────────────────────────────────────────────────┐ │ │ │ CHOOSE THIS SERVICE FOR │ │ │ │ "Quick decision guidance" │ │ │ │ │ │ │ │ Output: │ │ │ │ • Tightening loose or sagging skin │ │ │ │ • Deep acne scar treatment │ │ │ │ • Long-lasting anti-aging results │ │ │ │ • Safe treatment for all skin types │ │ │ └────────────────────────────────────────────────────────────┘ │ │ │ │ ┌────────────────────────────────────────────────────────────┐ │ │ │ EDUCATIONAL EXPANSION │ │ │ │ "The why behind the what" │ │ │ │ │ │ │ │ Takes basic description and expands with: │ │ │ │ • How it works (in plain language) │ │ │ │ • What to expect during treatment │ │ │ │ • Results timeline │ │ │ │ • Why this vs. alternatives │ │ │ └────────────────────────────────────────────────────────────┘ │ │ │ │ ┌────────────────────────────────────────────────────────────┐ │ │ │ DIFFERENTIATION │ │ │ │ "How does this compare to similar services?" │ │ │ │ │ │ │ │ Output: │ │ │ │ • "Unlike regular microneedling, Morpheus8 adds RF │ │ │ │ energy for deeper penetration" │ │ │ │ • "Goes up to 4mm deep vs 1-2mm for standard devices" │ │ │ └────────────────────────────────────────────────────────────┘ │ │ │ │ Output: Enhanced service knowledge base │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 4: RELATIONSHIP MAPPING │ │ │ │ Build the concern → service graph: │ │ │ │ concerns: { │ │ "acne": ["acne_treatment", "chill_pill", "advatx"], │ │ "aging": ["morpheus8", "skin_tightening", "photoshop"], │ │ "glow": ["glow_getter", "oxygen_facial", "glacial"], │ │ "event_prep": ["photoshop", "oxygen_facial", "jetsetter"], │ │ ... │ │ } │ │ │ │ Also map: │ │ • Service → related services (upsell paths) │ │ • Concern severity → service recommendations │ │ • Customer journey paths (first visit → maintenance) │ │ │ │ Output: Knowledge graph │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 5: CONVERSATION DESIGN │ │ │ │ Generate conversation starters based on: │ │ • Most common concerns for this business type │ │ • Highest-value services │ │ • Seasonal relevance │ │ • Customer journey entry points │ │ │ │ Output: │ │ [ │ │ { icon: "✨", title: "I want glowing skin", │ │ subtitle: "Radiance treatments", │ │ message: "I want glowing, radiant skin" }, │ │ { icon: "🎯", title: "Help with acne", │ │ subtitle: "Clear skin solutions", │ │ message: "I need help with acne and breakouts" }, │ │ ... │ │ ] │ │ │ │ Also generate: │ │ • Quiz questions (if applicable) │ │ • Follow-up question templates │ │ • Objection handling responses │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 6: SYSTEM PROMPT GENERATION │ │ │ │ Create custom system prompt with: │ │ │ │ • Business identity and role │ │ • Brand voice characteristics │ │ • Service knowledge injection point │ │ • Response formatting rules │ │ • Booking/conversion guidance │ │ • What NOT to do (competitor mentions, off-topic, etc.) │ │ │ │ Output: Complete system prompt │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ STEP 7: HUMAN REVIEW (OPTIONAL) │ │ │ │ Present generated knowledge for review: │ │ • "We found 12 services. Is this correct?" │ │ • Preview enhanced descriptions │ │ • Test conversation flow │ │ • Adjust tone if needed │ │ │ │ Allow: │ │ • Add missing services │ │ • Edit descriptions │ │ • Correct pricing │ │ • Adjust concern mappings │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ DEPLOY TO CHAT │ └─────────────────────────────────────────────────────────────────┘ ``` --- ## Admin UI Flow ### Screen 1: Getting Started ``` ┌─────────────────────────────────────────────────────────────────┐ │ │ │ 🌟 Train Your AI │ │ │ │ We'll analyze your website to create a knowledgeable │ │ AI assistant that truly understands your business. │ │ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ https:// │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ [ Analyze My Website ] │ │ │ │ This usually takes 2-3 minutes. │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` ### Screen 2: Analysis Progress ``` ┌─────────────────────────────────────────────────────────────────┐ │ │ │ Analyzing your website... │ │ │ │ ✓ Found 24 pages │ │ ✓ Identified business type: Medical Spa │ │ ✓ Extracted 14 services │ │ ◐ Generating knowledge base... │ │ ○ Creating conversation flows │ │ ○ Building your AI assistant │ │ │ │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━░░░░░░░ 65% │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` ### Screen 3: Review Services ``` ┌─────────────────────────────────────────────────────────────────┐ │ │ │ We found 14 services [ + Add ] │ │ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ ✓ Morpheus8 RF Microneedling $800+ │ │ │ │ Concerns: aging, scars, texture, laxity [Edit]│ │ │ ├─────────────────────────────────────────────────────┤ │ │ │ ✓ Glow Getter Facial $225+ │ │ │ │ Concerns: dullness, hydration, glow [Edit] │ │ │ ├─────────────────────────────────────────────────────┤ │ │ │ ✓ Acne Treatment $175+ │ │ │ │ Concerns: acne, breakouts, oily [Edit] │ │ │ ├─────────────────────────────────────────────────────┤ │ │ │ ... │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ Missing something? You can add services manually. │ │ │ │ [ Continue ] │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` ### Screen 4: Review Knowledge (Expandable) ``` ┌─────────────────────────────────────────────────────────────────┐ │ │ │ Review AI Knowledge │ │ │ │ ▼ Morpheus8 RF Microneedling │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ Description: │ │ │ │ The deepest RF microneedling available, Morpheus8 │ │ │ │ combines gold-coated microneedles with... │ │ │ │ │ │ │ │ Choose this service for: │ │ │ │ • Tightening loose or sagging skin │ │ │ │ • Deep acne scar treatment │ │ │ │ • Long-lasting anti-aging results │ │ │ │ │ │ │ │ Perfect for people who: │ │ │ │ • Notice their jawline isn't as defined │ │ │ │ • Are bothered by acne scars │ │ │ │ • Want results without surgery │ │ │ │ │ │ │ │ [ Edit ] │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ ▶ Glow Getter Facial │ │ ▶ Acne Treatment │ │ ▶ Chill Pill Facial │ │ │ │ [ Continue ] │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` ### Screen 5: Conversation Style ``` ┌─────────────────────────────────────────────────────────────────┐ │ │ │ Your AI's Personality │ │ │ │ Based on your website, we detected: │ │ │ │ Tone: ○ Professional ● Warm ○ Casual │ │ Formality: ○ Formal ● Conversational ○ Friendly │ │ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ Sample response: │ │ │ │ │ │ │ │ "I'd love to help you with that! For acne-prone │ │ │ │ skin, our Chill Pill facial is amazing—it uses RF │ │ │ │ technology to calm oil glands and reduce │ │ │ │ inflammation. Many clients see 50-70% improvement │ │ │ │ after just one session! Would you like to know │ │ │ │ more about how it works?" │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ [ Continue ] │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` ### Screen 6: Test & Launch ``` ┌─────────────────────────────────────────────────────────────────┐ │ │ │ Test Your AI Assistant │ │ │ │ ┌─────────────────────────────────────────────────────┐ │ │ │ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │ │ │ │ │ │ │ [Live Chat Preview] │ │ │ │ │ │ │ │ │ │ │ │ User: I have acne scars from high school │ │ │ │ │ │ │ │ │ │ │ │ AI: I completely understand—acne scars │ │ │ │ │ │ can really affect how you feel about │ │ │ │ │ │ your skin. The good news is we have │ │ │ │ │ │ excellent options for this! │ │ │ │ │ │ │ │ │ │ │ │ [Service Card: Morpheus8] │ │ │ │ │ │ [Service Card: TCA CROSS] │ │ │ │ │ │ │ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ │ │ │ │ │ ┌─────────────────────────────────────────────┐ │ │ │ │ │ Type a test message... │ │ │ │ │ └─────────────────────────────────────────────┘ │ │ │ └─────────────────────────────────────────────────────┘ │ │ │ │ [ 🚀 Launch AI ] │ │ │ └─────────────────────────────────────────────────────────────────┘ ``` --- ## Data Schema ```javascript // What gets stored after training { // Basic business info business: { name: "Skin Beauty Med Spa", type: "med_spa", // used for industry-specific prompting location: { address: "750 Hammond Dr", city: "Atlanta", state: "GA", zip: "30328" }, phone: "(404) 992-4345", website: "https://skinbeauty.skin", bookingUrl: "https://skinbeauty.skin/book", }, // Brand voice settings brandVoice: { tone: "warm", // warm | professional | casual formality: "conversational", // formal | conversational | friendly characteristics: ["empathetic", "knowledgeable", "reassuring"], avoidWords: ["cheap", "discount", "deal"], // luxury positioning preferredPhrases: ["investment in yourself", "your skin journey"], }, // Enhanced service data services: [ { id: "morpheus8", name: "Morpheus8 RF Microneedling", // Basic info (from website) description: "FDA-cleared fractional RF microneedling...", price: "$800+", duration: "60-90 min", // AI-generated enhancements expandedEducation: "Unlike standard microneedling that only...", chooseThisFor: [ "Tightening loose or sagging skin", "Deep acne scar treatment", "Long-lasting anti-aging results", "Safe treatment for all skin types" ], perfectFor: [ "You notice your jawline isn't as defined as it used to be", "You're bothered by acne scars from your teenage years", "You want real results without going under the knife" ], concerns: ["aging", "skin_laxity", "acne_scars", "texture", "wrinkles"], relatedServices: ["skin_tightening", "facial_scars", "tca_cross"], differentiators: [ "Deepest RF microneedling available (up to 4mm on face)", "First device FDA-cleared for soft tissue contraction", "Safe for all skin types including Fitzpatrick VI" ], faqs: [ { q: "How many treatments do I need?", a: "Most clients see..." }, { q: "Is it painful?", a: "We use topical numbing..." } ], tags: ["anti-aging", "scars", "tightening", "all-skin-types"], requiresConsultation: true, }, // ... more services ], // Concern-to-service mapping concernMap: { "acne": { primary: ["acne_treatment", "chill_pill"], secondary: ["advatx", "glacial"], severity: { mild: ["chill_pill", "glow_getter"], moderate: ["acne_treatment", "advatx"], severe: ["acne_treatment"] // consultation required } }, "aging": { primary: ["morpheus8", "skin_tightening"], secondary: ["photoshop_facial", "trilift"], byArea: { eyes: ["eye_treatment"], jawline: ["morpheus8", "trilift"], fullFace: ["skin_tightening"] } }, // ... more concerns }, // Conversation starters conversationStarters: [ { icon: "✨", title: "I want glowing skin", subtitle: "Radiance treatments", message: "I want glowing, radiant skin", targetConcern: "glow" }, // ... more starters ], // Generated system prompt systemPrompt: `You are the AI assistant for Skin Beauty Med Spa...`, // Quiz configuration (if enabled) quiz: { enabled: true, questions: [...], scoringLogic: {...} }, // Metadata training: { sourceUrl: "https://skinbeauty.skin", crawledAt: "2026-01-09T...", pagesAnalyzed: 24, lastUpdated: "2026-01-09T...", version: 1 } } ``` --- ## AI Prompts for Each Transformation Step ### Prompt: Entity Extraction ``` Analyze this website content and extract structured business information. Content: {raw_crawled_content} Extract and return JSON: { "business": { "name": "", "type": "", // e.g., "med_spa", "dental", "salon", "fitness", "restaurant" "location": {}, "phone": "", "hours": "", "bookingUrl": "" }, "services": [ { "name": "", "description": "", "price": "", "duration": "" } ], "brandSignals": { "tone": "", // warm, professional, casual "targetDemographic": "", "uniqueSellingPoints": [] } } ``` ### Prompt: Knowledge Enhancement (per service) ``` You are creating a knowledge base entry for an AI assistant that helps customers find the right service. Business: {business_name} ({business_type}) Service: {service_name} Current Description: {service_description} Price: {price} Generate enhanced knowledge that helps customers self-identify if this service is right for them. Write in a {tone} tone. Return JSON: { "expandedEducation": "2-3 paragraphs explaining how this works and why, in plain language a customer would understand", "chooseThisFor": [ "3-5 clear use cases, starting with action verbs" ], "perfectFor": [ "3-5 statements that help customers self-identify, written as 'You...' statements describing their situation" ], "concerns": [ "list of concern keywords this service addresses" ], "differentiators": [ "what makes this different from similar services or DIY alternatives" ], "commonQuestions": [ { "q": "question customers often ask", "a": "helpful answer" } ] } ``` ### Prompt: System Prompt Generation ``` Create a system prompt for an AI assistant for this business: Business: {business_name} Type: {business_type} Tone: {brand_tone} Services: {service_list} The AI should: - Act as a knowledgeable consultant, not a salesperson - Help customers identify their needs through questions - Only recommend services this business offers - Guide toward booking when appropriate - Never mention competitors - Use the specified brand voice Generate a complete system prompt (500-800 words) that will make this AI helpful, on-brand, and conversion-focused while feeling genuinely caring. ``` --- ## Technical Implementation ### Crawling Options 1. **Firecrawl** \- Best for JS-heavy sites, returns clean markdown 2. **Apify** \- More control, handles complex sites 3. **Custom Puppeteer** \- Full control, more maintenance ### Processing Pipeline ```javascript // Simplified flow async function trainFromUrl(url) { // Step 1: Crawl const pages = await firecrawl.crawl(url, { maxPages: 50, includeHtml: false }); // Step 2: Extract entities const extraction = await ai.chat({ model: 'claude-sonnet-4-20250514', messages: [{ role: 'user', content: ENTITY_EXTRACTION_PROMPT.replace('{content}', pages.markdown) }] }); const businessData = JSON.parse(extraction.content); // Step 3: Enhance each service const enhancedServices = await Promise.all( businessData.services.map(service => enhanceService(service, businessData.business) ) ); // Step 4: Build concern map const concernMap = buildConcernMap(enhancedServices); // Step 5: Generate conversation starters const starters = await generateStarters(businessData, enhancedServices); // Step 6: Generate system prompt const systemPrompt = await generateSystemPrompt(businessData, enhancedServices); // Step 7: Compile final knowledge base return { business: businessData.business, brandVoice: businessData.brandSignals, services: enhancedServices, concernMap, conversationStarters: starters, systemPrompt, training: { sourceUrl: url, crawledAt: new Date().toISOString(), pagesAnalyzed: pages.length } }; } ``` --- ## Cost Estimation Per training run (typical business with 10-20 services): | Step | Tokens | Cost (Claude Sonnet) | | :---- | :---- | :---- | | Crawl | N/A | \~$0.01 (Firecrawl) | | Entity extraction | \~10K in, \~2K out | \~$0.04 | | Service enhancement (×15) | \~30K in, \~15K out | \~$0.20 | | Concern mapping | \~5K in, \~2K out | \~$0.02 | | Starter generation | \~5K in, \~1K out | \~$0.02 | | System prompt | \~8K in, \~2K out | \~$0.03 | | **Total** | | **\~$0.35** | This is a one-time cost per business setup, with optional re-training when they update their website. --- ## Success Metrics A well-trained AI should: 1. **Recommend relevant services** for any concern mentioned 2. **Never hallucinate** services or prices that don't exist 3. **Maintain brand voice** consistently 4. **Guide toward booking** naturally 5. **Handle edge cases** gracefully (unknown concerns, competitor questions) 6. **Self-identify triggers** resonate with actual customers --- ## Future Enhancements 1. **Automatic re-training** \- Monitor website for changes, re-crawl weekly 2. **Analytics feedback loop** \- Learn from which recommendations convert 3. **Industry templates** \- Pre-built concern maps for common business types 4. **Multi-location support** \- Different services/prices per location 5. **Seasonal adjustments** \- Automatically suggest seasonal services --- ## Kb Generator **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/kb-generator **Description:** Documents in Kb Generator. --- ## KB Generator Onboarding — Design Build Instructions **URL:** https://secure-docs.aiconnected.ai/docs/knowledge-base/aiconnected-apps-and-modules/modules/kb-generator/kb-generator-design-build-instructions # KB Generator Onboarding — Design Build Instructions These instructions are for Claude to follow when building the aiConnected Knowledge Base Generator onboarding flow. This document is the single source of truth for design decisions. Do not deviate. Do not improvise. Do not take shortcuts. --- ## 1. ANTI-SLOP RULES (Read First, Follow Always) These are the specific failure modes that have appeared in every recent build. Each one must be actively avoided. ### What NOT to do: - **No emojis anywhere.** Not in headings, not in labels, not in placeholder text, not in feature descriptions. Zero. - **No centered paragraph text.** All body text and labels are left-aligned. The only centered text allowed is on interstitial screens (headline + feature cards) and the final launch screen. - **No uniform rounded corners on everything.** Use `rounded-xl` (12px) on cards and containers. Use `rounded-full` on pill buttons only. Use `rounded-lg` (8px) on input fields. Use `rounded-2xl` (16px) on the main screen container. Vary the radii deliberately. - **No purple gradients.** The palette is derived from the agency's primary color (HSL custom property). Gradients use the agency hue, not purple/violet. - **No Inter font.** Use DM Sans (already imported). Headers at 600 weight, body at 400. Do not use system fonts. - **No thin, barely-visible progress bars.** The progress bar segments must be at least 6px tall (`h-1.5`) with 8px gaps. - **No placeholder-quality SVG icons.** Every icon must be a proper, detailed SVG path — not a circle with a letter in it. If a real icon is needed (business, location, industry, website, document), draw a real recognizable SVG. - **No cramped layouts.** Minimum 24px horizontal padding on mobile. 32px on tablet+. Generous vertical spacing between all elements — 20px minimum between form fields, 32px between sections. - **No disabled buttons that look barely different from active.** Disabled state must be obviously, unmistakably different: `opacity-40` + `cursor-not-allowed` + desaturated bg. Active state must be bold and confident. ### What TO do: - Build each screen as if it were the only screen a designer would judge you on. - Treat whitespace as a design element, not leftover space. More whitespace = more premium. - Every interactive element must have three clearly distinct visual states: default, hover, active/selected. - Test the visual hierarchy by squinting: can you tell what's most important? --- ## 2. DESIGN SYSTEM TOKENS ### Agency Theming The entire color system derives from three CSS custom properties set once at the root: ```css :root { --agency-h: 210; /* hue — swapped per white-label agency */ --agency-s: 80%; /* saturation */ --agency-l: 50%; /* lightness */ } ``` All brand colors are computed from these: | Token | Value | Usage | |---|---|---| | `--agency` | `hsl(var(--agency-h), var(--agency-s), var(--agency-l))` | Primary brand color | | `--agency-light` | `hsl(var(--agency-h), var(--agency-s), 95%)` | Subtle tinted backgrounds | | `--agency-dark` | `hsl(var(--agency-h), calc(var(--agency-s) - 10%), 25%)` | Dark gradient base | | `--agency-mid` | `hsl(var(--agency-h), var(--agency-s), 40%)` | Dark gradient mid-tone | ### Color Palette (Non-Agency) | Token | Hex | Usage | |---|---|---| | Surface white | `#FAFAFA` | Form screen backgrounds (not pure white) | | Text primary | `#111111` | Headings, primary body text | | Text secondary | `#6B7280` | Subtitles, helper text, secondary labels | | Text tertiary | `#9CA3AF` | Placeholder text, disabled text | | Border default | `#E5E7EB` | Input borders, card borders, dividers | | Border hover | `#D1D5DB` | Input focus pre-ring | | Success | `#10B981` | Validation success, connected indicators | | Error | `#EF4444` | Validation errors, limit warnings | ### Typography | Role | Font | Size | Weight | Line Height | |---|---|---|---|---| | Screen heading | DM Sans | 28px (1.75rem) | 600 | 1.2 | | Screen subheading | DM Sans | 16px (1rem) | 400 | 1.5 | | Input label | DM Sans | 14px (0.875rem) | 500 | 1.4 | | Input value | DM Sans | 16px (1rem) | 400 | 1.5 | | Button text | DM Sans | 16px (1rem) | 600 | 1.0 | | Helper text | DM Sans | 13px (0.8125rem) | 400 | 1.4 | | Interstitial headline | DM Sans | 32px (2rem) | 700 | 1.15 | | Interstitial body | DM Sans | 15px (0.9375rem) | 400 | 1.5 | ### Spacing Scale Use Tailwind spacing, but maintain these minimums: - Screen horizontal padding: `px-6` (24px) mobile, `px-8` (32px) sm+ - Between form fields: `space-y-5` (20px) - Between sections (heading → first field): `mt-8` (32px) - Button bottom padding from last element: `mt-8` (32px) - Interstitial card internal padding: `p-6` (24px) ### Shadows | Level | Value | Usage | |---|---|---| | Subtle | `0 1px 2px rgba(0,0,0,0.05)` | Input fields at rest | | Card | `0 2px 8px rgba(0,0,0,0.08)` | Elevated cards, connectors | | Lifted | `0 4px 16px rgba(0,0,0,0.12)` | Hover states on cards, selected items | | Dramatic | `0 8px 32px rgba(0,0,0,0.16)` | Modal overlays, bottom sheets | ### Border Radius Scale | Token | Value | Usage | |---|---|---| | `rounded-lg` | 8px | Input fields, small cards | | `rounded-xl` | 12px | Selection cards, connector cards | | `rounded-2xl` | 16px | Screen container, large cards | | `rounded-full` | 9999px | Pill buttons, tags, counter badges | --- ## 3. SCREEN FLOW ARCHITECTURE The flow follows the Vibecoder pattern: form screens on light backgrounds, interstitials on dark agency-gradient backgrounds. The dark↔light alternation creates emotional rhythm. ``` [SignUp] → dark agency gradient (conditional — invited users only) ↓ [BusinessIdentity] → light (#FAFAFA) ↓ [Industry] → light (#FAFAFA) ↓ [Interstitial 1] → dark agency gradient ("We'll scan your website...") ↓ [WebsiteURL] → light (#FAFAFA) ↓ [Interstitial 2] → dark agency gradient, DIFFERENT hue shift ("Add your expert knowledge...") ↓ [KnowledgeSources] → light (#FAFAFA) ↓ [Launch] → dark agency gradient ("Ready to build your AI") ``` ### Progress Bar - Appears ONLY on form screens (Business, Industry, URL, Sources) — 4 segments. - Does NOT appear on interstitials, sign-up, or launch. - Segments are thick: `h-1.5` (6px), separated by `gap-2` (8px). - Filled segments use the agency color. Unfilled segments use `#E5E7EB`. - Full width of the content area. - Positioned below the back arrow, with `mb-8` before the heading. ### Back Navigation - A back arrow (left chevron SVG, not a text link) appears on all form screens and interstitials except the first screen in the flow. - The arrow is 20×20px, positioned at top-left of the content area. - Tap target is at least 44×44px. --- ## 4. SCREEN-BY-SCREEN SPECIFICATIONS ### Screen: Sign Up (Conditional) **Layout:** Split — top 45% is a dark agency gradient with the agency logo centered. Bottom 55% is a white card with rounded top corners (`rounded-t-3xl`), sliding up over the gradient. **Agency logo:** A proper SVG placeholder — a rounded square (radius 12px) with the agency's primary color fill + a subtle lighter highlight shape inside. No text in the logo. The logo should be 64×64px on this screen. **White card contents:** - "Create your account" — 24px, weight 600, left-aligned - "Set up your AI assistant in minutes." — 16px, weight 400, text-secondary, left-aligned - Full name input field - Password input field with show/hide toggle (eye icon SVG) - Password requirement: "At least 8 characters" in helper text below - Primary button: "Get Started" — full-width, agency-colored bg, white text, `rounded-full`, `h-12` - Below button: "Already have an account? Sign in" — text link in agency color **States:** - Button disabled (gray, `opacity-40`) until both fields valid - Password field: eye icon toggles between open/closed, field type switches --- ### Screen: Business Identity **Layout:** Light background (#FAFAFA). Back arrow + progress bar (segment 1 of 4 filled). Left-aligned heading. **Heading:** "Let's set up your AI" **Subheading:** "Tell us about your business so we can build the smartest assistant possible." **Fields (stacked vertically, `space-y-5`):** 1. Business Name — text input, full width 2. Street Address — text input, full width, labeled "Street Address (optional)" 3. City / State / Zip — responsive grid: - Mobile: City full width, then State (50%) + Zip (50%) - Desktop: City (flex-1) + State (120px dropdown) + Zip (100px) 4. State: a native `