1. 🎯 EXECUTIVE SUMMARY Business Objective Build a multi-tenant SaaS platform that allows small businesses to deploy AI-powered customer-support chatbots using their own business information. Primary Users Organization Owner - manages chatbot configuration and knowledge. Website Visitor - interacts with the chatbot. System Administrator - not explicitly required for MVP and therefore remains an unresolved decision. Core Capabilities 1. Authentication 2. Organization management 3. Chatbot creation 4. Knowledge-document upload 5. Document processing 6. AI-powered responses 7. Chatbot customization 8. Website embedding 9. Conversation history 10. Basic analytics 11. Audit logging Architecture Recommendation Proposed: Modular monolith. Browser ↓ Next.js Application ↓ Application/API Layer ↓ Domain Services ↓ PostgreSQL ↓ Object Storage AI Chat Request ↓ Chat Service ↓ Knowledge Retrieval ↓ OpenAI API ↓ Response No microservices are required for the stated MVP. --- 2. 📋 REQUIREMENTS SPECIFICATION Confirmed Requirements Area Requirement Product AI customer-support SaaS Users Business owners + website visitors Authentication Required Chatbots Create/configure Knowledge Upload documents AI OpenAI integration Deployment Website embed Analytics Basic analytics Database PostgreSQL Storage S3-compatible Architecture Modular monolith preferred Timeline 4-week MVP Inferred Requirements These are reasonable interpretations but should be confirmed: A user may belong to an organization. An organization may have multiple chatbots. Uploaded documents belong to an organization or chatbot. Conversations belong to a chatbot. Messages belong to conversations. Website visitors do not need accounts. Chatbot API must be publicly callable through an embed client. Proposed Design Organization-based tenant isolation. UUID primary keys. Server-side authorization for every protected operation. Separate public chatbot endpoint from dashboard APIs. Database-backed conversation storage. Object storage for original uploaded files. Background document-processing workflow. Unresolved Decisions Maximum document size. Supported file formats. Maximum number of chatbots per organization. AI model selection. Data retention period. Whether billing is required in MVP. Whether team members/roles beyond owner are required. Exact analytics metrics. Whether visitor IP/user-agent data should be stored. --- 3. 👥 USER & WORKFLOW ARCHITECTURE Workflow A - User Registration Actor: Business owner Trigger: User visits signup page. Preconditions: User does not have an authenticated session. Flow: Signup ↓ Authentication Provider ↓ Create User ↓ Create Organization ↓ Create Membership ↓ Dashboard Data Changes: users organizations organization_members Failure Cases: Duplicate account Authentication failure Database failure Completion: User reaches authenticated dashboard. --- Workflow B - Create Chatbot Actor: Organization owner Dashboard ↓ Create Chatbot ↓ Validate request ↓ Verify organization access ↓ Create chatbot ↓ Return chatbot Security Boundary: Authenticated User ↓ Organization Membership Check ↓ Chatbot Ownership Check ↓ Database Mutation --- Workflow C - Upload Knowledge Document Dashboard ↓ Select File ↓ Client Validation ↓ Upload API ↓ Server Validation ↓ Object Storage ↓ Create Document Record ↓ Document Processing ↓ Extract Text ↓ Chunk Content ↓ Generate Embeddings ↓ Store Searchable Knowledge Failure Paths Invalid file File too large Storage failure Text extraction failure AI API failure Embedding failure The document should not be marked READY until processing succeeds. --- 4. 🧩 FEATURE & MODULE MAP Module Responsibility Data Authentication Login/session Users Organizations Tenant management Organizations Chatbots Bot configuration Chatbots Knowledge Documents + processing Documents Chat Visitor conversations Conversations/Messages AI Model interaction AI requests Analytics Usage metrics Conversation data Audit Security-sensitive actions Audit logs Module Dependency Authentication ↓ Organizations ↓ Chatbots ↓ Knowledge ↓ Chat ↓ AI Provider Chat ↓ Analytics All protected modules ↓ Authorization --- 5. 🏗️ SYSTEM ARCHITECTURE Proposed Architecture ┌──────────────────┐ │ Business Owner │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ Next.js Dashboard│ └────────┬─────────┘ ↓ ┌──────────────────┐ │ API/Application │ │ Layer │ └───────┬──────────┘ ↓ ┌─────────────┼──────────────┐ ↓ ↓ ↓ PostgreSQL Object Storage AI Service ↓ OpenAI API Website Visitor ↓ Embedded Chat Widget ↓ Public Chat API ↓ Chat Service ↓ Knowledge Retrieval ↓ OpenAI API ↓ Response Architecture Choice Modular monolith rather than microservices. Why? Small team 4-week MVP Single product Limited infrastructure requirements Lower operational complexity Easier local development Easier deployment --- 6. 📁 CODEBASE & FILE STRUCTURE These paths are proposed, because no repository was supplied. src/ ├── app/ │ ├── (auth)/ │ ├── dashboard/ │ ├── api/ │ │ ├── chatbots/ │ │ ├── documents/ │ │ ├── conversations/ │ │ └── analytics/ │ └── embed/ │ ├── components/ │ ├── ui/ │ ├── chatbot/ │ ├── documents/ │ ├── conversations/ │ └── analytics/ │ ├── modules/ │ ├── auth/ │ ├── organizations/ │ ├── chatbots/ │ ├── knowledge/ │ ├── conversations/ │ ├── analytics/ │ └── audit/ │ ├── lib/ │ ├── db/ │ ├── auth/ │ ├── storage/ │ ├── ai/ │ ├── validation/ │ └── rate-limit/ │ ├── jobs/ │ └── document-processing/ │ └── types/ Major File Plan Action Proposed Path Responsibility Dependencies Reason CREATE src/modules/chatbots/service.ts Chatbot business logic DB Centralize rules CREATE src/modules/chatbots/repository.ts Chatbot persistence DB Data access isolation CREATE src/modules/knowledge/service.ts Document processing orchestration Storage/AI Separate domain logic CREATE src/lib/ai/client.ts AI provider integration OpenAI SDK Isolate vendor dependency CREATE src/lib/storage/client.ts Object storage S3 SDK Storage abstraction CREATE src/lib/auth/authorization.ts Permission checks Auth Trusted authorization CREATE src/components/chatbot/ChatbotEditor.tsx Bot settings UI API Reusable UI No existing files are claimed because the project is greenfield. --- 7. 🎨 FRONTEND IMPLEMENTATION PLAN Pages /signup /login /dashboard /dashboard/chatbots /dashboard/chatbots/[id] /dashboard/chatbots/[id]/knowledge /dashboard/chatbots/[id]/conversations /dashboard/chatbots/[id]/analytics Core Components DashboardLayout ChatbotCard ChatbotEditor KnowledgeUploader DocumentList ConversationList ConversationViewer AnalyticsOverview EmbedCodePanel LoadingState EmptyState ErrorState UI States Every major API-driven component should support: Loading ↓ Success ├── Data └── Empty ↓ Error Accessibility Keyboard navigation Accessible labels Form validation messages Focus management Sufficient semantic HTML Screen-reader-friendly status messages Responsive Behavior Dashboard: Desktop → Sidebar + Main Content Tablet → Collapsible Sidebar Mobile → Navigation Drawer + Stacked Content --- 8. ⚙️ BACKEND & DOMAIN IMPLEMENTATION Backend Layers Route Handler ↓ Request Validation ↓ Authorization ↓ Application Service ↓ Domain Logic ↓ Repository ↓ Database Chatbot Service Responsibilities: Create chatbot Update chatbot Delete chatbot Validate organization access Manage chatbot configuration Knowledge Service Responsibilities: Validate document Store document Process document Extract text Chunk content Generate embeddings Store searchable representation Update document status Chat Service Responsibilities: Validate public chatbot ID Apply rate limits Retrieve relevant knowledge Construct AI request Call AI provider Persist conversation Return response --- 9. 🔌 API CONTRACT SPECIFICATION POST /api/chatbots Status: NEW Authentication: Required Authorization: Organization owner/member with chatbot-create permission. Request { "name": "Support Bot", "tone": "friendly", "welcomeMessage": "How can I help?" } Response { "id": "uuid", "name": "Support Bot", "status": "ACTIVE" } Errors 400 Invalid request 401 Unauthenticated 403 Unauthorized 500 Internal server error --- GET /api/chatbots Status: NEW Returns chatbots accessible to the authenticated organization. --- PATCH /api/chatbots/:id Status: NEW Updates chatbot configuration. Authorization must be checked server-side. --- DELETE /api/chatbots/:id Status: NEW Deletes or deactivates a chatbot. Deletion behavior remains an unresolved product decision. --- POST /api/chatbots/:id/documents Status: NEW Uploads/creates a knowledge document. Validation: File type File size Chatbot ownership Upload authorization --- POST /api/public/chat/:chatbotId Status: NEW Used by the embedded website chatbot. Authentication: No user login required. Protection: Chatbot public identifier Rate limiting Request validation Abuse controls --- GET /api/chatbots/:id/conversations Status: NEW Authenticated dashboard API. Supports: Pagination Date filtering Chatbot filtering --- GET /api/chatbots/:id/analytics Status: NEW Returns basic usage metrics. Exact metrics remain an unresolved decision. --- 10. 🗄️ DATABASE & DATA MODEL users id email name created_at updated_at organizations id name created_at updated_at organization_members id organization_id user_id role created_at Unique constraint: organization_id + user_id --- chatbots id organization_id name tone welcome_message status created_at updated_at Relationship: Organization 1 ─── N Chatbots --- documents id chatbot_id file_name storage_key mime_type file_size status error_message created_at updated_at Possible statuses: UPLOADED PROCESSING READY FAILED --- conversations id chatbot_id visitor_identifier created_at updated_at messages id conversation_id role content created_at --- audit_logs id organization_id actor_user_id action resource_type resource_id metadata created_at Important Indexes Proposed: organization_members(user_id) chatbots(organization_id) documents(chatbot_id, status) conversations(chatbot_id, created_at) messages(conversation_id, created_at) audit_logs(organization_id, created_at) Migration Strategy Database changes should use versioned migrations. Deployment order: Compatible schema migration ↓ Application deployment ↓ Validation ↓ Optional cleanup migration Avoid destructive migrations in the same release as application code that still depends on the old schema. --- 11. 🔐 SECURITY & AUTHORIZATION ARCHITECTURE Authentication Auth.js handles user authentication and sessions. Authorization Authentication answers: > Who is this user? Authorization answers: > What can this user access? Every protected backend request should perform both. Tenant Isolation Example: Request ↓ Authenticated User ↓ Organization Membership ↓ Requested Resource ↓ Verify resource.organization_id ↓ Allow/Deny Never trust an organization ID supplied by the browser. File Security Server-side validation should check: MIME type File extension File size Storage key Upload authorization API Security Rate limiting Request validation Authentication Authorization Secure headers Secret isolation Error sanitization AI Security Potential controls: Prompt-injection-resistant retrieval design Do not expose internal system prompts Do not send unnecessary private data to AI provider Validate external document content Limit retrieved context Log AI failures without exposing sensitive content --- 12. 🔄 INTEGRATION & ASYNC PROCESSING ARCHITECTURE Document Processing Flow User ↓ Upload API ↓ Object Storage ↓ Document Record = PROCESSING ↓ Processing Worker ↓ Text Extraction ↓ Chunking ↓ Embedding Generation ↓ Knowledge Storage ↓ Document = READY Retry Strategy Transient failures should be retryable. Example: Processing ↓ Failure ↓ Retry ↓ Retry ↓ FAILED The exact retry count should remain configurable rather than hard-coded into the architecture. Idempotency Document processing should be safe to retry. A processing job must not create duplicate knowledge records when the same document-processing operation is repeated. External AI Failure Chat Request ↓ OpenAI unavailable ↓ Capture failure ↓ Return controlled error ↓ Do not expose provider internals Queue Decision A dedicated queue is proposed only if document processing cannot reliably execute within the selected deployment model. For the MVP, the team should choose the simplest supported background-processing mechanism compatible with Vercel and the chosen infrastructure. --- 13. 🧪 TESTING & QUALITY STRATEGY Unit Tests Test: Chatbot business rules Authorization helpers Document status transitions Input validation Analytics calculations API Tests Test: POST /chatbots GET /chatbots PATCH /chatbots/:id DELETE /chatbots/:id POST /documents POST /public/chat/:id Integration Tests Validate: API ↓ Service ↓ Repository ↓ PostgreSQL E2E Test Critical flow: Signup ↓ Create Organization ↓ Create Chatbot ↓ Upload Document ↓ Process Document ↓ Open Embed ↓ Ask Question ↓ Receive Answer ↓ Conversation Appears in Dashboard Security Tests Verify: User cannot access another organization's chatbot. User cannot modify another organization's documents. Public API is rate limited. Invalid uploads are rejected. Secrets never reach client-side code. Requirement Traceability Requirement Implementation Test Acceptance Create chatbot Chatbot service/API API test Authorized user can create bot Upload documents Knowledge module Integration test Valid document reaches processing AI chat Chat service E2E test Visitor receives response Tenant isolation Authorization layer Security test Cross-tenant access denied Conversation history Conversation module API/E2E test Messages appear in dashboard --- 14. 📊 OBSERVABILITY & PRODUCTION OPERATIONS Logging Use structured logs containing: Timestamp Request ID User ID where appropriate Organization ID Operation Status Error category Do not log secrets or unnecessary sensitive content. Metrics Track: API error rate API latency Chat request failures AI provider failures Document processing failures Document processing duration Active conversations Rate-limit events Health Checks At minimum: Application health Database connectivity Critical dependency availability Alerts Potential alerts: Elevated API errors Repeated AI provider failures Document processing failures Database connectivity failures Abnormal rate-limit activity SLO/SLI Exact numerical SLO targets are intentionally unresolved because the requirements provide no workload or availability target. --- 15. 🚀 INFRASTRUCTURE, CI/CD & DEPLOYMENT Environments Local Development ↓ Test/Staging ↓ Production CI Pipeline Push ↓ Install Dependencies ↓ Lint ↓ Type Check ↓ Unit Tests ↓ Integration Tests ↓ Build CD Pipeline Validated Build ↓ Deploy Staging ↓ Smoke Tests ↓ Production Approval ↓ Production Deployment Secrets Store: Authentication secrets Database credentials OpenAI API key Object storage credentials in the deployment platform's secret-management mechanism. Never commit them to source control. Deployment Strategy For the MVP: Rolling/standard deployment is sufficient unless traffic, downtime requirements, or infrastructure constraints justify blue/green or canary deployment. Database Rollback Application rollback and database rollback must be treated separately. Prefer backward-compatible migrations. --- 16. 🧠 IMPLEMENTATION TASK GRAPH T001 - Project Foundation Objective: Establish application structure. Dependencies: None Files: Proposed project configuration src/ Testing configuration Tests: Build Type check Lint Output: Running Next.js application. --- T002 - Database Foundation Dependencies: T001 Objective: Configure PostgreSQL and migrations. Files: src/lib/db/ Database schema/migrations Tests: Database connection Migration test --- T003 - Authentication Dependencies: T002 Objective: Implement authentication and sessions. Tests: Login Logout Unauthorized access --- T004 - Organization & Authorization Dependencies: T003 Objective: Establish tenant boundaries. Tests: Organization membership Cross-tenant access rejection --- T005 - Chatbot Management Dependencies: T004 Objective: CRUD chatbot functionality. Tests: Create Read Update Delete/deactivate Authorization --- T006 - Knowledge Upload Dependencies: T005 Objective: Upload and register documents. Tests: Valid file Invalid file Unauthorized upload Storage failure --- T007 - Document Processing Dependencies: T006 Objective: Extract and process document knowledge. Tests: Successful processing Retry Failure state Duplicate processing --- T008 - AI Chat Service Dependencies: T007 Objective: Generate responses using chatbot knowledge. Tests: Relevant question No relevant knowledge AI provider failure Rate limiting --- T009 - Embedded Chat UI Dependencies: T008 Objective: Provide website visitor chat interface. Tests: Load widget Send message Receive response Error state --- T010 - Conversation Dashboard Dependencies: T008 Objective: Allow owners to review conversations. Tests: Pagination Organization isolation Message rendering --- T011 - Analytics Dependencies: T010 Objective: Provide basic usage analytics. Tests: Metric calculation Empty dataset Date filtering --- T012 - Audit Logging Dependencies: T004 Objective: Record security-sensitive actions. Tests: Create chatbot audit event Document upload audit event Configuration change audit event --- T013 - Observability Dependencies: T001 Objective: Add logging, metrics, health checks, and error tracking. --- T014 - CI/CD Dependencies: T001, T002 Objective: Automate validation and deployment. --- T015 - Security Hardening Dependencies: T003-T013 Objective: Validate production security controls. --- T016 - End-to-End Validation Dependencies: T005-T015 Objective: Validate complete business workflow. --- 🔗 DEPENDENCY GRAPH T001 ↓ T002 ↓ T003 ↓ T004 ↓ T005 ↓ T006 ↓ T007 ↓ T008 ├────────→ T009 └────────→ T010 ↓ T011 T004 ───────→ T012 T001 ───────→ T013 T001+T002 ──→ T014 T003-T013 ──→ T015 T005-T015 ──→ T016 🚦 CRITICAL PATH Foundation → Database → Authentication → Authorization → Chatbot → Knowledge → Document Processing → AI Chat → E2E Validation Parallelizable Work After the foundation: Frontend dashboard Backend modules Authentication UI Observability CI/CD can partially proceed in parallel. --- 🗓️ SUGGESTED 4-WEEK DELIVERY PLAN Week 1 - Foundation 🏗️ T001 T002 T003 T004 Week 2 - Core Product ⚙️ T005 T006 T007 Dashboard UI Week 3 - AI Chat 🤖 T008 T009 T010 T012 Week 4 - Production Readiness 🚀 T011 T013 T014 T015 T016 --- 🧠 ARCHITECTURE DECISION REGISTER Decision Reason Alternatives Tradeoff Consequence Modular monolith Small team + MVP Microservices Less independent scaling Simpler operations PostgreSQL Relational tenant/data model NoSQL Requires schema design Strong consistency Object storage Large file storage Database blobs Additional service Better file separation Server-side authorization Security boundary Client checks More backend work Stronger tenant isolation Separate public chat API Visitor access Dashboard API reuse Additional endpoint Cleaner security model Async document processing Potentially expensive work Synchronous processing More workflow complexity Better reliability --- ⚠️ RISK REGISTER Risk Cause Impact Likelihood Mitigation Owner/Decision AI provider outage External dependency Chat unavailable Medium Controlled errors + monitoring Engineering Cross-tenant data exposure Authorization defect High security impact Medium Server-side authorization tests Backend Large uploads User-controlled files Processing/storage issues Medium Size/type limits Engineering AI cost growth High chat usage Higher operating cost Medium Usage tracking + limits Product Document processing failure Invalid/corrupt files Knowledge unavailable Medium Status + retry handling Backend Deployment migration failure Schema incompatibility Application outage Low/Medium Backward-compatible migrations DevOps Scope expansion MVP requirements grow Delivery delay High Explicit unresolved decisions Product --- 🔍 OPEN DECISIONS These should not be silently invented by the implementation team. 1. Maximum file size. 2. Supported document formats. 3. AI model selection. 4. Pricing/billing requirements. 5. Number of chatbots per organization. 6. Data retention policy. 7. Exact analytics metrics. 8. Team-member roles. 9. Admin console requirements. 10. Visitor privacy requirements. 11. Chat history retention. 12. Production availability target. --- ✅ DEFINITION OF DONE The MVP is implementation-ready when: Requirements are documented. Confirmed and inferred requirements are separated. Open decisions are explicitly recorded. Architecture boundaries are defined. Module ownership is clear. Proposed file structure is documented. API contracts are specified. Database entities and relationships are defined. Tenant isolation is designed. Authentication and authorization are separated. External dependencies are identified. Failure and retry behavior is defined. Testing strategy is mapped to requirements. CI/CD is defined. Deployment and rollback strategy is documented. Observability requirements are documented. Implementation tasks have dependencies. Critical path is identified. Acceptance criteria exist for important functionality. 🔗 FINAL TRACEABILITY EXAMPLE Requirement "Business can create an AI chatbot" ↓ Architecture Chatbot Module ↓ Module Chatbot Service ↓ Files chatbots/service.ts chatbots/repository.ts ↓ API POST /api/chatbots ↓ Database chatbots table ↓ Task T005 - Chatbot Management ↓ Test Create chatbot API test ↓ Acceptance Criterion Authorized organization member can create a chatbot and retrieve it from the dashboard. Result: This test demonstrates that your prompt can take a short 7-input specification and expand it into a Phase 1-16 implementation blueprint with architecture, files, APIs, database, security, integrations, testing, deployment, task dependencies, risks, and traceability—without pretending that unresolved requirements are already decided.