Conversational Intelligence Meets CRM
This expert-level architectural design prompt guides the creation of a production-ready enterprise CRM application enhanced with an AI-powered conversational chatbot. The system allows users to manage customers, contacts, deals, and activities while querying CRM data using natural language.
The chatbot leverages LLMs, vector embeddings, and semantic search to generate accurate, contextual answers strictly grounded in CRM data, with support for multi-turn conversations, function calling, and real-time streaming responses.
Natural language queries with multi-turn conversations, context awareness, and function calling for autonomous data retrieval
Semantic similarity search using PostgreSQL pgvector with automatic embedding generation and cosine similarity
Token-by-token response streaming via WebSocket with typing indicators and incremental UI updates
JWT authentication, RBAC with role-based chatbot access, multi-tenancy, and comprehensive audit logging
## **Prompt: Design an Enterprise CRM Application with AI Chatbot** You are a **senior software architect and product designer**. Design a **production-ready, enterprise CRM application enhanced with an AI-powered conversational chatbot**. ### **Objective** Create a full-stack CRM system that allows users to manage customers, contacts, deals, and activities, and **query CRM data using natural language via an AI chatbot**. The chatbot must use **LLMs + vector embeddings + semantic search** to generate accurate, contextual answers strictly grounded in CRM data. --- ## **1. Core CRM Capabilities** Design CRM functionality that supports: * Customer, Contact, Deal, and Activity management (full CRUD) * Strong entity relationships: * Customers → Contacts * Customers → Deals * Customers → Activities * Timeline view of all customer interactions * Traditional UI access **and** chatbot-based access * Example chatbot queries: * "Show me high-value deals closing this month" * "What's the status of ABC Corp?" * "Which customers are in the software industry?" --- ## **2. AI Chatbot & Conversational Intelligence** Design an AI chatbot that: * Accepts natural language queries * Converts queries into vector embeddings * Performs semantic similarity search against CRM data * Constructs contextual prompts using retrieved CRM records * Uses an LLM to generate responses * Supports **multi-turn conversations** with memory * Handles follow-up questions referencing earlier answers * Uses **function calling** to autonomously: * Query customers * Fetch deal details * Calculate revenue metrics * Retrieve activities and timelines --- ## **3. Vector Embeddings & Semantic Search** Design a background embedding pipeline that: * Automatically generates embeddings when CRM records are created or updated * Builds a rich textual representation for each entity * Stores vectors in **PostgreSQL using pgvector** * Supports cosine similarity search * Supports batch embedding generation for existing data * Includes retry logic with exponential backoff --- ## **4. Real-Time Chat Streaming** Design real-time chatbot behavior: * Token-by-token streaming responses * WebSocket-based communication * Typing indicators during processing * Graceful handling of disconnections and errors * Incremental UI updates during response generation --- ## **5. Database & Multi-Tenancy Architecture** Design a multi-tenant database schema including: * Customers * Contacts * Deals * Activities * ChatConversation (full chat history + context) * VectorEmbedding (entity type, entity ID, vector, metadata) Requirements: * Tenant isolation * UUID primary keys * Soft deletes * JSONB metadata * Optimized indexing strategy --- ## **6. Authentication & Authorization** Design security with: * JWT-based authentication * Role-based access control: * Admin * Manager * Sales Representative * Token refresh support * API-level permission enforcement * Chatbot-level data access enforcement (AI must respect RBAC) --- ## **7. Backend API Design** Define REST APIs for: * CRM CRUD operations * Chatbot query processing * Chat history retrieval * User feedback on chatbot responses * Suggested questions based on role and context Requirements: * REST best practices * Input validation * Consistent error handling * Swagger/OpenAPI documentation --- ## **8. Frontend Experience** Design a responsive React-based UI that includes: * CRM dashboards (metrics, charts, KPIs) * Searchable, filterable data tables * Customer detail pages with full context * Activity timelines * Notes and collaboration areas * Floating chatbot widget: * Expandable chat window * Message bubbles * Suggested prompts * Typing indicator * Copyable answers --- ## **9. Performance & Scalability** Design for: * <500ms CRUD APIs * <2s chatbot responses * 1,000+ concurrent users * 10,000+ CRM records * Sub-second vector search * Redis caching (data + chatbot responses) * Rate limiting * CDN for static assets * Cost-optimized AI usage with response caching --- ## **10. External Integrations** Design integration with: * LLMs for response generation * Embedding models for semantic search * Function calling for dynamic DB queries Requirements: * Configurable model switching * Retry logic and fallback behavior * API usage monitoring and cost controls * Secure environment variable management --- ## **11. Testing Strategy** Design tests for: * Unit testing (services, chatbot logic, embeddings) * Integration testing (end-to-end chatbot flow) * API testing * WebSocket streaming validation * Authorization & rate limiting * Seed data generation * ≥80% code coverage --- ## **12. Deployment & Infrastructure** Design a containerized deployment using: * Docker (multi-stage builds) * Backend API service * Frontend static app * PostgreSQL with vector extension * Redis cache * Reverse proxy * Health checks * Persistent volumes * Environment-based configuration --- ## **13. Analytics & Monitoring** Design an admin analytics dashboard showing: * Chatbot usage trends * Query categories * Response times * Confidence scores * Success vs fallback rates * User feedback ratings * Conversation explorer with filters * CSV export * Real-time updates --- ## **14. Security & Data Protection** Ensure: * HTTPS everywhere * Secure password hashing * Token expiration and validation * Input sanitization * SQL injection & XSS prevention * CORS policies * Audit logging * Data retention controls * User-controlled conversation deletion --- ## **15. Error Handling & Resilience** Design for: * Graceful AI API failures * Circuit breakers * Retry queues * Friendly fallback responses * Centralized logging * Monitoring alerts * Frontend error boundaries --- ## **16. User Experience Principles** Ensure: * Natural language usage (no commands) * Context-aware follow-ups * Clarifying questions for ambiguity * Readable, well-formatted responses * Confidence indicators or citations * Seamless transition between chatbot and UI * Response rating and feedback loops --- ### **Output Expectations** Provide: * Clear system architecture * Data models * API contracts * Chatbot flow diagrams * Embedding pipeline design * Security and scalability considerations * Production-grade best practices **Do NOT write implementation code unless explicitly requested.**
Complete CRUD for customers, contacts, deals, and activities with strong entity relationships and timeline views of all interactions
Natural language query processing with LLMs, multi-turn conversations with memory, function calling for autonomous data retrieval, and contextual response generation
Automatic embedding generation on record creation/updates, PostgreSQL pgvector storage, cosine similarity search, and batch processing with retry logic
WebSocket-based token-by-token streaming, typing indicators, graceful error handling, and incremental UI updates during response generation
Complete tenant isolation, UUID primary keys, soft deletes, JSONB metadata, optimized indexing, and role-based access control at both API and chatbot levels
Sub-500ms CRUD APIs, sub-2s chatbot responses, Redis caching, rate limiting, support for 1,000+ concurrent users and 10,000+ records, cost-optimized AI usage
Admin dashboard with chatbot usage trends, query categories, response times, confidence scores, user feedback ratings, and conversation explorer with CSV export
Architecture First: Focus on system design, data models, and API contracts rather than implementation code. This is a design-focused prompt.
Vector Search Strategy: Plan your embedding generation pipeline carefully - consider when to generate embeddings (sync vs async) and how to handle updates.
Cost Optimization: Implement aggressive caching for both data and AI responses to minimize LLM API costs while maintaining performance.
Security & Privacy: Ensure the chatbot respects role-based access control and never exposes data outside the user's permission scope.
User Experience: Design for natural conversation flow with context awareness, clarifying questions, and seamless transitions between chatbot and traditional UI.