AI-Powered CRM

Project Highlights

4

AI Features

85%

Test Coverage

500

Daily AI Requests

1536

Vector Dimensions

🤖 Core AI Features

🧠

Deal Coach AI

Intelligent deal progression assistant that analyzes historical patterns and provides actionable next steps with confidence scores.

👤

Persona Builder

AI-powered behavioral analysis that automatically generates comprehensive customer profiles from interaction patterns.

🛡️

Objection Handler

Smart response generation system that provides contextual, multi-angle approaches to customer objections.

📊

Win/Loss Explainer

Deal outcome analysis engine that provides comprehensive insights to improve future sales performance.

🏗️ System Architecture

graph TB subgraph "Frontend Layer" UI[Next.js UI Components] AI_COMP[AI Feature Components] DASH[Dashboard & Analytics] end subgraph "API Gateway" AUTH[Authentication Middleware] RATE[Rate Limiting] VALID[Input Validation] end subgraph "Business Logic Layer" AI_CTRL[AI Controller] DEAL_CTRL[Deal Controller] CONTACT_CTRL[Contact Controller] end subgraph "AI Services Layer" AI_SVC[AI Service] RAG_SVC[RAG Indexing Service] VECTOR_SVC[Vector Service] CONTENT_FILTER[Content Filter] end subgraph "External AI Services" OPENAI[OpenAI GPT-4 API] EMBED[OpenAI Embeddings API] end subgraph "Data Layer" MONGO[(MongoDB)] CHROMA[(ChromaDB Vector Store)] CACHE[Redis Cache] end UI --> AUTH AI_COMP --> AUTH DASH --> AUTH AUTH --> RATE RATE --> VALID VALID --> AI_CTRL VALID --> DEAL_CTRL VALID --> CONTACT_CTRL AI_CTRL --> AI_SVC AI_CTRL --> RAG_SVC AI_SVC --> VECTOR_SVC AI_SVC --> CONTENT_FILTER AI_SVC --> OPENAI VECTOR_SVC --> EMBED VECTOR_SVC --> CHROMA RAG_SVC --> VECTOR_SVC RAG_SVC --> MONGO DEAL_CTRL --> MONGO CONTACT_CTRL --> MONGO AI_SVC --> CACHE classDef frontend fill:#e1f5fe classDef api fill:#f3e5f5 classDef business fill:#e8f5e8 classDef ai fill:#fff3e0 classDef external fill:#ffebee classDef data fill:#f1f8e9 class UI,AI_COMP,DASH frontend class AUTH,RATE,VALID api class AI_CTRL,DEAL_CTRL,CONTACT_CTRL business class AI_SVC,RAG_SVC,VECTOR_SVC,CONTENT_FILTER ai class OPENAI,EMBED external class MONGO,CHROMA,CACHE data

🔍 RAG Implementation Flow

sequenceDiagram participant User participant Frontend participant AI_Controller participant AI_Service participant Vector_Service participant ChromaDB participant OpenAI participant MongoDB User->>Frontend: Request AI Suggestion Frontend->>AI_Controller: API Call with Context AI_Controller->>AI_Service: Process Request Note over AI_Service: Rate Limit Check (100/day) AI_Service->>AI_Service: Check Cache alt Cache Miss AI_Service->>Vector_Service: Search Similar Context Vector_Service->>OpenAI: Generate Query Embedding OpenAI-->>Vector_Service: Embedding Vector Vector_Service->>ChromaDB: Similarity Search ChromaDB-->>Vector_Service: Top 5 Similar Records Vector_Service-->>AI_Service: RAG Context AI_Service->>OpenAI: Generate Response with Context OpenAI-->>AI_Service: AI Response AI_Service->>AI_Service: Cache Response else Cache Hit AI_Service->>AI_Service: Return Cached Response end AI_Service->>MongoDB: Log AI Request AI_Service-->>AI_Controller: Structured Response AI_Controller-->>Frontend: JSON Response Frontend-->>User: Display AI Suggestions

🔄 AI Processing Pipeline

1

User Request & Authentication

User submits AI request through the frontend. System validates JWT token and checks user permissions.

2

Rate Limiting & Content Filtering

System checks daily AI usage limit (500 requests) and filters content for safety and appropriateness.

3

RAG Context Retrieval

Vector database searches for similar historical data using OpenAI embeddings and cosine similarity.

4

AI Response Generation

GPT-4 generates contextual response using RAG data, with confidence scoring and structured output.

🗄️ Vector Database Collections

flowchart TD A[New CRM Data] --> B{Data Type?} B -->|Deal Closed| C[Extract Deal Context] B -->|Objection Resolved| D[Extract Objection Pattern] B -->|Interaction Logged| E[Extract Communication Pattern] C --> F[Generate Deal Summary] D --> G[Generate Objection Summary] E --> H[Generate Interaction Summary] F --> I[OpenAI Embedding API] G --> I H --> I I --> J[1536-dim Vector] J --> K{Store in ChromaDB} K --> L[historical_deals Collection] K --> M[objection_responses Collection] K --> N[customer_interactions Collection] L --> O[Available for Deal Coach] M --> P[Available for Objection Handler] N --> Q[Available for Persona Builder] style A fill:#e3f2fd style I fill:#fff3e0 style K fill:#f3e5f5

                🎯 Key Vector Database Features:
                4 Specialized Collections: Deals, Objections, Interactions, Personas
Weighted Similarity: Industry (40%), Deal Size (30%), Objection Type (30%)
Real-time Indexing: Automatic background processing
Metadata Filtering: Time-based and contextual constraints

            

💻 Technology Stack

🎨 Frontend

Next.js 15.3.3, React 19, Tailwind CSS, ShadCN UI

⚙️ Backend

Node.js 18+, Express.js, MongoDB, Mongoose ODM

🤖 AI & ML

OpenAI GPT-4, text-embedding-3-small, ChromaDB

🔒 Security

JWT Authentication, bcrypt, Helmet.js, Rate Limiting

🧪 Testing

Jest, Supertest, React Testing Library, 85% Coverage

🚀 Deployment

Docker, Vercel, MongoDB Atlas, ChromaDB Cloud

📊 Sample AI Response

{
  "success": true,
  "data": {
    "suggestions": [
      {
        "action": "Schedule decision-maker call",
        "priority": "high",
        "confidence": 85,
        "reasoning": "Based on 3 similar $50K+ deals that closed after executive involvement"
      },
      {
        "action": "Send detailed ROI analysis",
        "priority": "medium", 
        "confidence": 72,
        "reasoning": "2 similar deals in your industry closed after ROI presentation"
      },
      {
        "action": "Follow up on technical questions",
        "priority": "low",
        "confidence": 65,
        "reasoning": "Standard follow-up for this stage"
      }
    ],
    "ragContext": [
      {
        "similarity": 0.89,
        "industry": "SaaS",
        "outcome": "closed_won",
        "value": 45000
      }
    ],
    "confidence": 85,
    "timestamp": "2024-01-15T10:30:00Z"
  }
}
            