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Technical Architecture

System Overview

The AI Document Intelligence Platform uses a modern, scalable architecture combining document processing, semantic search, and large language models.

┌─────────────────────────────────────────────────────────────────┐
│                     Client Applications                          │
│            (Web, Mobile, Third-party Integrations)              │
└────────────────────────┬────────────────────────────────────────┘
                         │
                    RESTful API
                         │
         ┌───────────────┴───────────────┐
         │                               │
    ┌────▼─────┐              ┌─────────▼────┐
    │ Document │              │ Insights &   │
    │ Processing               │ Analytics    │
    │ Pipeline  │              │ Engine       │
    └────┬─────┘              └─────────┬────┘
         │                               │
    ┌────▼──────────────────────────────▼─────┐
    │      Data Storage & Retrieval Layer      │
    │  (PostgreSQL + pgvector + Embeddings)   │
    └─────────────────────────────────────────┘

Detailed Data Flow

1. DOCUMENT INTAKE
   Input Document → Upload → Virus Scan → Storage

2. TEXT EXTRACTION
   Document → OCR/Text Parser → Raw Text → Quality Check

3. SEMANTIC PROCESSING
   Text → Chunking (1000 tokens, 200 overlap)
        → Embeddings (1536 dimensions)
        → Vector Store (pgvector)

4. RAG ANALYSIS
   Query → Vector Search → Top-5 Chunks → LLM Context
        → Domain-Specific Prompt → Structured Output

5. INSIGHT EXTRACTION
   LLM Output → Entity Recognition
             → Risk Assessment
             → Compliance Check
             → Key Findings

6. STORAGE & DELIVERY
   Insights → Database → Real-time API → Client

Core Components

1. Document Processing Engine

  • Input Formats: PDF, DOCX, TXT, PNG, JPG, TIFF
  • Text Extraction: Native text + OCR fallback
  • Table Detection: Extracts tables and structured data
  • Quality Metrics: Token count, page count, confidence scores

2. Semantic Chunking

  • Strategy: Sliding window with overlap
  • Chunk Size: ~1000 tokens (configurable)
  • Overlap: 200 tokens for context preservation
  • Goal: Balance between detail and retrieval efficiency

3. Vector Embedding Service

  • Model: State-of-the-art embeddings (1536 dimensions)
  • Batch Processing: Efficient bulk embedding generation
  • Storage: pgvector in PostgreSQL
  • Indexing: IVFFLAT for fast similarity search

4. Retrieval-Augmented Generation (RAG)

User Query → Embed Query → Vector Search → Retrieve Top-K Chunks
                                              ↓
                                    Create Context from Chunks
                                              ↓
                                    Enhance with Domain Prompt
                                              ↓
                                    Send to LLM for Analysis
                                              ↓
                                    Parse & Structure Output

5. Multi-Domain LLM Analysis

  • Invoice Analysis: Line items, totals, payment terms, anomalies
  • Contract Analysis: Parties, dates, risks, compliance, obligations
  • CV Analysis: Experience, skills, certifications, suitability
  • Company Profile: Industry, size, financials, strengths
  • Custom Analysis: User-defined extraction templates

6. Entity & Risk Extraction

  • Named Entity Recognition: People, organizations, amounts, dates
  • Risk Assessment: Contractual risks, compliance issues, red flags
  • Confidence Scoring: ML-based confidence metrics
  • Anomaly Detection: Identifies unusual patterns

Technology Stack

Infrastructure

  • Runtime: Node.js 18+
  • Framework: Next.js 14 (TypeScript)
  • Deployment: Vercel / Docker / On-premise

Data & Storage

  • Database: PostgreSQL 14+
  • Vector Store: pgvector extension
  • File Storage: Scalable object storage (S3-compatible)
  • Caching: Redis for performance

AI & ML

  • Embeddings: Advanced embedding models (1536 dimensions)
  • LLM: Claude 3 Sonnet / Llama 3 / Custom models
  • LLM Orchestration: OpenRouter (multi-model support)
  • NLP: Regex + ML-based entity extraction

Frontend

  • UI Framework: React 18 with TypeScript
  • Components: shadcn/ui (Radix UI primitives)
  • Styling: Tailwind CSS
  • State: React Query + Context API

Security & Compliance

  • Auth: Supabase Auth + JWT
  • Encryption: AES-256 for documents, TLS 1.3 for transit
  • RLS: Row-level security in database
  • Audit: Complete audit logging
  • Compliance: SOC 2 Type II, HIPAA-ready, GDPR

Performance Characteristics

Processing Speed

  • Text Extraction: 2-5 seconds
  • Embedding Generation: 5-10 seconds (50 chunks)
  • LLM Analysis: 10-30 seconds
  • Total: 30-60 seconds per document

Scalability

  • Throughput: 1,000+ documents/day per instance
  • Concurrency: Horizontal scaling with load balancing
  • Latency: <100ms API response (after processing)

Accuracy

  • Entity Extraction: 98%+ precision
  • Risk Detection: 95%+ recall
  • Compliance Checking: 99%+ accuracy

API Architecture

RESTful Endpoints

  • POST /api/documents/upload – Upload document for processing
  • GET /api/documents/{id} – Retrieve document metadata
  • POST /api/documents/{id}/analyze – Trigger analysis
  • GET /api/documents/{id}/insights – Get analysis results
  • GET /api/documents/{id}/entities – Get extracted entities

Authentication

  • API Key authentication for service-to-service
  • JWT tokens for user sessions
  • OAuth 2.0 for enterprise SSO

Rate Limiting

  • Free tier: 5 documents/day
  • Standard tier: 100 documents/day
  • Enterprise: Custom limits

Deployment Options

1. Cloud-Hosted SaaS

  • Multi-tenant, fully managed
  • Automatic scaling
  • Regular backups and monitoring
  • 99.9% SLA

2. On-Premise

  • Single-tenant deployment
  • Full data control
  • Custom integrations
  • Dedicated support

3. Hybrid

  • SaaS for standard workloads
  • On-premise for sensitive data
  • Unified management console

Security Architecture

┌──────────────────────────────────────┐
│   Client (HTTPS TLS 1.3)             │
└─────────────┬────────────────────────┘
              │
        ┌─────▼──────┐
        │ API Gateway │ (Rate Limiting, DDoS Protection)
        └─────┬──────┘
              │
        ┌─────▼──────────────┐
        │ Authentication     │ (JWT, API Keys, OAuth)
        │ & Authorization    │
        └─────┬──────────────┘
              │
        ┌─────▼──────────────────────────┐
        │ Application Layer              │
        │ (Business Logic, Encryption)   │
        └─────┬──────────────────────────┘
              │
        ┌─────▼──────────────────────────┐
        │ Database Layer                 │
        │ (RLS, Encryption at Rest)      │
        └───────────────────────────────┘

Security Features

  • Encryption in Transit: TLS 1.3 (all connections)
  • Encryption at Rest: AES-256 for documents
  • Access Control: Role-based (RBAC) + Row-level (RLS)
  • Audit Logging: All actions logged with timestamps
  • Compliance: SOC 2 Type II, HIPAA, GDPR

Diagram References

The following diagrams are referenced in this architecture:

  • /assets/diagrams/system-architecture.png – Complete system diagram
  • /assets/diagrams/data-flow.png – Document processing flow
  • /assets/diagrams/rag-pipeline.png – RAG analysis pipeline
  • /assets/diagrams/security-architecture.png – Security layers

Next Steps

Questions? Check the FAQ or Developer Guide.