AI Extraction Pipeline and Chat RAG

DOCUMENT.INTELLIGENCE

AI Document Processing & Intelligence Platform

DOCUMENT.INTELLIGENCE is a complete enterprise system that transforms how organizations extract, analyze, and interact with information from unstructured documents.

What It Does

The platform automates the entire document intelligence workflow:

Extracts structured data from PDFs, scanned images, Word, Excel, and PowerPoint files using AI

Enables natural language chat with document collections using RAG (Retrieval-Augmented Generation)

Provides human review workflows with annotations, field corrections, and re-extraction capabilities

Compares documents intelligently to identify patterns, differences, and anomalies

Delivers enterprise controls including role-based access, usage quotas, audit logs, and analytics

System Architecture

A full-stack application built with:

Backend: Flask API, Celery workers, Redis, ChromaDB vector database

Frontend: React 19 with Ant Design UI

AI Integration: OpenAI models for extraction, OCR, chat, and embeddings

Authentication: Firebase Auth with role-based permissions

Deployment: Docker Compose with persistent storage

Key Capabilities

Area Capabilities

Document Processing Multi-format support (PDF, images, DOCX, XLSX, PPTX), OCR for scanned files, AI classification, structured JSON output

Intelligence Multi-agent extraction pipeline, confidence scoring, anomaly detection, cross-document comparison

User Experience Batch upload, real-time progress streaming, chat assistant, template management

Review Workflow Field annotations, AI-powered single-field correction, annotation-aware re-extraction

Administration Role-based access (super_admin, admin, user), usage quotas, audit logs, analytics dashboard, system health monitoring, runtime configuration

Security Per-user data isolation, ownership verification on all operations, Firebase authentication

Technical Highlights

Async processing with Celery for handling large documents and batch operations

Vector search via ChromaDB for semantic document retrieval in chat

Streaming responses for real-time chat and extraction progress

Runtime configuration allowing API keys and model selection without redeployment

Dockerized microservices including frontend, backend, workers, Redis, and ChromaDB

Deployment

Containerized with Docker Compose for easy deployment on any infrastructure. Supports both self-hosted and managed options with persistent volumes for uploads, Redis state, and vector database storage.

Current Status

Fully implemented and production-ready with all major features including extraction, RAG chat, templates, batch processing, review workflows, comparisons, audit logging, analytics, quotas, and user management.