Enterprise-grade AI document processing platform that extracts structured data from PDFs, performs OCR on scanned documents, enables natural-language chat with citations using RAG, and exports structured JSON for downstream systems. Built for legal, finance, and enterprise document workflows.
Organizations handling contracts, invoices, financial records, and legal documents often spend countless hours manually reviewing files, extracting key information, and searching through large document collections.
Traditional OCR solutions only extract text but lack semantic understanding, making it difficult to ask natural-language questions or integrate structured data into downstream business systems.
The client needed a scalable AI-powered platform capable of processing both text-based and scanned PDFs while providing intelligent document search and structured data extraction.
Built an enterprise-grade AI Document Intelligence Platform that combines OCR, Retrieval-Augmented Generation (RAG), semantic search, and structured document extraction into a single workflow.
Users can upload one or many PDF documents, automatically process scanned pages through OCR, generate embeddings for semantic retrieval, and ask natural-language questions across their documents with page-level citations.
The platform also extracts structured fields and exports standardized JSON for integration with ERP, CRM, accounting, and document management systems.
The platform follows a Retrieval-Augmented Generation (RAG) architecture.
Uploaded PDFs are processed through OCR when necessary, parsed into structured content, embedded using OpenAI embeddings, and indexed inside Qdrant for high-performance semantic retrieval.
Chat requests search the vector database for relevant context before generating streaming AI responses with page-level citations.
Structured extraction results can be exported as JSON for downstream business workflows.
The platform dramatically reduces manual document review by automating extraction, semantic search, and structured data generation.
Business users can instantly locate information across hundreds of pages using natural language while maintaining traceable citations back to original documents.
The modular architecture also allows organizations to integrate extracted data into existing enterprise systems without changing the ingestion pipeline.
This project demonstrates expertise in AI integrations, Retrieval-Augmented Generation (RAG), OCR pipelines, vector databases, enterprise SaaS architecture, API development, scalable cloud deployment, and modern document intelligence systems.