AI-Powered Document Processing System

Designed and developed a cloud-ready AI document processing platform that enables users to upload single or multiple PDF files, process scanned documents with OCR, generate embeddings, and interact with document content through a real-time conversational interface.

Built a Retrieval-Augmented Generation (RAG) pipeline for semantic search and document Q&A, providing fast responses with page-level citations and direct links to source content. Implemented OCR processing for scanned PDFs, document chunking, vector indexing, and high-accuracy information retrieval.

Developed structured data extraction workflows for legal and financial documents, including configurable field-level extraction and JSON export APIs for downstream systems.

Key responsibilities included:
- Building PDF upload and document-processing pipelines
- Implementing OCR for scanned and image-based PDFs
- Generating embeddings and managing vector databases
- Developing RAG-based document chat systems
- Integrating OpenAI and/or enterprise-ready LLMs
- Implementing streaming AI responses with source citations
- Building semantic and vector search functionality
- Developing structured field extraction pipelines
- Creating REST APIs with JSON output
- Optimizing retrieval latency and system performance
- Implementing secure document storage and access controls
- Adding logging, monitoring, and error handling
- Containerizing services with Docker and Docker Compose
- Preparing deployment documentation and technical README files