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