Inspiration
The rapid advancements in Vision Language Models in 2025, especially the arrival of Nanonets OCR2-3B and NVIDIA NeMo Retriever, inspired me to develop a practical system to tackle real-world document chaos. I wanted to help sectors like healthcare and government improve efficiency by combining powerful OCR with semantic retrieval.
What it does
This system transforms scanned or photographed documents into searchable, structured knowledge. It extracts complex text, tables, equations, and more via OCR, converts the content into semantic embeddings, and enables natural language queries that return precise answers with context.
How I built it
I combined vLLM-served Nanonets OCR2-3B for state-of-the-art text extraction, NVIDIA NeMo Retriever for embedding and reranking, and a vector store powered by FAISS. The backend APIs run on FastAPI, and the user-friendly interface is built with Streamlit—creating a scalable, production-ready pipeline.
Challenges I ran into
Handling complex layouts with embedded images and mathematical notations was tough. Latency optimization between OCR, embeddings, and retrieval needed careful tuning. Integrating heterogeneous AI services efficiently and ensuring deployment with GPU acceleration also posed challenges.
Accomplishments that I'm proud of
Delivering 70-80% reduction in manual document processing time and achieving production-grade accuracy on messy healthcare and government documents. Building a fully integrated OCR + RAG system capable of answering natural language queries over real-world documents.
What I learned
The power of combining vision language models with semantic retrieval and reranking. The importance of chunking documents smartly for efficient querying. How effective architecture and GPU-accelerated serving maximize throughput and user experience.
What's next for Document Intelligence System
Scaling vector store backend to Milvus for billion-scale production, adding support for multilingual documents, and integrating active human-in-the-loop validation workflows for critical healthcare use cases. Expanding to other industries and adding multi-modal data (images, forms, handwriting).
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