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Most document automation projects fail because they rely too heavily on OCR.
OCR reads text — it doesn’t understand structure.
We’ve been working on a different approach: sending full-page documents to AI along with a structured field definition, allowing the system to return clean, usable data instead of raw text.
This shift improves accuracy, handles complex layouts, and supports multi-record extraction.
Curious how others are approaching document extraction beyond OCR.
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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Complete stack RAG driven document intelligence assistant developed using FastAPI and React. Features multi-format ingestion support (PDF, DOCX, and scanned images through EasyOCR), with FAISS vector indexing, and 6 LLM driven modes operating on Groq’s Llama 3.3 70B model. Dockerized application via GitHub actions CI pipeline for automatic linting and build validation with sub-2 second response time in local tests. FAISS is chosen over Pinecone for quick in-memory computation without relying on cloud infrastructure, while Groq is selected over other options for quicker and cheaper inference than GPT-4 alternatives.
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