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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.
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Developed an AI-powered document assistant that allows users to upload multiple PDF files and ask questions about their content. The application retrieves relevant information from uploaded documents and uses a large language model (LLM) to generate context-aware answers.
Key Features
Multi-PDF document upload and processing
Retrieval-Augmented Generation (RAG) for document-based question answering
Semantic search for retrieving relevant information
AI-powered responses using LLM integration
FastAPI backend for API-based interactions
Streamlit interface for user interaction
LangChain and LangGraph for AI workflow development
Technology Stack
Python, LangChain, LangGraph, RAG, LLMs, FastAPI, Streamlit, FAISS/Vector Store, Groq API.
Business Value
Helps users find information across multiple documents without manually searching through every PDF. The solution can be adapted for internal knowledge bases, business reports, technical documentation, and document-heavy workflows.
My Role
Designed and developed the application, integrated document retrieval and LLM-based question answering, and connected the backend API with the user interface.
Available Services
Custom AI chatbot development
RAG pipeline development and customization
PDF-based knowledge assistants
Python backend and API integration
AI application prototyping
