RAG AI Chatbot That Processes 1,000 Legal Documents Daily
A US fintech company needed to automate the review of 1,000+ loan agreements per day. We built a custom LangChain RAG system that reduced review time from 6 hours to 8 minutes per document, with 99.1% accuracy.
The Challenge
The client's compliance team was manually reviewing 1,000+ loan agreements daily - each requiring 6 hours of analyst time to check for clause violations, missing disclosures, and regulatory compliance issues. Manual review was causing 3–4 day processing delays and limiting loan volume.
The Solution
We built a RAG pipeline using LangChain + GPT-4 + Pinecone. Documents are parsed via AWS Textract, embedded with OpenAI's text-embedding-3-large, and stored in Pinecone. The LangChain agent performs clause-by-clause analysis against a vector database of regulatory requirements, flagging violations with citation-backed evidence. A Next.js dashboard gives compliance officers a review interface.
Project Overview
- Client
- US Fintech Lending Platform (Confidential)
- Industry
- Financial Technology / Lending
- Tech Stack
- PythonLangChainOpenAI GPT-4PineconeFastAPINext.jsAWS Lambda
Measurable Results
"The ROI on this project was so clear that we approved it in a single board meeting. DelhiStack's AI team understood fintech compliance from day one - they weren't just plugging in an API, they were building a real system.