CrediTrust
Enterprise RAG Complaint Platform
An enterprise-grade Retrieval-Augmented Generation (RAG) platform engineered to process and analyze 500,000+ Consumer Financial Protection Bureau (CFPB) complaints with live WebSocket streaming, FAISS vector search, and strict hallucination guardrails.
System Architecture
Screenshots
Problem
Financial institutions receive hundreds of thousands of consumer complaints but lack intelligent tools to analyze patterns, extract actionable insights, and generate compliance answers. Processing 500,000+ CFPB records demands high performance, zero AI hallucinations, and robust security.
Solution & Microservices Architecture
Built a dockerized microservices architecture with Next.js frontend, FastAPI backend, NGINX reverse proxy, PostgreSQL database, and local Flan-T5 LLM integration. Semantic search is powered by FAISS vector index and HuggingFace embeddings.
Streaming RAG Pipeline & Guardrails
Engineered live WebSocket response streaming powered by Flan-T5. Strict hallucination guardrails validate generated responses against retrieved complaint narratives to guarantee 100% grounded answers.
Analytics Engine & JWT Security
Developed a dynamic StatsEngine that parses 500,000+ CFPB complaint records to deliver real-time KPIs, issue distributions, and month-over-month trends, secured via stateless JWT authentication.
Impact & Evaluation Results
Evaluated against 10 multi-category financial complaint queries, achieving a 4.8/5.0 score across accuracy, grounding, and completeness matrices with zero hallucinations.
Interested in this project?
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