Suvrakamal Das

Designed and deployed an end-to-end retrieval-augmented generation (RAG) system for banks, featuring advanced query processing, custom chunking, and hybrid BM25 + vector search. Achieved a 40% improvement in response accuracy vs traditional chatbot solutions. Built a production-grade data ingestion pipeline with automated web scraping, real-time preprocessing, and a PostgreSQL + pgVector architecture using HNSW indexing. Managed 10K+ financial documents with 99% uptime and delivered client demos to Forrester Research.

RAG Application for Banks: Engineered end-to-end retrieval-augmented generation system for financial institutions featuring advanced query processing, custom chunking algorithms, and hybrid search (BM25 + vector similarity), resulting in 40% improved response accuracy over traditional chatbot solutions. Pilot with banks and other firms: Built production-scale data ingestion pipeline with automated web scraping, real-time preprocessing, and PostgreSQL + pgVector architecture using HNSW indexing, processing 10K+ financial documents with 99% uptime and successfully delivering client demonstration to Forrester Research.

RAG Application for Banks: Engineered end-to-end retrieval-augmented generation system for financial institutions featuring advanced query processing, custom chunking algorithms, and hybrid search (BM25 + vector similarity), achieving a 40% improvement in response accuracy over traditional chatbot solutions. Pilot with banks and other firms: Built production-scale data ingestion pipeline with automated web scraping, real-time preprocessing, and PostgreSQL + pgVector architecture using HNSW indexing, processing 10K+ financial documents with 99% uptime and delivered client demonstration to Forrester Research.