• Designed and implemented an enterprise-grade Retrieval-Augmented Generation (RAG) system to enable intelligent querying across financial documents (10K invoices and reports). • Built metadata-aware semantic retrieval using FAISS vector indexing with structured filters (year, month, document type) for precise context selection. • Integrated local LLM inference using Ollama (LLaMA) to ensure data privacy and on-prem enterprise deployment. • Engineered document chunking, embedding pipeline, and retrieval orchestration using LangChain. • Implemented source-grounded answer generation with document traceability to reduce hallucination risk. • Developed an interactive Streamlit UI for real-time finance workflow simulations. • Achieved ~40% reduction in query resolution time in simulated enterprise scenarios.