Originally published on iNextLabs Casestudy The Problem A leading Malaysian bank had 25+ legal and compliance professionals manually searching through thousands of contracts and regulatory documents every week. Simple queries like "which clauses are affected by the latest BNM guidelines?" took hours. That's not a search problem it's an architecture problem. Here's how we solved it.. The Stack LLMs for contextual understanding and clause analysis Semantic search (vector embeddings) instead of keyword matching RAG (Retrieval-Augmented Generation) to ground responses in actual documents RBAC with database-driven permission management PDPA-aligned data governance controls What We Built Natural Language Query Engine Users ask plain-English questions. The system retrieves semantically relevant document chunks, passes them to the LLM with context, and returns a precise answer not a list of files.…