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.
- Automated Compliance Analysis LLMs scan policies against regulatory frameworks (BNM, PDPA Malaysia), flag inconsistencies, and summarize obligations. No manual cross-referencing.
- Contract Diff & Risk Engine Compares contract versions, highlights changed clauses, and scores risk across thousands of documents simultaneously.
- Secure Multi-Tenant Access Role-based permissions ensure users only query documents they're authorized to see. Critical in a banking environment. ---
The Results
- 85% reduction in document review time
- Hour-long searches → seconds
- Improved compliance accuracy and consistency
Key Takeaway
Keyword search is dead for enterprise document workflows. Semantic search + RAG + LLMs is the architecture that actually works at scale in regulated industries.
Happy to go deeper on any part of the stack drop a comment.
Follow iNextLabs for more insights on AI, automation, and next-generation intelligent systems.
