Compliance Document Management System
A powerful document management system designed for regulatory compliance, using MongoDB for document storage and Azure OpenAI for intelligent document analysis and question answering
An experiment in document intelligence for regulatory compliance work. Compliance teams live inside large, slow-moving documents — policies, standards, regulations — and the questions they ask ("what does this require of us?", "where is data retention addressed?") are semantic, not lexical. Keyword search does badly at this.
The system stores documents in MongoDB with their metadata, embeds their contents as vectors, and offers two things on top: semantic search, which finds the relevant sections even when the wording differs from the query, and question answering, where responses are grounded in citations back to the exact passages they came from. The citation part matters most — in compliance work an unsourced answer is worthless.
It is Python throughout, with Azure OpenAI providing the embeddings and the language model, and the codebase kept honest by MyPy, Black, and pytest. There is no public deployment — it is a working exploration of the pattern rather than a product.