LLM integration & RAG

What it is

A large language model has no knowledge of your loan agreements, your internal policy documents, or the filing your compliance team submitted last quarter. Retrieval-augmented generation (RAG) closes that gap: the system searches your own document set for the relevant passages, and the model generates an answer grounded in what it found, with the source attached. Done well, this is the difference between an analyst copilot that's actually useful and one that gets quietly stopped being used after the first confidently wrong answer.

The hard part of RAG in finance is rarely the model call. It's the retrieval. Financial documents are long, dense, and full of numbers and defined terms that a naive chunking strategy shreds into meaningless fragments. We build hybrid retrieval, combining semantic search with keyword and metadata filtering, tuned against your actual document types: credit agreements, prospectuses, policy manuals, filings, internal memos. Every answer the system produces carries a citation back to the specific passage it used, so an analyst can verify it in seconds instead of trusting it blind.

We also build the evaluation layer most RAG projects skip: a test set of real questions with known correct answers, so you can measure retrieval accuracy and answer quality before rollout, and keep measuring it as your document set grows. Where the underlying documents are subject to access restrictions, such as client-confidential deal documents or non-public information, retrieval respects the same access controls the documents already carry, rather than treating the corpus as one flat pool.