BFSI · Use Case
Regulatory Document Analysis: Answers With a Citation Trail.
A compliance question usually means someone opening a rulebook, finding the relevant clauses across a few documents, and writing up what they say. A retrieval-grounded agent does the finding and the first write-up instead. It answers from your indexed rulebooks, CBUAE, FinCEN, internal policy, and links every point back to the paragraph it came from. Anything feeding a filing or a policy position still goes to a compliance reviewer.
The Problem
The rules are public. Finding the right ones is the work.
Regulatory text is long, cross-referenced, and spread across primary rulebooks, circulars, and internal policy that restates or tightens them. Answering a single question means knowing which documents apply, reading the relevant sections together, and writing a defensible summary with the sources attached. It is careful work, and it does not scale with the number of questions a compliance team gets.
The judgment stays with a person. What a model can take on is the retrieval and the first draft of the answer that sits around that judgment.
Retrieval and drafting, not interpretation
The agent surfaces what the rulebooks say and cites each point. It does not decide how a rule applies to your situation. That call stays with compliance and legal.
The Approach
Retrieval over your own rulebook set.
We build a retrieval pipeline over the regulatory and policy documents you nominate, then wrap it in an agent that pulls the passages relevant to a question and drafts an answer with each point tied to its source. A reviewer can open the cited paragraph in seconds and check it against the answer. RAG & LLM Fine-Tuning covers how the index, the retrieval, and the evaluation are built.
Because the answer is grounded in retrieval rather than the model’s memory, a regulatory update flows through by re-indexing the changed document. Nothing is retrained, and there is no drift between what the model “knows” and what the current rulebook says.
Citations, not confidence scores
Every line of an answer points to a specific passage. A reviewer audits the source, and a point the agent cannot cite is withheld rather than guessed.
Guardrails
The same delivery standard as every BFSI engagement.
This is the boundary published sitewide for BFSI: full delivery on analytics, summarisation, and decision-support, with human sign-off on anything that gates a regulatory filing or a policy position. The pipeline runs in your cloud tenant on weights you own. Rulebooks, internal policy, and query text stay inside your environment, and the agent is red-teamed before production.
An answer is a research aid, not a compliance opinion. It carries its sources so a reviewer can stand behind it in front of an examiner.
Built for examiner scrutiny
Every answer traces to a cited passage. That is what lets a compliance reviewer put the output, and their name, in front of a regulator.
Who This Is For
Compliance teams answering the same questions at volume.
Banks, NBFCs, and fintechs operating across more than one regulator or a thick internal policy set, where compliance and legal field a steady stream of rulebook questions and every answer has to trace to a written source.
FAQ
Common questions about regulatory document analysis.
Does it interpret the rules or make the compliance call?
No. It retrieves and summarises what your rulebooks say, with a citation on every point. The interpretation and the decision stay with your compliance and legal teams. For anything that feeds a filing or a policy position, a reviewer signs off.
How current are the answers?
Answers come from whatever is in the index. Re-index an updated rulebook or circular and the next answer reflects it. Nothing is retrained, so a regulatory change is a re-indexing job, not a model project.
What stops it from inventing a rule that is not there?
Retrieval grounding. Every statement in an answer links to a retrieved passage, and the agent is built to withhold a point it cannot cite rather than fill the gap. A reviewer checks the citation, not a confidence score.
Where do our queries and rulebooks go?
Nowhere. Indexing and inference both run inside your cloud environment on model weights you own. Query text and source documents stay in your tenant.
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