A Regulation-to-Execution engine for Indian BFSI.
Compliance teams don't just need to know that a regulation changed. They need to know what to implement, who owns it, by when, what evidence to keep, and what to tell the board. LexGuard turns a regulatory circular into that plan, in minutes.
A side project I built solo, nights and weekends, to prove I can take an AI product end to end — problem, PRD, working MVP, and my own QA. It's a skills demo, not a company I'm raising for.
The problem
A compliance officer facing a new RBI or SEBI circular has to read 50 to 100 pages of legal language, decide whether it even applies, extract every obligation and deadline, ask product whether customer journeys are affected, ask engineering what systems must change, draft a board note, update policies and SOPs, and maintain audit evidence for all of it. It's slow, expensive, and inconsistent, and the handoff to product and engineering is usually a vague email.
Existing tools track workflows and send alerts. Almost none of them do the hard part: translating an unstructured regulation into implementation-ready actions across compliance, product, engineering, and the board. That's the whitespace.
What it does
Upload a circular and LexGuard returns a structured, board-ready to engineering-ready execution plan, with a source reference and a confidence signal on every output.
A board impact note: risks framed as financial, regulatory, operational, and reputational, plus the specific decisions leadership must make.
Applicability analysis and obligations, each with owner, deadline, risk-if-missed, implementation steps, and the evidence to retain.
User-flow impact plus product requirements and Jira-ready tickets, with acceptance criteria and no invented customer-facing changes when the impact is internal.
System impact, API / database / logging changes, and Jira-ready engineering tickets mapped back to the exact obligation.
How the MVP works
The strategy behind it
The MVP proves one hypothesis: a regulatory PDF can become board, compliance, product, and engineering output in minutes. I also wrote the PRD as a PM exercise — to practise sizing and scoping a product bet the way I would on the job. It's product thinking, not a business plan I'm executing.
How I pressure-tested it
I evaluated the engine on synthetic test cases scored across schema completeness, content quality, context accuracy, actionability, and hallucination risk.
{"is_regulatory": false} to stop the pipeline if not. The restaurant menu now gets rejected cleanly. I also tightened citation rules so exact sections and penalty amounts (Section 35A, Rs. 1 crore) are copied verbatim instead of generalised.
The point isn't the 93.8%. It's that I built the thing, then tried to break it, found the failure that mattered, and knew how to close it.
Where I'd take it
A thought exercise in sequencing, not a plan I'm executing. It shows how I'd stage an AI product from a working analyzer to an embedded compliance layer.
/check-compliance API validating KYC, onboarding, eSign, lending, and grievance flows inline.