RegImpact AI
AI-powered regulatory compliance assessment for Indian fintech — every finding traced back to a real, verifiable regulatory clause.
Designed & built by Shriyashish Mishra, Product Manager
A 60-second walkthrough of the product — from a plain-language description to a citation-backed compliance report.
Manual reviews take weeks
Compliance assessments today mean a person reading a product spec against a regulation line by line — slow, and it doesn't scale with how fast fintech products ship.
Interpretation varies reviewer to reviewer
Two reviewers reading the same product against the same clause can reach different conclusions — there's no consistent, repeatable standard being applied.
Findings without a paper trail
Most AI compliance tools produce a plausible-sounding verdict with no way to check it against the actual regulatory text — an unverifiable finding is worse than no finding.
Five orchestrated stages, each feeding the next — not a single black-box prompt. Deterministic rules decide what to test before AI reasoning ever runs, and compute the report after. See the full system design.
Why AI at all
Reading a product against a regulation is language work, not arithmetic — the part AI is actually good at. The value isn't speed alone, it's applying the same standard every time, which manual review can't guarantee.
Why retrieval, not memory (RAG)
Findings are tested against real clauses pulled from a regulatory corpus at request time — retrieval-augmented generation, not recalled from what the model memorized during training — so every finding can be traced back to the exact text it's based on. See the architecture for the honest version of this, including its current scale limits.
Why adaptive questioning
A generic intake form asks the same twenty questions regardless of what you're building. RegImpact only asks the 3-5 questions that would actually change the compliance answer for your specific product.
Every finding — compliant or not — cites the exact clause it's based on, verbatim, joined back to the source by id rather than free text. A finding claiming high confidence with no supporting evidence gets flagged automatically, not hidden. Read the full case study for the product decisions behind that.