Indian banks process more UPI transactions in a day than Visa does globally. AI in BFSI here has to clear RBI scrutiny at that scale, or it does not ship.

Where we work

Tier-1 private banks and PSU banks, NBFCs and SFBs, life and general insurers, asset managers, and capital-markets infrastructure. The common thread: regulator-visible workloads where explainability, audit, and data residency are not features — they are the licence to operate.

Where AI changes the economics

• AML and surveillance. LLM-assisted alert triage and SAR drafting that cuts investigator load 50–70%, with the full audit trail an FIU expects.
• Underwriting. Alternative-data scoring for thin-file segments — gig workers, new-to-credit, SME — with explainable adverse action and DPDPA-clean data provenance.
• Contact-centre and RM copilots. Vernacular speech-to-text, intent classification, and grounded responses against your policy and product knowledge bases. Hindi and major Indic languages, not just English.
• Document-heavy back office. Claims adjudication, loan operations, KYC review, custody operations — all candidates for retrieval-grounded automation with human-in-loop where the regulator demands it.
• Capital markets. Research-assistant copilots over filings, internal notes, and market data with MNPI segregation enforced by retrieval-layer access control.

Regulatory architecture

Every BFSI deployment we ship maps to a control framework before code is written: RBI model risk tiers, DPDPA consent and purpose mapping, SEBI disclosure obligations where relevant, and the firm’s internal model risk management (MRM) policy. We build the audit log that the next inspection will ask for, not the one the engineering team improvises after the fact.

What you get

Production AI on your data residency, your IAM, your network perimeter. Open and commercial components configured to your risk taxonomy — no vendor lock-in unless you choose it. A system that survives an RBI inspection and a model-risk review the same week.