Banking, Financial Services, Insurance.
Production AI in a bank is not the same problem as production AI anywhere else. Regulatory frameworks shape architecture choices that generic deployment patterns ignore.
The regulatory frame
RBI Master Directions
IT governance, outsourcing guidelines, and risk management frameworks that shape how AI systems must be architected in Indian banking.
SEBI Cybersecurity
Cybersecurity directives for market infrastructure institutions, depositories, and regulated entities. Security controls, incident reporting, and audit requirements.
DPDP Enforcement
Data protection requirements, notice obligations, and consent management under the Digital Personal Data Protection Act. Data residency and processing constraints.
GDPR for Cross-Border
For banks operating across jurisdictions: data transfer mechanisms, adequacy decisions, and controller-processor obligations.
What BFSI-grade AI deployment requires
Generic deployment patterns work in a demo. Production AI in regulated finance requires different architecture choices:
- Audit logs that satisfy auditor questions — not just storage, but queryable evidentiary chains
- Evidentiary RAG with citation provenance — every response traceable to source documents
- Model card discipline — documented capabilities, limitations, and evaluation results
- Isolated tenancy patterns — customer data separation that survives regulatory examination
- Regional data residency — processing constraints that match regulatory geography
- Evaluation pipelines that exec sponsors can defend in writing
"We design for the second day in production, not the demo."
Common engagement patterns
Document QA for compliance teams
Internal AI systems that answer questions against policy documents, regulatory texts, and internal guidelines — with citation chains audit can follow.
Internal copilots with vetted sources
Engineering and operations assistants constrained to approved knowledge bases. No hallucination of policy or procedure.
Fraud detection model lifecycle
Model development, validation, deployment, and monitoring pipelines that match regulatory expectations for model risk management.
Customer service AI with governance overlay
Customer-facing AI with human escalation paths, conversation logging, and compliance guardrails.
Working on production AI in BFSI?
Start with a 30-minute conversation about your architecture, your regulatory context, and where you need depth.