Fintech.
Fintech moves faster, but compliance requirements are similar to traditional finance. RBI sandbox rules, SEBI directives, and DPDP apply. We work with fintech teams who need production AI that scales without sacrificing governance.
Where we work in fintech
UPI Ecosystem
Payment processing, transaction monitoring, and fraud detection at UPI scale. Architecture patterns that handle regulatory reporting and reconciliation.
Payment Infrastructure
Core payment systems, settlement engines, and treasury operations. AI applications that understand financial messaging and clearing mechanics.
KYC and AML
Customer verification, anti-money laundering detection, and sanctions screening. AI systems with explainability requirements built in.
Fraud Detection
Real-time transaction scoring, behavioral analytics, and fraud pattern detection. Model governance that survives regulatory examination.
Customer Service AI
Support automation with escalation paths, conversation logging, and compliance guardrails appropriate for financial services.
Scale is different. Compliance is similar.
Fintech teams often operate at transaction volumes traditional banks took decades to reach. The infrastructure challenges are different — real-time processing, elastic scaling, and cost efficiency at volume.
But the regulatory requirements are converging. RBI, SEBI, and DPDP apply regardless of founding year. Audit expectations, model risk management, and data governance requirements are becoming consistent across traditional and modern financial services.
We work with fintech teams who need to scale their AI capabilities without building compliance debt that slows them down later.
Building production AI for fintech?
Let's discuss your architecture, your scale requirements, and where governance needs attention.