Proven with Industry Leaders
The governance gate
Why bank AI pilots stall in 2026
The technology works in the demo. It dies in review — for four reasons, one of which only landed this spring.
Public models are off-limits
Customer records, transactions, MNPI — none of it can go to a consumer LLM. Staff route around the rules anyway; shadow AI is already inside the bank.
The model-risk ground just moved
April 2026's revised interagency guidance replaced SR 11-7 — and put generative and agentic AI explicitly outside its scope. There's no examiner template; the bank has to govern it itself.
Agentic risk cascades
One unattributed or compromised agent can amplify across workflows. Without per-agent identity and an audit trail, there’s no containment and no chain of custody.
Shift left, or stall
The new guidance pushes controls to the start of the model lifecycle. Pilots that bolt governance on at the end die in validation.
What we deliver
From stalled pilot to production
Start with the low-risk read; grow into the flagship build — and, only if it's the right fit, optional managed operation. You own the result either way.
Every workload we take toward production is read against three dimensions. That read — not the pilot result — is what separates a demo that impresses from a system you'd actually deploy.
AI governance review
A fast, fixed-scope read of your AI workload — the value it creates, the burden it must demonstrably honor, and whether it holds up in production rather than just passing the pilot. An architecture decision, not a compliance opinion.
Private AI on Cloudera
The flagship build — AI on transaction and customer data, in-perimeter, model-agnostic.
Governed account access
WSO2 agent identity, API governance and audit evidence — answer "which agent touched which account, under whose authority."
AML & KYC intelligence
Investigator copilots and screening intelligence on your own data — alert triage, narrative drafting, network views.
Explore the KYC/AML practicePOC rescue & production readiness
The pilot passes but you wouldn’t approve the deployment. We read it against outcome, compliance and surety and produce the design that says go, no-go, or go-if — and names what production actually requires.
Not every workload is a decision
The heaviest model-risk scrutiny falls on models that drive decisions — credit, fraud, AML disposition. But much of the highest-value bank AI decides nothing. Common patterns we see, grouped by the governance weight they carry:
LOWER SCRUTINY · THE ON-RAMPS
Decide nothing — the clean place to start
- Regulatory & board-reporting drafting from your own filings and data
- Document intelligence — credit memos, loan files, contracts, prospectuses
- Reconciliation & break investigation
- Complaint & surveillance summarization
- Call-center & ops copilots over internal knowledge
HIGHER SCRUTINY · DECISIONING
Full model-risk treatment — designed for the evidence from the start
- AML/KYC alert triage & disposition — our established practice
- Fraud & transaction-risk scoring
- Credit & underwriting assistance
- Trade surveillance & market-abuse detection
Building AI for banks as a vendor? We make yours the product that survives model-risk and vendor review.
Depth where it's regulated
WSO2 Partner Compliance and model risk have their owners — your risk, legal and validation teams. We design the architecture that makes their posture demonstrable and durable in production. We're integrators, not counsel.