Federal regulators just excluded generative and agentic AI from model risk management. They didn't excuse banks from governing them. Tamper-evident capture for every prompt, tool call and decision your agents make. Examiner-defensible at banks, 510(k)-ready at health systems.
Examiner-defensible AI agent governance, ECOA disparate impact reporting, model inventory and validation artifacts.
FDA 510(k) submission assist, PCCP-aware governance, HIPAA-architected from day zero.
The compliance gauntlet
Pre-deploy security scan. Runtime audit logging. Governance documentation. Third-party sign-off. Today, each step is a separate vendor, a separate workflow and a separate gap in the file your examiner is going to read.
I.
Credo AI, Fiddler, Arthur - none of them ship the documentation a bank needs when its examiner walks in. They were built for "AI governance" in the abstract.
II.
Most AI observability tools capture latency and cost. None capture the feature snapshots and decision metadata that a governance reconstruction requires.
III.
Banks still need independent model validation. There is no productized auditor network for AI agents - you hire a Big 4 consulting team at $500K and wait six months.
"The audit log isn't a feature. It's the spine. Every other artifact regulators require - model cards, disparate-impact reports, 510(k) submissions - hangs off it".From AI Agent Governance After SR 11-7 · Ashish K. Saxena
The architecture
Every AI decision flows through one capture layer. From there, four product surfaces share evidence, share schema and produce regulator-ready artifacts without manual stitching.
Watch the spine work
Forty seconds. Watch a single agent decision become four regulator-ready documents through the audit-trail spine.
The founder
Fifteen years shipping AI inside large institutions. Two AI-ethics bestsellers. IEEE Senior Member. BCS Fellow.
Caventia exists because the people building AI in banks and hospitals don't have what they need from horizontal AI platforms. After fifteen years deploying machine learning at scale, including the platform engineering behind some of Amazon's largest financial systems, the gap between AI capability and AI accountability became impossible to ignore.
Notes from the founder
Model Risk
Eight principles and no checklist. Here is each of the five controls in full, with the artifact it produces, the failure it prevents, the build order nobody tells you and the drill that proves the whole thing works.
Model Risk
The April 2026 guidance rescinded SR 11-7 and then excluded generative and agentic AI from its own scope. Out of scope is not off the hook. Here is what survived, what quietly vanished, and the five controls that turn eight principles into evidence.
Spanning fraud detection at Amazon, LSTM hospital systems, AI policy and TRiSM frameworks. The kind of credentials banks ask for and rarely find in an AI infrastructure founder.
To the model risk officer reading this -
Caventia is taking five design partners in 2026. Banks $250M+ in assets. FinTech with a named compliance officer. Health systems planning clinical AI rollouts. Size flexible for right-fit teams.
The conversations are with me directly. There is no sales team. We will spend thirty minutes on your specific model-risk exposure, your specific agent inventory or your specific FDA Q-Sub timeline - and figure out together whether the platform we're building fits your gauntlet. If not, you'll leave with a one-page framework you can use anyway.