The audit-trail spine
for regulated
AI agents.
For FinTech
SR 11-7 model risk documentation, ECOA disparate impact reporting, OCC-examiner-ready artifacts.
For Healthcare
FDA 510(k) submission assist, PCCP-aware governance, HIPAA-architected from day zero.
The compliance gauntlet
A four-step gauntlet, stitched together with spreadsheets.
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 model risk file your examiner is going to read.
I.
Horizontal tools, vertical regulators
Credo AI, Fiddler, Arthur — none of them ship the SR 11-7 model risk documentation pack an OCC examiner expects. They were built for "AI governance" in the abstract.
II.
Audit logs that aren't audit logs
Most AI observability tools capture latency and cost. None capture the feature snapshots and decision metadata that an SR 11-7 reconstruction requires.
III.
No certified human in the loop
Banks need third-party model validation under SR 11-7. 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 the SR 11-7 whitepaper · Ashish K. Saxena
The architecture
One platform. Four artifacts. A single spine.
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.
The founder
Credibility that pre-sells the platform.
Ashish K. Saxena
FinTech engineer · AI ethicist · IJSR reviewer
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 in regulated industries — at Amazon FinTech, in hospital management systems, and in published frameworks read by the field — the gap became impossible to ignore.
- Amazon FinTech — 40% fraud reduction, 75% processing-error reduction at scale2018–2023
- Author, The Ethics of Artificial Intelligence & Society and the Machine2024
- 42 peer-reviewed papers · 37 citations on flagship FinTech fraud-detection paperIJSR · IEEE
- IEEE TEMSCON & ISTAS contributor · IJSR reviewerOngoing
- Best Technical Researcher of AI 2024 · London Book Festival winner2024
Peer-reviewed papers, two books, thirty-seven citations.
Spanning fraud detection, LSTM hospital systems, AI ethics, 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 ten design partners in 2026. US banks at $10B+ AUM. Series B+ fintechs with lending or fraud products. Health systems planning clinical AI rollouts.
The conversations are with me directly. There is no sales team. We will spend thirty minutes on your specific SR 11-7 exam, your specific model 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.
Reaching out: ashish@caventia.com