The governance layer between your AI agents and your regulators. Prove compliance at every inference, not every quarter.
But the governance stack was built for a world before AI inference. Policies exist on paper. Evidence is pulled after the fact. No one can reconstruct what the AI actually saw when it made a decision.
AI usage is scattered across teams, models, and vendors. No single view of what agents exist or what they do. Shadow AI is a known problem with no known solution.
AI policies exist on paper but are never evaluated at runtime. Controls fire before or after execution — never during. Your policy framework has no connection to your AI behavior.
When the examiner asks why the AI said what it said, you cannot reconstruct the inputs, policy context, or retrieval state. Audit response takes weeks, not hours.
Evident AI sits between your AI agents and your regulators — recording decisions, evaluating policy, and producing audit-grade evidence continuously.
Your AI policies should be evaluated at every inference, not every quarter. Evident AI binds governance controls to AI workflows and evaluates them in real time — so compliance is a continuous state, not a periodic exercise.
When the examiner asks why the AI said what it said, you need an answer in hours — not weeks. Evident AI captures the full decision context at runtime and makes it reconstructable on demand, with regulator-grade integrity.
Where AI decisions carry regulatory weight and audit exposure — and where "we have an AI policy" is no longer a sufficient answer.
Banks, insurers, asset managers, wealth managers, and health systems deploying LLM-powered applications into decision workflows — credit decisioning, underwriting, portfolio recommendations, claims processing.
CROs, CTOs, CIOs, CCOs, and Model Risk leaders who need to prove to examiners that every AI-assisted decision was governed by policy — not just monitored after the fact.
OCC, FDIC, SEC, and FINRA have made AI governance an examination priority. EU AI Act high-risk obligations are approaching. The window to get governance right is closing.
Evident AI was founded by operators who have spent their careers shipping products into regulated environments — and who have lived through what happens when governance is an afterthought.
Padma brings extensive experience building and operationalizing products from concept to scaled production. Across her career she has led product organizations through every stage — defining 0-to-1 wedges, architecting platforms that hold up under enterprise load, and shepherding launches into regulated industries where the bar for trust is not negotiable.
Her focus at Evident AI is translating the messy realities of AI adoption inside banks, insurers, and health systems into product surfaces that risk, compliance, and engineering teams can all stand behind. She is a believer that great enterprise software disappears into a workflow — and that governance, done right, becomes a multiplier rather than a tax.
Raghu has spent his career at the intersection of security and governance, designing and operationalizing the controls that let regulated enterprises ship modern technology without losing their footing with examiners. He has led security and governance functions through audits, regulatory reviews, and the kind of incident response that teaches you exactly which controls actually matter at 3am.
At Evident AI, he leads the work of turning policy frameworks — model risk, AI governance, third-party risk, data protection — into runtime controls that fire at the moment of inference. His thesis: the next generation of governance is not written in PDFs, it is enforced in the request path.
We're working with a small number of design partners to co-develop the platform against real governance requirements. Request a demo or express interest below.
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