Governance that holds up to scrutiny.
Every engagement is built around the same principle: AI adoption should survive regulatory review, internal audit, and board-level questioning — not just a pilot demo.
Runtime Governance & Reliability Scoring
Approval is a point in time; risk is continuous. We instrument live systems so the assumptions behind a deployment decision keep being tested — and so a system that drifts outside them is escalated or contained, rather than discovered at the next review.
The reliability score is open source and model- and system-agnostic. It embeds in the evaluation layer or telemetry you already run.
- Reliability scoring across consistency, robustness, uncertainty and safety
- Threshold, escalation and containment design owned by your second line
- Embedding into existing evaluation, monitoring or telemetry pipelines
- Evidence packs that answer “how do you know?” with a record, not an assertion
AI & GenAI Governance Frameworks
We design governance structures — policy, roles, controls, and escalation paths — that let institutions adopt AI and GenAI with a defensible story for regulators, auditors, and the board.
- AI governance policy and standards development
- Risk taxonomy and control mapping for AI/GenAI use cases
- Committee structure and escalation design
- Regulatory-readiness review ahead of exams
Model Risk Management
Traditional model risk discipline, extended to cover the questions generative and agentic systems raise that legacy MRM frameworks weren't built to answer.
- Model inventory and tiering for traditional and generative models
- Independent validation approach and challenge
- Ongoing monitoring design for GenAI and agentic systems
- Third-party and vendor model risk assessment
Advisory & Implementation
Hands-on support for institutions standing up an AI governance function for the first time, or maturing one that hasn't kept pace with how fast AI is moving.
- Governance program stand-up, start to finish
- Gap assessments against current supervisory expectations
- Executive and board education on AI risk
- Interim / fractional AI risk leadership
Independent Research
Practitioner-level analysis of how AI risk and governance practice is evolving — used to keep our own frameworks current, and shared with clients directly.
- Ongoing tracking of supervisory guidance and enforcement trends
- Emerging practice benchmarking across peer institutions
- Point-of-view papers on agentic AI and governance maturity
Not sure which of these you need first?
That's normal — most engagements start with a short scoping conversation.