Jev, LLMs and the gap between confidence and authorization.
Decision models return bounded answers that software can act on directly. That makes agentic AI easier to govern, and raises a harder question: calibrated against what?
Why AI Risk ConsultingEveryone governs AI before launch.
Everyone governs AI before launch.
The risk shows up after.
Approval assumes things stay true: that the system keeps behaving the way it did in testing. In production those assumptions quietly stop holding. We build a runtime reliability score that keeps checking — and tells you the moment the system drifts outside what you approved.
The score is open source, and model- and system-agnostic. We help you embed it in the evaluation layer or telemetry you already run — no new platform to buy, and nothing that ties you to us.
Governance todayWith ARC
Risk assessed once, before go-liveChecked continuously, while the system runs
“Is it AI? Is it agentic?”“What happens if it fails, and can we contain it?”
Assurance rests on documentationAssurance rests on evidence from the running system
You find out at the next reviewYou find out when it happens
Four dimensions, not one number
Consistency, Robustness, Uncertainty and Safety are measured separately. A system that looks strong on average but is weak in one dimension does not get waved through.
Safety is never averaged away
A single serious violation stops the system, whatever the other scores say. Some failures are not survivable on average.
You set what “acceptable” means
The thresholds are your risk appetite, owned by your second line and open to challenge — not a vendor default you inherit.
Evidence, not assertion
Every score traces back to how the system actually behaved. When the board or an examiner asks how you know, the answer is a record.
Checks that can stop a harmful action run inline, as it happens. The broader reliability picture refreshes continuously rather than at the next review cycle.
Latest ResearchAll insights →
AI Governance
AI governance is a control infrastructure problem in a policy costume.
The challenge is not defining how AI should be governed. It is building the capacity to govern it at the speed it is being deployed.
Read the analysis →SupervisionSupervision just moved the goalposts. Model and AI risk fell outside them.
The OCC and FDIC redrew supervision around material financial harm. Four routes to a finding on AI now miss.
Read the analysis →What We Do
Governance built for regulated environments
One discipline, applied from approval through to production.
01
Runtime Governance
Reliability scoring on live systems, so drift is escalated or contained rather than found at the next review.
02AI & GenAI Governance
Frameworks, policies, and controls built to withstand regulatory and internal audit scrutiny.
03Model Risk Management
Validation, monitoring, and inventory practices spanning traditional and generative models alike.
Experience
Twenty years across all three lines of defense.
Most AI governance advice comes from people who have only ever worked in one of them. Having sat on every side of the challenge shapes what we recommend — and what we leave out. We know which controls hold up under scrutiny, and which ones only generate work.
First line
Where the models are built and owned
What it takes to get a system into production, and what a control costs the people living with it every day.
Second line
Where they are validated and challenged
Setting the standard, running independent challenge, and defending both to supervisors and the board.
Third line
Where the standard itself is tested
Assurance over whether the framework was genuinely applied — or only documented as though it were.
Ready to talk through your AI governance gaps?
A short conversation is usually enough to tell you where to start.