AI you can verify — and defend.
Assurance is how an institution makes its AI verifiable — to a board, a regulator, an examiner, or its own audit committee.
It is the difference between a system that appears to work and one whose behavior can be evidenced, explained, and defended on demand. We put the controls and the proof in place so that when someone asks how your AI reached a decision, there is a clear, documented answer.
Proof, not hope.
Most organizations can build a capable model. Far fewer can demonstrate, after the fact, why it behaved the way it did — which inputs it saw, which controls applied, who approved what, and whether its outputs stayed inside agreed boundaries.
Governance is the set of decisions and controls that determine what your AI is allowed to do, who is responsible for it, and where the limits sit. Assurance is the evidence that those controls actually held in production — the record that lets a leader sign their name to it.
Treated together, they let an institution prove what its AI does, rather than hope it behaves. That is the standard a board expects of any function that touches customers, capital, or regulated decisions — and the standard AI is now held to as well.
If a decision can't be explained and evidenced, it can't be governed. Assurance turns AI behavior into a record that can be explained and defended.
Four controls that make AI defensible.
A working assurance posture is built from a small number of disciplines that reinforce each other. We put each in place and tie them to clear ownership.
Governance framework
Clear policies, roles, and decision rights for what AI may do, who owns it, and how change is approved — aligned to recognized model-risk and AI-governance practice.
Evaluation & observability
Continuous measurement of accuracy, drift, and failure modes, with the instrumentation to see what a system is doing in production — not only in testing.
Audit-ready evidence
A durable, tamper-evident trail of inputs, decisions, approvals, and overrides — so behavior can be reconstructed and defended long after the fact.
Risk & compliance controls
SOC 2-style operational controls, data handling, and human oversight mapped to your obligations — built to satisfy reviewers without slowing the business.
Frameworks are tailored to your sector and obligations. We put these disciplines in place — and we never claim, on your behalf, a certification you do not hold.
From risk to proof.
Assurance is a sequence, not a document. We move from understanding the risk to producing evidence a reviewer will accept.
Assess
Map where AI is used, what it can affect, and where the real exposure sits — against your obligations and risk appetite.
Instrument
Put controls, logging, and oversight in place so that every consequential decision is observable and recorded as it happens.
Evaluate
Measure behavior continuously against defined standards — accuracy, drift, boundaries, and failure modes — and act on what it shows.
Attest
Produce the evidence pack that lets a leader, reviewer, or examiner confirm the AI did what it was meant to — and only that.
Every decision, linked to its proof.
An assured system leaves a continuous, verifiable trail. Inputs, controls, decisions, and approvals connect end to end — so any output can be traced back to the evidence that justifies it, and forward to who signed off.
For institutions that must prove it.
PE & M&A diligence
Buyers and sponsors who need an objective read on whether an asset's AI is sound, governed, and defensible before — and after — the deal closes.
Regulated institutions
Organizations answerable to examiners and audit committees, where every consequential AI decision must be explainable and supported by evidence.
Growth companies scaling responsibly
Teams moving AI from pilot to production who want to scale on a foundation of controls rather than retrofit governance under pressure later.
Answers, up front.
What is AI assurance & governance?
AI assurance & governance is the discipline of proving that a production AI system does what it claims, within defined limits, and that someone is accountable for it. Assurance is the evidence — objective evaluation, monitoring, and documented controls; governance is the operating structure — the policies, roles, and decision rights that keep the system inside its mandate. Together they turn AI from a capability you have deployed into a system you can prove and defend.
How do you make production AI auditable for a board, regulator, or examiner?
We build an evidence chain that links every material AI decision back to its inputs, the model and version that produced it, the controls applied, and the human responsible at each step. That record is captured continuously in production rather than reconstructed after the fact, so when a board, regulator, or examiner asks how an outcome was reached, the answer is already documented and retrievable. The standard we work to is simple: nothing the system does should be unexplainable to the people who must defend it.
What controls and oversight do you put in place?
We establish a governance framework first — clear ownership, decision rights, escalation paths, and policy aligned to your risk appetite — then instrument the system with continuous evaluation and observability so drift, failures, and edge cases are caught in production, not by customers. On top of that sits an audit-ready evidence layer that captures the proof of each control operating, alongside risk and compliance controls mapped to your regulatory obligations. The result is oversight that is demonstrable, not assumed.
Do you hold a specific certification?
We do not claim to hold a certification we have not earned, and we would be cautious of any firm that did. What we do is operate to recognized control standards — SOC 2-style control design, established model-risk practices — and produce the audit-ready evidence those frameworks expect, so your own auditors and examiners have something concrete to test. Where a formal attestation is required, we structure the program so it can stand up to that scrutiny.
Begin with a Charter.
A fixed-fee diagnostic that maps where your AI is exposed, what controls it needs, and the evidence required to defend it — turning a governance question into a costed, defensible plan.