App Assurance
AI-powered applications, copilots, and user-facing experiences.
What “control layer” means
The independent layer between your AI ambition and your operational risk.
Traditional QA tests whether software does what it did yesterday. Traditional governance describes what should be true on paper. Neither can make a non-deterministic AI system signable.
It is how visibility, evaluation, control and evidence work together — continuously — so that AI systems can be trusted, governed and signed off.
Three assurance dimensions
Where enterprise AI actually lives and fails.
The control layer works across the three places where enterprise AI actually lives and fails.
Four continuous motions
Not once at launch — continuously.
Across every dimension, the control layer runs four motions — not once at launch, but continuously, because AI systems don’t hold still.
Evaluation
Score behaviour, test failure modes adversarially, and surface what cannot be signed off.
Control
Constrain what the system can’t be trusted to do alone.
Verification
Produce the audit-ready proof that a control was applied.
Continuous monitoring
Watch behaviour and drift in production, where AI systems change.
Why independent
An operating capability — not a tool, not a committee.
The control layer sits independently between your AI and your promises — the same way a financial audit sits independently between a business and the numbers it reports.
It is not the team that builds the AI, and it is not a slide deck describing what should be true. It is how visibility, evaluation, control and evidence work together, continuously, so that AI systems can be trusted, governed and signed off.
That independence is what turns “we think it’s fine” into something you can actually stand behind — an operating capability, running across App, Agent and Data, that makes enterprise AI signable.