Policies don’t
pass audits.
Evidence does.
Qapitol converts AI policies and obligations into system-level controls, verification, and audit-ready evidence — proof, in a form someone outside the building will accept.
Governance stays on paper.
Most AI governance lives in documents — a policy, a committee, a risk register, a set of principles. It describes what should be true. It rarely proves what is true.
When a regulator asks how an automated decision was made, or an auditor asks for the controls on a production system, a policy document is not evidence. The gap between “we have a governance framework” and “here is proof the control was applied to this system on this decision” is exactly where AI compliance fails.
Policy-to-system mapping
Qapitol connects each obligation to the system it governs: which policy applies to which AI system, which control implements it, and where the evidence that the control works actually lives. Governance stops being a parallel paper exercise and becomes a property of the running system.
- ObligationA policy or regulatory requirement
- AI systemThe app, agent or model it governs
- ControlWhat implements the obligation
- EvidenceWhere proof the control works lives
Control evidence, built for audit
Control validation
Confirm the controls a policy requires are actually in place and effective.
Evidence collection
The traces, logs, eval results and attestations an auditor accepts.
Risk classification
Systems mapped to their regulatory exposure (EU AI Act, sector rules).
Sign-off packs
The assembled proof a leader signs and a regulator reviews.
One artefact: proof someone outside will accept
Sold standalone for teams facing an audit or a regulatory deadline, or as the evidence layer of a Sign-Off Program. The output is the same artefact: proof, in a form someone outside the building will accept.
- Standalone — when you are facing an audit or a regulatory deadline.
- As the evidence layer of a Sign-Off Program.