AI governance that creates evidence, not just policy.
Regulated organisations need AI to be auditable, explainable and controllable. We help you embed evidence-grade governance into how AI decisions are made, released and refused.
AI cannot be a PDF policy sitting outside the system.
Boards, risk teams and regulators need to see what AI did, on what data, with what human oversight, and what was refused. Without that, AI adoption stalls at the audit gate.
Auditability
Every AI-influenced decision must be reconstructable end-to-end.
Explainability
Show the data, the reasoning path and the human oversight.
Data sovereignty
Control where data is processed and by which models.
Risk sign-off
Governance owned by the second line, evidenced in the first.
Refusal logic
AI must be allowed to refuse — and record why.
Release control
No evidence, no release.
Six things governance has to produce.
Governance that satisfies boards and regulators must produce evidence artefacts on every AI-influenced decision.
- Input data lineage and permissions
- Model, version and prompt used
- Recommendation and confidence signal
- Human oversight and approval record
- Refusal-with-reasons where blocked
- Immutable, exportable audit pack
An evidence and governance layer for enterprise AI.
Not a chatbot. Not a dashboard. A governance layer that sits between AI recommendations and enterprise action — capturing evidence, applying policy, releasing or refusing.
Where governance changes outcomes.
AI release approval
Structured release gates with evidence attached to every decision.
Refusal-with-reasons
AI can decline to proceed — and the record shows why.
Decision evidence
Every AI-influenced outcome carries a reconstructable trail.
Audit pack
Exportable pack for internal and external review.
Risk review
Second-line review with signal, not noise.
Human oversight
Explicit oversight records on high-impact decisions.
Own where it runs.
Governance controls sit closest to the estate they protect. We support private cloud, on-prem and client-controlled deployment models.
Deploy inside your enterprise cloud with your identity and controls.
For sensitive estates or strict residency needs.
Bring your own keys and identity provider.
Enterprise roles that need evidence.
Owns platform, integration and delivery risk.
Needs evidence for every AI-influenced decision.
Owns lineage, sovereignty and data quality.
Owns the operating model for AI adoption.
Independent review with structured evidence.
Sovereign AI Governance Pilot.
A time-boxed engagement to prove governance value on one high-priority use case, with an evidence pack you can take to the board.
- 1StepWeeks 1–2
Use-case selection and evidence mapping.
- 2StepWeeks 3–4
Governance workflow configuration.
- 3StepWeeks 5–8
Refused-with-reasons demo and iteration.
- 4StepWeeks 9–12
Evidence pack and expansion roadmap.
Common questions.
Move AI adoption past the audit gate.
Talk to us about an evidence-grade governance pilot in your environment.