A defined evidence workstream for active reviews.
- Energy, utility, transportation, and telecommunications operators
- Vendors supplying AI-enabled systems into regulated environments
- Security, risk, and internal-audit teams reviewing operational AI
- Organizations preparing customer assurance or procurement evidence packages
What enters the review package.
System boundaries, operational purpose, model or service version, and accountable owner
Testing, monitoring, human intervention, change approval, and incident artifacts
Identity, access, segregation, data handling, retention, and vendor records
Control mappings, risk decisions, remediation tasks, and reviewer approvals
Where procurement and assurance reviews stall.
Operational controls exist, but evidence is not tied to the AI system version under review.
Monitoring records are available, but ownership, escalation, or human intervention decisions are unclear.
Supplier documents describe capabilities without demonstrating how local controls are implemented and reviewed.
Changes are recorded in technical systems but not translated into reviewer-readable evidence dependencies.
One fixed-scope engagement. Five reviewer-facing outputs.
Evidence Inventory, Mapping Summary, Gap Register, Ordered Remediation Plan, and Chain-of-Custody Statement.
- Define the AI system and review context.
- Receive and inventory the approved evidence set.
- Verify evidence relationships and record gaps.
- Order remediation by review dependency.
- Deliver the point-in-time evidence package.
Give reviewers a package they can examine without reconstruction.
$3,500. One defined AI system and review context. No annual license required.