Governance platform
The operating layer between AI policy and production.
AIGR™ is designed to maintain the evidence, controls, review decisions and monitoring events behind enterprise AI governance in one system of record—so a portfolio can be governed continuously instead of reconstructed for every review.
One record from system inventory to ongoing review.
The platform keeps the operating model compact: identify the system, map the controls, attach evidence, record reviewer decisions and monitor material change.
System registry
System, accountable owner, intended use, lifecycle stage and risk context.
Inventory
Control map
Governance requirements translated into reviewable control expectations.
Controls
Evidence record
Artifacts connected to owners, dates, versions and reviewer states.
Evidence
Decision record
Findings, exceptions, critical gates, challenge and approval history.
Review
Ongoing review
Material change, incidents and evidence expiry can trigger reassessment.
Monitor
Every requirement resolves to a controlled state.
The platform separates “not yet reviewed” from “failed,” which prevents ambiguous spreadsheet statuses from entering the rating process.
Evidence passes relevance, currency, traceability and sufficiency review.
Evidence is present but reviewer determination is not complete.
Excluded only with an explicit scope-based justification.
A material failure that can hold the governed outcome regardless of the aggregate score.
Designed for more than one model and more than one review.
Executive portfolio
See systems by business owner, risk class, status, sector framework and open conditions.
Evidence continuity
Track upcoming expiries, missing artifacts and control changes before they become audit surprises.
Governed change
Use model updates, vendor changes, incidents and policy changes as formal re-assessment triggers.
Governance should behave like infrastructure.
Persistent, permissioned, auditable and connected to operating decisions—not a presentation assembled after the fact.