No Management System Behind The AI
- AI shipped without a documented AIMS
- Impact assessments happen after the fact
- Model lifecycle triggers are unwritten
- No inventory of AI systems and their owners
Scores your AI governance against ISO/IEC 42001, grounded in real evidence, with a readiness percentage and observations on every gap. Extends to NIST AI RMF and the EU AI Act.
Why It's Hard
The obligations arrive faster than the evidence. Boards want assurance now, certification bodies want it in the standard's own language, and neither reads the same dashboard.

Building an AI management system by hand is a job that predates the standards. Agent workflows keep the evidence collected, the controls scored, and the readiness percentage current so governance operates as a program rather than an annual scramble.
The Coverage
Clauses 4-10 for the AI management system itself, plus the 38 Annex A reference controls (A.2 through A.10), scored as a single AIMS conformity assessment with a colour-banded readiness percentage.
See it workEvery control opens to the observations behind its verdict, a severity, and a named remediation, 'ad hoc for 1 of 5 systems, no lifecycle triggers, nothing retained' rather than 'needs improvement'.
The assessment knows impact assessment (6.1.4, A.5) is the control ISO 42001 hangs responsible-AI on, and cascades a missing process into every control that depends on it.
Cross-framework mapping so a single evidence pipeline scores your AIMS against ISO 42001 today and extends into NIST AI RMF and the EU AI Act as they come into scope, one collection, multiple assessments.
01
Evidence is pulled from cloud and AI-platform sources, the model registry, IAM, drift metrics, the data catalog, into a raw evidence store.
02
Every control across clauses 4-10 and Annex A is scored against what the evidence actually shows, producing an overall readiness percentage with a verdict summary of In Place, Partial and Not In Place.
03
Each control carries assessor-grade observations, a severity and a named remediation, and the auditor chat reasons over the whole assessment on demand.
04
Controls with no evidence become one-click requests to the customer, so the assessment drives the next round of collection instead of stalling on a spreadsheet of outstanding asks.
What The Programme Produces
Grounded verdicts, prioritised blockers, and the artifacts a Stage 1 review actually consumes.
Verdicts cite real evidence collected from cloud and AI-platform sources, so a control is judged against what the evidence shows, 'drift metrics missing on 3 of 5 models', not on whether a document was uploaded.
A scoping questionnaire marks controls Not Applicable by your role, provider, developer, deployer or user, so you are not scored against controls that do not apply to you.
The blockers that actually stop a Stage 1 review, impact assessment, internal audit, the SoA, are surfaced ahead of the noise.
The assessment exports to a formatted readiness and gap report and to CSV and JSON, ready for the board and the certification body.
Boards and certification bodies both want assurance. This one produces both, a readiness percentage a board can act on, and a control-by-control record a certification body can review.
Business Outcomes
Grounded
Verdicts from real evidence, not uploaded documents.
Specific
Every gap carries an observation and a fix.
Prioritised
Stage 1 blockers surfaced ahead of the noise.
Score your AI management system against all 65 controls, grounded in evidence, with every gap carrying its fix and a path into NIST AI RMF and the EU AI Act.