Governance agents that stay on watch
Continuously inventory AI systems, map controls against the EU AI Act and ISO 27001, and surface evidence gaps before review day.
Continuous controls · Evidence trails · Risk register
APBAI audits enterprise AI controls, repairs data connections, improves RAG quality and monitors token spend—without adding a full architecture team.
Built for UK enterprises operating across EU AI Act and ISO 27001 requirements.
24/7
control monitoring
Live
MCP health
Unit-level
token attribution
What to expect
Every finding links to a system, control and timestamped record.
Agents automate the work; accountable owners retain final decisions.
Connection repairs and policy actions are logged and reviewable.
The governance loop
APBAI turns fragmented architecture work into a continuous, auditable cycle your technical and risk teams can share.
Continuously inventory AI systems, map controls against the EU AI Act and ISO 27001, and surface evidence gaps before review day.
Continuous controls · Evidence trails · Risk register
Custom MCP servers monitor enterprise data routes, diagnose broken dependencies, and restore connections without waiting for an integration sprint.
Connection health · Automated recovery · Change history
Test retrieval performance, identify weak context and track token costs by model, workflow and business unit from one operating view.
Retrieval evaluation · Token attribution · Cost alerts
Monthly plans
Subscription pricing is scoped to system count and connector complexity. Every engagement begins with a paid assessment; no surprise implementation fee follows.
From £2,500
/month
For a team establishing governance around one production AI system.
From £6,500
/month
For enterprises that need continuous oversight and resilient AI data operations.
Custom
annual scope
For multi-team estates with bespoke controls, integrations and operating requirements.
Before you deploy
Make the black box accountable
Begin with a scoped assessment of your systems, obligations, data connections and model spend.