Answers built for cross-functional rollout

Agent Mai ingests your AI system documentation, estimates likely EU AI Act risk positioning, surfaces gaps against Annex IV–style expectations, and turns that into a prioritized remediation backlog with ownership hints. It is built for cross-functional teams: product sees scope, engineering sees evidence gaps, and compliance sees what still needs sign-off.

Most teams see a first-pass assessment in minutes after connecting sources or uploading documents. The output is deliberately actionable: top risks, missing artifacts, and suggested next steps — not a wall of generic text — so you can convene a review the same day.

No. Partial documentation is the norm. Agent Mai highlights what is missing and in what order to fix it, so you make progress while you iterate on model cards, data sheets, and governance records. The goal is continuous improvement, not a documentation freeze.

No. It accelerates technical readiness, traceability, and consistency of evidence. Legal interpretation, regulatory strategy, and formal conformity decisions stay with qualified counsel and authorities. Think of Agent Mai as structured preparation — not a substitute for professional judgment.

Yes. Exports are structured for governance forums: clear assumptions, cited inputs, open gaps, and remediation paths. Legal and leadership can see what was automated versus what still needs human attestation, which shortens review cycles.

After meaningful changes to models, training data governance, safety policies, or product scope. Many teams keep a monthly health check and run a focused audit before high-impact releases, procurement renewals, or regulator interactions.

Yes. Private Vault and customer-controlled deployment patterns exist for organizations that need strict residency, egress control, or separation from multi-tenant SaaS. Your security team can align network boundaries with internal policy.

Cloud is the fastest path: managed upgrades, elastic capacity, and standard security baselines. Vault adds deeper infrastructure control — tighter sovereignty boundaries, custom integration patterns, and operational responsibility split that maps to regulated industries.

Product, engineering, legal/compliance, and security should share one evidence workflow. Agent Mai works best when ownership is explicit: who attests to model behavior, who owns data processing records, and who signs off on residual risk.