Make production AI accountable by infrastructure.
Trust and control for autonomous AI.Trust infrastructure for autonomous AI.AAI establishes what an AI system is, what it can do, what evidence supports it, whether it is behaving inside its authority, and what should happen when that state changes.
One control layer across the AI lifecycle.
AAI connects discovery, authority, independent evaluation, evidence, policy and runtime action instead of leaving them as separate dashboards.
AI estate + authority graph
Agents, models, tools, MCP servers, APIs, data, identities, owners, environments and dependencies become durable records.
Observe → attack → evaluate → explain
Run realistic and adversarial scenarios, inspect execution traces, identify failures and attach evidence to the exact asset version.
Policy → decision → action
Translate current trust and authority into deployment gates, approval workflows and runtime enforcement.
The system behind the decision.
ALLOW, REVIEW and DENY are only the final actuator. The product is the evidence chain underneath them.
AI Estate Registry
Find sanctioned, unknown and shadow AI across systems, models, agents, tools and environments.
Permission graph
Trace agent → identity → permission → tool → data → business action.
Evidence fabric
Hash, provenance, timestamps, trace links, approvals, findings and assurance history.
Failure + change graph
Connect recurring failures, dependencies, incidents, remediation and re-assessment.
From a production event to a defensible action.
material change detected ↓ impact graph identifies affected systems + dependencies ↓ targeted evaluation suite executes ↓ observed traces + findings + evidence are verified ↓ trust state recalculated ↓ release / runtime action: ALLOW · REVIEW · DENY ↓ incident → remediation → retest → verified assurance
Govern the systems that can actually change the world.
Customer support agents, legal workflows, finance operations, enterprise search, autonomous engineering, internal agents and AI vendors serving regulated or high-value workflows.
AI agents
Authority, tools, data boundaries, delegation, approvals and runtime controls.
AI platforms & vendors
Independent evidence for customers who need production assurance beyond model-level testing.
Enterprise AI estate
Fleet visibility, policy, evidence, deployment gates, incidents and continuous assurance.
Independent assurance assessment →
Run a deterministic reference assessment that produces findings, evidence, risk and next actions instead of echoing submitted metrics.
AI Failure Observatory →
Map failure modes to controls, affected components and remediation patterns.
Assurance Protocol →
Portable machine-readable identity, evidence, trust and enforcement state.
Have a production AI system that needs independent assurance?
Start with one consequential workflow. Expand into the control plane once the evidence is proven.