The governed AI control plane your other three capabilities run on.
Runtime, continuous evaluation, unified observability, and regulatory-grade governance. The substrate that lets regulated enterprises operate AI safely, prove it, and scale it without re-architecting.
Runtime, evaluation, observability, governance — one control plane.
A model in production is not a system in operation.
Most enterprises now have models in production. Few have control planes around them. The runtime is improvised. Evaluation is point-in-time. Observability is whatever the application team logged. Governance is a document, not a runtime property. The first time the regulator asks who decided, why, and on what — the gap shows.
Runtime improvised per project
Each AI project ships its own scaffolding. Reuse is accidental. Lifecycle management is whatever the team had time for.
Evaluation once, not always
Models are evaluated at launch. Drift is detected by a downstream complaint. Continuous evaluation is a research aspiration.
Observability without decisions
Application logs exist. Decision-level traces do not. The question why did the system decide this requires a forensic project.
Governance as a document
The AI policy lives in a PDF on a shared drive. The runtime cannot enforce it. The regulator can read both.
A control plane built for regulated AI.
We build the governed AI control plane the rest of the AI estate runs on. It is the runtime, the evaluation surface, the observability layer, and the governance enforcement point — all of which need to exist in code, not in a document.
Agent runtime and control plane
An operational layer above execution: routing, controls, and lifecycle management for every agent in the estate.
Continuous evaluation
Pre-production eval studios for release gates. Live scoring of accuracy, drift, and task success in production.
Unified observability
Trace every agent decision end to end. OpenTelemetry, distributed tracing, decision logs. One pane, every call.
Regulatory-grade governance
Identity, RBAC, audit, and controls aligned to the EU AI Act and to sector-specific regulation. Policy is a runtime property.
The five primitives every control plane needs.
Every governed AI control plane we build sits on the same five primitives. Together they let an enterprise bring any compliant agent in, prove what it does, and govern what it can do.
What a governed AI control plane includes.
Agent runtime
A managed runtime for agents and models, with routing, lifecycle management, and policy enforcement at the edge of every call.
Pre-production evaluation studios
Environments where any candidate agent runs against named test suites before release. Pass criteria are explicit.
Live evaluation & drift detection
Production scoring of accuracy, drift, and task success on every active agent. Alerts route to the supervisory mesh.
Unified observability surface
OpenTelemetry-backed distributed tracing for agent decisions. Decision logs queryable by case, by agent, by policy state.
Identity and access control
RBAC and identity for every agent and every tool call. Built for the audit, not for the demo.
Sovereign deployment
The control plane deployable in client cloud, hybrid, or on-prem when regulation requires it.
Regulated AI, proven and governed.
A bank consolidated 14 model and agent deployments under one control plane in 22 weeks. EU AI Act readiness completed without a remediation backlog.
Read the engagementA claims operation moved from quarterly release reviews to continuous evaluation gates. Incident rate dropped alongside.
Read the engagementA regulated public-sector entity deployed the control plane on-prem, with all observability and governance inside the controlled environment.
Read the engagementNotes on the governed control plane.
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