AI Governance
Organise access across organisations, departments and projects. Apply model and region policies at the credential boundary.
Explore pillarEnterprise AI Control Plane
The Enterprise
AI Control Plane.
Manage who can use AI, monitor spending and choose from supported models through one platform — without forcing every team into the same provider or deployment model.
- Governed access
- Live usage visibility
- Policy-based routing
MISSION CONTROL / ROUTE POLICYIllustrative
Request identityProject · research-euScoped credential accepted
Policy evaluationAllowed
MODELSApproved catalogueExplicit or opt-in route
LOCATIONEU policyEligible lanes only
SPENDAttributedProject usage evidence
TRUSTObservableRequest context logged
The operating problem
AI access grew quickly. Control should catch up without slowing teams down.
Provider accounts, model choices and invoices fragment across an organisation. Mission Control creates one governed layer between applications and supported model lanes, so platform owners can set boundaries while builders keep a practical path to production.
Five integrated pillars
Control every decision around the model call.
AI Trust forms the foundation beneath access, economics, sovereignty and route selection.
AI FinOps
Connect token activity to accountable teams and nominated billing relationships, with usable cost and usage evidence.
Explore pillarAI Sovereignty
Choose supported geographic lanes and keep deployment options open as governance requirements evolve.
Explore pillarAI Optimisation
Use explicit model selection or opt in to chat routing informed by model fit, cost and available operational telemetry.
Explore pillarUniversal AI Access
Connect applications to supported models through documented compatible, native and specialist endpoints.
Explore pillarFoundation
AI Trust is the foundation
Review trust Identity, policy, observability and evidence sit beneath every pillar. Controls are scoped to the route, region and integration you select.
Architecture
A visible path from request to evidence.
Current controls govern supported hosted and direct-provider lanes. Customer-owned GPU integration is available through assisted onboarding.
01Workload
→02Identity + policy
→03Eligible route
→04Usage evidence
Governed model lanes
Supported hosted and selected direct-provider paths with model, supplier and location controls.
Customer-owned GPU integration
Manual architecture and onboarding service with explicit serving and operational boundaries.
Enterprise identity and hard budgets
Discuss identity-provider rollout and authoritative enforcement scope during your briefing.
Two ways in
For the teams building AI — and the teams accountable for it.
Establish control without creating a ticket queue.
Define allowed models and regions, assign accountable structures, inspect usage and plan enterprise identity and spending requirements.
Explore controlsUse familiar interfaces with policy already attached.
Select a supported model directly or opt in to chat routing. Keep application integration separate from provider-account sprawl.
Start with the APILive catalogue preview
Real models. One public source.
Explore the current public catalogue, then compare supported capabilities, locations and pricing.
GPT 6 Astra
gpt-6-astraContext: 1.1M
Claude Fable 5.1
claude-fable-5.1Context: 1M
Deepseek V4 Pro
deepseek-v4-proContext: 1.0M
Kimi K3
kimi-k3Context: 1.0M
GPT Image 2
gpt-image-2Context: -
Whisper
whisperContext: -
Pricing with context
Compare current rates, then govern the workload.
Use live public pricing alongside project attribution and control requirements. Choose a model and deployment approach that fits your requirements.
Trust and resources
Evaluate the boundary, not just the feature list.
Your AI estate, under control
Start with one workload and a clear set of boundaries.
Map model, access, cost, location and integration requirements in an AI briefing.