Govern AI where it runs, not after the bill arrives.
Control AI spend, risk and compliance at the point where AI runs: budgets and caps per team and use case, scoped access for every agent, and audit evidence produced by the platform instead of reconstructed by hand.
Today, AI governance lives on paper.
With Mia-Platform, AI governance lives where AI runs.
AI spend grows with no clear owner per team or use case. The bill arrives after the fact, and attributing it means asking around.
Spend is attributed to a team and a use case as it happens, and a cap is something you set in advance rather than something you discover.
Agents reach data and tools with no consistent policy. Governance exists on paper, but not at the point where AI actually runs.
Policy is applied where the agent calls a model or a tool, so what is written down and what happens are the same thing.
Regulatory obligations are phasing in while evidence for an audit has to be reconstructed by hand, months after the activity it describes.
The trail is a by-product of running under policy, so producing evidence is an export rather than a project.
Three fronts, one place where they are enforced.
Not three phases: three things that break separately and are held together by the same mechanism, the platform deciding what an agent may do before it does it.
Spend
Token tracking, budgets and caps per team and use case, with caching, model routing and context retrieval tuned before the tokens are spent.
Risk
Agent identity, scoped access, separation of duties, environment-scoped autonomy and policy enforcement, with an audit trail behind all of it.
Compliance
Evidence for audits and the readiness work that the incoming AI rules ask for, produced from what the platform already records.
What sits at the execution layer.
Four controls that act at the moment of the call, which is the last moment anything can still be decided.
Token tracking and attribution
Spend broken down by team, agent, use case and model, while it happens. The question of who is spending what stops being answered once a month by a spreadsheet.
Budgets, caps and routing
A budget per team and use case, and what happens when it runs out decided in advance: block the call, or route it to a cheaper model. Caching and tuned context retrieval reduce the tokens each call needs in the first place.
Agent identity and scoped access
Every agent has an identity, the access that identity carries is scoped, and separation of duties and environment-scoped autonomy apply to it as company-wide policy rather than per project.
Audit evidence
What ran, under which policy, with which data and at what cost, recorded as it happens. An audit or an internal review is served from that record instead of from a reconstruction.
Why the usual answers stop short.
Policy tools define, FinOps tools report, gateways see the traffic they are given. None of them is standing at the point where the decision is made.
Each row is the same job: the answer most organizations reach for, and what changes here.
A governance tool that defines the policies, the risk register and the model documentation.
Enforced at the call
The policy is applied when an agent calls a model or a tool, not when someone opens the document that describes it.
A FinOps tool that reads the billing export after the money is gone.
Prevented, not reported
Context, agent and model are decided before the tokens are spent, which is the only moment a cost can still be avoided.
A gateway sitting beside the platform, governing the traffic that happens to pass through it.
Inside the substrate
Enforcement lives in the platform your teams already build on, so there is no path around it to find.
What you will be able to measure.
Governance is only real if it shows up as numbers. These are the ones this use case is accountable for, on your own baseline.
Spend
What it costs, and what it stopped costing.
- AI spend per team and per use case
- Spend avoided by caps and routing
Coverage
How much of the AI in the organization is actually governed.
- Share of AI spend under policy
- Ungoverned AI tools found and onboarded
Compliance
How long the evidence takes.
- Time to produce audit evidence
We publish the indicators, not an average saving: the baseline is yours, and so is the delta. Measuring them before anything changes is part of the work.
What makes it work.
Three parts of the platform carry this use case. The rest of it is there when you need it.
AI Foundry
Registers the agents, governs their MCP access and models, and records the tokens each one spends.
Context Catalog
Holds the access rules on the data and the compliance campaigns that drive the gaps to zero.
Console
Carries the environments and the deploy gates where separation of duties and scoped autonomy are applied.
The objections this raises, answered.
No, and it is not trying to. Those tools are where policies, risk registers and model documentation live, and they stay there. What they cannot do is apply a policy at the moment an agent calls a model. That is the part this covers, which makes the two complementary rather than competing.
Where this goes next.
Scale Agentic Engineering
Turn your standards and knowledge into rules every AI agent follows, and prove the investment pays off.
ExploreRun the AI-Native SDLC
Agents working through every stage of delivery, under guardrails your platform team controls.
ExplorePilot to Production
Move the business AI initiative you already believe in to a governed production service.
Explore