Wants one enforcement point for all AI traffic, instead of policy that differs per tool.
Platform · Control plane
The heart of Numaga: every AI interaction in your organisation runs through one central gate, where policy is enforced deterministically — before a model comes into play.
Wants one enforcement point for all AI traffic, instead of policy that differs per tool.
Wants personal data masked before the model call — demonstrably, not on trust.
Wants governance that is central, not reinvented for every new use case.
The problem
In most organisations AI policy lives in documents and good intentions, not in the system. What an employee sends a model is barely auditable afterwards, and certainly not preventable.
The control plane inverts that: policy is code that runs before every model call. What does not pass the gate does not happen.
Control plane — the journey of a prompt
Pick a prompt and follow it through the control plane. Every step runs before the model call — no compensation after the fact.
Sensitivity label assigned — public · internal · confidential · strict.
Special-category personal data masked before it leaves the perimeter — GDPR Art. 9.
Checked against the EU AI Act risk class — prohibited use is blocked.
The best-fitting model chosen internally based on risk and sensitivity.
Budgets and rate limits enforced per user and per team.
Immutable audit record written — tamper-evident, time-synced.

Every step runs before the model call — no compensation after the fact.
Policy rules, not LLM judgement. Reproducible for inspectors.
Routing tables, sensitivity rules and budgets tuned to your policy.
If it doesn't pass the control plane, it doesn't happen.
We set up a demo environment and run the six checkpoints against your own policy.
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