Authorize
checks the agent's ceilings and has the action risk-scored.
The agent drafted standups and opened pull requests for six weeks.
Then procurement asked what it costs at forty people, and where content is processed.
Nobody had either answer in writing, and the pilot ended there.

A model risk-scores every action and a rate card prices it before it runs. A log line records it after.
€0.004
per executed action, on a published rate card
EU only
executed and risk-scored in Sweden, never copied out
10,000 actions
free, no card, metered and logged like paid ones
Compare
| What a buyer checks | Ferrule | CORE |
|---|---|---|
| Cost at 40 people | ✓€0.004 per action, published | ✕Usage, storage and tier fees, unpublished |
| Processing region | ✓Stockholm and Azure Sweden Central | ✕None named |
| Model providers | ✓Azure OpenAI, zero retention, to score risk | ✕OpenAI, Anthropic or Google Gemini |
| No-training commitment | ✓Yes; baselines fit one tenant's data only | ✕Google user data only |
| Apps out of the box | ✓New MCPs on request | ✓50+ through one MCP endpoint |
Scope
Pricing
One rate for every tool and operation.
€0.004
per action
Why it works this way
A published rate is easy to undercut. Some deals a negotiated quote would win, we lose on arithmetic.
An unpriced platform never reaches procurement, only a sales cycle that takes a quarter.
The terms name the region too: Stockholm for the gateway and log, Azure Sweden Central for both models.

Features
Each one leaves a record you can export.
checks the agent's ceilings and has the action risk-scored.
quotes the public rate card before the action runs.
makes the call with your grant and reports the result.
records the call, risk score, price, authorizer, result and trace id for 400 days.
The AI layer
A risk classifier inside Authorize, and anomaly detection over the Log.
A routine action and a harmful one can share an endpoint and parameter shape. Only a model can read what the parameters say.

Risk classifier - scores tool, operation and parameters for risk and intent.
Over your risk ceiling - refused, or held for your named person.
Learning - that person's decisions tune only your tenant's risk threshold.
Anomaly detection - flags an agent's new tool, new volume or rising refusals.
Never approve - a fooled model passes only what your ceilings allow.
Runs in Sweden Central - Azure OpenAI with Azure Machine Learning.