Start with the AI question on your desk.
- Jobs, each with its own failure cost
- 10
- Steps in total
- 40
- Capabilities they draw on
- 14
- Bytes of your data sent to the vendor
- 0
- OpenAI
- Anthropic
- Google Gemini
- OpenRouter
- Amazon Bedrock
- Azure OpenAI
- Your own endpoint
Cloud setup offers OpenAI, Anthropic, xAI and Google. Use separate billing and inference credentials.
Find out what AI costs this business, line by line
A vendor, a tool, a team and an owner on every line. Billed kept apart from metered.
The board gets a number off the statement and it holds for ten seconds. Which teams, which tools, who signed them off: that answer is a spreadsheet nobody has updated.
4 stepsKnow which AI tools people are paying for, and whether anyone approved them
Three states with unknown as the default, an owner on every entry, coverage on its own axis.
The approval that happened is in a thread somebody can nearly find. Meanwhile a tool nobody signed off renews on a card and a leaver's seat is still billing.
4 stepsStop agents leaking personal data and secrets to model providers
Detect sensitive data in requests and responses. Mask or tokenise supported text, with the detector limits set out below.
A card number or a live API key goes out inside a prompt. The question is not what it cost but who can say it will not happen again, and the answer is that a regular expression ran somewhere in a codebase.
4 stepsCap what an AI agent can spend before it spends it
Refused before egress, priced against everything the route could reach, never estimated at zero.
A retry loop or a promoted model turns an ordinary week into an invoice nobody budgeted for. You find out from the vendor, and the control everybody assumed was on turns out to have been a chart.
4 stepsProduce audit evidence for AI agents that an auditor accepts
One bundle with a verification verdict inside it, and the protection level beside the verdict.
You assemble exports the week before the audit, and every one was produced by, or about, the party under examination. The auditor asks how you would know the file was not edited, and nothing in the folder answers it.
4 stepsControl which tools an AI agent may call
Grants re-checked at execution rather than at the list, and a changed descriptor is quarantined.
A whole server was granted rather than an action, so the endpoint that issues refunds sits next to the one that reads orders. Nobody outside the team that wrote the configuration can say what the agent may do.
4 stepsFind the AI use happening outside your controls
Bring five evidence sources together. See which feeds are connected, stale or missing.
The export died in July. Nothing on the screen distinguishes that from an estate with nothing to find, so a green banner has been asserting a clean result over a dead feed.
4 stepsPut a person in front of a consequential agent action
One decision, bound to one exact payload, spendable once. Nothing calls the agent back.
The approval was somebody typing yes in a chat channel. Nothing binds it to the action that ran, and a slightly different call two minutes later is covered by the same yes.
4 stepsStop one agent, a whole team, or everything, now
Checked before every other control. Stops new governed work; it cannot recall actions already dispatched.
The stop takes as long as a deploy takes and depends on the owning team being awake. The person who noticed the problem and the person who can end it are in different time zones.
4 stepsAnswer whether an agent was allowed to do that, after the fact
The authority as it stood at the moment of the call, and a review bound to a digest.
The current configuration answers a different question, because the role has been edited twice since. The access review recorded that the agent held the role and not what was in it.
4 steps- beat 01Name the caller
- 1Authenticate
- 2Resolve the agent
Nothing is decided for an anonymous caller. The key resolves to one registered agent with a named owner, and its roles, its ceilings and any stop switch in force are read live from the store on every request, not from a cached copy, so a suspension takes effect on the next call instead of after a redeploy.
- beat 02Start the record, then read what is being sent
- 3Open the trace
- 4Sanitise
- 5Scan
The trace is opened before anything can refuse the request, which is why a blocked call is recorded as thoroughly as one that went through. Only then is the text normalised and scanned, in that order, so the detectors see what the model will read rather than what a human reviewer can see.
- beat 03Decide once
- 6Govern
- 6bIntersect with the named human
- 7Enact
One function returns one verdict: allow, block, or hold it for a named person to approve. Every check lives in that one place rather than scattered along the path, which is what makes it possible to read your rules and know what they do.
- beat 04Send it, and only then spend the money
- 8Route
- 9Call upstream
Routing fixes which provider and model will serve the call, so where an agent has a USD ceiling it is tested against the price you will pay, not the one the caller typed. Routing is also the last step that can still refuse locally: after it, the request has left your network.
- beat 05Govern the answer, then count the cost
- 10Govern the response
- 11Meter and record
A tool call the model proposes is judged before it reaches your agent. Metering runs last and on every ending, including the refusals and the streams a client abandoned halfway, because the tokens were spent either way and a partial trace is still evidence.
Step names and their grouping are the product’s own, and the order is encoded in the evaluator rather than drawn for the page.
Check the product limits alongside the job you want to do.
What we do not claimWhich of these is the one costing you?
Say what your estate contains, and the reply will say where to start.