Service · AI Ops, Governance & Gateway
Know what AI your company is using, and what it touched
Most companies discover their AI usage from a credit card statement. A gateway puts every model call through one door with a key, a budget, and a log, without blocking the people doing useful work.
- One gateway, one key per team, one bill you can read
- Hard spend caps rather than after-the-fact surprise
- Every call logged: who, what model, what it cost
- A written policy short enough that people read it
What you get
Deliverables, not slideware
- 01
Usage audit
Which tools and models are in use today, by whom, and what they cost per month.
- 02
Gateway in place
A single access point with keys per team, model allow-lists, and hard budget limits.
- 03
Audit log
A queryable record of calls and what data category they involved, for security reviews.
- 04
Usage policy
One page: what may be sent to a model, what may not, and who to ask.
- 01allowed: internal docs, anonymised customer records
- 02never: full payment data, unredacted contracts
- 03budgets: support $200/mo · ops $350/mo · hard cap
- 04logging: every call retained 90 days
- 05exception route: named owner, 1 business day
How it runs
What actually happens, step by step
No discovery theatre. Each step ends with something you can read or use.
- 01
Find the shadow usage
Subscriptions, keys, and browser tools already in play.
- 02
Route through one door
Keys per team, allow-listed models, budget caps.
- 03
Log and attribute
Cost and usage per team, per workflow, per month.
- 04
Publish the policy
One page people actually follow.
Tangible
What lands in your hands
Named objects with a format and a week attached, so the handover is easy to picture.
Working automation
One process running end to end, on your data, with a human approval step.
Deployed servicePhase oneOperating runbook
What to do when it fails, who to call, how to roll back.
PDF · 8 pagesFinal weekEvidence dashboard
The numbers that prove it worked, refreshed daily.
Live URLWeek 5Source repository
Yours from day one, including the prompts and the evaluation set.
Git repo + READMEWeek 1
Operating Runbook
Failure modes, rollback steps, and who owns each alert.
This is the real template, with client figures replaced by representative ones. Read it before you talk to us — if the format is not useful to you, the engagement will not be either.
Before and after
What the change looks like in the week
Red is the cost you carry today. Green is what the system gives back.
Today
- Personal accounts and keys nobody tracks
- A monthly AI bill with no owner
- No answer when a customer asks what data models see
- Policy that exists only in a Slack thread
After
- One gateway, keys issued and revocable
- Spend capped and attributed per team
- An audit log you can hand to a reviewer
- A one-page policy people can follow
Objections
Answered before you ask
Will this slow our team down?
It should not. The gateway is a drop-in endpoint. The friction people notice is the budget cap, which is the point.
Do we have to standardise on one model provider?
No. The gateway sits in front of several, which is usually cheaper because you can route cheap work to cheap models.
Is this compliance certification?
No. It gives you the evidence a reviewer asks for. Certification programmes are outside what we do.
Keep going
Where people look next
Related work, the category this sits in, and the proof behind it.
One next step, and it is a paid one on purpose
The AI Opportunity Diagnostic is a fixed-scope engagement. You leave with a ranked plan you can act on with us or without us.
Still sizing the problem? Write to us in your own words first. A person reads it.