Two days to find out whether it's worth doing.
It starts small, with decision criteria agreed before anything begins. If the honest answer is that you don't need an audit, that's the answer you'll get.
The spike
A small sample of your real work, the instructions your team already uses, and time with the people who review and approve the finished work. At the end you'll know whether your rules are holding — and whether anything more is worth doing.
Decision criteria, agreed before we start:
- If the model already follows your rules, you'll be told a simple set of checks is all you need.
- If every problem found is a formatting problem, a template fixes it and no audit is needed.
The audit
Your rules tested against your model, on your own documents — including the rules that really decide whether a document is accepted, which aren't always the ones written down.
You get a clear answer for every rule: whether it holds, whether it does anything at all, and, for the ones better wording can't fix, what to put in their place. Your IT team gets the fixes, and the fixes are tested again so you know they worked.
The watch
Hosted models change without notice. The watch tells you when a rule that used to hold stops holding, and when the model underneath has changed.
The details depend on your documents, your model and how your organization reviews its work — which is why it starts with a conversation rather than a price list.
What a result from us is made of.
Every rule is tested with it and without it. Some rules turn out to do nothing the model wouldn't have done on its own, and some make things worse. You'll know which.
A rule that holds on easy cases tells you little. Your rules are measured where your model actually struggles, not only where every rule looks perfect.
Every result is read by a person before it reaches you, so what you report upward holds up.
Measured repeatedly as the model changes and evolves. A rule that held last quarter can stop holding after an update nobody announced.
Each rule is written, measured, re-measured, and kept only if it fixed the failure without breaking something else.
What people usually ask
| “Isn't this the AI companies' problem?” | They control the model. You control how it's used in your organization — and your name is on what it produces. |
| “Our IT team is building this.” | Good — let them. Then let someone who didn't build it measure it. You wouldn't accept a contractor's own inspection report. |
| “We read samples.” | A sample finds the mistakes that look like mistakes. The dangerous ones look like sentences. |
| “The new model fixed that.” | At the weather station, the newer model stopped inventing the reviewer's name and left a blank beside the word “reviewed.” Better hidden, same hole. |
| “Security.” | Your security team can review all of it before the work starts, and how the work is set up is agreed with you before anything runs. |
| “How long?” | Two days to know whether it's worth doing. A few weeks to the full picture. |
Start with two days.
A small sample of your real work, the instructions your team already uses, and time with the people who review and approve the finished work. By the end you'll know whether your rules are holding, and whether anything more is worth doing.
Decision criteria are agreed before it starts. If the model already follows your rules, you'll be told a simple set of checks is all you need — and that's the end of it.
Thank you — that's arrived.
You'll get a personal reply.
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