A first validation, scoped and run in a few weeks.
Most engagements begin with a single, fixed-scope project. You see exactly where your AI is sound and where it isn't, and you get the evidence to back it. Here's what that looks like.
The process
Four steps, agreed with you upfront. No open-ended retainers to start, a clear scope, a clear deliverable.
We define the standard
Together we set what "correct" means for your product, the rules it must follow, the edge cases it must handle, and the bar a qualified professional in your field would hold it to. This becomes the rubric everything is graded against.
We bring the right experts
We match credentialed, practising professionals to your domain, people who hold the standard your AI is trying to meet. Every expert is vetted; you know who is reviewing your product and why they're qualified to.
Experts grade real outputs
Your AI's outputs are assessed against the agreed rubric. Experts flag where it fails, surface the high-stakes cases where a plausible answer is the wrong one, and produce worked, correct examples you can learn from.
You get a defensible record
A clear report of what was tested, where it held up, where it didn't, and the evidence that qualified professionals reviewed it, mapped to the regimes you answer to, ready to show a board or a reviewer.
After the first project
Models drift. Rules change. New edge cases arrive.
Teams running AI in production often move to ongoing re-validation, a regular review of live outputs that keeps your evidence current as your model and the regulations around it evolve. We'll only suggest it when your product is live and it genuinely earns its place. The first project stands on its own.