The questions teams ask first.
Straight answers on how validation works, who does it, and what it costs. If yours isn't here, ask us directly.
How is this different from automated evaluation tools?
Automated tools and LLM-as-judge approaches tell you whether a model sounds right and stays consistent. They struggle with domain-specific correctness: whether advice was suitable, an assessment sound, an answer compliant with rules the model was never taught. Catching those takes someone who holds the professional standard. We provide that human layer; we don't replace your tooling, we cover what it can't see.
How do you vet your experts?
Every expert is a credentialed, practising professional in the relevant field. We verify qualifications and standing before anyone reviews your product, and you know who is assessing your AI and why they're qualified to. Vetting is the core of what you're paying for, so we don't shortcut it.
What does a project cost?
It depends on the scope: how many experts, how many outputs, and how deep the review. Most first engagements are a fixed-scope project rather than an open-ended retainer, so you know the cost before we start. We'll scope it with you on a first call and give you a clear figure.
How quickly can you start?
Once we've agreed scope and the rubric, we assemble the right experts and begin. A typical first validation runs over a few weeks. We'll give you a realistic timeline for your specific project rather than a generic promise.
What do I actually receive?
A clear report: what was tested, where your AI held up, where it failed, and the high-stakes edge cases to address, plus worked, correct examples from the experts, and a record that qualified professionals reviewed it, mapped to the regimes you answer to.
Do you guarantee regulatory compliance?
No, and be wary of anyone who does. We provide expert validation and the supporting evidence that qualified humans reviewed your AI against the relevant standards. That strengthens your compliance position and gives you something concrete to show, but the regulatory responsibility for your product remains yours.
How do you handle confidentiality?
Your product, data, and results are treated as confidential, and we'll put appropriate agreements in place before any work begins. If you have specific data-handling requirements, raise them on the first call and we'll work to them.
Is this a one-off or ongoing?
Most teams start with a single, fixed-scope project. Those running AI in production often move to ongoing re-validation, because models drift and rules change, but only when it genuinely earns its place. The first project stands on its own.