Most teams already know their AI should be fair. What they're missing is the policy, the test, and the person whose job it is to check. That's the work.
We help you write AI policy that actually addresses fairness, accessibility, and responsible use — then build the governance framework and review process that keeps it real after the memo goes out.
Models fail on the people who weren't in the training data. We advise on collecting representative datasets and measuring performance across the populations and regions you actually serve.
Hiring and evaluation are where AI touches people most directly, and where getting it wrong is most expensive. We audit the tools your HR team already uses and train the people using them.
Fairness fails when leadership treats it as someone else's job. We run workshops for the people setting direction, and hands-on training for the people shipping the code.
Same engineers who build our AI products, same delivery model as our custom work: senior people, remote from day one, measurable output at the end.
Which systems touch people, and how? We map every model, tool, and vendor that shapes an outcome for a candidate, customer, patient, or employee — including the ones nobody registered as AI.
We measure performance across the groups and regions you serve, not just in aggregate. An accuracy number that looks fine overall routinely hides a system that fails badly for one group.
A framework with named owners, review gates, and escalation paths. If it can't tell an engineer what to do on a Tuesday, it isn't a policy — it's a poster.
Leadership workshops and hands-on sessions for engineers, product, and recruiting. Governance survives on habit, not documents.
The tests, the docs, and the review process are yours, in your repos and your systems. We'd rather your team ran this next year than re-hired us to.
Most teams start with a review, because it's hard to argue about fairness in the abstract. Findings on your own systems end that argument fast.
Both, and that's the point. Policy teams write frameworks engineers can't implement; engineers ship models nobody governs. We do both sides, which is why the tests end up wired into your pipeline instead of living in a PDF.
Alongside it, not on top of it. Fairness review slots into the intake, assessment, and audit motions you already run — we'd rather extend a program your team knows than stand up a parallel one they'll ignore.
Yes, and that's the more common case. Most bias exposure walks in through a vendor — hiring platforms especially. We evaluate what the tool actually does with your candidates, not what the sales deck claims.
We break performance out across the populations and regions you serve and look for where the system fails a group it shouldn't. Aggregate accuracy hides this routinely — a model at 94% overall can be near-useless for a subset of your users.
Coco HR is our AI recruiting platform, so we sit on the exact hot seat we're describing. Its scoring is built to be bias-resistant, and the fairness reasoning is published in our docs rather than kept internal.