HR & Recruitment AI Engineering

Yes, but hiring AI is high-risk under the EU AI Act and subject to anti-discrimination law, so it must be documented, bias-tested, and overseen by a person. We build to those obligations from the start. Using AI in hiring is not the risk; using it without fairness testing and human review is.

We test for adverse impact across protected groups, hunt for proxy features that stand in for those characteristics, and keep a recruiter reviewing decisions. Bias usually comes from historical hiring data, so correcting for it is deliberate work, not a checkbox. We report the fairness metrics rather than asserting the model is fair.

We strongly advise against it, and design so it does not. The model assists by surfacing and ranking candidates, but a person makes and can explain every rejection. Fully automated rejection is both a legal exposure and a brand risk, and the time it saves is not worth what it costs when challenged.

They should, and transparency is part of compliant hiring AI. We design the workflow so candidates can be informed and, where required, request review by a person. Hidden automated decisions are exactly what regulators are targeting, so building for transparency now avoids a rebuild later.

You do. Your applicant data, the trained models, and the code remain yours, under strict access control and privacy handling. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single resume screening build is a modest, weeks-long project, while a wider rollout across your ATS and HR systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from faster screening and better matches, without fairness or legal risk, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as resume screening usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are resume screening and ranking, candidate-role matching, employee support, and onboarding automation. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.