Take-homes and whiteboards miss the skill that now defines great engineers — and a single mis-hire burns months and budget. HireCulture measures AI judgment directly and returns an evidence-backed hire signal your panel can trust.
20-minute walkthrough · on a role you're actually hiring for
Strong grounding. Prompted precisely, caught a hallucinated API instead of trusting it, and reasoned clearly about token budgets.
Trusted by teams hiring for AI-heavy roles · [ your logos here ]
Coding tests miss the skill that now defines great engineers: judgment with AI. We measure it directly — not as an afterthought.
Every signal is backed by what a candidate actually did. A record your panel can review, not a gut call you have to defend.
A weighted consensus across multiple models holds every candidate to the same bar, on the role you're actually hiring for.
Every dimension comes back with evidence you can audit — and a weighted consensus across multiple models so the bar stays consistent.
How candidates actually use AI — prompt quality, judgment, and whether they verify rather than trust.
Can they explain what they built and why — beyond the happy path?
Trade-offs, failure modes, and how the pieces fit under real load.
Depth measured against the actual job, not a generic checklist.
Clarity under questioning. Honest about edges and unknowns.
Reliability, cost, and the failure modes that separate demos from systems.
Usage-based, no per-seat traps. Start with a single role — see a tailored quote in your demo.
Run one role and see the signal for yourself.
For teams hiring across multiple roles.
Scale, security, and compliance.
We stopped guessing who was actually good with AI. The evidence does the arguing for us.
Cut our early-screen time in half and our shortlist got noticeably stronger.
Finally a signal our whole panel trusts — because they can read the reasoning behind it.
Those test syntax and algorithms in isolation. HireCulture evaluates how a candidate reasons about real work — and, crucially, how well they use AI to do it. You get a calibrated signal with the evidence behind it, not a pass/fail on puzzles.
Using AI isn't cheating — it's the job. The point is to see how well they wield it: prompt quality, judgment, and whether they verify the model's output instead of trusting it. We measure that directly.
Every candidate is scored across the same six dimensions by a weighted consensus of multiple models, then backed by evidence your panel can review. The bar stays the same for everyone.
Typically under an hour for the candidate. Your team gets a structured result with a clear hire signal — no scheduling, no live panel time spent on early screens.
No. Invite candidates, then review results and share the evidence with your panel. It slots in ahead of your existing interviews as a stronger first signal.
Yes. Assessments are grounded in the role you're hiring for, so the signal reflects the job rather than a generic checklist.
A 20-minute walkthrough on a role you're hiring for. See exactly what the assessment returns — and decide for yourself.