The shortlist arrives ranked.
With reasons.
Paste your JD — the actual JD, not a Boolean translation of it — and get up to 1,000 candidates from 4M+ Japan-focused profiles, each scored 0–100 with a written case for why they fit. Your reading time goes into candidates worth reading.
A score you can argue with
Most AI scoring asks you to trust a number. This one shows its work: every candidate carries a written fit rationale — what matched your role, what didn't, in plain language. When a score looks wrong, you can see exactly where the reasoning went, and tighten your guidance so the next search knows better. That's the difference between a black box and a colleague.
Your rules are enforced, not politely considered
Write screening rules the way you'd brief a junior recruiter: “must have led a team,” “no pure consultants,” “needs hands-on fintech experience.” Your guidance shapes every score — and a disqualify rule is enforced by the system, not weighed as a suggestion. The candidate scores 0, never appears in your results, and costs you nothing. You don't pay to be shown people you already ruled out.
Deep enough for the hard roles
A search returns up to 1,000 ranked candidates, and when a niche role needs more, you keep expanding — up to 5,000 cumulative on a single search. The next time a client asks “did we really cover the market?”, you'll have an answer with a number in it.
The 4M+ profile database is licensed and audited — the posture, and the paper trail to check it yourself, is on the trust page.
The meter only runs on candidates worth reading
Credits are consumed only by candidates who score 50 or above and pass your filters. Everyone weaker is free to ignore. Bad matches cost the tool credibility — they shouldn't also cost you money.