Every hiring platform eventually ends up with some version of a match score. It's a useful idea — reduce a pile of resumes to a ranked list — but most implementations stop there. You get a number, and you're expected to trust it.
We didn't think that was good enough. A score without evidence pushes the actual work of evaluation onto a hiring manager who now has to reverse-engineer why the algorithm thinks a candidate is a fit, or just take it on faith.
So Explainable Matching is built differently from the ground up. Every score is deterministic — the same job and candidate always produce the same result — and every point on that score traces back to something concrete: a line in a resume, an answer in a screening question, a result from an assessment.
That also means the gaps are visible, not hidden. A high score doesn't paper over a missing requirement, and a lower score doesn't bury genuine strengths. You see the whole picture, and you make the call.
We also made a deliberate choice about what goes into that score: never a protected attribute. Skills, experience, and role-relevant signals only. Matching narrows the list and explains its reasoning — it never makes the hire or no-hire decision. That's always a person's call.