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Case studies · Recruiting & staffing

Case study 26 / 35

How We Built a Candidate-Role Match Score That Shows Its Work Instead of Guessing for a Recruiting Technology Platform

A deterministic matching engine scores fit and risk from cited reasons — and reports honestly when there isn’t enough data to be confident.

How we did it

  1. Step 1
    Deterministic Matching

    Scores candidate-role fit from specific, named reasons — not a black box.

  2. Step 2
    Cited Risk Factors

    Surfaces the exact risk reasons behind every match.

  3. Step 3
    Honest on Missing Data

    Reports an explicit low-confidence state instead of inventing a plausible score.

  4. Step 4
    One Logic, Every Match

    The same scoring logic runs behind every match the platform surfaces.

Explainable MatchingCandidate ScoringRecruiting Intelligence
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Deterministic, explainable matching for two-sided marketplaces.

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Client names are withheld by default and replaced with an industry description throughout — the work speaks for itself either way.