A GenAI screening tool that parses and scores resumes against role requirements — faster, more consistent, and designed to reduce bias.
High-volume hiring meant recruiters manually reading large numbers of resumes against role requirements — slow, inconsistent between reviewers, and vulnerable to unconscious bias.
The goal was to speed up first-pass screening and make it more consistent, while actively reducing bias rather than encoding it — a real risk with naive AI screening.
The tool parses each resume and extracts the signals that actually map to role requirements, rather than keyword-matching.
Candidates are scored against defined criteria, giving recruiters a consistent first-pass ranking to review.
Bias-reduction is built into the scoring approach, with a feedback loop so the system's quality and fairness improve as recruiters confirm or correct its calls.
Resumes arrive in every format imaginable — different layouts, different ways of representing experience and education, PDFs with inconsistent text extraction. The parsing layer extracts structured data (experience history, skills, education, relevant keywords) from this variety, which is the prerequisite for any consistent scoring — you can't compare candidates fairly if the underlying data extracted from their resumes isn't comparable to begin with.
The scoring model evaluates parsed resume data against the specific requirements for a role — not a generic 'resume quality' score, but a match score against the criteria that actually matter for that position. Criteria are defined explicitly and structured (required experience, specific skills, education requirements) rather than left implicit in a model's general judgement, which is both more accurate for the specific role and more auditable — a recruiter can see which criteria a candidate matched or didn't, not just a single opaque score.
Bias reduction was a design requirement from the start, not a post-hoc adjustment. The scoring criteria are structured around job-relevant qualifications, and the pipeline deliberately excludes or de-emphasises signals that correlate with protected characteristics but aren't job-relevant (e.g., gaps in employment history are assessed in context rather than penalised by default, and demographic-correlated signals like name or address aren't part of the scoring input). The goal was consistency — the same criteria applied the same way to every resume — which is itself a bias reduction relative to the variability of unstructured human review, while being explicit about the limits of what automated screening should and shouldn't decide.
First-pass screening became faster and more consistent — designed to reduce screening time by roughly 30% while improving the quality of candidate matches and keeping a human in the loop on final decisions.
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