Recruiting
AI candidate evaluation engine
A sales hiring intelligence platform
Recruiters stopped reading every candidate cold, and started from a ranked shortlist with the reasoning already laid out.
The situation
Hiring managers and recruiters were manually reading through dozens of candidate records per role: resumes, ATS entries, screening notes, to decide who was worth an interview. Evaluation was inconsistent from one reviewer to the next, slow, and easy to get wrong on high-stakes sales roles where a bad hire costs six figures.
What we built
A multi-agent evaluation layer that plugs into the existing ATS rather than replacing it. It ingests candidate data directly (resume, work history, screening answers) with no manual re-entry. It extracts structured signal from unstructured text: tenure, quota attainment, deal size, industry background, reasons for leaving, employment gaps. It scores and ranks every candidate against a weighted ideal-candidate profile for the role (must-haves, nice-to-haves, and automatic disqualifiers) and flags gaps explicitly, with context, instead of burying them in a wall of resume text.
What changed
Recruiters move from reading every record cold to reviewing a ranked shortlist with the reasoning already visible. Evaluation stays consistent across reviewers.
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