Screens 1,000+ resumes per role, scores fit against a JD, and auto-schedules interviews with calendar-aware reasoning.
An enterprise TA team was hiring across 14 European markets with a 9-day average time-to-shortlist. Recruiters spent the bulk of their time on the first pass — reading CVs in three languages and copying notes into Greenhouse.
We built a Claude-powered screener that parses CVs into a normalized profile (skills, seniority, sector, language fluency), scores each candidate against the JD with an explainable rubric, and proposes interview slots against the recruiter's actual calendar. Bias-sensitive fields are masked before scoring and every decision ships with an audit log.
Within a quarter, time-to-shortlist dropped 87% and recruiter NPS on candidate quality jumped 34%. The same pipeline now powers internal mobility — matching existing employees to open roles before external sourcing kicks in.
An enterprise recruitment team was losing two days per role to manual resume screening, and good candidates were going cold.
An AI screener parses CVs into structured profiles, scores them against the JD with explainable reasoning, and books interviews against recruiter availability.
Designed for clarity. Built to scale.
Explainable JD-to-candidate match scoring
Bias-aware screening with audit logs
Calendar-aware interview scheduling
Native ATS integrations (Greenhouse, Lever)
