Candidate screening and shortlisting
Recruiters spend 40 to 60 percent of their time screening CVs, the majority of it on clearly unqualified candidates. The good candidates wait in the queue while this happens.
AI extracts structured signals from each CV, scores against the job description, and produces a shortlist with the reasoning every recruiter can read and challenge.
- 01
CVs are parsed into structured fields (experience, skills, qualifications, tenure).
- 02
Each candidate is scored against the job description using criteria agreed with the hiring manager.
- 03
Shortlisted candidates are surfaced with a written rationale citing the CV text.
- 04
Disqualifications include reasoning so recruiters and candidates get a fair explanation.
- 05
Bias monitoring is built in, flagging patterns in outcomes across demographic signals where permitted.
with faster time-to-shortlist on volume hiring.
Ranges drawn from production deployments and public enterprise benchmarks. For a specific rupee or dollar figure tailored to your volume, use the calculator below.
Prerequisites for a clean deployment.
- API access to your ATS (Workday, Greenhouse, SmartRecruiters)
- A structured JD library with consistent criteria
- A hiring manager feedback loop to tune scoring
- Bias monitoring approved by your HR compliance team
Put your own numbers on it.
“At 1,000 documents a month and a loaded monthly cost of ₹1,50,000 per person, candidate screening and shortlisting would typically save ₹9.0 L to ₹12 L a year.”
Range uses this use case’s typical automation rate (60 to 80 percent) against the baseline time per task for documents work, with your cost per person converted at 160 working hours a month.