Stop reading 200 resumes.
200 applicants. Read 10.
Match score ranks every candidate against your job requirements — skills, trajectory, comp fit, location. The 10 worth your time surface first. Every score breaks down by dimension. No black box.
0.4 sec
To rank a candidate
100%
Transparent — every score explained
Free
Included with every job post
The shortlist builds itself
AI reads candidates the way a great hiring manager would.
Skills, career trajectory, comp alignment, location match — not keyword overlap. Every score breaks down by dimension so you see exactly why a candidate ranked where they did. No black-box number. No 'trust us' scoring. Full transparency.
- Ranked in 0.4 seconds
- Every dimension visible
- Free with every job post
Five dimensions. One score. Full transparency.
Skills match
AI maps candidate skills against your requirements. Exact matches and semantically related skills both count — 'React' and 'React.js' aren't two different things.
Career trajectory
Not just years of experience — the shape of the career. A founding engineer at a Series A scores differently from a services consultant with the same tenure.
Location fit
City match, willingness to relocate, remote/hybrid/onsite preference. All factored in before you look at the candidate.
Comp alignment
Candidate expectations vs your budget — flagged upfront so you never waste a final-round slot on a mismatch.
Pipeline auto-sorted
Your applicant pipeline sorts by match score. Best fits at the top, always. No manual re-ranking.
Every score explained
Click any score and see the dimension breakdown. Why 91%? Because skills: 9, trajectory: 8, domain: 9, comp: 10, location: 9. No mysteries.
Keyword matching is broken
Your ATS is ranking resumes by word overlap. That's the problem.
Ranked in 0.4 seconds. Every dimension visible.
Recruiters see exactly why each candidate scored the way they did. No black-box numbers.
The industry today
Most ATS systems still rank candidates by literal keyword match. 'Computer Vision Engineer' doesn't surface for 'ML Engineer with image recognition' — even though they're the same person. Great candidates get rejected for phrasing. Bad candidates with keyword-stuffed CVs surge to the top.
Recruiters know this. So they manually re-sort the 'top 20' anyway, defeating the entire point of automated screening. The tool meant to save time creates more work.
Why we built this
We watched our own customers re-rank our algorithm's output for six months. So we rebuilt the algorithm to match their manual re-ranking. Match score now reads candidates the way a hiring manager would — skills, project shape, trajectory, comp fit — not keyword overlap.
The result: the first 10 candidates you see are the 10 a great hiring manager would have picked. No re-sorting required.
Compare candidates
Three look-alike CVs. One is the right hire.
Same job. Three candidates whose resumes look similar at a glance. The breakdown shows why one of them is the actual fit.
Compare candidates · Sr Backend Engineer · Bangalore · Payments
Aman
0
6 yrs · ex-Razorpay · Go + payments
- Skills overlap9/10
- Trajectory8/10
- Domain (payments)9/10
- Comp fit10/10
- Location9/10
Real fit. Built rails-style payment systems at scale. Domain knowledge transfers without ramp-up.
Sneha
0
6 yrs · ex-Infosys · Java + Spring · keyword-perfect CV
- Skills overlap9/10
- Trajectory4/10
- Domain (payments)5/10
- Comp fit7/10
- Location9/10
Keyword-perfect but trajectory mismatch. Most experience is services-style delivery, not product engineering. Would need 3-6 months to ramp.
Rajiv
0
5 yrs · founding eng at fintech startup · Python + Postgres
- Skills overlap9/10
- Trajectory10/10
- Domain (payments)10/10
- Comp fit9/10
- Location10/10
Strongest fit. Built payments infra from zero at a Series A. High ownership trajectory matches role's scope. Worth jumping the queue.
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Frequently asked questions
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Your shortlist, built in seconds.
Every candidate ranked. Every score explained. Free with every job post.