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Case study · Talent Screening

From inbox to shortlist, while you blink.

TwinMind reads every CV the moment it lands, normalises the skills, scores each candidate against your role scorecard and stamps shortlist, screen or reject - with a reason for every score and the inflated claims caught along the way.

94% in our demo3.4s / CVevidence-checked
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Live reconstruction

Watch two candidates run the screening pipeline end to end

94%straight-through in the demo below
3.4sper CV, in the demo
100%scored consistently
The problem

More applications than anyone can fairly read

Roles like this pull more applications than anyone can fairly read. The best candidate is often screened out by fatigue, not by fit.

And every reviewer scores differently, so there is no consistent bar and no defensible reason when someone asks why they were rejected.

What we built

A 7-step screening pipeline on every application

A 7-step pipeline runs on every application the moment it lands: receive, parse and OCR the CV, lift a structured profile, normalise every skill - K8s and Kube become Kubernetes - then score against your role scorecard and stamp shortlist, screen or reject.

Each verdict carries a plain-English rationale, and the right next action fires automatically - so the shortlist builds itself while the pile is still landing.

What changed

The outcomes, in plain numbers

  • In the demo on this page, 94% of applications go straight through at 3.4 s per CV - every score with its evidence
  • Every application read and ranked within minutes of landing
  • Skills matched on meaning, not spelling - no candidate lost to a synonym
  • Inflated CVs caught - a skill listed but evidenced in no role is flagged
  • Consistent, auditable scoring across 100% of applicants - no fatigue drift
Why it matters

Could this be your workflow?

If a role pulls more applications than anyone can fairly read, this pipeline reads all of them - your ATS stays, the pile disappears, and every rejection has a reason you can stand behind.

Best for · recruiters, in-house talent teams, agencies, any high-volume role

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