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Case study · Talent Screening

From inbox to shortlist, while you blink.

TwinMind reads each CV as it arrives, standardises the skill names and scores the candidate against your role criteria. It marks shortlist, screen or reject, explains each score and flags inflated claims.

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 hundredth CV is hard to read as carefully as the first. A shared scorecard keeps the same criteria in front of every candidate.

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 process starts as each application arrives. It receives the CV, reads it using text recognition and builds a structured profile. It standardises skill names, treating K8s and Kube as Kubernetes. It then scores the candidate against your role criteria and marks 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 attracts more applications than anyone can fairly read, this process reads them all. Your applicant tracking system stays in place. Each rejection has a reason you can explain.

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

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