Glimpse

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Predictive hiring signals built from your own high performers

SI Score
68/100
Pricing
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Glimpse builds a predictive model from people who already succeeded in your environment, then matches candidates against it. Each person goes through one brief neuroscience-based assessment that captures 257 markers without self-reporting. The output is a likelihood-to-succeed signal you can use in selection decisions.

Checked against the site's own pages on .

Our take

Worth a look if you hire at volume and already have a clear pool of proven performers to model from. The pitch is potential rather than past performance, and the case studies lean on contact centers, defense programs and editorial teams. Pricing isn't published, so you'll need a demo conversation before you know if it fits your budget.

Features

  • Success model building

    Glimpse builds models from individuals who have already succeeded in your specific environment, capturing the cognitive patterns behind that success.

  • Single assessment

    Each individual is assessed once through a brief neuroscience-based digital experience, creating a reusable capability pattern.

  • Performance-based markers

    The assessment captures 257 unique markers with no self-reporting, according to Glimpse.

  • Candidate matching

    Each person's capability pattern is matched against your success model to produce a signal of likelihood to succeed.

Pricing

Model
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Best for

  • High-volume hiring teams in contact centers
  • Defense and security training program selectors
  • Early-career and editorial hiring managers
  • Organizations with a defined pool of proven performers

FAQ

How does Glimpse predict who will succeed in a role?
It builds a model from people who already succeeded in your environment, then matches each candidate's capability pattern against that model to produce a likelihood-to-succeed signal.
Does Glimpse require candidates to retest?
No. Each individual is assessed once, and the resulting capability pattern can be matched against any model without retesting.

Sources