Glimpse combines neuroscience-based measurement with machine learning to
generate objective predictions of future success.
We measure 250+ unique cognitive and behavioral markers - how individuals
make decisions under pressure, adapt to change, learn, and collaborate in
real conditions. These are performance-based measurements, not self-reports
or questionnaires - capturing what people do, not what they say they do.
From these measurements, we build Predictive Potential Patterns (P³) - success
models trained on individuals who have already succeeded in your specific
context. These models identify the cognitive patterns that statistically
distinguish success from average performance. When applied to new individuals,
they produce a decision-grade signal of likelihood to succeed - specific
to your organization, your challenge, your definition of success.
Every high-stakes decision about people is a prediction of future success.
But the signals most organizations rely on - resumes, interviews, and past
performance - that used to predict success no longer do in today's
fast-evolving industries. Credentials show what someone has done. Glimpse
predicts what they are capable of.
Through a brief neuroscience-based digital experience, Glimpse captures how
individuals think, decide and adapt under pressure - with no self-reporting,
no prior knowledge required, and bias-neutral by design. From these signals,
we model the Predictive Potential Patterns (P³) that distinguish your top performers,
creating a reusable capability pattern applicable to any future decision.
The result is a decision-grade signal of likelihood to succeed - before you
commit, not after the consequences. Glimpse doesn't replace your judgment or
your process. It augments both with the one layer of insight that has always
been missing: not what someone has done, but what they are capable of achieving.