Rank 1,000+ people, not 1,000+ keyword matches

One result per applicant, against what good looks like in that specific role, so you can rank and prioritize. It is deliberately not a report: no dashboard, no development guidance and no interview questions, and it measures behavior rather than technical skill, so it pairs with the eligibility and experience questions your ATS already asks. The two together are what produce a usable longlist. 

Because the questions are chosen for prediction rather than coverage. The scoring equations are built from a large data set and weighted differently for each role, so the same twelve items behave differently for a contact center role and a warehouse role. The result reaches 0.38 against job performance, about 90% of what a 45 to 60 minute structured interview achieves. 

Fewer than you would expect. The Screener is new, so we do not yet have completion data for it at scale. Across our existing behavioral assessments, around 85% of invited candidates complete and almost everyone who starts finishes, so the friction sits in signing in rather than in the questions. We expect the Screener to land at least there, and possibly better, because it is shorter than anything else we run. 

In most cases yes. It is available across 115 connectors with no integration fee, and setup is measured in days rather than months. Candidates are invited by email as they enter your pipeline and the result comes back against them in the list view your recruiters already work in. There is no implementation project and no new system for anyone to learn. 

The platform detects abnormally strong or unusual response patterns and flags any candidate who should be re-tested, and the forced-choice format means a candidate cannot improve their result by agreeing strongly with everything. Candidates are not asked to do anything extra. For roles where the stakes justify it, remote supervision is available on top.  

You do. We return a result and nothing else. Nothing on our side advances a candidate or rejects one, and the score at which candidates surface for review is set by you in your own system. Every result comes with the reasoning behind it, so a decision can be explained to a candidate who asks. 

The scoring equations are built to reduce age, gender and ethnicity differences, and we monitor for adverse impact. We will share the fairness evidence for what you are using and review your own hiring data with you.