Product · Assessment
Eight years of psychometric science, now one stage of the pipeline.
The item bank we built at Neuroworx since 2018, validated against real hires, audited for bias, and now running adaptively per role. Not a single test. A library, with the right instrument chosen for the work.
Post a role →Read the validation methodology
The four families
Four kinds of signal, mixed per role.
01
Cognitive ability.
Numerical reasoning, verbal reasoning, abstract reasoning, working memory. Adaptive difficulty per item. Norms across 200k-plus candidates.
02
Personality.
Big Five, plus role-relevant trait composites. Self-report with attention checks and forced-choice items to limit social desirability bias.
03
Situational judgement.
Scenario-based items written for the role family. Sales, customer success, operations, line-management, all have their own.
04
Role-specific simulations.
Coding tasks for engineers. Writing samples for content roles. Numerical modelling for finance. Sales role-play (voice) for AEs. Each generated per role from the brief.
Selection
One library. One assessment chosen per role.

When you post a role, the assessment selector picks the right mix from the library based on the role family, the must-haves, and the seniority. An SDR does not get a numerical reasoning battery they will never use. A senior engineer does not get a coding task that screens out by language preference. The selection is visible to you and editable before the role goes live.

  • Role family maps to a default battery (e.g. AE → personality + SJT + 20-minute role-play).
  • Must-haves modify it (e.g. "must have managed a quota team" adds a leadership SJT module).
  • Seniority modifies length (junior 25 min, senior 40 min, head-of 60 min).
  • You can swap any module for an alternative before the role publishes.
Adaptivity
Items adjust to the candidate, not the other way around.

On cognitive batteries, the next item depends on the last response. A strong run pushes the candidate into harder items quickly; a weak run keeps them at a fair level. Two outcomes: a more precise score, and a shorter test for the candidate. The median assessment runs 22 minutes, not 45.

22 min
median assessment length.
0.78 to 0.91
internal reliability (alpha) across batteries.
14%
reduction in candidate dropout vs the fixed-form test.
Anti-fraud
Four layers, no proctoring.
01
AI-assistance detection.
Patterns of paste, paste timing, response cadence, and answer fingerprinting against known-LLM outputs. Flagged, not auto-rejected.
02
Proxy detection.
Voice biometrics (where the candidate consents) compared between screen, assessment, and interview. Cross-stage signal, not a single test.
03
Copy-paste detection.
Open-ended items track keystroke patterns and clipboard events. Honest answers do not get flagged; verbatim paste does.
04
Deepfake video (V2).
When video lands, we run liveness and face-consistency checks across the session. V1 is voice-only so this is deferred.
No camera, no screen-share, no remote-take-over. Intrusive proctoring punishes honest candidates more than it catches dishonest ones.
Fairness
Monitored per role, per stage, per protected group.

Every assessment in production is monitored for adverse impact against protected groups, per role, per stage, per audit cycle. The 4/5ths rule is the floor. When a flag fires, the role's hiring manager and our internal review team both see it before the next candidate is processed.

  • Annual independent bias audit, summary published.
  • Per-role drift detection: if a role's pass rates shift on a protected attribute, we know and so do you.
  • Adverse-impact dashboard in product, shareable with HR or legal.
  • See /trust/fairness for the full posture.
Read the science
Every model in production has a card. We publish them.
Capability, training data, evaluation, known limitations, the version history, and the failure modes we monitor for. Updated quarterly.
Read the model cards →
Want to see the assessment on your role?
Post the role. Pick the modules. Watch it run.
Post a role →See the trust posture
Assessment · Picked.ai