NeuroFrame is an ML-powered behavioral assessment platform. A tower-defense game captures thousands of behavioral micro-signals and maps them to 8 validated competencies in 30–60 min.
Strongest: planning and synthesis — Progress Monitoring and Openness to New above the comparison median, Conscientiousness below it.
A reference range for 287 professions at each of the 8 stages of a company's life — 2,296 combinations in total. Pick a stage and a role, see the benchmark corridor across eight behavioural parameters, and compare a profile against it.
Open the calculatorThe ranges are a reference point, not a verdict — and the tool asks for nothing in return.
Three shifts that separate behavioural measurement from self-report questionnaires
Instead of questionnaires and self-reports — an immersive simulation. The candidate plays while the system captures thousands of digital traces: clicks, pauses, reaction time, strategy changes. Social-desirability distortion is far lower than in self-reports: the candidate solves a task instead of describing themselves.
A single game session replaces a battery of 3–5 traditional tests. Automatic report with 8 parameters, growth areas, and recommendations — no manual processing and no assessor panel.
The comparison sample is 14,850 real executives from 500+ companies. Your candidate is scored in percentiles against that sample, not against abstract norms. Details in the Science section.
Psychometric tests, interviews, and assessment centers have low predictive validity — they're vulnerable to distortions, biases, and social desirability effects
Replacing a wrong hire costs twice their salary: search, onboarding, lost know-how, team morale collapse. Interviews and resumes don't predict role success.
The global economy loses $8.8T annually to disengaged employees. 85% of people show up to work but don't engage — traditional assessments can't detect this.
Classic questionnaires (MBTI, DiSC) are vulnerable to social desirability bias and deliberate distortion. Candidates know the "right" answer — and pick it.
Managers rely on gut feeling and stereotypes. Interview outcomes depend on gender, age, and appearance — not on actual candidate competencies.
An end-to-end ML pipeline transforms 30–60 min of gameplay into a structured behavioural profile
The candidate plays a Tower Defense game for 30–60 min. The game captures thousands of behavioural micro-signals: click timing, strategy changes, resource allocation, error correction speed.
The stream of play becomes a structured picture of behaviour: what the person chose, what they passed up, and how the approach changed as pressure grew.
ML models compare the profile against 14,850 validated executives from 500+ companies. Every parameter has its own calibrated model, with its metrics published.
A personalized report across 8 competencies with growth areas and recommendations. Team analytics: heatmaps, role distribution, conflict detection.
A sample behavioural profile built from a 30–60 min game session. 8 competencies, cognitive DNA, and actionable insights — all in one document.
Strongest: planning and synthesis — Progress Monitoring and Openness to New above the comparison median, Conscientiousness below it.
Detailed analysis — what the individual did during the game, specific numerical indicators, and practical workplace insights.
Lower expression. Works at pace rather than systematically and spends little time re-checking: with subtle interface changes, misses 34% of low-salience signals (norm: 18%).
This is not a deficit — it's a cognitive style optimized for speed and synthesis. Where precision is the deliverable (financial models, contracts, QA), pair this individual with a detail-oriented team member: checklists and peer review move the checking into the process and keep the pace.
Takes calculated risk for a later advantage: commits early and accepts a temporarily weaker position when the payoff is bigger.
Moving before the picture is complete is an asset in fast markets and a risk on irreversible calls. Where the bet cannot be unwound (hiring, pricing, M&A), add an explicit go/no-go checkpoint.
How the candidate responds to pressure, uncertainty, and high workload — recorded from in-game behaviour, not from self-report.
Stable productivity under escalating pressure. Performance sustains as task complexity increases.
Efficient cognitive resource management. Pauses and shifts occur before overload.
Moderate caution under uncertainty. Balances speed and accuracy of decisions effectively.
Rapid strategy switching when rules change. High cognitive flexibility.
Errors don't cause fixation — returns to productive behavior quickly after setbacks.
Maintains work quality throughout the full session. Minimal decline toward the end.
Behaviour under load — sustains productivity under pressure, recovers quickly from setbacks, and manages cognitive resources efficiently. Well-suited for high-autonomy roles with intensive task flow.
Every metric is published. Every claim is verifiable. Here's what NeuroFrame's psychometric properties actually mean — in plain English.
CFI shows how well the assessment model fits real data. A score of 1.0 is perfect; above 0.95 is excellent.
A CFI exists only for instruments that have actually been through confirmatory factor analysis. NeuroFrame publishes 0.96 for a two-domain model — cognition and personality.
Cronbach's alpha measures whether the items in a scale are all "talking about the same thing." 0.70 is the commonly accepted threshold.
Reported as a range across the eight scales, because that is what it is — a single averaged figure would hide the spread.
The employees whose assessment results were set against real KPIs and manager ratings. A predictive figure is worth exactly as much as the sample it was measured on, so the sample is published next to it.
Not the same number as the comparison base of 14,850 executives an individual score is ranked against — two figures that are easy to confuse.
ROC AUC shows how well the model separates profiles that match a role's requirements from profiles that do not. 0.5 is random; 1.0 is perfect. NeuroFrame's 0.77 means high accuracy.
Measured against real KPIs and manager ratings on 3,000+ employees, and published in full. MBTI and DiSC publish no predictive-validity data at all.
How stable results are across sessions. If the same person takes the assessment twice, do they get a consistent profile? Above 0.80 is excellent.
Reliability you can check yourself: take the assessment twice and compare the two profiles. NeuroFrame publishes > 0.83 — stable and reproducible.
A single platform adapts to your task: hiring, team analytics, or identifying future leaders
Mass candidate screening through game-based assessment. Filter before expensive stages — interviews and assessment centers. One assessment cycle: 30 minutes, report generated automatically.
Diagnose your existing team. Competency heatmaps, team chemistry analysis, role distribution, and hidden conflict detection.
Identify high-potential employees. The algorithm recognizes hidden behavioral patterns of high-performing leaders. Build your talent pipeline based on data, not subjective opinions.
Every case follows the same framework: Problem → Investigation → Solution → Result. These are real deployments, and the numbers below are the results of those specific projects — not an average effect of the method.
The holding needed to select 50 high-potential young leaders from 400+ internal candidates for a management training program. Previous selection relied on interviews — resulting in 40% dropout.
NeuroFrame diagnosed the existing process. Supervisor nominations correlated poorly (r = 0.12) with actual post-program performance. Interview-based screening was essentially random.
~400 candidates completed the 30–60 min assessment. The algorithm generated a ranked list with leadership potential scores and fit indices against a "successful leader" benchmark.
The program shortlist came out of the ranking, with the gaps named for each finalist. Training completion rate jumped from 60% to 92%. Saved $50K per cycle. Expanded to 3 additional divisions.
Behind NeuroFrame is a multidisciplinary team from science, ML, and the HR industry
Neuroscience + ML + HR-Tech. Three domains in one team.
5 PhDs: psychometrics, neurophysiology, organizational psychology.
ML engineers and full-stack developers building adaptive systems.
Long-form work on how human capital gets measured and priced — with sources, methodology and numbers you can check.
Seventy to ninety per cent of acquisitions destroy value, and people and culture are among the most-documented causes — yet no line in the model holds them. A step-by-step method for turning integration risk into a coefficient a valuation committee can argue with.
An analytical framework for integrating cognitive data into deal valuation — and the introduction of HC-Beta, a Human Capital Coefficient for M&A
One 30-minute game session. Eight validated competencies. A behavioral profile based on data — not guesswork.