Assess peoplethrough datanot bias
NeuroFrame is an ML-powered behavioral assessment platform. A tower-defense game captures 4,000+ behavioral micro-signals and maps them to 8 validated competencies in 30 minutes.
"Analytical Strategist" — higher expression in conscientiousness + learnability + openness to new approaches.
Assessment that works differently
Three fundamental shifts that make game-based behavioral assessment 5–9× more accurate than traditional tools
Observe, Don't Ask
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. No social desirability — only real behavior.
30 Minutes Instead of 4 Hours
A single game session replaces a battery of 3–5 traditional tests. Automatic report with 8 competencies, growth areas, and recommendations — no manual processing, no assessors, no subjectivity.
Data, Not Opinions
The model is trained on 10,000+ real executives from 500+ companies. Your candidate is compared to validated benchmarks, not abstract norms. Details in the Science section.
Why current assessment doesn't work
Psychometric tests, interviews, and assessment centers have low predictive validity — they're vulnerable to distortions, biases, and social desirability effects
A bad hire costs 2× annual salary
Replacing a wrong hire costs twice their salary: search, onboarding, lost know-how, team morale collapse. Interviews and resumes don't predict role success.
$8.8 trillion — the price of disengagement
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.
Tests are easy to fake
Classic questionnaires (MBTI, DiSC) are vulnerable to social desirability bias and deliberate distortion. Candidates know the "right" answer — and pick it.
Assessor bias
Managers rely on gut feeling and stereotypes. Interview outcomes depend on gender, age, and appearance — not on actual candidate competencies.
From game patterns to business results
An end-to-end ML pipeline transforms 30 minutes of gameplay into an objective performance profile
Digital Traces
The candidate plays a Tower Defense game for 30 minutes. The game captures 4,000+ behavioral micro-signals: click timing, strategy changes, resource allocation, error correction speed.
Behavioral Patterns
The algorithm computes optimal decisions for each situation and measures deviations — decision trees, gradient analysis, trade-off evaluation.
Psychometric Scales
Random Forest, Gradient Boosting, and neural networks compare the profile against 10,000+ validated executives from 500+ companies.
Holistic Profile
A personalized report across 8 competencies with growth areas and recommendations. Team analytics: heatmaps, role distribution, conflict detection.
See what you get
A real behavioral profile generated from a 30-minute game session. 8 competencies, cognitive DNA, and actionable insights — all in one document.
"Analytical Strategist" — higher expression in conscientiousness + learnability + openness to new approaches.
Unpacking each competency
Detailed analysis — what the individual did during the game, specific numerical indicators, and practical workplace insights.
Lower expression. This individual naturally prioritizes speed and big-picture patterns over micro-level precision. In tasks with subtle interface changes, they miss 34% of signals (norm: 18%).
- Error detection decreases 28% after minute 15
- Gravitates toward "good enough" under high cognitive load
- Strong at macro-level; naturally filters out micro-level signals
This is not a deficit — it's a cognitive style optimized for speed and synthesis. In practice, pair this individual with a detail-oriented team member for tasks requiring precision (financial models, contracts, QA). Checklists and peer review processes naturally complement this style.
Pronounced initiative. In multi-agent scenarios, takes the coordinator role from the first actions. Allocates resources in favor of the group vs. self — 62/38.
- Initiates 73% of collaborative actions (norm: 45%)
- Style: transformational leader, oriented toward developing others
- Delegation: tends to take on more than optimal (complementary zone)
Will naturally lead cross-functional projects. Current style leans toward "do it myself" + "inspire" rather than "delegate and monitor." For scaling to VP-level, practicing trust-based delegation would expand this individual's leadership range.
Risk & Resilience Map
How the candidate responds to pressure, uncertainty, and high workload — measured objectively through in-game behavior.
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.
"Resilient Performer" — sustains productivity under pressure, recovers quickly from setbacks, and manages cognitive resources efficiently. Well-suited for high-autonomy roles with intensive task flow.
What's behind the numbers
Every metric is published. Every claim is verifiable. Here's what NeuroFrame's psychometric properties actually mean — in plain English.
CFI (Confirmatory Fit Index)
CFI shows how well the assessment model fits real data. A score of 1.0 is perfect; above 0.95 is excellent.
MBTI: 0.80–0.89. NeuroFrame: 0.96 — our model is rigorously validated.
Cronbach α (Internal Consistency)
Cronbach's alpha measures whether the questions in a test are all "talking about the same thing." Above 0.70 is the accepted threshold.
Many commercial tests don't publish α. NeuroFrame exceeds the 0.70 threshold — your data is reliable.
R² (Predictive Power)
R² shows what share of job performance NeuroFrame can predict. 0.46 means our model explains 46% of variance — exceptional in psychometrics.
Interviews: R² = 0.05–0.10. Assessment centers: R² ≈ 0.15. NeuroFrame: R² = 0.46 — 5–9× more accurate.
ROC AUC (Classification Quality)
ROC AUC shows how well the model separates a "good fit" from a "bad fit." 0.5 is random; 1.0 is perfect. NeuroFrame's 0.77 means high accuracy.
Resume screening: ~0.55. NeuroFrame: 0.77 — reliable separation of top performers.
Test-Retest Reliability
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.
Structured interviews: near-zero test-retest. NeuroFrame: 0.83+ — stable and reproducible.
Three products — one engine
A single platform adapts to your task: hiring, team analytics, or identifying future leaders
NeuroFrame Test
Mass candidate screening through game-based assessment. Filter before expensive stages — interviews and assessment centers. One assessment cycle: 30 minutes, report generated automatically.
- Volume hiring from 1 to 5,000+ people
- Remote assessment — download the app and play
- Automatic personal report across 8 competencies
- Candidate ranking with hire / don't hire recommendation
NeuroFrame Team
Diagnose your existing team. Competency heatmaps, team chemistry analysis, role distribution, and hidden conflict detection.
- Team map — aggregated group profile
- Heatmap: strengths and gaps for each member
- Role balance and conflict zone analysis
- Recommendations for role redistribution
NeuroFrame HiPo
Identify high-potential employees. The algorithm recognizes hidden behavioral patterns of high-performing leaders. Build your talent pipeline based on data, not subjective opinions.
- Identify Value Creators — who drives the business
- Leadership potential and decision-making style
- Succession planning
- Individual development plan
Proven by real business
Every case follows the same framework: Problem → Investigation → Solution → Result. These are real deployments with real metrics — from Fortune 500 to fast-growing startups.
Major Petrochemical Holding
25,000+ employeesThe 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-minute assessment. The algorithm generated a ranked list with leadership potential scores and fit indices against a "successful leader" benchmark.
Identified high-potential employees with ≥ 95% promotion probability. Training completion rate jumped from 60% to 92%. Saved $50K per cycle. Expanded to 3 additional divisions.
Scientists & Engineers
Behind NeuroFrame is a multidisciplinary team from science, ML, and the HR industry
Founders
Neuroscience + ML + HR-Tech. Three domains in one team.
Scientific Board
5 PhDs: psychometrics, neurophysiology, organizational psychology.
Engineers
ML engineers and full-stack developers building adaptive systems.
Unlock the true potential of your team
One 30-minute game session. Eight validated competencies. A behavioral profile based on data — not guesswork.