Assess peoplethrough datanot bias
NeuroFrame is an AI-powered behavioral assessment platform. An adaptive game measures cognitive abilities and personality traits in 30 minutes with scientific precision.
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.
Trusted by companies across 20+ sectors and 27 industries
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 ecosystem transforms 30 minutes of gameplay into an objective performance profile
Immersive Simulation
The candidate plays a Tower Defense game for 30 minutes. The game models real situations: resource allocation, working under pressure, strategic planning. The "flow" state eliminates socially desirable answers.
Decision Mathematics
The algorithm computes the optimal decision for each situation and measures the player's deviation — decision trees, gradient analysis, evaluation of trade-offs.
ML Pattern Analysis
Random Forest, Gradient Boosting, and neural networks compare the profile against a database of 10,000+ executives from 500+ companies.
Predictive Analytics
A personalized report with a profile across 8 competencies, growth areas, and recommendations. Team analytics: heatmaps, role distribution, conflict detection.
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.
NeuroFrame vs. traditional tools
MBTI, DiSC, SHL, Saville, Hogan — how NeuroFrame compares on every dimension that matters
| Criterion | NeuroFrame | Traditional Tests |
|---|---|---|
| What it measures | Real behavior in a simulation | Self-report (what a person thinks about themselves) |
| Can it be faked? | No — data is behavioral | Yes — candidate picks the "right" answer |
| Performance prediction | R² = 0.46 (5–9× higher than interviews) | R² = 0.05–0.10 (close to random) |
| Time per candidate | 30 minutes, one game session | 4+ hours (battery of tests + interview) |
| Assessor bias | Zero — algorithm assesses everyone equally | Depends on gender, age, appearance of evaluator |
| Model validation | Published: CFI = 0.96, α = 0.74 | MBTI: no predictive validity published |
| Result stability | Test-retest > 0.83 | Varies widely between sessions |
What it measures
Real behavior in a simulation
Self-report (what a person thinks about themselves)
Can it be faked?
No — data is behavioral
Yes — candidate picks the "right" answer
Performance prediction
R² = 0.46 (5–9× higher than interviews)
R² = 0.05–0.10 (close to random)
Time per candidate
30 minutes, one game session
4+ hours (battery of tests + interview)
Assessor bias
Zero — algorithm assesses everyone equally
Depends on gender, age, appearance of evaluator
Model validation
Published: CFI = 0.96, α = 0.74
MBTI: no predictive validity published
Result stability
Test-retest > 0.83
Varies widely between sessions
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
What your business gets
Real numbers: time, money, and decision quality
Instead of 4 hours of testing
A single game session replaces a battery of 3–5 traditional tests (MBTI + SHL + interview). The candidate downloads the app, plays for 30 minutes — HR gets a ready report.
Saved on training costs
One development program cycle costs ≈ $50,000. Precise selection through NeuroFrame eliminates investment in employees who won't deliver results.
Executives in the database
Profiles of 10,000+ top managers from 500+ companies across 27 industries. Your candidate is compared to real leaders — not an abstract norm.
More accurate than classic tests
NeuroFrame's predictive accuracy is 3× higher than self-report tests (MBTI, DiSC). More details in the Science section below.
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 + AI/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.
See the true potential of your team
Book a demo and get your personalized profile. Assess people through data, not assumptions.
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