How It Works
From a 30-minute game to an objective report — step-by-step neuro-assessment process
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.
NeuroFrame vs. traditional tools
MBTI, DiSC, SHL, Saville, Hogan — how NeuroFrame compares on every dimension that matters
| Criterion | NeuroFrame | Traditional Tests | Winner |
|---|---|---|---|
| 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 | |
7/7 — NeuroFrame wins on every metric | |||
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 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.