Today's data
OutputsContainsText, images, video and code
ShowsWhat people produced
MissingHow the result was reached
Mission
We are building the intelligence layer for human strengths.
Play is fundamental to human nature, and it is where people most honestly reveal how they think, adapt and perform. Skillprint reads real gameplay and turns it into strengths — for the people playing, and for the enterprises, developers and AI labs building around them.
The problem
AI learns from what humans produce. Not how we think.
Models are trained on outputs. The process that produced them — the decisions, the adaptation, the emotion, the collaboration — is missing from the record.
The missing layer
ProcessContainsDecisions, adaptation, emotion and collaboration
ShowsHow people solve problems
EnablesMeasuring which AI systems genuinely improve human performance
Why gameplay
Games have taught machines to plan, adapt and cooperate.
From Deep Blue to Cicero, games have repeatedly unlocked new machine capabilities. Skillprint extends that precedent in the other direction: turning human gameplay into a measure of how people actually reason.
“Games are a microcosm of reality”
Demis Hassabis · Google DeepMind
01
Behaviour, not self reportingGameplay captures natural behaviour continuously, a richer read than another survey or isolated test.
02
Repeatable and comparableThe same mechanics, played by many people, make strengths comparable across sessions and populations.
03
People come backReal games hold attention, so the picture builds over months rather than fixing a single moment.
The model
One model turns play into strengths that hold up outside the game.
The model and its ontology map play patterns into comparable cognitive, emotional and collaboration strengths. It is the layer that connects the player portal, Flow and the Signal benchmark.
Mood
Desired and achieved states such as focus, relaxation, energy and emotional balance. Nine mindsets in the current model.
Cognition
Pattern matching, attention, memory, reasoning, planning and reaction. The cognitive skill set, read from how a session is played.
Personality
Stable traits and preferences that help interpret engagement and individual differences. Sixty traits across the ontology.
01
How change is describedTrend, momentum, volatility, value score and confidence describe direction, acceleration, consistency, level and reliability.
02
Confidence is part of the outputEvery measurement carries a reliability score, so recommendations and claims can be qualified rather than overstated.
03
Governed by consentPrivacy, consent and data governance controls are explicit wherever gameplay derived strengths are shared.
Research foundation
Six years of research behind every session.
The mapping between game mechanics and human performance is grounded in published neuropsychology, not invented for the product. Over 800 studies inform how mechanics are linked to cognition, mood and personality, and 150+ game mechanics are mapped to the signals a session can produce.
01
Grounded mappingGame mechanics are linked to established cognitive and affective constructs before they are used for measurement.
02
Measured against modelsThe public AI Benchmark evaluates how well AI systems predict player mindset shifts during real gameplay.
03
Open to inspectionEvaluated games, model rankings and methodology are published on Skillprint Labs.
Team
Built by operators who scaled games, data and AI.
A team with exits, platform scale, research depth and enterprise route to market experience.
Chethan Ramachandran
CEO & Founder
Founded Playnomics, acquired by Unity. Platform data reached 2B+ devices monthly.
Gabriel Farah
CTO
AI and ML leader, fintech CTO and Berkeley M.S. in Data Science.
George Kachergis, Ph.D.
Lead Research Scientist
Stanford Language Cognition Lab. Former Assistant Professor of AI.
Ian Atkinson
Head of BD
Former SVP at Esports League, THQ and Playnomics. 20+ years in business development.
Tony Lam
COO / Tech Ops
Former COO and CTO across EA Sports studios. Former VC and Playnomics investor.
Davin Miyoshi
Product / Board
Founded Mesmo, acquired by GSN. Built products reaching 75M+ users and $100M+ revenue.
Long term vision
Utility drives data. Data drives better products.
Each session strengthens the model, the benchmarks and what every participant receives back. That is why the ecosystem is built as one loop rather than three separate products.
See how it works01
More sessions
Real games people want to return to produce continuous behavioural signal.
play02
Better labels
Every session adds labelled evidence to the model and its ontology.
label03
Stronger benchmarks
Better labels make the human+AI benchmarks sharper and more useful to researchers.
measure04
Higher value products
Each product returns more value as the others grow, which brings more sessions.
compoundNext step
See how gameplay becomes intelligence.
Explore the products or speak with Skillprint about the path that fits your organisation.
