Player intelligence for live games.
The same recommendation and prediction stack, tuned for player behaviour: who's at risk, who to match, and what to surface next.
Player modelling
Represent players from behaviour and context to power downstream prediction and personalisation.
Matchmaking
Balance skill, latency and engagement to build matches players actually want.
Churn prediction
Spot disengagement early and target the right players with the right intervention.
In-game recommendation
Surface the next item, mode or content most likely to resonate with each player.
Player data is noisy, fast and deeply sequential.
Sessions are bursty, behaviour shifts patch to patch, and yesterday's model drifts fast. Graph + sequence modelling and real compute are what keep predictions honest.
Model the player
Combine event sequences, social graph and account features.
Predict the moment
Churn risk, match quality or the next best recommendation.
Close the loop
Ship it, measure lift against a baseline, and iterate.
Building a live game?
Let's talk about player modelling, matchmaking or churn, and how to validate it properly.