AI Integrations · Case file 10
Recommendations that learn from the individual and the crowd.
An AI recommendation workflow for an iGaming platform that analyses player behaviour and feeds the engine recommendations based on individual and overall gaming trends.
The brief
Buxholm builds the player-facing layer for gaming operators. Showing every player the same lobby wastes the one thing the platform knows: how people actually play. They needed recommendations that reflect each player's own behaviour and what the wider player base is trending toward.
What we built
- 01
Behaviour analysis that builds a picture of each player from how they actually use the platform.
- 02
Trend analysis across the whole player base, so the engine knows what is rising before any one player does.
- 03
A workflow that combines the two and feeds recommendations back to the platform's engine, tailored per player and refreshed as behaviour changes.
What it does now
The platform serves each player recommendations shaped by their own behaviour and the trends across everyone else's.
Got a build like this in your head?
Twenty minutes, no pitch. Tell us how your business runs and we’ll tell you what’s worth building.