Recommendation intelligence, built on serious AI compute.
PeachTech designs recommendation, prediction and intelligent-decision systems, backed by H200 and B300-class GPU infrastructure, so proper validation is actually possible.
Research-grade systems, from model design to production output.
We take messy, real-world behaviour data and turn it into models you can actually trust, validated against strong baselines, not just demoed.
AI Research
Model design, experimental validation, ablation studies and clear technical reporting you can defend.
Recommendation Engines
Cross-platform ranking, user-interest prediction, cold-start handling and Top-K output.
GPU Infrastructure
Access to H200 and B300-class resources for graph learning and large-scale experimentation.
Gaming Applications
Player modelling, matchmaking, churn prediction and in-game recommendation.
Real behaviour data is distributed, incomplete and time-dependent.
Most teams have signals scattered across platforms and no reliable way to know whether a new model is actually better. We fix that.
Connect signals across platforms
Unify graph relationships, behavioural sequences and tabular business features into one coherent view.
Compare models against baselines
Every hybrid design is measured against strong, honest baselines, with ablations to show what carries the weight.
Convert findings into usable outputs
Turn validated research into ranked, Top-K predictions and decisions your product can ship.
Bring us the data problem. We'll help define the model question.
If it involves cross-platform user behaviour, personalisation, recommendation, ranking or prediction, we'd like to hear about it.