XGaming provides an AI‑driven platform that lets game studios define feature goals in plain English and automatically generates, tests, and optimizes game content. Its agents analyze telemetry, create player segments from dozens of signals, and run contextual bandit experiments in real time, continuously rolling out winning features without manual A/B testing.
Funding
Funding not disclosed
HEFounders
Product
Problem
Game studios often spend extensive time and resources manually designing, segmenting, and testing new gameplay features, leading to slow iteration cycles and suboptimal player experiences. Traditional A/B testing methods can be inefficient, requiring static traffic splits and lengthy analysis before deploying successful changes.
Solution
XGaming offers an AI‑driven copilot that automates the end‑to‑end workflow of game feature development. Studios input goals in plain English, and the platform’s agents generate detailed player segments using over 40 behavioral and contextual signals, propose data‑backed hypotheses, and launch contextual bandit experiments that allocate live traffic to the best performing variants in real time. Successful variants are automatically rolled out to production, and each experiment feeds back into the system to improve future recommendations, enabling continuous, rapid optimization of retention, monetization, and engagement metrics.
Target Audience
Primary customers are mid‑size to large game development studios and live‑ops teams that need to accelerate feature iteration and optimize player retention and monetization at scale.
Features
- Natural‑language goal input that triggers automated planning, hypothesis generation, and experiment design
- Segmentation engine building hyper‑specific cohorts from 40+ telemetry signals
- Data‑backed hypothesis generation for offers, tuning, and content variants
- Real‑time contextual bandit optimizer that shifts traffic to winning variants without static splits
- Automated rollout of winning features directly into production
- Continuous learning loop where each experiment informs subsequent AI recommendations