Products
Human Gameplay Data
Large-scale datasets capturing real human decisions across hundreds of games. These structured logs provide high-quality signals for studying strategic reasoning, long-horizon planning, and decision-making under uncertainty at scale.
RL Environments
Interactive game environments purpose-built for reinforcement learning research. Agents can train, simulate, and test strategies in controlled settings with standardized rules, reward structures, and reproducible outcomes.
EVAL
A systematic framework for evaluating AI models on complex game tasks. Models are tested against human gameplay patterns and measurable objectives to track improvements in reasoning, strategy, and generalization.
Benchmarks
Standardized challenge suites built from real-world games that measure model performance over time. These benchmarks create consistent comparisons across systems in areas such as planning, imperfect information, and strategic decision-making.
Training Infrastructure
Infrastructure and scalable pipelines designed to train AI models using gameplay data and interactive environments. This platform supports large-scale experimentation across architectures, learning strategies, and reinforcement learning workflows.
SFT (Supervised Fine-Tuning)
Human gameplay examples are used to fine-tune models through supervised learning, enabling models to learn strong baseline behaviors and replicate effective human decision-making patterns.
World Models
Simulation models that learn the underlying dynamics of games and environments, enabling AI systems to predict future states, reason about consequences, and plan actions over long horizons.
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