Products
Advanced reasoning isn't going to come from static scrapeable data - and it likely won't even come from LLMs. And true real world readiness requires the space to iterate and improve. That's why gaming is the ideal arena to unlock AI's next level. Gamelab leverages our own dynamic data and structured environments to create benchmarking and training capabilities that only gaming can provide.
Human Data
Our information-rich decision data is exactly what AI needs to up its cognitive potential. Gamelab’s proprietary data comes from 22 million monthly players of our own games that we’ve created and hosted for decades.
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Bespoke Solutions
GameLab’s Bespoke Datasets offering enables the creation of entirely new games designed with data capture in mind. Rather than relying solely on existing environments, we work backwards from the research objective, identifying the specific behaviors, decision points, or cognitive patterns that need to be studied, and build engaging game mechanics that naturally elicit those signals during gameplay.
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Environments
We offer a wide array of training environments - our own spaces where AI can safely train and transform its capability. From strategy to spatial to reasoning up to reinforcement learning with human feedback.
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Benchmarking
Our benchmarking goes deeper than specialized tasks and surface intelligence. We’ve created a new Cognitive Index Score to measure AI capabilities across an array of eight brain functions.
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RL & Training
Open models, Vision Language Models, Language Models or World Models - our data is multimodal and our training environments have been crafted to support them all. Training with Gamelab will improve your model, whether specialized or general, in areas you wouldn’t have expected.
Continue reading >FAQ: High-Fidelity AI Data Solutions
Our AI training data services are built to address the specific challenges of frontier AI development, from benchmark saturation to data contamination. Explore the questions below to understand how GameLab’s unique methodology ensures data integrity, strategic depth, and high-performance results for your models.
We produce structured, sequential records of human decision-making across a “Multiverse” of games. Our AI training data services include full-state game records, move-by-move decision trees, and outcome-based trajectories. This data is delivered in machine-readable formats (JSON/CSV) optimized for rapid integration into existing training pipelines.
- Strategic Planning Sets: Data from long-term horizon games (e.g., Chess, Strategy puzzles).
- Probabilistic Inference Sets: Data from non-perfect information card games (e.g., Gin Rummy).
- Adversarial Modeling Sets: Data focused on competitive multi-agent interactions.
- The “Human Game Multiverse” Corpus: A diverse, multi-genre dataset for general-purpose reasoning fine-tuning.
Most training data for AI is currently scraped from the public web, which is increasingly contaminated with AI-generated text. GameLab data is proprietary and generated in “cleanroom” human environments. Because games involve non-deterministic outcomes, our datasets provide a more rigorous test of a model’s ability to handle uncertainty than any static text corpus can.
- Reinforcement Learning from Human Feedback (RLHF): Aligning models with expert human strategic logic.
- Fine-Tuning: Teaching models spatial reasoning, deductive logic, and long-term planning.
- Agentic Evaluation: Verifying how autonomous agents handle multi-step tasks in non-perfect information environments.
Absolutely. Beyond our standard library, we offer bespoke AI training data services. We can design custom game environments or curate specific gameplay distributions, such as “edge case” scenarios or high-level expert play, to meet your specific research objectives.
Every data point is sourced from our private network of human players, ensuring it has never touched the open internet. We use automated verification to ensure sequential logic is preserved, providing a “clean” environment where models must solve problems from first principles rather than retrieved memory.
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