Worldmodeldata, a Cambridge-based startup building a database of video game-generated training data for next-generation AI, has raised €8 million (£7 million) in Seed funding as it emerges from stealth.
The round was led by Iona Star Capital.
Rhea Loucas, Founder and CEO, says: “World models represent a fundamental paradigm shift in AI, but progress like this needs fuel: internet-scale data to help AI systems make predictions and reason in physical environments. Such a comprehensive dataset does not yet exist; however, video games, as safe, controlled environments, are the perfect setting for generating the action-conditioned data needed to train the next generation of AI at the required scale. Worldmodeldata is built to bridge that gap.”
Worldmodeldata’s Seed round comes amid a wider 2026 funding pattern around AI data infrastructure, world models and physical AI in Europe.
EU-Startups has reported sizeable adjacent rounds this year, including AMI’s €890 million raise in Paris for world-model AI systems, London-based Encord’s €50 million Series C to scale data infrastructure for physical AI, and London-based Stanhope AI’s €6.7 million Seed round for adaptive AI in robotics and defence applications.
UK-based comparables include Encord, Stanhope AI and BeyondMath, making Worldmodeldata part of a same-country cluster of companies addressing the data, simulation and deployment layers needed for AI systems operating beyond text-based models.
Richard Allan, Chairman, adds: “We are proud to anchor ourselves in the UK’s AI ecosystem, a strategic choice driven by the urgent push for sovereign AI capabilities and the robust infrastructure that powers them. The deep expertise of this team is hugely exciting and positions them perfectly to lead this charge.
“In tackling critical gaps that LLMs cannot bridge, this isn’t about improving AI model training, but building an essential foundation for deploying AI in sectors where the demand is vast but the solutions remain limited.”
Founded in 2025, Worldmodeldata aims to deliver the most diverse, scalable, and high-quality training data to accelerate the next generation of AI-world models, embodied agents, and physical AI.
They turn modern video games into a source of structured training data captured directly from the engine with humans in the loop.
The company was founded by serial entrepreneur Rhea Loucas and is supported by Lord Richard Allan, UK technology policy specialist and Meta’s former VP of Public Policy, who joins the board as Chairman.
Gerry Buggy from Iona Star says: “AI spent the last few years learning to describe the world, constrained primarily by compute and architecture. The next era is about acting in it, and you cannot learn to act from passive video or text, because acting requires having seen how the world answers back. That coupling between action and consequence is the scarcest resource in AI today.
“Worldmodeldata is manufacturing that missing ingredient, and every company building Physical AI or Digital AI will eventually face the same challenge. That conviction is what led IonaStar to lead the investment in Worldmodeldata, and that is why I joined the board.”
According to Worldmodeldata, world models, an AI system’s internal understanding of how the world works, will form the backbone of the next generation of AI. Rather than simply reacting to inputs, world models learn how things look, interact, and change over time.
This allows them to predict what will happen next and plan actions accordingly to operate safely in complex environments.
However, these models are only as capable and robust as the data they are trained on. Unlike generative models, which had a head start benefiting from the internet, the data world models need are harder to find. This data scarcity represents a critical limitation for developing AI capabilities in high-stakes industries where trial-and-error training is not an option.
Worldmodeldata looks to overcome this bottleneck by aggregating and structuring rich datasets from modern video games that capture real human behaviour and interactions in complex, dynamic environments.
Delivered as curated datasets, it gives customers, such as frontier labs building world models, physical AI systems, and robotic companies, a powerful foundation for training models that need to understand dynamics, predict outcomes, and make safer decisions in the real world.
For instance, enabling world models to act as an internal simulator for self-driving cars as they navigate traffic and predict pedestrian movement.
This data is sourced directly from real gameplay in titles built on engines such as Unreal and Unity. It is acquired via formal licensing agreements that allow the gaming community and developers to monetise their gameplay and assets built with Worldmodeldata, rather than using web scrapers.
The company aims to build a data library of 1 million hours by the end of 2026, compared to the current largest database, which has just 40,000 hours. The funding will help advance this goal by fuelling product development, team expansion, and the securing of crucial data-sourcing agreements.
Read the orginal article: https://www.eu-startups.com/2026/07/uk-startup-worldmodeldata-raises-e8-million-to-turn-video-games-into-training-data-for-physical-ai/
