Physicl, a startup that builds realistic digital worlds to train robots, has raised $10 million in seed funding from Inovia Capital. Founder and chief executive Alex de Vigan previously built nfinite, a platform for 3D product renderings for e-commerce. Generative AI overtook that business, so he is repurposing the technology to make simulations that follow real-world physics. Physicl is releasing Studio, which has 10,000 3D environments and 100,000 object assets. Users can create scenes to generate synthetic data for specific problems, such as a robot struggling to spot a cup on a cluttered counter or in shadow. World model builder SpAItial is an early customer. Physicl faces a crowded field of data and tooling providers.

Physicl raises $10 million to solve physical AI's edge case gap. New Studio app lets researchers generate actionable data tailored to their models' weak spots. November 06, 2026 08:00 ET | Source: Physicl PARIS, Nov. 06, 2026 (GLOBE NEWSWIRE) - Physicl, a platform for generating targeted training data for physical AI, today announced $10 million USD in seed funding, led by Inovia Capital, with participation from US Venture Partners, to address physical AI's edge-case problem. Physicl also introduced Studio, which lets researchers generate customized environments and high-fidelity physical training data with precise ground-truth, targeted to improve models on specific shortcomings. For physical AI to advance from labs to broad deployment, models need to be able to operate reliably within every situation and environment. However, capturing sufficient quantities of data to accomplish this in pre-training is nearly impossible given the long-tail of scenarios they can encounter in the real world. Physicl lets teams generate ground-truth data for targeted post-training on the specific situations their models fail in, giving researchers a reference against which to train and evaluate their models. The company works with AI labs including Meta, World Labs, Epic Games and SpAItial, helping them create the data needed to train and evaluate increasingly capable models. "An impressive demo doesn't tell you how a model will perform in all the situations it could encounter " said Alex de Vigan, founder and CEO of Physicl. "Models fail on edge cases. When researchers find these weaknesses, they need a way to generate data that specifically addresses them and test whether the model improves. We built Physicl to make that process easy and repeatable." Founded by entrepreneur Alex de Vigan, Physicl represents the culmination of six years of work in 3D, simulation, and machine learning. The company grew out of Vigan's experience building 3D infrastructure at scale and is now applying that expertise to one of physical AI's emerging bottlenecks: providing researchers with the data required to systematically train and evaluate models against the long tail of real-world conditions. The seed funding will be used to expand the team and further advance Physicl's platform as robotics and world models move toward broad deployment. "We believe physical AI represents one of the most significant emerging opportunities in technology, but the industry still faces fundamental challenges in translating model capabilities into real-world performance. Physicl is tackling a critical piece of that puzzle: scalable, high-quality physical data grounded in real-world physics. That's what makes this team and its approach so compelling to us," said Steve Woods, Partner at Inovia Capital. Introducing Studio The company's first offering, Studio, provides access to 10,000 ground-truth 3D environments, more than 100,000 assets, and billions of possible scene variations. AI researchers can select an environment or describe the scene in which their model struggles, and then adjust variables like lighting, camera viewpoint, and object placement to recreate specific conditions. The app then generates targeted data focused on the identified weakness. With Studio, edge cases that have been nearly impossible to capture sufficient data for can now be reproduced at scale. Researchers can recreate a failure, test fixes and measure progress in a continuous loop, providing a direct path to reach the level of reliability needed for broad deployment. "A home robot might be able to recognize a cup on a clear counter, but miss it when clutter hides half of it or once the sun sets and shadows change in the kitchen," continued de Vigan. "Physical AI teams are faced with challenges like this daily. Studio helps teams recreate those situations, generate variations with precise ground-truth, and train directly against the conditions where their model has failed in the past." Physicl's collaboration with SpAItial, a developer of world models that generate explorable 3D environments, shows how its high-fidelity physical data can address a specific model weakness. SpAItial's model was struggling with high error rates when reconstructing real rooms. By adding data generated with Physicl library to its model's training data, SpAItial more than halved the model's reconstruction error rate. "Physicl gives any researcher access to virtually any physical training data they can imagine. That changes what a small team can build in physical AI, and we saw it in our results right away," said Matthias Niessner, SpAItial's CEO and Professor of Computer Science at Technical University of Munich. About Physicl Physicl was founded by entrepreneur Alex de Vigan and represents the culmination of six years of work in 3D, simulation, and machine learning to build the data infrastructure for the deployment stage of physical AI. The company works with leading AI labs and universities, including AWS, NVIDIA, Meta, World Labs, and Technical University of Munich to deliver the targeted, actionable training data needed to continue advancing physical AI. Physicl is supported by advisers with expertise from World Labs, Hugging Face, Adobe, Carnegie Mellon University, and more. The company is backed by Inovia Capital, U.S. Venture Partners, and Insight Partners.