Physicl secured $10 million in seed funding led by Inovia Capital to build tools that convert unstructured data into high-fidelity physical representations. The startup leverages a pipeline from its previous e-commerce company to create 3D assets. The funding will support the development of a platform designed to generate synthetic data for training robots on complex physical tasks.
The company released a product called Studio containing 10,000 3D environments and 100,000 assets. SpAItial, a world model builder, utilized Physicl’s data to enhance its Echo-3 product. This collaboration demonstrates how the startup’s synthetic environments can improve the performance of physical AI models by providing realistic training scenarios.
Investors are backing Physicl as the demand for synthetic data grows in the robotics sector. The startup’s ability to transform unstructured inputs into structured 3D environments addresses a key bottleneck in training physical AI agents. Developers working on robotics and physical AI models will find these synthetic datasets essential for testing and training in safe, controlled environments.
