We are building the physics layer for physical AI.
> SYSTEM_STATUS: PRIVATE_BETA
Simulation is the primary bottleneck in robotics.
Before a vision-language-action (VLA) model can learn to manipulate a new object, an engineer has to manually rebuild its collision geometry, guess its mass, and trial-and-error its friction boundaries. It takes weeks of manual configuration before a robot sees its first training episode.
We built EnviScale because physics shouldn't be a design problem. It is a compute problem.
By combining Vision-Language Models with deterministic geometry extraction, we automate the hardest parts of System Identification (SysID). EnviScale turns raw 3D meshes into mathematically grounded, domain-randomized simulation environments in under 5 seconds.
No pipelines. No guesswork. Just a compiler for physics.
HEADQUARTERS:Built in Hyderabad, India.
ACCESS:Early access is rolling out in batches to robotics teams training on MuJoCo, Gazebo, PyBullet, and Isaac Sim.