Antioch has raised $32 million in Series A funding to accelerate how physical AI systems are developed and tested.
The New York-based startup, founded in 2025, is building a simulation platform that allows robotics companies to design, train and test physical systems in the cloud.
Led by Greylock, with participation from A*, Category Ventures, Box Group and Icehouse Ventures, the round backs Antioch’s push to take robotics and autonomy teams from simulation to real-world deployment faster.
The company is tackling a growing bottleneck in the sector: testing physical systems in the real world is slow, expensive and difficult to scale.
While software teams can run thousands of experiments in minutes, robots, drones and autonomous systems still rely heavily on physical hardware to validate performance.
Antioch’s platform is designed to shift more of that process into high-fidelity simulation. Its environments can be calibrated to a customer’s hardware, allowing teams to run thousands of parallel evaluations before deploying systems in the real world. Antioch said its simulations have closely matched physical test results for customers including Amazon’s Ring.
“Simulation isn’t new to the industry, but low fidelity, lack of scale, and tooling fragmentation have limited its role in the development process,” said Alex Langshur, Co-founder of Antioch. “Antioch is the first platform that takes autonomous systems from design to validation to mass deployment in a single simulation environment.”
The company is also building partnerships across the Physical AI stack, including with NVIDIA, integrating Omniverse libraries and robotics frameworks such as Isaac Sim and Isaac Lab.
As IZON explored in an earlier deep dive, the rapid expansion of AI infrastructure is putting “intelligence on tap”, opening the floodgates for physical AI and increasing the need for infrastructure that can test and validate what these systems do in the real world.
With the new capital, Antioch plans to expand its engineering team, deepen its simulation capabilities and support growing demand across robotics, drones, industrial automation and other Physical AI applications.
