Xolver is a physical intelligence platform providing robotics foundation models, a deterministic enforcement layer, and embedded runtimes for safe, auditable machine operations.
It is designed as an embedded translation device, an edge runtime, and a model layer you can evaluate.
In pilots today, production in constrained environments.
Works with standard robotics middleware and industrial controllers.
Industry Partnership
Xolver is a member partner of NVIDIA Inception program that nurtures cutting-edge startups. We leverage the NVIDIA ecosystem to scale our foundation models from research to high-stakes physical environments.
Cloud Infrastructure
Xolver is supported by the AWS Startups program, leveraging AWS Activate credits and benefits under the Activate Portfolio. This enables us to build and scale our high-performance robotics infrastructure with enterprise-grade cloud services.
Physical systems do not fail because perception is useless. They fail because interpreting the world is not the same as deciding what a machine is allowed to do next.
The hard problem is not generating intent. It is turning intent into physical behavior without losing safety, policy, or control.
Bounded autonomy is a design principle for physical systems. Learned models can interpret, adapt, and propose, but physical action remains governed by explicit limits.
That keeps autonomy useful under real operating conditions instead of depending on ideal scenes, perfect connectivity, or unchecked model confidence.
Models propose, enforcement constrains, runtime executes. Xolver separates these roles so perception, validation, and execution do not collapse into a single unchecked loop.
ERP and IAM feed policy and permissions, not perception.