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.
Large language models and vision-language models changed what machines can understand. They made planning, interpretation, and adaptation far more capable than traditional software allowed.
What they did not solve is the final step: how a physical system should act safely once intent has been inferred.
A warehouse vehicle is asked to move inventory to a staging zone while avoiding a restricted area and adapting to a blocked aisle.
The system forms a view of the world state, tracks the obstruction, and proposes a new route aligned to the task intent.
Candidate routes are validated against traffic rules, restricted zones, kinematic feasibility, and safety policy.
The allowed action is executed locally with bounded latency and continuous monitoring against intent and drift.
If no safe route exists, the system refuses, logs the reason, and escalates instead of improvising an unsafe move.
Auditability means machine behavior can be reconstructed and reviewed, not just observed in the moment. The system records how intent was interpreted, what actions were permitted, and how execution unfolded.
This gives operators and integrators a usable record of machine decisions instead of leaving critical behavior trapped inside opaque model output.
Xolver sits between learned perception and physical actuation. It works with robotics middleware, industrial controllers, and existing operational systems rather than replacing the surrounding stack.
That lets teams introduce stronger intelligence and control boundaries without rebuilding everything around the machine.
CTO Robotics • Head of Automation • Plant Ops • Systems Integrator Lead
We start with a technical assessment, then a controlled pilot.