Compute where robots work.
We deploy and manage shared GPU systems inside the facilities where robots operate, bringing powerful inference close to the fleet. From individual compute boxes to full GPU racks, we build infrastructure for robotics at scale.
Deployment
As robotics moves to scaled deployment, compute demand will grow exponentially. We expect millions, potentially billions, of robots in the next decade. The infrastructure to support them still needs to be built.
Scaling laws suggest that more data, larger models, and greater compute can expand what robots can do. Many physical AI models are two to three orders of magnitude smaller than the largest language models. As diverse, high-quality interaction data becomes available, we expect larger and more capable vision-language-action models and world models to emerge.
Why?
Physical AI needs reliable inference within strict latency constraints. Larger models push onboard memory, power, and processing limits. Remote cloud inference adds network delays and connectivity risks. Shared compute inside the facility brings capacity close to where robots operate.
Control and safety stay on each robot. Demanding perception and planning can use shared GPUs nearby.
A local GPU pool serves the fleet. Keiso brings deployment, inference coordination, and ongoing operations together.
Training and fleet analytics run in the cloud. Time-sensitive robot decisions stay close to the machines.
Our solution
Compute boxes built for physical AI give robots shared access to powerful inference on site. Deployments scale from a single box to full GPU racks as your fleet grows.
The software coordinates inference across robot fleets to meet latency and reliability requirements. We handle deployment and ongoing operations, tuning each system as models and workloads evolve.
Team
We are partnering with robotics companies, AI labs, and datacenter builders to bring shared GPU infrastructure into facilities where robots operate.
Our team includes Olympiad medalists and engineers and researchers from Stanford, Harvard, EPFL, and ETH Zurich, combining expertise in AI systems, inference optimization, and infrastructure.