Foundation model companies
Deploy your model across different robot bodies and edge devices without worrying about every runtime integration.
Our Manifesto
Physical AI is moving from the lab into the real world. Models can’t simply be “good enough”—they must respond on time, every time. A few milliseconds of latency can mean a robot missing an object or a machine failing to react. Today, engineers manually optimize inference across models, hardware, workloads, and control loops. This breaks the moment robotic systems get into production. We built RAVN to fix it.
We are building the low-latency inference and runtime engine for physical AI.
Make your robot production-readyHow RAVN works
A hardware- and model-agnostic runtime that finds the fastest way to run your inference—and keeps improving that strategy while the robot is operating.
Built for companies moving physical AI from demo to production.
RAVN is the runtime layer for teams deploying physical AI in real-world environments.
Robotics companies
Deploy proprietary and third-party models on your robots without manually optimizing every single deployment.
Full-stack robotics companies
Keep perception, planning, action, and recovery within the robot’s timing budget.
Robotics platforms & integrators
Give customers a consistent runtime across different robot bodies, sensors, compute configurations, and deployment sites.
Robot fleet operators
Keep every deployed robot within its performance envelope as workloads, environments, and hardware conditions change.
A customer type not listed?
If you are building or deploying physical AI, RAVN helps your systems run faster, adapt to new hardware, and perform reliably in the field.
Your models are ready. Your hardware is capable. RAVN is the runtime that makes them work together.
Install RAVN once and it continuously measures your robot’s full control loop, selects the fastest reliable execution path, and adapts as your models, workloads, hardware, and environment change.
Take your model from checkpoint to production robot.
Your model may work on a reference platform. RAVN helps it run across the hardware your customers actually deploy—different robot bodies, sensors, accelerators, and latency budgets—without building a custom runtime for every embodiment.
# deploy your model on a robotdeployment = ravn.deploy( model="your-foundation-model", robot="customer-robot",) # optimize for the target hardwaredeployment = ravn.optimize(deployment) # monitor production performanceravn.observe(deployment) → optimized · 42 ms p99 · running on-device- deploy(model)
- Connect your model to a production robot without rebuilding the inference stack for every embodiment.
- optimize(deployment)
- RAVN evaluates model, runtime, and hardware paths against real workloads, then selects the fastest reliable configuration.
- observe(deployment)
- See how your model performs across robots, tasks, hardware, and real-world conditions.
RAVN gives your model a runtime that adapts to the physical world—so you can support more embodiments, reach more customers, and spend less time on integration.
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Don’t let inference be the reason your robot fails in production.
RAVN makes your inference stack faster, more portable, and more reliable across the hardware, workloads, and environments your robot actually faces.