Bring physical AI from demo to production
The production layer
for physical AI
LazyInfer is an SDK and runtime for Bayesian model updates on customer hardware. It is designed to enhance existing foundation model stacks, such as π0, Gemini Robotics or GR00T, or replace them with Bayesian models for fast, reliable policy execution.
Discuss an integrationA working demo isn’t production-ready AI.
Task success: best tested policy (π₀.₅) vs expert teleoperation
12.8% vs 100%
RoboDojo, 18 real-world tasks, July 2026Moving from demo to production exposes models to new sites, tasks and conditions. Performance can drop without warning. Engineers have to diagnose and fix the problem at each deployment.
When production conditions change
Robot arms
The grasp works with the surface conditions in the demo.
Drones
Estimates position under the flight conditions in the demo.
Wearables
Speech is clear for the listener and room used in the demo.
LazyInfer
From a working demoto AI that keeps working.
LazyInfer is an SDK and runtime for Bayesian model updates on customer hardware. It is designed to enhance existing foundation model stacks, such as π0, Gemini Robotics or GR00T, or replace them with Bayesian models for fast, reliable policy execution.
It plugs into the customer’s existing stack and runs on the device. As conditions change, the model is updated locally, so the system keeps working without a new engineering cycle.
Adaptation under the same changed conditions
Robot arms
Lower friction · the object slips
Drones
New flight conditions · estimates drift
Wearables
New listener or room · speech degrades
Example response
One approach
tested across physical systems.
Everything the microphone hears.
Wearables
Adapt parameters to the user and environment.
Built by a team with two decades of research in Bayesian machine learning, computational neuroscience and electrical engineering.
LazyInfer
Bring physical AI from demo to production.
Tell us about your hardware and stack. We will walk through how LazyInfer fits in.