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 2026Physical systems encounter conditions their models were not prepared for. Managing that uncertainty requires engineers to repeatedly diagnose, adjust and redeploy.
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
When operating conditions change, LazyInfer estimates what has changed and uses those estimates and their uncertainty to adjust decisions during operation. This reduces reliance on retraining and case-specific recovery logic.
We’re packaging the same Bayesian inference core across perception, state estimation, learning and planning into a reusable SDK and runtime—turning adaptation from repeated engineering work into a product capability.
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
Illustrative response
One Bayesian inference core across the stack
Everything the microphone hears.
Learning
Adapting wearable parameters to the user and environment.
GN Group (Jabra) · paid work on GN’s testbed
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.