SOTA Physical AI models can't be trusted in production.
A model proposes actions. Nothing verifies them before they reach the robot.
The Physical AI Deployment Engine.
Morix Robotics builds the enterprise-grade deployment engine for production robot fleets. It turns Physical AI models from demos into deployable systems: enforcing safe actions, tracking outcomes, and converting field failures into fixes.
Morix turns every action into safe behaviour.
Morix runs inline between the model and the robot. Every action proposal becomes safe physical behaviour before it executes.
Unsafe actions are corrected, not blocked.
Every model action is checked against deterministic safety boundaries at runtime, before it becomes physical robot behaviour.
A failure on one robot updates the whole fleet.
A failure is detected, root-caused and turned into a validated fix that propagates across the fleet.
Failures become signatures that improve the models.
Morix abstracts universal experience from every failure: failure signatures, recovery patterns, skill boundaries. They compound across the network.
Every action made safe. Every failure, a lesson.
The governed action path from model output to robot behaviour. Model agnostic. Hardware agnostic. Built for enterprise deployment.
Watch it run.
The engine in the action path, on real hardware. Actions checked before they become motion, and the loop closing back to the model.
The control point for Physical AI.
Morix sits in the action path between the model and the robot. Every model action is checked against deterministic safety boundaries at runtime, and the learned parts can never override the hard boundary.
- 01
Action Guards
Low level · per step
Deterministic checks on every proposed action: workspace bounds, force and velocity limits, payload and reach.
- 02
Safety Barriers
Model based · trajectory level
Continuous safety invariants over the whole trajectory: collision avoidance, joint-space envelopes, keep-out zones.
- 03
Competence Monitor
Learned · calibrated
Flags when the model is operating outside the skills it has been validated on.
- 04
Arbitration
Off the control loop
Routes the result into a graded recovery: retry, replan, then halt and call a human.
Gate budget
Sub-10 ms
inline, per control step
Placement
On robot
edge, in the action path
On failure
Retry · replan · halt
graded recovery
Compliance
ISO 27001 · SOC 2 · ISO 10218
built in
Bring any brain
Model-agnostic. VLA, RL, MPC or classical skills, behind one interface.
Bring any body
Humanoids, AMRs and fixed arms, on the same runtime.
Runs on the robot
The action path stays at the edge. Only telemetry leaves.
Failure-to-Fix
Detect, cluster, root-cause, fix. Validated fixes propagate fleet-wide.
Fits the existing stack
Native connectors for WMS, MES and ERP.
Traceable by default
Full lineage from raw telemetry to live deployment, for every decision.
What crosses the boundary, and what never does.
- Your model stays yours
- The task policy is owned by you. Morix wraps it, it does not train it and does not replace it.
- Your data stays yours
- Raw episodes, telemetry and operational context are customer-scoped. They do not cross into another customer.
- Only abstractions compound
- Anonymised failure signatures, recovery patterns and skill boundaries are what travel across the network. The separation is architectural.
What comes back is the real-world field data that model validation pipelines lack.
We built this layer once already.
Autonomous driving reached the same point a decade ago. Capability was real, and scale still waited on one thing: a layer that could stand between the model and the road and be trusted with the consequences. We helped build that layer.
Physical AI is at that point now. We are building the same layer for robots, before the industry needs it rather than after.
The layer that sits in the action path owns the live workflow, the failure data and the customer trust. In driving, that is the layer that won.
Working with a deliberately small set of design partners
Find your way in.
Robot builders
A safety runtime, not a research project.
- Safety runtime
- Skill-boundary enforcement
- Fleet-scale behaviour updates
Model companies
The field data your pipeline structurally lacks.
- Failure signatures
- Recovery patterns
- Skill boundaries
- Real-world evaluation
System integrators
Guarded deployment across every site.
- Guarded deployment
- Fleet rollout
- Multi-site ops
- Failure-to-fix
Integration
Bring a checkpoint
Any policy behind one uniform contract, tracking community standards such as LeRobot. No retraining, and no rewrite of your inference path.
First step
One cell, in production
A guarded deployment on a single cell under real conditions, where the runtime is doing the work rather than a demo.
Then
Promote or roll back
Widen to the fleet once it holds, or roll back without stopping the line. Both are the same mechanism.
Put a model on a real line.
Tell us what you are deploying and where it stops being safe. If it is a fit, we will show you the engine running against it.
- You keep
- Your model, your data. Nothing is trained on.
- We reply
- To every enquiry that names real hardware.
- First step
- A call, not a procurement cycle.