Physical AI Platform

From working prototype to production system

The tools to take robots from lab performance to production reliability — monitored, compliant, and connected to enterprise systems.

The Hard Part

Why most robots never leave the lab

Getting a robot to work is one thing. Getting it to work reliably, safely, and connected to your business — that's where projects stall.

The real world is different

Lab conditions don't match production. Lighting changes, backgrounds vary, edge cases multiply. That 95% lab accuracy often drops to 60% in the field.

Average isn't good enough

Research papers report mean success rates. In production, 95% success means dozens of failures per day — each one needing human intervention.

Speed vs. capability

The best models are often too slow for real-time control. Edge deployment means hard tradeoffs between capability and response time.

Everything needs to talk

Robots don't exist in isolation. They need to connect to warehouse systems, fleet management, monitoring dashboards, and compliance logging.

Certification is unclear

Safety standards were written for robots that follow explicit rules. There's no established playbook for certifying behavior that emerges from neural networks.

Debugging is hard

When a traditional robot fails, engineers read the code. When a learned policy fails, there are only weights to inspect. New observability tools are needed.

What You Get

The tools to go from prototype to production

Monitoring

Real-time visibility into production behavior. Alerts when conditions drift from training data, before things break.

Compliance

Audit trails and behavioral documentation for regulated environments. Built for how ML systems actually work.

Integration

Pre-built connectors for warehouse management, ERP, and fleet systems. No more custom glue code for every deployment.

Sim-to-real validation

Test against real-world physics before shipping. Catch the gaps between simulation and production early.

Multi-platform

Deploy to Isaac Sim, Unity, Unreal, or edge devices. Same infrastructure, consistent behavior across environments.

Developer SDKs

Python, JavaScript, Rust, and C++ libraries. Fits into existing CI/CD pipelines, not around them.

How it works

Three steps to simulation-ready assets

1

Describe

Natural language prompt for your asset

prompt: "6-DOF robotic arm"

2

Generate

AI creates 3D asset with physics properties

await client.generate()

3

Export

One-click export to your simulation engine

format: "usd", target: "isaac-sim"

Validate before you export

Run physics validation checks on every asset before exporting. Catch simulation issues before they become real-world problems.

  • Mass distribution analysis
  • Collision geometry validation
  • Joint limits verification
  • Inertia tensor computation
validate.ts
// Validate physics properties before export
const validation = await client.validate({
assetId: asset.id,
checks: [
'mass-distribution',
'collision-geometry',
'joint-limits',
'inertia-tensor',
],
})
if (validation.passed) {
await client.export({
assetId: asset.id,
format: 'usd',
target: 'isaac-sim',
})
}

Industries

For environments where failures matter

Healthcare

Surgical assistance, pharmacy automation, patient logistics. HIPAA compliance built in from the start.

Manufacturing

Assembly, quality inspection, material handling. Safety certification for learned policies in factory environments.

Logistics

Pick-and-pack, autonomous forklifts, fleet coordination. Connects to existing warehouse and ERP systems.

The Virtuous Cycle

Deployed robots make better robots

Robots in production collect real-world training data while generating value. Better data leads to better models, which enables more deployment.

Teams that can deploy early start compounding these advantages. Those stuck in the lab fall further behind with every passing month.

1

Deploy learned systems

In production environments that generate value

2

Collect real-world data

Training data from actual deployment scenarios

3

Improve models continuously

Better models enable more deployment

Platform support

Export to any engine

Generate once, export everywhere. Native support for industry-leading simulation and game engines.

NVIDIA Isaac Sim

Robotics

NVIDIA Omniverse

Simulation

Unity

Game Engine

Unreal Engine

Game Engine

Godot

Game Engine

Bevy

Rust Engine

Pricing

Scale with your simulation needs

Starter

$99/month
  • 100 generations/month
  • Standard validation
  • GLB/USD export
  • Community support
Get started
Most Popular

Pro

$299/month
  • 500 generations/month
  • Advanced physics validation
  • Sim-to-real validation
  • All engine exports
  • Priority support
Get started

Enterprise

Custom
  • Unlimited generations
  • Custom model training
  • On-premise deployment
  • Dedicated support
  • SLA guarantee
Contact sales

Ready to go from lab to production?

For teams building robots that need to work in the real world.