Where AI Learns to Understand and Create in Three Dimensions
Before a robot touches the real world, it lives in simulation. Before an autonomous vehicle navigates a city, it drives millions of miles in synthetic environments. Before embodied AI can act, it must learn.
Simulation is the training ground. We’re building the infrastructure.
The Reality Gap
AI is hungry for data. But physical-world data is:
- Expensive. Every real-world test costs time, equipment, and risk.
- Slow. You can’t run a million trials overnight in a physical warehouse.
- Dangerous. Crashes, collisions, and failures have real consequences.
- Limited. You can’t manufacture edge cases on demand.
Simulation solves this—if the simulation is good enough.
The challenge isn’t building a game engine. It’s building environments that transfer to reality. Synthetic data that trains models that actually work.
That requires native 3D understanding. Not rendering. Understanding.
What We Enable
Synthetic Environment Generation
AI that designs the worlds AI trains in.
Not hand-crafted levels. Not artist-built scenes. AI-generated environments with:
- Procedural variation — Infinite configurations from parametric rules
- Semantic richness — Objects with meaning, not just geometry
- Physical accuracy — Materials, friction, mass, dynamics that match reality
- Edge case generation — Deliberately create the scenarios that break models
Design a warehouse template. Generate ten thousand variations. Train on all of them.
Synthetic Data at Scale
Training data without the labeling bottleneck.
When you own the simulation, you own the ground truth:
- Perfect annotations — Every pixel labeled, every object tracked
- Sensor simulation — Cameras, LiDAR, depth sensors, radar
- Domain randomization — Vary lighting, textures, positions systematically
- Failure injection — Simulate sensor noise, occlusion, adversarial conditions
No manual labeling. No annotation errors. Unlimited volume.
Physics-Based Simulation
Reality has physics. Your simulation must too.
NATS JetStream architecture enables:
- Distributed physics — Scale simulation across compute clusters
- Real-time and faster — Train at 1000x real-time when you can
- Accurate dynamics — Rigid body, soft body, fluids, cables
- Contact modeling — Grasping, manipulation, assembly that transfers to real robots
Simulation that lies to your AI produces AI that fails in reality.
Reinforcement Learning Environments
The gym for embodied AI.
- Customizable task spaces — Define objectives, rewards, success criteria
- Curriculum generation — Progressive difficulty, automated
- Parallel rollouts — Thousands of agents training simultaneously
- State inspection — Full observability for debugging and analysis
Your RL agents need environments designed for learning, not just visualization.
Sim-to-Real Pipeline
The gap between simulation and reality is where projects die.
Close it with:
- Digital twin calibration — Tune simulation to match real-world measurements
- Reality capture integration — Scan physical spaces into simulation
- Transfer learning validation — Test synthetic-trained models against real data
- Continuous refinement — Production data flows back to improve simulation
Not simulation OR reality. Simulation TO reality.
Open Standards
Your training environments shouldn’t be locked in a game engine.
STEP geometry means:
- Import CAD from any source
- Export environments to any simulator
- Maintain engineering precision, not game-engine approximations
- Archive training environments for reproducibility
Simulation built on open standards. Training data you actually own.
Use Cases
Autonomous Vehicles
Millions of miles of synthetic driving. Every weather condition. Every edge case. Every near-miss scenario you can imagine—and many you can’t.
- Urban environments at scale
- Sensor simulation for full stack testing
- Scenario generation for safety validation
- Synthetic data for perception training
Robotics Training
Teach robots manipulation, navigation, and interaction without destroying hardware.
- Grasping and manipulation tasks
- Mobile robot navigation
- Multi-robot coordination
- Human-robot interaction scenarios
Humanoid Development
Before your humanoid walks in a factory, it walks in simulation. Before it hands a tool to a human, it learns the motion synthetically.
- Locomotion training across terrain types
- Manipulation skill learning
- Human collaboration scenarios
- Fall recovery and edge case handling
Warehouse & Logistics
Optimize layouts and train systems before building.
- Goods-to-person system simulation
- Traffic flow optimization
- Pick-and-place training data
- Throughput prediction
Industrial Digital Twins
Mirror your facility. Train against it. Optimize it.
- Factory floor simulation
- Process optimization
- Predictive maintenance training
- What-if scenario analysis
For AI Teams
Training embodied AI? You need environments that:
- Generate unlimited variations
- Produce perfect ground truth labels
- Scale across your compute infrastructure
- Transfer to the real world
Stop building custom simulation infrastructure. Build on ours.
For Robotics Companies
Your robots need to train before they ship.
- Accelerate development cycles
- Reduce physical prototype costs
- Catch failures before customers do
- Generate training data continuously
Simulation as competitive advantage.
For Autonomous Systems
Vehicles, drones, mobile robots—anything that moves needs simulation.
- Safety validation at scale
- Edge case coverage
- Regulatory compliance evidence
- Continuous improvement pipeline
Prove safety before deployment.
The Numbers
Real-world testing:
- $1000s per hour of operation
- Days to set up scenarios
- One crash = weeks of delay
Simulation:
- Marginal cost approaching zero
- Millions of scenarios overnight
- Failures are data, not disasters
The math is obvious. The infrastructure is what’s been missing.
Complete the Loop with Publish
Simulation generates more than trained models—it generates documentation.
- Test reports — Scenario results, edge case coverage, validation data
- Training datasets — Annotated data with full provenance
- Compliance documentation — Safety validation evidence for regulators
- Configuration specs — Environment parameters, sensor setups, test conditions
All from single source. All versioned alongside your simulation environments.
Get Started
Every AI that will act in the physical world needs to learn in simulation first.
We’re building where that learning happens.