Building Your Digital Universe
Sawatch AI generates high-fidelity synthetic training data and automated OpenUSD environment simulations for vision models, physical AI agents, and autonomous systems.
Get StartedCapabilities
Synthetic Data Generation
Automated procedural scene generation, domain randomization, and multi-modal annotation pipelines designed to train edge computer vision models.
OpenUSD • PyTorch • Synthetic DataSpatial AI Simulation
Physics-accurate digital twins and robotic reinforcement learning environments accelerated using NVIDIA Omniverse and Isaac Sim platforms.
NVIDIA Omniverse • Isaac Sim • CUDAHow It Works
From asset preparation to annotated datasets — our pipeline delivers production-ready synthetic data in days, not months.
Asset Ingestion & Preparation
Import CAD/mesh assets, clean geometry, apply PBR materials, and convert to optimized OpenUSD format.
Scene Composition & Randomization
Programmatically build scenes in Isaac Sim with domain randomization across lighting, materials, camera poses, and environmental clutter.
Simulation & Rendering
Execute distributed rendering across GPU clusters, capturing RGB, depth, segmentation, and surface normals at scale.
Auto-Annotation & Export
Generate ground-truth annotations (2D/3D boxes, segmentation, depth) and export to COCO, YOLO, or KITTI formats for immediate training.