See what you can control.
Pick a domain, choose a variation — and watch the synthetic environment respond. Every frame is annotation-ready, covering everything from standard BBoxes to complex trajectories and spectral data.
Base scene
Select a variation to see the scene parameters change.
retail / SKU detection
agro / object detection
all projects
Common questions.
What is synthetic data and when does it make sense?
Data generated in simulation — 3D scenes with controlled lighting, materials and object placement — rendered into images with automatic annotation. It makes sense when real data collection is slow, expensive, or when edge cases simply don’t occur often enough in the field to train on. See all use cases →
Does it replace real data entirely?
Not necessarily. Synthetic data expands coverage and generates edge cases at scale. Real data is still valuable for validation and fine-tuning. The strongest pipelines combine both — synthetic for quantity and control, real for grounding.
What annotation formats do you deliver?
COCO, YOLO, segmentation masks, depth maps, ego-motion trajectories, and spectral metadata — depending on your task. Formats are defined at project start based on your training pipeline.
How is this different from generative AI (Stable Diffusion, etc.)?
3D simulation gives you explicit control over every variable and full annotation traceability. Generative AI produces visually diverse output but lacks the parametric control needed for reliable ground truth. Learn more about our approach →
If data is still limiting your model, we can fix that.
Tell us your use case — we’ll map the right synthetic data strategy together.