Perception infrastructure for high fidelity physical AI
Build, train, and ship real world perception systems that scale. Safer, and in a fraction of the time and cost.
Real-world perception, without the real-world bottleneck
Most teams lose months to simulation, labeling, and tooling before a model sees its first frame. The data bottleneck is the real cost, and the stakes keep climbing.
of AI and automation projects fail due to poor or missing data.
estimated training-data market by 2032.
industrial robots installed in the EU during 2023.
End to end synthetic data creation. Generate enterprise grade synthetic data tailored to your needs, from scene to sensor.
- Step 1
Set up a scene
Build the exact scenario you need in the scene editor. Bring your own assets in 6+ formats or start from scratch.
- Step 2
Render your dataset
Hit render and your dataset processes in the cloud. Ready to download in minutes.
- Step 3
Export, share and train
Download and start training, or publish your dataset as a template for others to use.
Everything you need in one place
One workspace to annotate, build, collaborate on, and ship perception datasets, with no glue code and no stitched-together tooling.
Annotate
Label an entire dataset in minutes, not months. Guided tooling does the heavy lifting, so you skip manual labeling and get straight to training.
Pipeline
Define the perfect data scenario visually, without writing a single line of code. Clear, repeatable, and faster than ever.
Collaborate
Work on projects together with your team for faster, shared dataset creation. Everyone builds on the same scene.
Marketplace
Download datasets from other Scailab users, or share your own. A head start you can plug straight into production.
Built for teams teaching machines to see
Logistics & warehousing
Train robots to pick, place, and navigate across the endless variation of real fulfilment sites.
Mining & heavy industry
Generate the dust, low light, and hazards of heavy-industry sites without sending a robot in.
Inspection & maintenance
Teach autonomous inspectors to spot faults across structures that are hard to reach.
Surveillance & security
Detect rare events across the angles, lighting, and conditions real cameras seldom capture.
And that is just the start. If a machine has to see it, you can generate the data to teach it.
Already changing how robots learn to see
Investors who understand deep tech
We're backed by investors who move early on deep tech and stay close as we build. The kind of partners who understand what it takes to turn hard technology into infrastructure physical AI can rely on.
Questions, answered
Still wondering about something? Reach out and we will get back to you.
How is synthetic data different from real-world data?
Synthetic data is generated from a 3D scene, so every frame is labeled by construction with pixel-perfect ground truth. You control the conditions exactly, which means no manual annotation and no waiting to encounter an edge case in the wild.
Will a model trained on synthetic data work in the real world?
Yes. We use domain randomization and high-fidelity sensor simulation to close the sim-to-real gap, and most teams blend synthetic data with a small amount of real data to fine-tune. The result matches or beats real-only datasets on rare cases.
How long does it take to generate a dataset?
Most datasets render in the cloud in minutes. You set up the scene once, hit render, and download a fully labeled dataset ready for training.
How do you handle our data and IP?
Your scenes, assets, and datasets are yours. We support private workspaces, and on-premise or VPC deployment is available for teams with stricter data requirements.
How do I get access?
Scailab is in private beta. Join the waitlist and we will reach out with onboarding, or contact us directly if you have an urgent project.
Start building perception data that scales
Join the teams shipping real-world AI faster, safer, and at a fraction of the cost.