Scailab

Build precise datasets for robotics perception models.

Powering perception for robotics, automation, and beyond. Built by talent from and backed by

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Mission statement. February 2026.

Scailab removes the data bottleneck in robotics so the breakthroughs that solve humanity's biggest challenges come from the best ideas worldwide, not just the most funded institutions.

End to end Synthetic data creation.

Effortlessly create enterprise grade synthetic data, tailored to your needs

Set up a scene

Effortlessly create the exact scenario you need in our intuitive scene editor. Bring your own assets in any of our 6+ supported formats or start from scratch.

Render your dataset

Once you've built your scene, hit render and your dataset will start processing in the cloud. Ready for download in minutes.

Export, share and train

Once your dataset is complete, download it and start training. You can also make it publicly available for others to use as a template or download directly.

An all in one one toolbox for synthetic data

Scailab distills the most complex parts of synthetic data creation into intuitive tools anyone can pick up in minutes.

Annotate

Annotate your entire dataset in minutes, not months. Our guided annotation tool does the heavy lifting so you can skip the manual labeling and get straight to training.

Pipeline

Set up your perfect data scenario without writing a single line of code. Define everything visually, clearly, and faster than ever before.

Collaborate

Work on projects with your team for quicker, more collaborative dataset creation.

Marketplace

Download datasets from other Scailab users or share your own. Use existing datasets and pipelines as a head start or plug them straight into production.

Use cases across industries.

From warehouses to open roads, synthetic data powers perception models wherever they need to see, detect, and understand the world.

Stop sourcing data. Synthesize it.

Synthetic data puts you in full control in edge cases with perfect data accuracy at unlimited scale, delivering higher quality than real world data, faster and cheaper.

Synthetic vs sourced data: Label accuracy

Source: MIT CSAIL, NeurIPS 2021 — ImageNet validation set

~94%
Real world data
100%
Synthetic data

Instant ground truth

Zero human annotation required. Every dataset delivers 100% label accuracy, built precisely for your use case. Real world data requires costly manual labeling, introduces human error, and leaves gaps in coverage.

Data used to be the bottleneck, these teams moved past it.

Scailab adapts to nearly any perception data requirement, however specific your use case may be.

Simple, transparent pricing that scales with you.

Start for free, upgrade when you're ready. Every plan includes access to our core platform.

Monthly Annually Save 20%
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Faq

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Experience the future of synthetic data creation with Scailab. Sign up now for free.