Scailab
Build precise datasets for robotics perception models.
Powering perception for robotics, automation, and beyond. Built by talent from and backed by
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.
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.
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.
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
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.
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.
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.
"We used to spend months collecting and labeling training data. With Scailab, we generate exactly what we need in hours. It's transformed our entire development cycle."
"The label accuracy alone justified the switch. Zero annotation errors means our models train cleaner and converge faster. We've cut our error rate by 40%."
"Synthetic data let us cover edge cases we could never capture in the real world. Our autonomous systems handle corner scenarios that used to cause failures."
"We went from a $200K annual data budget to a fraction of that. The ROI was immediate and the data quality actually improved."
"Scailab adapts to our very specific warehouse scenarios. No other tool gave us this level of control over scene composition and annotation types."
"We iterate on model architectures weekly now instead of quarterly. The bottleneck completely shifted from data to ideas, which is exactly where it should be."
"We used to spend months collecting and labeling training data. With Scailab, we generate exactly what we need in hours. It's transformed our entire development cycle."
"The label accuracy alone justified the switch. Zero annotation errors means our models train cleaner and converge faster. We've cut our error rate by 40%."
"Synthetic data let us cover edge cases we could never capture in the real world. Our autonomous systems handle corner scenarios that used to cause failures."
"We went from a $200K annual data budget to a fraction of that. The ROI was immediate and the data quality actually improved."
"Scailab adapts to our very specific warehouse scenarios. No other tool gave us this level of control over scene composition and annotation types."
"We iterate on model architectures weekly now instead of quarterly. The bottleneck completely shifted from data to ideas, which is exactly where it should be."