Building the data infrastructure for embodied intelligence.
Robots can’t learn from the internet. They learn from experience. GaitLabs captures, annotates, and delivers the real-world demonstration data that Physical AI systems train on.
Founded
2026 · Istanbul, Turkey
Operations
Global collector network
Stage
Early access · First cohort
Roadmap
Building toward infrastructure
We start as a service — but the destination is infrastructure.
Dataset Production
Custom egocentric training datasets — scoped, captured, annotated, and delivered to robotics teams.
Service revenue
Managed Data Ops
Continuous data operations as a service — ongoing capture, annotation, and delivery as models iterate.
Recurring revenue
Data Infrastructure
Infrastructure layer for Physical AI teams — APIs, pipelines, and tooling for large-scale data operations.
Platform + network effects
Operating Layer for Physical AI
Category-defining data infrastructure — the foundational layer that Physical AI systems train on.
Infrastructure monopoly
Why now
Three waves converging
Physical AI is leaving the lab
Humanoid and autonomous systems are entering real-world deployment — demand for diverse, real-world training data is accelerating.
Foundation models can reason
VLAs and world models require rich, structured demonstrations — not just raw video. The annotation layer is what makes data trainable.
Sensor costs are collapsing
Egocentric capture is now achievable at scale. The infrastructure to process and deliver that data at quality is the missing piece.
$15.3B
Humanoid robot market by 2030
39.2%
CAGR, 2025–2030
$26B
Robotics VC funding in 2025
$200B+
Potential humanoid economy by 2035
Careers
We’re building the first cohort.
Interested in working on the data layer for Physical AI? Get in touch →
Want to work with us?
Tell us what you’re building — we’ll find the fit.