The Generalization Engine for Physical AI.
We provide engineering-grade manipulation data at scale. Bridging the Sim-to-Real gap through a global network of kinematic proxies, validated by physics.
How We Work
From Request to Training-Ready Data
Step 1: Distributed Collection (Source)
We leverage a global network of operators to capture visual and physical diversity using standardized kinematic proxies.
Step 2: Internal Refining (Process)
Data undergoes rigorous cleaning. We remove PII, filter for quality, and apply high-precision labeling.
Step 3: Quality Delivery (Output)
We deliver structured, engineering-grade datasets. They are formatted and ready for immediate Foundation Model training.
THE CHALLENGE
The "Lab-to-Real" Gap: Robots trained on sterile, repetitive laboratory data become fragile. They perform perfectly in a controlled demo but fail the moment they face the chaos of the real world. This is the Generalization Bottleneck.
The Diversity Deficit
In-house labs are biased environments. A model trained on clean white tables and consistent lighting will hallucinate when faced with shadows, clutter, or unfamiliar textures in a user's home. You cannot simulate the entropy of daily life.
The Simulation Ceiling
Simulators provide infinite data but zero reality. They struggle to accurately model deformable objects (like clothing), fluids, and complex friction. Without real-world ground truth, your 'Sim-to-Real' transfer is destined to fail.
The Scaling Trap
Building an internal data farm is a massive CAPEX trap. Managing hundreds of teleoperators distracts your core engineering team from what matters most: Algorithm Architecture. Stop managing crowds; start training models.