Data and deployments infrastructure for Physical AI. Capture. Annotate. Deploy.
PhiBuild collects multimodal data of people and robots doing real work, annotates it densely, and runs the infrastructure that puts models in the field and keeps them learning.
From real work to robots in the field.
Real work, any viewpoint.
People and robots doing real tasks in kitchens, warehouses, labs and homes, captured however the task needs.
Dense, reviewed labels.
Every session segmented into action steps and checked by a second annotator.
Data shaped to your robot.
Datasets and custom collection built around your embodiment and target tasks.
Keep the data coming back.
Fleet infrastructure that runs policies in the field and returns episodes to training.
Hours aren't the metric. Task success is.
Public manipulation datasets are large, but scripted, single-modality and lab-bound. Models trained on them stall the moment they meet a real kitchen.
- Tabletop tasks under lab lighting, seconds long
- Single RGB stream, sparse or crowd-sourced labels
- Human demonstrations that never map onto a real robot
- Collection ends at release; nothing flows back from the field
- Real work in real environments, full tasks end to end
- Aligned multimodal streams with reviewed, dense labels
- Human and robot data shaped to your embodiment
- Deployments feed field data back into training
We still barely understand how humans move, handle and work. The data is how we find out.
PhiBuild's datasets and deployment infrastructure are the foundation for automating manual labor and for understanding cognition, spatial computing and prosthetics.
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