Embodied Data
Real-world data · Labeled
Training-ready datasets and evaluation sets for robotics teams. Captured in production environments. Delivered with a schema, a benchmark split, and task-specific acceptance notes.
Offering
Design Sprint
Task selection, label schema, and acceptance criteria before a single frame is captured.
- Task family definition
- Label schema
- Sample clips
- Delivery plan
Pilot & Production Datasets
Real work captured in warehouses, factories, and logistics sites. Labeled with AI plus human QA.
- Wearable & fixed multi-view
- Step, object, and outcome labels
- Failure and exception coverage
- JSONL / Parquet / RLDS export
Evaluation & Refresh
Held-out benchmarks and recurring capture of new failure cases as deployments evolve.
- Eval & benchmark packs
- Pass/fail protocols
- Monthly drift & edge-case refresh
- Versioned releases
Process
Specify
A written data spec, label schema, and acceptance criteria. Buyer-funded from day one.
Map Context
Site context, operator workflow notes, redaction policy, and capture metadata before production collection.
Capture
Standardized field protocol with multi-view capture and event markers in real production environments.
Label & QA
Auto-labeling with multimodal models, validated by human reviewers against the buyer spec.
Deliver
Versioned release with data card, manifest, benchmark split, and a short limitations note.
Principles
No surveillance, no facial-recognition use cases. Blur by default.
Schema first, footage second. Raw video is not a product.
Source operators are partners, not raw material.
Start with a spec.
Tell us the task family and the deployment problem. We respond with scope, schema, and a fixed-price plan.