// process
A closed-loop data process for physical AI.
From model failures to certified training data — and back again. Each step feeds the next, and deployment feedback restarts the loop.
01
Diagnose
Failure review
02
Design
Data spec
03
Capture
Real + synthetic
04
Annotate
HITL + QA
05
Certify
Quality + rights
06
Improve
Loop again
deployment feedback → restart
[01]
Discover
Understand model goals, deployment environment, available data, and failure patterns.
[02]
Diagnose
Find missing scenarios, label issues, bias, imbalance, duplication, and weak coverage.
[03]
Design
Create a data specification for collection, annotation, QA, metadata, and certification.
[04]
Capture
Collect real-world data, egocentric video, task demonstrations, field data, or synthetic scenarios.
[05]
Annotate
Apply expert human-in-the-loop annotation and QA workflows.
[06]
Filter
Remove low-quality, duplicate, risky, or irrelevant data.
[07]
Certify
Deliver a Model-Ready Data Certificate.
[08]
Improve
Continue updating the dataset based on model performance.
// faq
Common questions
A foundry that creates, collects, annotates, filters, certifies, and continuously improves model-ready datasets for physical-world AI through a closed loop.
Timelines vary by scope. We scope on an intro call and share an estimate before starting; the audit is usually the fastest, first step.
Yes — discovery and diagnosis typically start with the data and model you already have.
A transparent report delivered at the certify step covering rights, privacy, coverage, balance, annotation quality, limitations, and a training-readiness score.
We validate synthetic data and measure synthetic-to-real transfer, blending it with real capture where it improves training usefulness.
Book a Data Failure Audit. It kicks off the loop and produces a prioritized roadmap for the data your model needs next.