// cognitive_dataops
Your model failures are data signals.
Before you collect more data, find out what data your model actually needs. The Data Failure Audit inspects your dataset and failure patterns to identify the missing data your model needs next.
// who_it_is_for
Run an audit when…
- Your model performs well in demos but fails in production.
- You do not know which edge cases are missing.
- Your annotation quality is inconsistent.
- Your dataset is imbalanced, duplicated, or too narrow.
- Your team needs a data roadmap before scaling training.
audit_deliverables.json
01Dataset health report
02Edge-case gap map
03Failure pattern analysis
04Label quality assessment
05Data risk summary
06Priority data creation roadmap
07Recommended next sprint
// signal_01
Coverage gaps
Where your data is thin, narrow, or missing entirely.
// signal_02
Label noise
Inconsistent, ambiguous, or training-breaking annotations.
// signal_03
Imbalance
Skewed classes, duplication, and over-represented scenarios.
// signal_04
Failure clusters
The recurring conditions where your model breaks.
// faq
Common questions
A data foundry creates, collects, annotates, filters, certifies, and continuously improves model-ready datasets for physical-world AI — not just one-off labeling.
We diagnose model failures, design data strategy, capture real-world and egocentric data, annotate with QA, filter, and certify. Labeling is one step inside a closed loop.
Yes. Many engagements start with an audit of data you already have, followed by targeted collection and annotation to fill the gaps we find.
Timelines vary by dataset size and scope. We scope each audit on an intro call and share an estimated timeline before starting.
A transparent report delivered with qualified datasets covering rights, privacy, coverage, balance, annotation quality, limitations, and a training-readiness score.
Book a Data Failure Audit. We'll review your model, dataset, and failure cases, then recommend the data your model needs next.