Data labeling
Expert labeling when crowd data isn’t enough.
We route each item to calibrated domain experts and return structured labels, rationales, and disagreement — not a forced majority vote in a spreadsheet.
How it works
Your data
Expert panel
Labels +
rationales
rationales
QA-ready
dataset
dataset
What you get
- ▸3–4 independent experts per item, when the task needs it
- ▸Soft-label distributions with rationales and confidence
- ▸Disagreement analysis you can train and evaluate on
- ▸JSONL / YAML exports and a short QA summary
Where it fits
- ▸Model evaluation and preference / post-training data
- ▸Private benchmarks and rubric-based scoring
- ▸Safety, policy, and failure-mode labeling
- ▸Engineering-heavy domains and other specialist fields
Run a labeling pilot.
Most engagements start small — so you can see the quality before you scale.