Collaboration
Turn production mistakes
into training gold
When your AI gets something wrong, don't just fix it—label it. Build training datasets from the real examples that matter most.
Annotations
Interactive preview
CollaborationCollaboration
Documentation →Real failures, real fixes
Synthetic data can't match production edge cases. Annotate what actually went wrong.
Team annotation at scale
Assign labeling tasks, track progress, resolve disagreements. Quality control built in.
Feed any training pipeline
Export in JSONL, HuggingFace, or custom formats. Your annotations, your models.
Annotation interface
Label outputs with categories, ratings, and corrections. Fast keyboard shortcuts.
- Category labels
- Quality ratings
- Text corrections
DETAILS
StatusActive
Last updated2 minutes ago
OwnerPipeline Agent
Duration1.2s
Tokens used2,847
Annotation workflow
Assign tasks, track progress, and review annotations before export.
- Task assignment
- Progress tracking
- Review queue
RECENT ITEMS
Pipeline flagged Acme Corp→
User approved TechStart→
Agent checked API health→
Delivery blocker resolved→
New agent registered→
How it works
1
Select
Choose outputs to annotate
2
Label
Add annotations and corrections
3
Export
Use for model improvement
Similar in Collaboration
All apps →Start annotating
Better training data.
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