Vertical: Data & ML Operations Tagline: First-pass AI labeling, human review only where it matters.
The Problem Teams training or fine-tuning models need clean labeled data, but pure-human labeling is slow and expensive, while pure-AI labeling introduces silent errors that corrupt downstream models — most teams have no good middle path.
Key Features
- Automated first-pass labeling for text classification, image tagging, and entity extraction
- Confidence-based routing — only low-confidence items go to human reviewers
- Inter-annotator agreement tracking for quality assurance
- Custom labeling schema support (client-defined taxonomies and edge-case rules)
- Reviewer dashboard with inline correction and feedback-to-model loop
- Exportable in standard ML formats (JSON, COCO, CoNLL, etc.)
How It Works
- Client uploads raw dataset and labeling schema/guidelines
- Agent performs first-pass labeling with per-item confidence scores
- Low-confidence items route to human reviewers via a review dashboard
- Corrections feed back into the labeling model to improve future confidence calibration
Tech Stack Claude API for classification/extraction tasks, active-learning confidence calibration, reviewer web dashboard, standard ML export pipelines
Ideal For ML teams and startups fine-tuning models who need labeled data faster than pure-human pipelines but more reliable than pure-AI labeling