Data Annotation
Labels built for
model accuracy
not just volume.
Specialist annotators, clear taxonomies, and multi-pass QA—so your computer vision, NLP, and agentic systems learn from data that holds up in production.
ML leads and product teams who need high-precision training data without sacrificing governance.
- Focus
- Precision
- QA
- Multi-pass
- Scale
- On demand
How it works
A clear path from intake to owned operations.
- 01
Lock the taxonomy
We align label definitions, edge cases, and acceptance criteria with your ML owners before volume ramps.
- 02
Annotate in controlled batches
Trained teams label with tooling that fits your pipeline—images, text, audio, or structured events.
- 03
QA and consensus
Gold sets, inter-annotator checks, and senior review catch drift before it hits training.
- 04
Ship model-ready packages
Versioned exports, audit notes, and feedback loops so the next batch gets sharper.
Use cases
Where teams deploy this.
- Vision datasets for detection, OCR, and document AI
- NLP intent / entity labeling for copilots and chat
- RLHF preference data for enterprise agents
- Continuous labeling for models already in production
Let's build what's next.
Ready to deploy Data Annotation? Tell us your coverage needs and SLAs.