Computer vision
Boxes, polygons, keypoints, and pixel masks for detection, tracking, and segmentation work.
Bounding boxesPolygonsSegmentation
Data annotation studio
Labels that hold up when the model ships.
Helix annotates vision, language, audio, and multimodal datasets for teams that cannot afford noisy training data. Written rubrics. Second-pass review. Exports that drop into your pipeline.
Batch 184 · street-sceneReviewed
pedestrian
vehicle
- pedestrian · boxagree
- vehicle · boxagree
- sign · pendingin review
Trusted by applied ML teamsNorthwind LabsHarbor RoboticsFieldwork AICedar HealthAtlas Mobility
Services
One studio for the datasets you actually train on.
Language & LLMs
Classification, NER, instruction data, and preference ranking from reviewers who read the spec.
NERSFTPreference pairs
Audio & speech
Transcription, diarization, and event labels for voice, call-center, and multimodal audio sets.
TranscriptionDiarizationEvents
Video & multimodal
Frame-accurate action labels, temporal spans, and paired text-image review for product models.
ActionsTemporal spansPaired review
Process
A short path from rubric to labeled batch.
01
Scope the task
We turn your rubric, edge cases, and export schema into a written labeling guide before anyone starts.
02
Label and review
Specialists annotate in batches. A second reviewer samples every lot against the same written guide.
03
Ship in your format
Exports land as COCO, YOLO, JSONL, or a custom schema. We match the fields your training job already expects.
Quality
Review is part of the job, not a later favor.
Every project keeps a living guideline, gold tasks, and a sampled second pass. Disagreements go back to the same written rule — not a hallway conversation. You get the labels and the audit trail.
- Labels delivered
- 40M+
- Consensus accuracy
- 99.2%
- Languages covered
- 120+
- Typical first batch
- 48h
Send a sample. Get a scoped plan.
Share the task, the format, and a small unlabeled slice. We reply with coverage, a review plan, and a pilot quote.