Data labeling studio
Every label reviewed. Then sealed.
SkySeal Labs prepares image, video, text, audio, and document data for teams training production models. A lead reviewer sees every batch before it leaves the studio.
A studio for the labels a model will actually train on.
SkySeal is a labeling studio. The people who write the guide are in reach of the people who apply it. Work is not sprayed across an anonymous crowd and collected at the end of the day.
We take a defined batch, label it against a written standard, and have a lead reviewer adjudicate it. You receive the labels, the disagreements, and a record of what was checked.
The kinds of judgment we take on.
Image and video
Boxes, masks, tracks, and attributes on stills and sequences. Classes stay stable across a batch.
Text and documents
Spans, entities, relations, and page structure. Source text is kept intact. Review notes sit beside it.
Speech
Transcription, speaker turns, and timed labels. Unclear audio is marked, not guessed.
Preference review
Side-by-side judgments against a written rubric. Disagreement is recorded, then resolved.
Guideline design
We turn a model requirement into a guide a reviewer can apply the same way on Friday as on Monday.
Audit
A sample can be reopened. You see who labeled, who checked, and what changed.
Four passes. Nothing informal in between.
- 01
Scope
Modality, volume, edge cases, and what “done” means for the model team.
- 02
Guide
A short written guide, with examples of the hard cases, before the first production batch.
- 03
Label
The same reviewers stay on the task. Questions go back to the guide, not into a side channel.
- 04
Adjudicate
A lead reviewer reads the disagreements and seals the batch, or sends it back.
Review is part of the delivery, not a later add-on.
- Agreement checks. A slice of each batch is labeled twice. Where the two passes differ, the item is adjudicated.
- Gold tasks. Known items sit inside the work so a drifting reviewer is caught in the batch, not after it.
- Escalation. An item that does not fit the guide is flagged and returned. It is not forced into the nearest class.
- Audit trail. Each delivered batch names the guide version, the reviewers, and the items a lead changed.
Where the batches usually come from.
- Autonomy
- Retail vision
- Document AI
- Speech systems
- Agriculture
- Content safety
We label data for software teams. We do not certify medical devices, and we do not claim a government clearance.
Tell us about the batch.
Modality, rough volume, and the decision the model has to learn. We reply from the studio inbox.