Labels you can actually audit.
Labeled datasets for vision, text, and audio, with clear guidelines, two-pass QA, inter-annotator agreement, and an audit trail on every span.
Deliverables
What you get, in writing.
Image & video
Boxes, polygons, keypoints, masks, and tracking IDs, with guidelines written and agreement reported.
Text
Span labeling, entity extraction, intent classification, preference data, and multilingual review.
Audio
Transcription including code-switched Indic speech, diarization, event tagging, and alignment.
Guidelines
Written guidelines with worked examples, edge cases, and a decision tree annotators actually use.
Two-pass QA
Independent second-pass review and agreement per label. Every error traces to a specific span.
Clean delivery
COCO, YOLO, JSONL, or Parquet, whatever your training pipeline expects, plus a data card.
90%
Agreement target on production splits
100%
Deliveries with a data card
24h
First response to every inquiry
Tools we reach for · documented, replaceable, no lock-in
Process
How it runs.
Four steps, agreed before any code is written: what the system should do, what it should refuse, and how success gets measured.
- 01
Spec & pilot
A 100-item pilot on your data, with agreement reported before anything scales.
- 02
Guideline lock
Written guidelines signed off by you. Later changes are re-scoped, not absorbed silently.
- 03
Annotate & QA
Rolling QA with agreement reported every milestone. Errors are fixed at the source.
- 04
Deliver & audit
Data, data card, and error analysis, plus fixes for anything caught in the first 30 days.
Labels that need to hold up?
Send a sample and the schema. We run a pilot, report agreement, and quote a fixed price.