Image
Bounding boxes, segmentation masks, keypoints, classification, OCR. Medical, industrial, retail, and consumer imagery.
BOXES · MASKS · KEYPOINTS
[Labels] your pipeline trusts.
Image, video, text, audio, and multimodal annotation by verified domain specialists. High agreement at scale, measured on every batch.
A dataset is only as good as the annotators who agreed on it.
Crowd platforms send your data to annotators who learned the task yesterday. Agreement gets sampled, not measured. Errors pass through as majority vote. By the time your eval catches them, the model has already learned the wrong answer.
The label is the lesson. Get the label wrong, the model learns wrong.
Why this holds up
Verified domain specialists
Radiologists label medical images. Linguists label text. Engineers label code. Not generalists learning on your data.
Agreement, measured per batch
Inter-annotator agreement tracked with Cohen's Kappa, Fleiss' Kappa, or Krippendorff's Alpha. Numbers ship with every delivery, not on request.
Double-verified, not sampled
Every label passes peer review and centralized QC. We measure every batch. Sampling is a confession, not a method.
Schema [precision]
COCO, YOLO, Pascal VOC, custom JSON. We match your training pipeline's schema, not our internal preference.
What we annotate
Video
Frame-by-frame tracking, temporal action recognition, scene segmentation, multi-object tracking across long sequences.
TRACKING · ACTION · TEMPORAL
Text & NLP
Named entity recognition, intent classification, sentiment, relation extraction, document parsing, and LLM preference ranking.
NER · INTENT · RANKING
Audio
Speaker diarization, emotion tagging, intent labeling, acoustic scene classification, and paralinguistic annotation.
DIARIZATION · EMOTION · ACOUSTIC
Multimodal
Cross-modal alignment: video-transcript, image-caption, audio-visual event sync, LiDAR-camera fusion.
ALIGNMENT · FUSION · PAIRED
RLHF & LLM
Preference pairs, instruction-response grading, red-teaming traces, constitutional AI alignment data.
PREFERENCE · RLHF · ALIGNMENT
How an annotation project runs
Calibrate
Share your guidelines, schema, and edge cases. Our specialists annotate a pilot set against your gold standard. We iterate on the guidelines together before the full pass begins.
Annotate & review
Matched domain specialists annotate to your schema. Every label passes peer review by a second specialist. Disagreements are flagged, resolved, and fed back into the guidelines.
Measure & deliver
Centralized QC audits every batch. Inter-annotator agreement, per-annotator metrics, and error categorization ship with the delivery. In your format: COCO, YOLO, Pascal VOC, or custom.
The Human Standard, applied to every label.
What ships with every annotation
- Label
- Annotation to your schema, peer-reviewed
- Specialist
- Verified annotator ID, domain credentials on file
- Agreement
- Inter-annotator agreement score, per batch
- Review log
- Every QC decision logged with actor and time
- Edge cases
- Flagged files with resolution notes
- Guidelines
- Version-locked guidelines used for this delivery
- Metrics
- Per-annotator accuracy and consistency scores
- Format
- Your schema: COCO, YOLO, Pascal VOC, or custom JSON
- Card
- Per-delivery documentation, covering everything above
Every label is traceable to who drew it, when, and against which guideline.
Who it's for
Foundation model teams
High-volume preference data, instruction-tuning sets, and multimodal alignment annotations for pre-training and post-training.
Computer vision teams
Production segmentation, detection, and tracking datasets across medical, industrial, and consumer domains.
Applied NLP & conversational AI teams
Entity, intent, sentiment, and dialogue annotations with domain-specific specialists matched to your vocabulary.
Questions
Tell us what to label.
Share the modality, the schema, and a few sample files. We come back within one business day with a labeled sample and a scoped plan.