Multi-modal labeling with multi-layer QA.
Overview
What is data annotation?
Data annotation is the process of labeling raw data, images, text, audio and video, so machine learning models can learn from it.
Foundation models train on billions of annotated examples. The quality and consistency of those labels decides model performance, safety and real-world reliability.
Bounding boxes, polygons, keypoints, semantic and instance segmentation.
Named entities, relation extraction, sentiment, intent and coreference.
Speaker diarization, speech alignment and sound event tagging.
95%+ accuracy guarantee with IAA scoring and multi-pass QA.
Workflow
How we do it
- 01Project scoping
We assess requirements, define the taxonomy and assign domain-specialized annotators.
- 02Style guide creation
Written guidelines and calibration batches lock in consistency before full production.
- 03Precision labeling
Expert annotators label boxes, polygons, keypoints, segments and relationships.
- 04Multi-layer QA
Automated consistency checks, inter-annotator agreement scoring and senior review.
- 05Structured delivery
Export-ready datasets in COCO, YOLO, Pascal VOC or JSON, with full documentation.
Case study
Annotate 300K product images for a visual search engine
Multi-label classification plus a bounding box pipeline for product attributes.
Ready to get startedwith data annotation?
Tell us about your project and we will scope a pilot within 48 hours.