The fuel for intelligence. We provide high-precision, human-annotated training data for supervised fine-tuning (SFT), computer vision, and NLP models. No crowdsourced noise. Just expert ground truth.
Semantic definitions of our data annotation and labeling methodologies.
Multi-modal data labeling tailored to your specific machine learning pipeline.
Precision labeling for NLP models: sentiment analysis, intent detection, toxicity flagging, and complex Named Entity Recognition (NER) for specific industry domains.
Computer vision annotation: 2D/3D bounding boxes, polygon segmentation, keypoint tracking, and object detection for autonomous systems and medical imaging.
Verbatim transcription, speaker diarization, and acoustic event labeling across 47 languages to train high-accuracy speech-to-text and voice recognition models.
Domain experts author complex prompt-response pairs to create supervised fine-tuning datasets that teach base models how to reason, code, or diagnose.
We collaborate with your ML team to write a comprehensive annotation guideline, defining edge-cases and edge-case handling.
A small batch is labeled by senior annotators. We calibrate Inter-Annotator Agreement (IAA) to ensure the rubric is objective.
The vetted workforce executes the labeling at scale, with real-time QA checks to prevent drift and maintain fidelity.
Final datasets are filtered through consensus algorithms to resolve disputes, delivering clean, model-ready ground truth.
Deploy elite annotators. Generate high-fidelity data. Train superior models.