Every service runs through the same annotation and review discipline — only the modality changes.
Bounding boxes, polygons, semantic and instance segmentation, keypoints, and landmark labeling for computer vision models — from retail shelf recognition to medical imaging.
Frame-by-frame object tracking, activity recognition, and temporal event labeling for surveillance, sports analytics, and behavioral AI.
Cuboid labeling and point-cloud segmentation for autonomous vehicles, drones, and robotics operating in three-dimensional space.
Multi-language transcription, speaker diarization, timestamping, and acoustic event tagging for voice, call-center, and speech-recognition systems.
Named entity recognition, text classification, sentiment analysis, and intent labeling for language models and conversational systems.
Purpose-built capture of image, audio, video, or text data when off-the-shelf sets don't cover your model's edge cases.
Beyond the six core modalities, we run dedicated workflows for domains with their own edge cases and compliance needs.
Multi-sensor perception, prediction, and planning annotation for self-driving and ADAS systems.
Imaging, clinical text, and doctor-patient conversation annotation under strict confidentiality controls.
Text region, layout, and form-field extraction for document-understanding models.
Object detection, land-use classification, and change detection for aerial and satellite imagery.
Explore Voxa for transcription and EchoLab for speech recording, or sign in to the secure internal workspace to manage assigned audio tasks and reviews.
Every deliverable — regardless of modality — passes through the same review chain before it reaches you.
Trained against your specific guidelines and edge-case library, labels the raw asset.
A second annotator checks every label against the acceptance criteria, flags disagreement.
A senior lead resolves flagged disagreements and signs off before delivery.
Send us a sample of your raw data — we'll recommend a labeling schema and pilot scope, free.