Multimodal
Annotation
Image, video, audio, and document annotation by trained human annotators — with schema-enforced labelling, QA review, and full audit trail from task definition to final delivery.
Four modalities, one governed delivery model
Whether you're training a vision model, an audio classifier, or a document parser, Fuzu Atlas provides the human intelligence layer with consistent quality standards across modalities.
Image
Bounding boxes, segmentation masks, keypoints, classification labels, and scene description captioning.
Video
Frame-level annotation, action labelling, temporal segmentation, and object tracking across video sequences.
Audio
Transcription, speaker diarization, emotion tagging, sound event classification, and dialect labelling.
Document
OCR correction, form parsing, table extraction, entity recognition in unstructured documents, and layout labelling.
Governed annotation pipeline
Every annotation workflow follows a structured pipeline — no ad-hoc tasking, no anonymous crowd.
Schema Design
Label schema, taxonomy, and edge case guidelines co-designed with your team before any annotation begins.
Annotator Matching
Annotators matched by task type, domain, and modality. Specialised visual or audio skills tested before assignment.
Calibration Batch
Small calibration batch reviewed jointly. Ambiguities resolved and schema updated before full production.
Production + QA
Production annotation with independent QA review. Inter-annotator agreement tracked per label type.
Verified Delivery
Output delivered with quality metrics, error taxonomy, and rework completion status. Audit trail included.
Commonly requested annotation programs
3D bounding boxes, lane markings, pedestrian segmentation, and traffic sign classification for autonomous driving pipelines.
Image-caption pairs, visual question answering (VQA) datasets, and image instruction tuning data for multimodal LLMs.
Multi-language transcription with dialect and accent tagging, word-error-rate evaluation, and accent coverage testing.
Structured field extraction from financial, legal, and medical documents. Table and entity annotation for document understanding models.
Ready to annotate at scale?
Start with a focused PoC — schema design, calibration batch, and first verified deliverable in weeks.