Annotation with a number attached.
Every batch runs through double-pass QA — acceptance scoring, reviewer notes and versioned guidelines, reported whenever you want the numbers. Image, video, audio, text and 3D — labelled by people we trained and certified ourselves.
/modalities
Whatever your model sees, hears or reads
Bounding boxes, polygons, segmentation masks, keypoints, classification.
image annotation →NER, classification, document processing, LLM output evaluation.
text annotation →Point-cloud cuboids, sensor-fusion labelling for autonomy stacks.
3d annotation →Custom guidelines and tooling are normal for us — describe the task.
/image
Image
Labeling assigns meaning to a whole image; annotation marks the regions inside it. Both feed computer-vision systems in autonomous driving, medical imaging, retail and security.
We label and annotate images at volume — expanding training sets, repairing imprecise labels that hold a model back, and running thorough quality checks so accuracy stays consistent across the whole dataset.
- Bounding boxes and polygons for object detection
- Semantic and instance segmentation masks
- Keypoint annotation for pose and movement analysis
- Whole-image classification and tagging
/video
Video
Video adds time to the picture: objects move, events unfold. Annotation tracks them across frames for self-driving stacks, surveillance, sports analytics and behaviour analysis.
We handle large video volumes frame by frame — tracking objects over time, tagging events and actions, and segmenting scenes to your guideline version, with QA sampling on every batch.
- Object tracking with per-frame bounding boxes
- Event tagging and action recognition
- Temporal and scene segmentation
- Clip-level classification
/audio
Audio
From speech recognition to sound-event detection, audio systems learn from precisely segmented, tagged and transcribed recordings — single speaker or crowded room.
We identify, segment and classify audio to your specification: transcribing speech word-for-word, separating speakers, timestamping events — consistent across any volume of files.
- Word-for-word transcription with timestamps
- Speaker diarisation for multi-speaker recordings
- Sound-event marking and classification
- Sentiment and emotion tagging
/text-documents
Text & Documents
NLP models live or die on context-aware annotation — every word, phrase and sentiment placed correctly, linguistic nuance included.
We label complex text datasets and process documents at scale: entity recognition, sentiment, contextual tagging and classification, handling diverse linguistic challenges under rigorous quality control.
- Named-entity recognition and key-phrase tagging
- Sentiment analysis and intent classification
- Document processing and structured extraction
- LLM output evaluation and ranking
/3d-lidar
3D LiDAR
Autonomy stacks perceive the world as point clouds. Annotation gives those points identity — vehicles, pedestrians, infrastructure — across space and time.
We annotate point-cloud data for perception systems: placing cuboids, classifying objects and aligning labels across sensor streams so your model trains on coherent, quality-scored ground truth.
- Point-cloud cuboid placement and classification
- Sensor-fusion labelling across camera and LiDAR
- Object tracking across sweeps
/quality
Quality is a process, not a promise
Every batch runs through the same loop — and you see the numbers, not just the labels. A dedicated project manager and QA manager oversee every project.
Everyone on your project passed the DeeLab General Skills Assessment and DeeLab Academy certification for that modality.
A second reviewer samples every batch against the guideline version it was labelled under.
Acceptance rate, disagreement notes and edge-case flags — ask, and any delivery ships with its numbers.
Individual annotators, a customized team, or a hybrid — on-demand for one-off datasets or long-term for continuous projects. Client-led or jointly tuned guidelines.
// start with a pilot batch