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 →Satellite and aerial imagery, land parcels, building footprints, change detection.
geospatial 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, whether 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
/geospatial
Geospatial
Mapping and monitoring models read the earth in pixels and parcels. Annotation is how satellite and aerial imagery becomes usable ground truth.
We annotate satellite, aerial and drone imagery to your specification: tagging, mapping and organising location-based data so mapping, monitoring and disaster-response models train on consistent, quality-scored labels.
- Land parcel, building footprint and boundary polygons
- Land-use and land-cover classification
- Change detection across time-series imagery
- Infrastructure and asset mapping from overhead imagery
/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 customised 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