Bounding boxes
Rectangular boxes for object localization, the foundation for real-time detection models.
Typical use casesFire & smoke, vehicles, PPE, defect localization
AI data annotation
Training data your models can trust. Labelled by people who build models.
Image and video annotation for computer vision, from bounding boxes to pixel-accurate segmentation and keypoints, delivered with multi-pass QA by a team that also trains and deploys the models.
01Boxes, polygons, keypoints02Multi-pass QA03COCO · YOLO · VOC
Annotation types
Every label type your vision models need.
Polygons & segmentation
Pixel-accurate polygons and semantic or instance masks for irregular shapes and surfaces.
Typical use casesSurface defects, lanes & areas, packaging inspection
Detection & classification
Multi-class labels with attributes, combining detection with classification for analytics.
Typical use casesVehicle types, product SKUs, anomaly tagging
Landmarks & keypoints
Keypoint labelling for pose, facial and structural feature points.
Typical use casesPose estimation, part alignment for assembly QA
Quality assurance
Bad labels make bad models. Our QA framework prevents both.
Agreed guidelines
Class definitions, edge-case examples and labelling rules agreed with you before work starts.
Multi-pass labelling
A primary annotator labels and a second reviews, so every image gets at least two sets of eyes.
Gold-standard QA
Samples checked against a gold-standard set, with agreement tracked per batch.
Validated exports
Datasets exported and validated in COCO, YOLO and VOC formats before hand-off.
95%+inter-annotator agreement target
2-tierreview before client hand-off
3export formats: COCO, YOLO, VOC
Process
An annotation pipeline built around the model.
Because we also build and deploy computer vision, we label with the model's needs in mind and close the loop with production data.
01
Collect
Images or video from your cameras, sensors or existing datasets.
02
Pre-label
AI-assisted pre-labels, for example Segment Anything, speed up human annotation.
03
Annotate & review
Boxes, polygons, classes and keypoints, with multi-pass review.
04
Train & validate
Optional model training to prove the dataset delivers the accuracy you need.
05
Re-annotate edge cases
Low-confidence cases from production come back for labelling: an active-learning loop.
Tools & formats
Industry-standard tooling. No lock-in.
Annotation platforms
CVATLabelImgRoboflowLabelboxV7 DarwinSuperAnnotate
AI-assisted labelling
Segment Anything (SAM)Model-assisted pre-labelsActive learning
Models we train
YOLOv8 / v9 / v11Detectron2Mask R-CNNMediaPipeOpenPose
Formats
COCO JSONYOLO txtPascal VOC XML
NDA-first
Every engagement starts under NDA, with controlled access to your images and video.
On-premise options
Annotation and model work on your infrastructure when data can't leave your site.
Controlled access
Only the assigned team can see your data, with access removed when the project ends.
Portable outputs
Standard formats and open tools, so your datasets and models stay yours.
Engagement models
Start with a pilot. Scale the way that suits you.
Per-image pricing
Pay per image or per annotation for well-defined labelling work.
Best whenYou have models and need quality labels
Pilot + model
A pilot dataset annotated and a proof-of-concept model trained to prove value.
Best whenYou want results before committing to scale
Dedicated team
A dedicated annotation team for continuous, high-volume labelling.
Best whenYou have a steady stream of new data
Proof
All AI case studiesDatasets that became production models.
Each of these projects started with annotation by our team.
Automated visual inspection on a camshaft production line
A line-side camera reads each camshaft's engraved serial number and flags surface defects in real time, logging every part for traceability.
2-stagedetection and OCR pipeline
4classes: serial ROI, scratch, dent, cast mark
Real-time fire and hazard detection from video
Live and recorded camera feeds are analyzed in real time, with fire and flame events flagged and streamed to a browser dashboard.
Real-timefire and flame detection on live video
0.7confidence threshold tuned for few false alarms
Highway traffic counting for outdoor advertising analytics
A camera on a highway billboard counts and classifies passing vehicles, producing impressions data advertisers use for media planning.
4vehicle classes counted
4×frame-skip for high-FPS streams
Bounding boxes, polygons and semantic or instance segmentation, multi-class detection with attributes, and landmark or keypoint annotation, for both images and video.
Agreed guidelines up front, multi-pass labelling with a second reviewer, gold-standard sample checks with agreement tracked per batch, and validated exports. We target 95%+ inter-annotator agreement.
COCO JSON, YOLO txt and Pascal VOC XML as standard. Other formats on request.
Yes. We can annotate only, or annotate, train, validate and deploy the model, including real-time APIs and video streams.
NDA-first engagements, controlled access for named annotators only, and on-premise options when data must not leave your site.
Per image or per annotation for pure labelling, fixed price for a pilot with a model, or a dedicated team for ongoing volume, scoped after a short discovery call.
Have images or video to label?
Send a sample. We'll annotate a pilot batch.
We agree the guidelines, label a pilot set with full QA, and can train a quick proof-of-concept model, so you see the quality before you scale.
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