Object detection & tracking
Tight bounding boxes around every object of interest, with consistent classes and attributes across the whole dataset.
Image annotation for computer vision
Accurate, human-labeled image data to power computer vision in autonomous systems, e-commerce, healthcare and beyond.
- Exports: COCO · YOLO · Pascal VOC · custom JSON
- Boxes · polygons · masks · keypoints
- 98%+ accuracy guarantee
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auto-detecting objects…detection · OCR
Image annotation types
A workforce trained for pixel-level precision
Our experienced workforce delivers high-precision image annotation, from object detection to instance segmentation. Deep domain knowledge and rigorous quality checks help your models learn faster and perform better.
Segmentation
Semantic and instance masks that separate each object from its background, down to the pixel.
Image classification
Whole-image and multi-label tags that sort your data into exactly the categories your model predicts.
Keypoint & pose
Landmarks and skeletons on people, faces and objects for pose estimation and gesture recognition.
3D cuboids
Cuboids drawn on 2D images to estimate an object's depth, orientation and dimensions.
OCR
Text regions boxed and transcribed on documents, receipts, signs and screens for text recognition.
One image, many label types
Pick the annotation your model needs
Detectors, segmenters, pose and OCR models all learn from different labels. Switch between them to see how each one looks on real data.
- Pixel-tight boxes and masks
- Attributes and occlusion flags per object
- Class taxonomies built with you
Tight boxes on every person and object, including small and partly hidden ones.
Use cases
Where teams put this data to work
Retail & e-commerce
Product detection, shelf monitoring and catalog tagging for visual search.
Medical imaging
Organ and lesion segmentation on X-ray, CT and pathology images.
Agriculture
Crop, weed, fruit and livestock labels from field and drone imagery.
Security & smart cities
People, vehicle and license-plate labels for safety and traffic analytics.
How we work
From pilot to production
We start by understanding your data and goals, annotate with the right techniques, validate through multiple QA layers, collect your feedback and deliver clean, structured data ready for training.
- Step 01
Consult
We review your data, edge cases and success criteria, then write annotation guidelines with you.
- Step 02
Annotate
A trained team labels a pilot batch, following the agreed guidelines to the letter.
- Step 03
Calibrate
You review the pilot. We fold your feedback into the guidelines before scaling up.
- Step 04
Multi-layer QA
Every batch passes peer review, expert QA and automated checks before it leaves us.
- Step 05
Deliver
Labels ship in your format on your schedule, ready to drop into training.
Quality pipeline
Five checkpoints between raw data and your model
98%+
Accuracy guarantee
- 01AnnotatorLabels against your guidelines
- 02Peer reviewA second annotator reviews the work
- 03Expert QASenior reviewer samples and corrects
- 04Automated checksRules catch missing or invalid labels
- 05Client sign-offYou approve before final delivery
Results
Quality you can measure
- Accuracy guarantee
- 98%+Accuracy guarantee
- Returning clients
- 96%Returning clients
- Cost reduction
- 2.5xCost reduction
- Projects completed
- 200+Projects completed
“Our fashion AI model required pixel-level segmentation across 72 garment categories, and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.”
Valerio Colamatteo
Sr. AI Scientist & Team Lead, MindEarth
Related
Supporting computer vision in autonomous vehicles, robotics, e-commerce, medical AI, security, agriculture and more.
FAQ
Questions, answered
Something else on your mind? Email sales@intellisane.ai.
Get started
Let's scope your project
Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.