Automating annotation for computer vision

Unlock data-centric AI by iterating on your training data creation and model building faster than ever, while retaining 100% control of your data.

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Labeled training data, radically faster

Our novel micro-model approach automates up to 99% of your manual labeling tasks. All with data never having to leave your private storage.

Stanford Medicine
Stanford Medicine

The Division of Nephrology reduced experiment duration by 80% while processing 3x more images.

Stanford was using three different pieces of software to identify, annotate, and count podocytes and glomeruli in microscopy images.
Stanford started using Cord’s training data platform & SDK to automate segmentations, count, and calculate sizes of segments.
With Cord, Stanford researchers reduced experiment duration from an average of 21 to 4 days while processing 3x the number of images.
Reduction in experiment duration
Number of images
1 platform
... and not 3
KCL Logo
King's College London

KCL used Cord to achieve a 6.4x average increase in labelling efficiency for GI videos.

Using clinicians to annotate pre-cancerous polyp videos had prohibitively high costs to produce large datasets.
Deployed Cord’s micro-model module to increase clinician labelling efficiency and automate 97% of produced labels.
Highest expense clinician saw 16x labelling efficiency improvement. Cut model development time from 1 year to 2 months.
Faster than manual labelling
Automated labels
Faster to AI in production

Explore the possibilities

We built Cord to support you as your company adopt and scales computer vision AI applications.


Expansive ergonomic image & video data labeling toolkit.


Embedded algorithms and heuristics help to replace manual labeling.


Condense the iteration cycle by finding the right data to label.


One-click state-of-the-art model training and inference.


Combine algorithms, heuristics, and models into data programs.


Discover biases and imbalances with advanced visualization features.

Check out our featured
research paper and tutorials

We collaborate with some of the world's leading companies, hospitals, and research institutions.

Why You Should Ditch Your In-House Training Data Tools
“ If your tool is built on top of CVAT — like most of the machine vision teams we’ve worked with — it quickly starts to succumb to the increased workload and comes down crashing faster than you can say Melvin Capital. “
Written by Ulrik Hansen
The article can be found here (Medium)

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Speak to a member of our team to learn how you can iterate faster.

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