Pricing

Whatever phase of development you’re in - from early research, to enterprise scale - our solution scales with you.

Scale better with the right plan

Research

For $0/mo, join a community of researchers on the cutting edge of AI in the medical and life sciences, leveraging free Centaur Labs capabilities

Start free today

Up to 3 users

1,000 files for labeling

Labels from the Centaur Labs network for most data types

Unlimited self-service labels

Request a demo for your team →

Scale

Scale your AI project with even more labeling capacity and expert support from a dedicated customer success manager
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Up to 10 users

100,000 files for labeling

API integration to send data to and from the platform

Labels from the Centaur Labs network for most data types

Dedicated customer success manager

Unlimited self-service labels

Request a demo for your team →
* Billed as $420 yearly

Most popular

Enterprise

Enhance your strategic AI initiatives with unlimited data labeling capacity from the Centaur Labs network, and custom data editing and setup

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Up to 30 users

Unlimited files for labeling

API integration to send data to and from the platform

Labels from the Centaur Labs network for all data types

Dedicated customer success manager

Custom data editing and setup

Unlimited self-service labels

Request a demo for your team →

FAQ

What’s the difference between self-service labels and labels generated by the Centaur Labs network?

Self-service labels are labels created by your team. You can upload data, create a labeling project, and invite up to 3 colleagues to label directly in the Centaur Labs platform.

The Centaur Labs network is a network of tens of thousands of medical doctors, students, and other professionals, all of whom compete with each other on our platform to annotate data most accurately. The Centaur Labs network has annotated a breadth of data formats (e.g., unstructured clinical notes, radiographic images, pathology slides, auscultation audio files, etc.) and can generate a range of annotations.

Labels generated by the Centaur Labs network are done so in collaboration with your team. You define what a high quality label looks like, by creating the gold standard labels and instructions used to train the Centaur Labs network. You then publish your labeling project to the Centaur Labs network, and immediately start to see thousands of annotations flowing in weekly.

What data types can you annotate with the Centaur Labs platform?

All customers can have images and  image series (.jpgs, .pngs), videos (.mp4), and text (.csv - plain text or HTML) annotated by the Centaur Labs network.
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Enterprise customers can have additional data types annotated by the Centaur Labs network - .svs, .dcm, .nii. Enterprise customers also have access to data editing services e.g. altering contrast levels.
 
As always, self-service annotation is available for with all products and for all data types.

How long does it take the Centaur Labs network to generate labels?

Centaur Labs knows the importance of both speed and quality of annotations.
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A dedicated customer success manager works with each Scale and Enterprise customer to set up your labeling project before publishing it to the Centaur Labs network. Together you will define what a quality annotation looks like, as well as what edge cases may be in your dataset. You’ll then run a test batch of annotations to confirm the quality meets your expectations. This phase of the annotation process takes 1-2 weeks.
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Once the annotation quality meets your expectations, the Centaur Labs network generates approximately 5,000 - 10,000 labels weekly.

Our customers

We are proud to work with organizations at the forefront of AI.

Brigham and Women's Hospital
Eko
Volastra
Massachussetts General Hospital
Massachussetts General Hospital
Brigham and Women's Hospital
Memorial Sloan Kettering Cancer Center
Volastra
Volastra
Brigham and Women's Hospital
Eko
Volastra
Brigham and Women's Hospital
Massachussetts General Hospital
Brigham and Women's Hospital

Ready to accelerate your AI development?