Experts to annotate
text at scale

NLP, named entity recognition, LLMs, and other AI methods generate insights from both structured and unstructured medical data stored as text.

Our annotation platform can quickly tag thousands of text strings, conversations, paragraphs and more, organizing large volumes of text data for AI applications. 

Use cases

Unstructured clinical notes

Tag conditions and severity. Identify drugs prescribed. Tag symptoms after a drug protocol begins, and challenges patient experiences related to adherence.

Scientific literature

Classifying research citations and determining drug-target relationships in pharmacological literature.

Chatbot messages

Tag words and phrases that communicate intent, the severity of the patient condition, and act with appropriate urgency. Identify tone and sentiment to offer suggestions and improve experience.

Insurance claims

Identify interventions to contextualize and rationalize most recent intervention. Tag payouts and procedures from past claims. Identify prescribing behavior of HCPs.

Social media

Tag medical misinformation. Identify sentiment and symptoms shared when patients discuss a medication in a public forum. 

Annotation types

Entity annotation
Entity linking
Sentiment annotation
Semantic annotation


Trusted by AI leaders across healthcare

“Annotations that would have taken me 3 months to complete with our prior data annotation system, Centaur Labs completed in only 2 weeks.”

Founder and CEO

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"Their user base combined with their application allowed us to easily and efficiently scale up our training data in a very controlled way that ensured it was high quality based on metrics we cared about in-house."

Co-Founder and CEO

"I get pitched on annotation tools all the time and Centaur Labs is simply the best. They offer you the accuracy and sophistication of medical experts at the price and speed of Mechanical Turk."



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