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We are humbled and excited to share our recent $15M Series A funding round led by Matrix Partners! We’re thankful to Matrix and our other investors in this round including Accel, Global Founders Capital, Susa Ventures, Y Combinator, Omega Venture Partners, and other individual investors. We’d also like to thank all of our advisors, partners, and customers for their support.
One of our core values at Centaur Labs is “Every Voice Counts.” This value is not only central to our company culture but also to our company history, our DNA. Our approach is based on Erik's PhD research at MIT’s Center for Collective Intelligence. The idea is that multiple opinions combined intelligently are going to be more accurate than any single opinion alone. Applying this theory to medicine and AI, we’re leveraging a network of medical experts and performance assessments to label training data. Our mission is to annotate the world’s medical data accurately so that medical AI can make the impact it’s destined to. When we first started about 2 years ago, we were collecting ~160,000 opinions per week. Today, we’re at 1 million!
Our Series A funding enables us to continue our work by growing our team (engineering, data science, marketing, and sales) and continuing to invest in our network of medical experts.
We’re thrilled to continue on our journey to build a global network of people and machines that are trusted based on their performance to solve medical problems.
Read more about our Series A funding round at Forbes here.
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Centaur.ai is heading to HumanX 2026 to address the biggest challenge in healthcare AI: data quality. Most teams struggle not with labeling, but with measuring accuracy. Using collective intelligence and expert consensus, Centaur delivers faster, higher-quality datasets that improve model performance and support FDA-ready, defensible AI deployment.
A Centaur Labs study found that disease prevalence and expert feedback significantly influence diagnostic accuracy in dermatology, highlighting the need for contextual data and ongoing guidance to reduce errors and improve clinical decision-making.
Explored data curation strategies to mitigate bias in medical AI, with a focus on diverse datasets, expert input, and ensuring fairness in results.