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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 CEO Erik Duhaime joined SegMed’s Bites of Innovation to explain how healthcare AI teams can achieve data quality at scale. He discusses collective intelligence, why credentials do not guarantee labeling quality, competitive annotation, dynamic escalation using disagreement, confidence scoring, continuous QC, regulatory datasets, de-identification, and how to use LLMs without blind trust.
A $750,000 grant from the Massachusetts Life Sciences Center will support Brigham & Women’s Hospital researchers in their efforts to transform medical research.
Emphasized the importance of data curation practices in reducing bias in medical AI, promoting diverse datasets, expert collaboration, and fairness metrics for more equitable outcomes.