Komodo Health is an Interoperability & Data Infrastructure company founded in 2014 and based in San Francisco, United States. It has raised $534M in total funding, most recently a Convertible in 2022.
| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| Nov 1, 2022 | Convertible | $200M | — | Coatue, Dragoneer Investment Group |
| Mar 22, 2021 | Series E | $220M | $3.3B | Tiger Global Management, Casdin Capital |
| Jan 12, 2021 | Series D | $44M | — | — |
| Jan 8, 2020 | Series C | $50M | — | Andreessen Horowitz |
| Jun 12, 2018 | Series B | $20M | — | — |
| Jan 1, 2016 | Series A | — | — | McKesson Ventures |
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Komodo's foundational data asset that tracks over 330 million de-identified patient journeys across the U.S. by linking claims, clinical, lab, genomics, pharmacy, and social determinants of health data. It provides longitudinal, patient-level insights that eliminate data silos and bias, giving life sciences, providers, and government organizations a complete view of healthcare encounters across all care settings.
A healthcare-native AI analytics platform purpose-built on the Healthcare Map. Marmot delivers reproducible, traceable, and governed AI insights that accelerate healthcare innovation for life sciences and provider organizations. Unlike generic AI models, it is trained on the industry's most authoritative real-world patient data, enabling users to run complex analyses, generate evidence, and derive actionable intelligence with full transparency and defensibility.
A unified analytics workspace that centralizes the workflow for life sciences and healthcare teams. Users can ask questions, build analyses, and run queries against Komodo's real-world data all in one environment, streamlining the journey from data exploration to insight generation without switching between disparate tools and platforms.
An AI-powered data enrichment offering that integrates with Cornerstone AI to clean, standardize, and enhance raw healthcare data before it enters the Komodo platform. MapEnhance improves the accuracy and volume of real-world insights by automating data cleansing at scale, ensuring that downstream analytics and AI models operate on the highest-fidelity data possible.