Lumiata raises $10M in Series B

What does Lumiata do?

Lumiata, is a predictive analytics company. It uses artificial intelligence powered analytics for helping organizations in precisely identifying and managing risk at the individual level. It applies big data driven medical science to patient data for optimizing every health care interaction. It delivers real time predictive analytics which helps insurance providers and hospital networks in delivering value care to more patients in little time. The company has developed the world’s first medical graph for producing accurate predictions and insights in symptoms, diagnosis, medications and procedures. What the graph does is, organizing and analyzing hundreds of millions of data points which are valuable. This company is venture backed and is composed of data scientists, clinicians and other experts.

How much Lumiata was funded?

Lumiata raised $10M in Series B on May 26, 2016 from abdulla waheed, Sandbox Industries, Blue Cross Blue Shield, Khosla Ventures and Intel Capital.

Previous funding

• $4M in Series A on January 8, 2014 from Khosla Ventures
• $6M in Series A on September 11, 2014 from BlueCross BlueShield Venture Partners and Sandbox Industries

What is next for Lumiata?

The company announced it’s funding while it is accelerating its deployment of medical AI which dramatically improves risk and care management for physicians, payers and population and health organizations. The company has made breakthrough improvements in the usability and precision of predictive analytics in healthcare by merging clinical science and data science.

More about Lumiata?

The company was founded in 2013 by Ash Damle. It has its headquarters in San Mateo, California. The company’s approach and technology to analytics enable organization in identifying and managing risk precisely, efficiently and transparently as required for the delivery of value based care. The company’s first flagship product is the Lumiata Risk Matrix, which provides current and evolving risk of each person within a population through precise and time based prediction along with medical reasoning.