For many health systems, the challenge has shifted from finding an AI vendor to solve a specific challenge to deciding which ones can move beyond pilots. And discerning which ones actually change how work gets done.
That is the backdrop for Cleveland Clinic’s partnership with Luminai, a San Francisco-based startup building an AI-native platform to automate complex administrative healthcare workflows. The collaboration comes as Luminai announces a $38 million Series B funding round, bringing its total capital raised to approximately $60 million, with backing from Peak XV Partners (formerly Sequoia Capital India & SEA), General Catalyst, Define Ventures, Y Combinator and others.
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The timing reflects a broader inflection point. Health systems are under mounting pressure to modernize operations while managing workforce shortages, rising costs and increasingly complex care delivery environments. Many are now looking beyond narrow AI tools toward systems that can operate across functions.
Cleveland Clinic, one of the largest and most operationally complex health systems in the world, offers a high-stakes proving ground. With more than 80,000 employees, 23 hospitals and hundreds of outpatient facilities, even incremental improvements in administrative workflows can have system-wide impact.
“Referrals were selected as the initial use case because workflows in this space are complex and involve several steps including manual intake, data validation, and coordination across systems,” Chandra said. “They also require frequent follow-up.”
Despite years of efforts to digitize, many referrals still arrive via fax, and for organizations the size of Cleveland Clinic, they can be in the thousands per day – often mixed in with faxes unrelated to referrals. Each document must be reviewed, interpreted, matched to the correct patient and provider, and routed appropriately before care can begin.
Kesava Kirupa Dinakaran, Luminai’s co-founder and CEO, has spent the past several years focused on this exact problem: how to make software handle the work that humans currently perform to bridge disconnected systems.
“The biggest barrier was the amount of unstructured data,” Dinakaran said. “Without structuring that data, you can’t actually automate the workflow.”
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