Luminai Raises $38M With Cleveland Clinic Using Its AI for Hospital Operations Leave a comment
Another AI startup helping providers automate administrative processes picked up a significant round of capital on Thursday.
San Francisco-based Luminai snagged $38 million in Series B funding, bringing its fundraising total to $60 million since its founding in 2020. The round was led by Peak XV Partners, with participation from General Catalyst, Y Combinator and Define Ventures.
The company seeks to replace fragmented, manual healthcare administrative workflows with an easy-to-manage AI system.
Luminai CEO Kesava Kirupa Dinakaran said the startup’s platform is built using 3 layers. The first is data transformation.
The AI engine first converts messy healthcare documents like faxes and PDFs into structured, usable data by distinguishing relevant clinical information from irrelevant noise, Dinakaran explained.
“For example with referrals, when a health system receives faxed documents, only a fraction are actual referrals. The rest could be pizza flyers, sales spam, thank-you notes from providers and dozens of other document types mixed in. Traditional software cannot determine what is a pizza flyer versus a referral. Luminai’s model is able to identify what’s a referral, extract the relevant clinical and administrative information, and hand off clean, structured data that can be read by software,” he remarked.
The second layer is a knowledge graph that has learned how each health system operates, including its routing rules, policies, exceptions and institutional judgment, Dinakaran said.
That means when a referral comes in, the system can automatically determine where it should go — even in complex cases where routing decisions aren’t explicitly documented — by replicating the judgment of long-tenured staff who would otherwise rely on their experience and institutional memory, he noted.
“Every health system has its own unique processes and policies,” Dinakaran declared. “Luminai encodes that operational logic into versioned, auditable infrastructure, rather than relying on ‘tribal knowledge’ that lives in people’s heads.”
He explained that the third layer, workflow execution, uses agents to then classify documents, match them to the right patient and provider, route them to the correct department and trigger any necessary downstream actions.
When the system isn’t confident enough to act autonomously, it routes the work to a human operator with all the context already in place, Dinakaran added.
But this only happens in rare cases. He said one of the main benefits the startup’s platform offers to customers is reducing the administrative burden on care teams.
“There could be over 100 people whose entire job is processing incoming faxes at a health system, which is work that nobody signed up to do, but has to get done for the institution to function,” Dinakaran remarked.
The company is currently partnered with Cleveland Clinic, which is using the platform to automate complex administrative workflows, starting with referral management.
Referrals are just the first of many use cases, according to Dinakaran. He said the partnership is designed to expand across “high-volume, operationally intensive workflows” in the administrative realm.
As the collaboration moves from pilots toward a wider rollout, it reflects a larger shift: health systems are looking for AI platforms that can take on end-to-end operational workflows — not just help with individual tasks.
Photo: sesame, Getty Images
