TriFetch Raises $1.9M in Funding Led by Nexus Venture Partners

The company plans to use the funds to grow its operations and continue developing its products.

TriFetch is starting by automating calls, referrals, and prior authorizations for specialty clinics. Over time, it plans to build an AI-powered system for healthcare operations, expanding across the country into multi-specialty groups, primary care, and hospital networks, while improving EHR integrations and connecting existing tools to daily workflows. The goal is simple: reduce paperwork and give healthcare providers more time to focus on patients.

TriFetch was created to reduce the administrative burden on clinics. Automating key tasks such as patient calls, referral processing, and prior authorizations helps clinics cut admin costs by over 50% while increasing revenue, potentially saving more than $1 million for a mid-size practice.

TriFetch is supported by more than 10 strategic advisors from top U.S. health systems. This includes Tim Eggena and senior leaders from Sutter Health, Johns Hopkins, Mayo Clinic, UW Health, Revere Health, Springfield Clinic, and UChicago Medicine. This group reflects TriFetch’s operator-first approach, with experienced professionals who have managed these workflows helping shape the product.

As AI use grows in healthcare, most tools focus on large systems, handle only one task, or work with a specific EMR. TriFetch offers a single, flexible automation layer designed for independent clinics. It works with different EMRs like NextGen, eCW, and Athena, can be set up in a matter of weeks, and reduces the need for clinics to rely on multiple vendors to manage their operations.

TriFetch is currently testing its solution with several specialty clinics across California. In one ophthalmology clinic, doctors and staff are overwhelmed by phone calls and prior authorizations while trying to provide quality care. In a cardiology clinic, staff are struggling to keep up with patient inquiries and internal coordination.

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Cofounders Varuni Sarwal and Rosemary He met at UCLA while earning their PhDs in Computer Science, where they focused on AI in healthcare. Varuni worked on machine learning with EHR data to predict conditions such as depression, and sepsis while Rosemary developed computer vision models to track Alzheimer’s progression using 3D medical images. They published their work in leading journals such as Nature and ICML.

“Clinics are doing everything they can to keep up, but the administrative workload keeps expanding,” said Varuni Sarwal, CEO and co-founder of TriFetch. “We built TriFetch to plug into how clinics already run and take the tasks staff dreads the most off their plate, calls, referrals, and prior auth, so teams can focus on the parts of care that require the human touch.”

Dr. Shashi Ganti, Ophthalmologist, Cal Retina MD, added: "Clinics up and down the US are facing the same administrative headache. Working with TriFetch, we've been able to relieve our staff from managing patient calls and scheduling, freeing them up from hundreds of voicemails and phone calls to focus on the patients in front of them. AI can be incredibly powerful when adopted safely, and I can't think of a better team to trust with that in my clinic.”

Jishnu Bhattacharjee, Partner at Nexus, commented: “Varuni and Rose are deep domain experts in healthcare AI. Healthcare administrative workflows represent one of the largest untapped opportunities for AI, and the Trifetch team is uniquely positioned to unlock it. They combine deep AI capabilities with real-world clinical understanding to build what we believe can become a category-defining company in healthcare AI."

About TriFetch

Founded by Varuni Sarwal and Rosemary He, TriFetch is an AI platform designed for independent clinics and multi specialty groups. It automates key tasks, such as patient calls and scheduling, referral processing and prior authorizations, to save staff time. The system connects with a clinic’s existing tools and workflows, so there is no need to change EHR systems or retrain staff.

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