Funding

Triomics Raises $22M in Series B Funding Led by Battery Ventures

May 29, 2026 | By Startuprise io

Triomics, a NYC-based oncology AI company helping cancer centers operationalize complex clinical information, has raised $22 million in a Series B funding round led by Battery Ventures.

The round also saw participation from Nexus Venture Partners, Lightspeed, and Y Combinator, alongside strategic backers Oncology Ventures and Precision Health Informatics. The raise brought the total amount to more than $36Million.

The company plans to use the funds to speed up adoption across health systems, oncology networks, and life sciences organizations.

The startup raised a $15 million Series A round in mid-2024. At first, Triomics focused on helping doctors find the best clinical trials for patients. As large language model (LLM) technology improved, the company expanded its platform.

Over the past few years, Triomics added verified patient summaries and visit-preparation tools that highlight important information directly inside the systems clinicians already use, without requiring them to switch between applications.

By reducing the time needed to prepare for appointments, Triomics gives oncologists more time to spend with patients. This is especially important in oncology, where patient histories are complex and staff burnout is common. Tools that reduce administrative work can make a major difference for healthcare teams. Triomics also automates the process of submitting tumor reports to government registries, which cancer centers are legally required to do.

"Oncology is the hardest place to build AI, yet the most important. Getting a model to reason reliably across thousands of pages of notes, pathology, imaging and evolving trial criteria, and show its work, is what separates a demo from software that clinicians actually use. We've spent four years building that foundation and this round lets us push even further, "said Hrituraj Singh, co-founder and CTO of Triomics.

"Oncology faces an information burden at a scale legacy systems were never designed to handle, and that burden can stand in the way of better outcomes. Clinicians, research coordinators and medical assistants are working against records that have become too large and too dynamic to process manually. We built Triomics to turn that complexity into usable intelligence inside the workflow, purpose-built for oncology. This financing allows us to bring that infrastructure to many more cancer centers and improve care for cancer patients, "said Sarim Khan, co-founder and CEO of Triomics.

"We are excited to partner with Triomics, our selected solution for oncology clinical trial matching, to extend our collaboration to an AI-enabled method for cancer registry abstraction and reporting. This activity is labor-intensive, subjective, and challenging to complete in a timely manner. Our goal is to produce autonomous chart abstraction of clinical registry quality that can be rapidly reviewed and finalized for reporting by human registrars to comply with mandatory state, federal and professional society reporting obligations, "said Lee Schwamm, MD, Chief Digital Health Officer, Yale New Haven Health System.

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"Triomics built what oncology has always needed: AI infrastructure that actually works on the full patient record. We are live at some of the top cancer centers and demonstrating measurable outcomes—faster enrollment, less manual chart review—and the same underlying AI infrastructure already powers multiple distinct workflows with no redundant integrations. That kind of platform leverage, inside a customer base this strong, is rare at this stage, "said Brandon Gleklen, Principal at Battery Ventures.

About Triomics

Founded by Sarim Khan and Hrituraj Singh, Triomics is an oncology AI company that transforms complex cancer records into structured and easy-to-understand data. Its platform helps healthcare teams with clinical trial matching, pre visit chart preparation, and oncology data organization, allowing doctors, researchers and health systems to make faster decisions using information already available in patient records.

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