What you should know
- Innovator in AI in oncology Triomics has announced a Series B financing round of $22 million led by battery companiesbringing its total venture funding to more than $36 million.
- The expansion round has the strategic support of speed of light, Nexus Venture Partners, Y Combinator, Oncology companiesand Precision Health Informatics (a subsidiary of Texas Oncology).
- Triomics replaces manual curation of medical records by deploying domain-specific AI agents that ingest unstructured longitudinal records, pathology data, biomarker panels, and radiology reports.
- Peer-reviewed validation published in Nature Digital Medicine documents that Triomics reduces manual chart review times by 67%, while increasing clinical trial matches by 40% and enrollment by more than 30%.
- The platform’s multi-workflow infrastructure has captured rapid adoption at elite networks including Memorial Sloan Kettering (MSK), MD Anderson, Yale Cancer Center, Mount Sinai, and Texas Oncology.
The clinical and operational lifecycles of modern cancer care are suffocating under an unprecedented data paradox. Cancer care is no longer limited by a paucity of clinical information; instead, it is profoundly hampered by the inability to synthesize and act on the enormous data sets that already exist. The longitudinal record of a single oncology patient often expands to hundreds of narrative-heavy clinical notes, multi-page pathology and radiology profiles, genetic biomarker sequences, inherited external records, and historical treatment regimens.
Compounding this complexity is the rapid evolution of clinical trial eligibility protocols and National Comprehensive Cancer Network (NCCN) guidelines. Because legacy electronic health records (EHRs) function as passive digital file cabinets rather than intelligent action systems, oncology networks are forced to rely on manual chart extraction. Highly trained research coordinators, physicians, and physician assistants spend hours manually auditing dense files to match patients to trials or compile required state records. This structural data fragmentation results in high operational friction, widespread clinical burnout, and lost enrollment periods for life-saving therapies.
To turn this multimodal text overload into explainable, workflow-integrated intelligence, oncology infrastructure pioneer Triomics has completed a $22 million Series B financing round. Directed by battery companieswith the participation of existing sponsors Nexus Venture Partners, speed of lightand Y Combinatoralong with strategic healthcare networks such as Oncology companies and Precision Health Informatics (Texas Oncology)The round brings Triomics’ total capitalization to more than $36 million. The funding will be used to expand its AI engineering teams, accelerate health system adoption, and scale its autonomous graph abstraction architecture across global life sciences and provider networks.
Source-Backed Reasoners vs. Light Summaries
Founded in 2021 by Sarim Khan and Hrituraj Singh, Triomics completely avoids the security risks of uncalibrated consumer LLMs by creating a highly specialized oncology reasoning engine. While lightweight, general-purpose summarization software often drops vital clinical parameters or hallucinates critical diagnostic links, Triomics’ AI agents read the entire longitudinal record to generate structured, explainable results. Crucially, each algorithmic recommendation is source-backed, mathematically traceable, and fully verifiable within the clinician’s native workflow.
“Oncology is the most difficult place to develop AI, but also the most important,” said Hrituraj Singh, co-founder and CTO of Triomics. “Getting a model to reliably reason through thousands of pages of notes, pathology, images, and evolving test criteria, and display its work, is what separates a demo from the software doctors actually use.”
This commitment to medical integrity has driven explosive adoption in elite academic cancer networks, including Memorial Sloan Kettering Cancer Center (MSK), MD Anderson, Yale Cancer Center, and Mount Sinai Tisch Cancer Center, as well as in dominant community networks such as Texas Oncology.
By deploying these autonomous agents, healthcare institutions are achieving dramatic operational efficiencies. Peer-reviewed validation published in Nature Digital Medicine and presented to the American Society of Clinical Oncology (ASCO) demonstrates that Triomics users reduce manual chart review times by 67%, while expanding clinical trial matches by 40% and overall enrollment numbers by more than 30%.
Automation of the complex cancer registration process
Beyond immediate bedside triage and assay comparison, Triomics is aggressively positioning its underlying AI infrastructure to handle the laborious burden of cancer registry extraction and mandatory reporting obligations. Lee Schwamm, MD, director of digital health at Yale New Haven Health System, emphasized that traditional graph abstraction is deeply subjective, time-consuming, and difficult to complete within federally mandated timelines. Yale’s expanded integration with Triomics aims to deliver true clinical record-quality self-contained chart abstraction, enabling human registrars to quickly review and finalize data to meet state, federal, and professional society reporting mandates without missing a beat.
Brandon Gleklen, principal at Battery Ventures, who will join Triomics’ board, said the company has built the precise infrastructure that oncology desperately needs. Gleklen highlighted the distinct advantage of Triomics’ platform: the exact same underlying AI infrastructure seamlessly powers clinical trial comparison, pre-visit chart preparation, and registry data abstraction without requiring redundant and costly EHR integrations. This architectural advantage offers an undeniable operating moat within a highly coveted enterprise customer base.
