Triomics Raises $22M to Combat Cancer Care's Data Overload with AI

NEW YORK, NY – May 28, 2026 – Triomics, an artificial intelligence company focused on oncology, announced today it has secured $22 million in Series B financing. The round, led by prominent tech investor Battery Ventures, aims to accelerate the adoption of its platform designed to help clinicians and researchers navigate the overwhelming amount of data in cancer care.

The new capital brings Triomics' total funding to over $36 million and will be used to scale its AI platform across more cancer centers nationwide, grow its engineering and deployment teams, and advance its AI agents for clinical and research applications. The company already boasts partnerships with some of the most respected names in cancer treatment, including Memorial Sloan Kettering Cancer Center, MD Anderson, Yale Cancer Center, and Mount Sinai Tisch Cancer Center, as well as large community practices like Texas Oncology.

Existing investors Nexus Venture Partners, Lightspeed, and Y Combinator also participated in the round, alongside strategic backers Oncology Ventures and Precision Health Informatics, a subsidiary of Texas Oncology.

The Challenge of Oncology's Data Deluge

Modern cancer care is a victim of its own success; the explosion of diagnostic tools, genomic data, and treatment options has created an information crisis. A single patient's history can encompass hundreds of pages of narrative-heavy clinical notes, pathology and radiology reports, biomarker results, and constantly evolving clinical trial criteria. For healthcare professionals, manually processing this deluge of unstructured data is a monumental and often unsustainable task.

"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," said Sarim Khan, co-founder and CEO of Triomics, in a statement. "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."

Founded in 2021 by Khan and CTO Hrituraj Singh, Triomics has developed a platform that uses AI agents to read a patient's complete longitudinal record. It converts this vast trove of unstructured information into structured, explainable outputs that are delivered directly into clinical workflows. Unlike simple summarization tools, the platform's outputs are source-backed, allowing clinicians to verify every piece of data. This technology supports critical tasks such as proactive clinical trial matching, pre-visit chart preparation, and data abstraction for registries and quality improvement.

A Strategic Bet on AI Infrastructure

The $22 million investment is more than just a vote of confidence in Triomics; it's a significant bet on the foundational role of AI in the future of oncology. The involvement of Battery Ventures, a firm with a history of backing transformative enterprise technology, signals a belief that Triomics is building essential infrastructure.

"Triomics built what oncology has always needed: AI infrastructure that actually works on the full patient record," said Brandon Gleklen, a principal at Battery Ventures who will join the company's board. "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."

The participation of strategic investors like Oncology Ventures and Precision Health Informatics further underscores this point. Oncology Ventures focuses specifically on companies tackling major challenges in cancer care, while Precision Health Informatics is the data and innovation arm of Texas Oncology, the largest community oncology provider in the United States. This backing indicates a deep strategic alignment, suggesting that those on the front lines of cancer treatment see Triomics' technology as a critical tool for implementing precision medicine at scale.

From Lab to Clinic: Proving AI's Worth in the Real World

In a field filled with AI hype, Triomics is backing its claims with measurable results and peer-reviewed validation. The company reports that cancer centers using its platform have seen a 40% increase in clinical trial matches and a more than 30% increase in trial enrollments. Perhaps most significantly for overworked clinical staff, the platform has reduced manual chart review times by an average of 67%.

These metrics are supported by external validation. The company's technology has been validated in the peer-reviewed journal Nature Digital Medicine and presented at the American Society of Clinical Oncology (ASCO) annual meeting, lending significant credibility within the clinical and research communities. This is part of a broader trend where AI tools are demonstrating remarkable efficiency gains. Independent studies, such as one from UCSF, have shown that large language models (LLMs) can extract data from medical charts with over 90% accuracy and at a pace 20 times faster than human researchers.

Leading healthcare systems are now looking to extend this capability beyond trial matching. "We are excited to partner with Triomics... to extend our collaboration to an AI-enabled method for cancer registry abstraction and reporting," said Dr. Lee Schwamm, chief digital health officer at Yale New Haven Health System. He noted that this work is traditionally "labor intensive, subjective and challenging to complete in a timely manner," and the goal is to produce autonomous chart abstraction that can be rapidly reviewed and finalized by human registrars.

Navigating 'The Hardest Place to Build AI'

Despite the promise and progress, the path to widespread AI adoption in oncology is fraught with challenges. Triomics' CTO, Hrituraj Singh, acknowledged this complexity, stating, "Oncology is the hardest place to build AI, yet the most important." He added, "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."

This difficulty stems from several factors. The data itself is incredibly complex and often unstructured. Integrating new technology into rigid, established clinical workflows is a major hurdle. Furthermore, the stakes are life-and-death, demanding an exceptionally high bar for accuracy, reliability, and safety.

Beyond the technical hurdles lie significant ethical and regulatory considerations. The use of AI in medicine raises critical questions about data privacy under HIPAA, the potential for algorithmic bias to perpetuate health disparities, and accountability when an AI system contributes to a clinical decision. Professional bodies like ASCO have released principles for the responsible use of AI, emphasizing transparency, equity, human oversight, and accountability. Navigating this landscape requires not just brilliant engineering, but a deep commitment to ethical development and a "human-in-the-loop" approach, ensuring that AI tools augment, rather than replace, the expertise and judgment of clinicians. As Triomics deploys its new funding to expand its reach, its success will depend as much on navigating these complexities as it does on the power of its algorithms.