Artificial intelligence startup Keebler Health has secured $16 million in a Series A financing round aimed at supporting the development of its infrastructure for value-based care. The round was led by Flare Capital Partners, with participation from Sands Capital, Tau Ventures, and several existing investors, including Freestyle Capital, Underdog Labs, and MBX Capital. Additional backing came from Everywhere Ventures, New Stack Ventures, Tweener Fund, Aviano Ventures, and Hustle Fund, according to a company press release.
With this latest funding, the company has raised a total of $23 million to date, as stated by Isaac Park, chief executive officer and co-founder of Keebler Health. The funds are intended to support hiring efforts, expand commercial operations, and strengthen infrastructure designed for organizations operating within value-based care models. The company also plans to extend its technology into related areas such as compliance and audit processes, including AI-enabled readiness for Risk Adjustment Data Validation (RADV) audits.
Keebler Health’s platform focuses on improving risk adjustment, a process that remains limited by fragmented healthcare data. Executives at the company noted that a large portion of clinically relevant information exists outside structured and coded fields, often embedded in unstructured formats such as physician notes, imaging reports, and discharge summaries. Approximately 80% of healthcare data falls into this category, creating challenges for accurate coding and reimbursement.
The company has developed a large language model (LLM)-native system designed to process this type of unstructured clinical documentation. Its platform generates Hierarchical Condition Category (HCC) codes and delivers insights intended to assist clinicians during patient care. According to company executives, the goal is to close the gap between documented clinical information and what is ultimately captured in structured data systems.
Research cited by the company highlights ongoing data consistency issues, noting that only 59.4% of chronic conditions are reliably documented across electronic health record systems. These inconsistencies contribute to incomplete risk capture and inaccuracies in reimbursement processes.
Isaac Park described the company’s focus on leveraging emerging technology to address these limitations. “As we were exploring that technology front, we realized that risk adjustment would actually be a really great place to be able to start affecting this kind of technology change that we were leveraging,” he said in an interview with Fierce Healthcare.
Ian Chiang, a partner at Flare Capital Partners, stated that the firm identified Keebler Health’s approach as addressing a persistent challenge in healthcare data. He said the platform aligns with the way clinical information is documented and has shown the ability to generate measurable outcomes, adding that the firm views it as an important capability for value-based care organizations.
Founded in 2023 and based in Durham, North Carolina, Keebler Health was established by Park, Andrew Stickney, Kevin Hill, Ph.D., and Chief Medical Officer Terrell Bacchus, M.D. Prior to the Series A round, the company raised $1.8 million in pre-seed funding led by New Stack Ventures and an additional $6 million in a seed round led by Freestyle Capital.
Park also noted that advances in LLM technology have enabled new approaches to processing clinical narratives, allowing software to analyze large volumes of unstructured patient information and identify relevant medical details across longitudinal records.
Series A Funding and Strategic Backers
The funding round for Keebler was led by Flare Capital Partners, with participation from Sands Capital and several existing investors. The capital will enable Keebler to scale its commercial operations, expand its workforce, and enhance platform capabilities.
With total funding now reaching approximately $23 million, Keebler is positioning itself for rapid growth in the competitive digital health and AI infrastructure space.
AI-Based Risk Adjustment Platform Explained
At the core of Keebler is an AI-native platform designed to process unstructured clinical data, such as physician notes and medical reports. Unlike traditional systems, Keebler uses large language models (LLMs) to extract meaningful insights directly from patient narratives.
This allows Keebler to identify missed Hierarchical Condition Categories (HCCs), improving the accuracy of risk scoring and reimbursement for healthcare providers.
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