Procode AI announced a $10 million Series A financing round, led by Health Velocity Capital, to accelerate expansion of its AI-powered revenue cycle management (RCM) platform into private practice surgical markets. The funding will support growth initiatives, including two planned acquisitions aimed at broadening the company’s footprint across surgical specialties and ambulatory surgical center (ASC) billing.
Procode AI’s stated strategy is to acquire and vertically integrate AI technologies within existing companies rather than selling standalone AI tools. The company uses a hybrid large language model to translate operative reports into billing and diagnostic codes. Executives say that capability reduces manual coding time and cuts downstream denials, which has direct financial implications for providers.
Co-founder Jeff Cripe, who spoke with Fierce Healthcare earlier this year, framed the approach in commercial terms: Procode bills on contingency and only gets paid when its clients are paid. In a statement accompanying the funding announcement, Cripe said the company’s growth is evidence that its technology is “putting more dollars in providers’ pockets, not just automating paperwork.”
Through its acquisition of The Auctus Group, Procode AI currently works with more than 350 plastic surgery and dermatology providers. The company has raised $14 million in venture funding to date, including the newly announced Series A.
Cripe also projected aggressive performance improvements tied to the acquisition, stating the company is "on track to double The Auctus Group’s revenue and quintuple its EBITDA margin." Those targets were presented as commercially important because of Procode’s contingency-fee revenue model.
Alongside the financing, Procode AI reported publication of its first peer-reviewed study in the American Society of Plastic Surgeons’ open-access journal, Plastic & Reconstructive Surgery (PRS) Global. The study compared Procode’s hybrid LLM against multiple large language models—OpenAI GPT-5, Google Gemini 2.5 Pro and Anthropic Claude Sonnet 4.5—as well as external professional auditors.
The analysis assessed 120 case reports across varying difficulty levels. According to the published findings cited in the company announcement, Procode achieved 87.5% accuracy, which the study said was more than double the best-performing generic LLM and twice the accuracy of human auditors. Procode Co-Founder and CMO Kameron Rezzadeh, M.D., described the result as showing that solutions trained specifically for surgical coding can outperform both humans and generic models, and called it “a step change in what billing accuracy looks like for private practice surgeons.”
The source article did not report additional methodological details of the study—such as case selection criteria, specific accuracy metrics used, or peer-review commentary beyond the summary cited by the company.
Procode AI said the new capital will back two additional acquisitions to accelerate the company’s move into all surgical specialties and ASC billing. Executives indicated those acquisition targets will be announced in the coming months. No further details on the identity, size or terms of the planned acquisitions were reported in the source.
Company leaders emphasized their focus on the private practice market, arguing that while large health systems have long drawn attention and investment from technology firms, the long tail of RCM companies serving private surgical practices remains underserved. Procode positions its acquisition-and-integration model as a way to bring specialized AI tools directly into that segment.
For private practice surgeons and RCM vendors focused on specialty care, improvements in coding accuracy and denial reduction have immediate financial consequences because many vendors operate on contingency or share collections with clients. Procode’s reported accuracy gains and the company’s business performance targets were presented as evidence the platform increases collections and margins for its clients.
The broader RCM market has seen growing interest in AI-based automation, but Procode’s pitch combines technology deployment with acquisition-led distribution. That model aims to embed AI capabilities inside established billing and RCM firms rather than offering standalone products to practices.
Procode will use the Series A proceeds to pursue two acquisitions and continue scaling its operations into additional surgical specialties and ASC billing, with announcements expected in coming months. The company also now has a peer-reviewed publication to reference when discussing the accuracy of its hybrid LLM for surgical coding.
Details omitted from the source include the identities and financial terms of upcoming acquisitions, granular results and methodology from the PRS Global study beyond the reported accuracy numbers, and specific timelines for rolling the platform into new surgical specialties or ASCs.
Procode AI’s $10 million Series A, led by Health Velocity Capital, expands the startup’s push into private practice surgical billing by combining acquisitions with an AI-based coding copilot. The company reports working with more than 350 providers through The Auctus Group, has raised $14 million to date, and cites a peer-reviewed study reporting 87.5% accuracy for its hybrid LLM versus other LLMs and human auditors. Future announcements are expected as the company moves to complete two additional acquisitions and extend its services across more surgical specialties and ASC billing.
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