Exclusive: XCures Lands $46M Series B To Clean Up Messy Medical Records With AI

XCures, a startup that uses AI to streamline patient data and medical records, has closed a $46 million Series B financing round, it tells Crunchbase News exclusively.

Innovius Capital led the financing, which included participation from iGrow, Spring Mountain Capital and existing backers. The raise brings the company’s total funding to more than $76 million since its 2018 inception and values it at $127 million post-money. That’s more than double the valuation of its previous funding round — a $25 million Series A that closed in December 2023.

“Healthcare has spent decades generating enormous amounts of patient data without a reliable way to make that information usable,” said xCures CEO Mika Newton in an exclusive interview with Crunchbase News. “We’re changing that.”

Venture investment in healthcare and biotech companies that have an AI bent has been on an upward trajectory in recent years. As of June 22, investors have put an estimated $8.5 billion into seed- to growth-stage funding for companies in AI-powered health tech categories, according to Crunchbase data. In 2025, funding to the sector across all stages totaled $15.8 billion. This year’s total is already nearly as much as the $8.6 billion raised in the category in all of 2024.

Pivoting to solve a problem

Founded in 2018 as a spinout from Cancer Commons by Marty Tenenbaum, xCures initially launched to provide decision-support tools for patients with advanced cancer. At its inception, the company focused on patients with Stage 3 or Stage 4 recalcitrant cancer diagnoses, where standard care options were exhausted.

While working with thousands of patients across the country in a direct-to-consumer setting to build its initial model, the company encountered a systemic bottleneck.

“What we learned in the process was that the decision-making was hard,” Newton said. “These are complicated things, but doable. But the even harder thing was to get our hands on the data and information about the patient that we needed in order to give them the advice in the first place.”

At the time, patient records were arriving at the company in FedEx boxes and over fax machines. This logistical hurdle prompted xCures to pivot to build the underlying infrastructure needed to connect directly to national healthcare interoperability networks. Today, xCures hooks into these electronic exchanges on behalf of its customers, shifting its primary focus to structuring what Newton described as the industry’s “dirty data.”

“The data in those medical records is incredibly dirty, so it’s duplicative. There are pictures of things, scans of things. There are errors that are caused because it’s all human entry,” Newton explained. “There’s lots of narrative information, and we turn it all into something that basically is clinical intelligence or the clinical clarity an organization needs to make its next decisions.”

Creating a ‘clinical clarity engine’

Patient information remains scattered across thousands of labs, hospitals, imaging centers and electronic medical records, often arriving as unstructured documents that are difficult to use in clinical workflows. This is where xCure can provide a differentiated experience, according to Newton.

“They’re [competitors] really in the transport business … moving data from Point A to Point B,” he noted. “We think of our product as the executor’s clinical clarity engine. We’re in the business of taking that transported data and making it into something that’s actually instantly useful, versus just moving it from one space to another.”

The xCures Clinical Clarity Engine, he said, solves this by integrating capabilities to generate decision-ready checklists from automated patient histories, backed by evidence-grade data. Newton estimates that the engine is three to five years ahead of anyone else in the market. To date, xCures has processed more than 300 million medical records sourced from more than 550,000 healthcare locations nationwide, supporting clinical decisions for millions of patients across the U.S., per the company.

To manage this volume without incurring the extreme processing costs associated with running massive, unstructured files through generic models, xCures utilizes a variety of AI, combining its own home-built machine learning models with commercial frontier models from existing vendors. The company manages these tools through a proprietary governance framework.

“We really see it as the harness for … the process for applying AI, and how we make sure that the tasks that we’re asking the AI to do are appropriate and well-governed, and that the rules of engagement are really clearly defined,” Newton said.

High growth and enterprise adoption

This technological approach has driven impressive traction. Operating on a usage-based SaaS model with committed caps, xCures grew from roughly $3 million to $10 million in annualized recurring revenue in 2025, according to Newton, and it’s on track to break $20 million in 2026.

While xCures achieved cash-flow breakeven last year, the company has intentionally entered a capital-burn phase to build its team for its 2027 business pipeline, he added.

The startup’s enterprise customer base consists of 25 clients, including lab diagnostic companies such as Exact Sciences, Caris Life Sciences and Novocure. Large hospital networks use the tool to “instantly” generate patient histories for operating room scheduling, screen for comorbidities and estimate operative times ahead of surgeries. The engine is also used by telehealth providers lacking robust Electronic Health Record architectures, as well as by Medicare Advantage plans seeking to automate population risk stratification, prior authorizations, medical-necessity documentation and administrative appeals.

Solving healthcare’s most expensive grunt work

Ultimately, Newton believes that reducing the immense administrative drag built into the American healthcare system is crucial.

“Companies like xCures really reduce the administrative burden and represent the fastest path to realizing value in healthcare for everybody who’s involved in it,” Newton said. “This idea that we can use AI not to do things that doctors should do, but just to make it all better, easier, faster, cheaper and better for everybody involved … there’s just a lot of, like, grunt work that you should do that’s really expensive, and so that’s probably the most immediate opportunity.”

Stu Posluns, partner at Innovius, wrote via email that his firm backed xCures because it was impressed with its ability to “locate, extract, and normalize messy data across thousands of incompatible sources.” By applying real clinical context to surface exactly what matters, the investor noted that Mika Newton and his team are successfully “building the foundational AI data layer that will power the entire healthcare industry.”

Related Crunchbase query:

• Global Funding To AI Health Tech Companies In 2026

Related reading:

• Crunchbase Sector Snapshot: Funding To AI-Related Healthcare Startups Is Robust This Year

Illustration: Dom Guzman