Bunkerhill Health has raised $55 million to scale its agentic AI platform, Carebricks.
The closing of the company’s Series B round, announced today, folds in continued participation from Sequoia Capital, Felicis, Optum Ventures, and Y Combinator. However, a funding total doesn’t answer the key question any hospital executive wants to know about healthcare AI: does the software run inside a working hospital?
That question is among the reasons why Khosla Ventures put its name on this deal. Healthcare organisations have put no shortage of funding behind machine learning pilots that perform well in a research setting and then never touch a live patient chart.
Bunkerhill’s argument to investors, and to the health systems already paying for it, is that Carebricks closes the space between a model that works in a sandbox and one that runs against live clinical data at institutional scale.
The backdrop is a spending number and a staffing problem. US healthcare spending hit $5.3 trillion in 2024, according to the Centers for Medicare & Medicaid Services, and labour shortages continue to strain providers nationwide.
Bunkerhill frames the opportunity around a gap between what health systems want to do for patients and what their staff have time to execute. Decades of investment went into documentation systems meant to ease the burden on clinicians. Bunkerhill’s bet is that the next round of technology spending goes toward software that acts on ideas clinicians already have, rather than just recording them.
“Medicine has advanced faster than our healthcare system’s ability to operationalise it,” explained Nishith Khandwala, Co-Founder and CEO of Bunkerhill Health. “Every leading health system has more opportunities to improve patient outcomes than its workforce has capacity to address. We believe AI agents can help them turn more of those ideas into reality.”
Carebricks lets hospitals build their own agents rather than buying a fixed product off the shelf. Some agents review cardiology imaging for early signs of heart disease and flag patients who need follow-up care. Others handle prior authorisations or keep registry data current, and the range extends into administrative work that rarely gets attention in AI pitches but consumes staff hours every week. Cleveland Clinic, the University of Texas Medical Branch, and Intermountain Health all run the platform today.
Vinod Khosla, Founder of Khosla Ventures, said: “The bottleneck in healthcare AI was never the technology, it was getting a health system to actually run it. Bunkerhill closed that gap. They made it much, much easier to adopt AI and already have traction inside critical health systems that would take most companies years to earn.”
Twenty AI agents running inside one hospital system
UTMB offers the clearest picture of what “running it” looks like once the pilot label comes off. The system now has more than 20 agents live on Carebricks, spanning clinical care, operations, and administration, according to Dr. Peter McCaffrey, UTMB’s Chief AI Officer.
In its first month running at UTMB, a coronary calcium detection agent built on an FDA-cleared algorithm flagged a patient as being at imminent risk of a heart attack. Cardiology confirmed the risk and performed a triple bypass.
UTMB’s care team credits the early detection with saving the patient’s life. It’s a single case, not a controlled trial, and Bunkerhill has published no data on how often the agent produces false positives or how it performs across a broader patient population over time.
Other figures at UTMB come from Bunkerhill and the health system itself rather than independent audit. A nephrology triage agent now prioritises patients by severity, escalating urgent cases and routing others to telemedicine, and UTMB reports this has cut average specialist wait times by more than 50 percent.
A lung nodule agent tracks incidental findings on CT scans through to the correct follow-up, with UTMB citing an 80 percent faster response on urgent cases and a doubling of guideline-concordant follow-up alongside a drop in manual coordinator work.
These are health system-reported operational results from live production use, not synthetic benchmark scores, which matters. It also means the numbers reflect one institution’s data conditions and staffing setup, not a guarantee that another hospital will see the same curve.
“We’ve already seen tremendous impact on patient care, and we’re only at the beginning of what becomes possible when a health system can operate with agentic AI at this scale,” McCaffrey said.
Of course, none of this removes the work health systems still have to do themselves. Bunkerhill says it will use the new funding to expand Carebricks into a wider range of clinical and operational use cases while building out governance, monitoring, and safeguards.
A platform that lets a nephrology department build its own triage agent also means that department owns the consequences of how that agent is tuned. Health system boards weighing a Carebricks-style deployment need answers on liability assignment, monitoring cadence, and what happens when an agent’s judgment and a clinician’s judgment disagree, before signing off on scale.
UTMB’s 20-agent footprint gives Bunkerhill a reference case few competitors can currently match. Whether that number holds up as a signal for the rest of the industry depends on how UTMB, and the other systems now running Carebricks, handle the governance side as the agent count climbs.
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