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C8 Health started with an AI that gives anesthesiologists guidance on demand — now it’s targeting whole hospitals

Medicine is one of the most highly regulated fields in the world, and for good reason — the difference between doing a process correctly and incorrectly can often be that of life or death.But think of the many people involved in providing care at hospitals: it’s not just doctors and nurses, but also the entire medical support staff who handle patient records, equipment, and dispose of medical waste. They all need to be following the rules and best practices to ensure the hospital remains a safe and healthy place to work, administer, and receive care. Yet each job duty and department has its own list of guidance and best practices to follow — often siloed away in different applications like SharePoint, SmartVault, Docuware or others. For New York City AI startup C8 Health, that disconnect isn’t just an inconvenience — it’s a $345 billion problem.“When I began practicing medicine, I was shocked by how hard it was to access the information I needed,” said Dr. Ido Zamberg, an anesthesiologist and C8’s Chief Medical Officer, in a recent video call interview with VentureBeat. “In a field so knowledge-intensive, it felt absurd to have to search across 10 or 15 different systems just to find an answer.”

Medicine is one of the most highly regulated fields in the world, and for good reason — the difference between doing a process correctly and incorrectly can often be that of life or death.

But think of the many people involved in providing care at hospitals: it’s not just doctors and nurses, but also the entire medical support staff who handle patient records, equipment, and dispose of medical waste. They all need to be following the rules and best practices to ensure the hospital remains a safe and healthy place to work, administer, and receive care. Yet each job duty and department has its own list of guidance and best practices to follow — often siloed away in different applications like SharePoint, SmartVault, Docuware or others.

For New York City AI startup C8 Health, that disconnect isn’t just an inconvenience — it’s a $345 billion problem.

“When I began practicing medicine, I was shocked by how hard it was to access the information I needed,” said Dr. Ido Zamberg, an anesthesiologist and C8’s Chief Medical Officer, in a recent video call interview with VentureBeat. “In a field so knowledge-intensive, it felt absurd to have to search across 10 or 15 different systems just to find an answer.”


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Now, the New York-based startup that initially focused on providing anesthesiologists with knowledge to put people under is betting that its AI-powered chatbot and knowledge platform, built to bring structure and immediacy to clinical best practices, can solve it.

“Our platform ensures knowledge is always accessible — whether on mobile, desktop, or within the Electronic Medical Records (EMR) — so clinicians don’t waste time hunting across 20 systems,” said CEO and co-founder Galia Rosen Schwarz in the same interview.

Backed by a fresh $12 million Series A round led by Team8, with 10D and Vertex Ventures also participating, the company plans to scale up deployments and broaden its reach across the U.S. healthcare system.

Funding to expand

The newly announced funding brings C8 Health’s total raised to $18 million.

According to Rosen Schwarz, the capital will go toward expanding the team, refining the product, and meeting the growing demand among hospital systems seeking a better way to deliver on their own standards of care.

Founded in 2022, the company is focused squarely on one of the most persistent challenges in healthcare: ensuring that evidence-based best practices actually make it into the hands of those delivering care — regardless of whether they’re on night shift, rotating in from another facility, or just starting their residency.

“In many hospitals, protocols are still printed out and taped to the walls,” said Zamberg. “Stakeholders know no one has time to dig through software to find them. It’s that rudimentary.”

Zamberg, a trained physician and former software engineer, built the first version of C8 Health as a workaround to this challenge in his own department. But he and the company’s other leadership saw the problem went far beyond the anesthesiology department.

As Rosen Schwarz put it: “I interviewed over 100 health professionals in the U.S. and Switzerland, and it became clear how massive this problem was — the impact on both providers and patients was undeniable.”

Little surprise, then C8’s application quickly spread, first to 13,000 employees across five hospitals in Switzerland, and then on to more than 100 hospitals across the U.S., with clients including Dartmouth Health, Mount Sinai, MetroHealth, and the University of Texas Medical Branch.

A Red Panda avatar provides suggestions without prompting

C8 Health’s platform aims to centralize every piece of clinical guidance—policies, protocols, guidelines, educational materials—and make it instantly accessible in a format tailored to the clinician’s role, department, and even daily schedule.

The system is available via mobile, desktop, and directly within EMRs, allowing hospital staff to engage with it in the flow of their work. A friendly red panda avatar serves up the knowledge from the organization’s own siloed databases, complete with citations to the underlying knowledge sources, files, and documents.

The system doesn’t just wait for users to query. Based on behavioral patterns, schedule data, and institutional context, it can proactively surface relevant protocols before a scheduled procedure, or deliver targeted quality reminders if an individual’s performance metrics are slipping.

“We don’t just let users search,” Rosen Schwartz explained. “We proactively push the right content to the right person, at the right time—based on their role, behavior, and what others like them are doing.”

Early impact earns rave reviews

In each deployment, the company reports clinician adoption rates above 90% within three to six months—a notable achievement in a sector where new software tools often struggle to gain traction.

At MetroHealth, Dr. Luis Tollinche, Chair of Anesthesiology, described the state of affairs before C8 as a mess of six different protocol locations—email threads, shared drives, and policy databases among them.

“We had protocols scattered across six different locations—emails, shared drives, policy databases, even cognitive aids in the EHR,” he wrote in a quote provided to VentureBeat by C8. “When clinicians needed guidance, they often couldn’t remember where to find it, or simply gave up trying. We needed a single, reliable source that made our best practices instantly accessible at the point of care.”

After deployment, over 750 knowledge items were centralized, daily engagement hit 3.49 views per user, and nearly 90% of that activity came from mobile devices.

Real-time feedback and performance tracking

One of C8’s defining features is its ability to integrate performance data into the same experience clinicians use to access knowledge.

Through dashboards and metrics tied to specific procedures, users can see how their performance stacks up against department goals—and receive guidance to help improve it.

“A clinician can see tomorrow’s cases, who they’re working with, the relevant best practices, their quality performance, and how to improve—all in one interface,” Zamberg said.

Dr. Brian Masel, Chief of Pediatric Anesthesiology at UTMB, noted that this kind of real-time, individualized feedback could shift how hospitals approach quality improvement.

Instead of relying on retrospective administrative reports, providers can now engage with their own performance data in the moment, with clear recommendations on how to improve.

Built to handle healthcare’s unique complexities

Unlike generic enterprise software, C8 was built specifically for the fragmented and time-sensitive environment of modern healthcare. The platform’s backend uses general-purpose LLMs, but all data ingestion, cleaning, structuring, and formatting is proprietary—designed to make clinical knowledge easy to access, trustworthy, and relevant.

“Our assistant distinguishes between a policy, a protocol, educational material, or a national guideline,” said Zamberg. “It’s tailored to each user’s role, training level, and department.”

Rosen Schwarz also emphasized the breadth and flexibility of the platform: “We built proprietary systems to ingest and structure any type of content—PDFs, videos, scanned docs—so clinicians can get clear, actionable answers without information overload.”

Growth and system integration

With the new funding, C8 plans to deepen its relationships within existing hospital clients and expand to broader system-level deployments.

Dartmouth Health, for example, is already using the platform to bridge best practices across its main campus and satellite locations.

While the company remains focused on hospitals for now, Rosen Schwarz says they see clear opportunities to extend the platform into outpatient and urgent care settings, where fragmented access to knowledge remains a significant barrier to consistent care delivery.

“Clinicians need knowledge instantly, often at 3 a.m. in the OR,” she said. “They don’t have time to wonder which app or drive might have the answer—that delay can lead to errors.”

Sarit Firon, Managing Partner at lead investor Team8, said the platform’s traction with clinicians is a sign that C8 is solving a real pain point. In her view, the company is well-positioned to become a foundational layer in how care quality is managed and improved.

As clinical workflows become more complex and staffing more fluid, C8 Health is betting that better access to institutional knowledge isn’t just helpful—it’s essential.

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Oil Posts First Monthly Loss Since April

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Namibia’s Ambition to Become Oil Hotspot Tested by Wildcatter

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Data Center Chip Giants Negotiate Political Moves, Tariffs, and Corporate Strategies

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Johnson Controls Brings Data Center Cooling into the “As-a-Service” Era

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Meta’s Dual-Track Data Center Strategy: Owning AI Campuses, Leasing Cloud, and Expanding Nationwide

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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