AIRA Health Landed USD 2M and Brings AI Decision Infrastructure to Clinical Development

• AIRA Health is building Colligo to help clinical teams make faster, evidence-based development decisions
• The platform brings regulatory precedents, registries and other evidence into protocol design workflows
• The team envisions AI as infrastructure for changing how clinical development decisions are made
• The funding supports Colligo’s development and expansion across clinical development workflows

Clinical development has become one of the most data-intensive parts of healthcare, yet many of the decisions that shape clinical trials still depend on fragmented information and significant amounts of manual work. American-Hungarian AIRA Health is betting that AI can change that by bringing evidence, regulatory precedent, and expert judgment together in one decision-making environment.

This April, the company, led by CEO and founder Levente Fazekas, presented Colligo as its AI-powered platform for clinical development, following the USD 2M Pre-Seed round of investment from Interactive Venture Partners and Nesprit. Rather than positioning the product simply as another tool for generating clinical trial protocols, AIRA Health describes its broader ambition as building ‘decision infrastructure’ for modern clinical development.

This distinction is central to how Mr Fazekas approaches the company. In his view, the problem is not that clinical teams lack data. There is more scientific knowledge, regulatory information and historical precedent available than ever before. The problem is that bringing all of that information together at the moment a decision needs to be made remains fragmented and time-consuming.

For the founder, the motivation is also personal. His own experience of needing treatment that was not available at the time helped shape his interest in the wider clinical development system. As he moved deeper into healthcare and technology, the question became less about individual scientific breakthroughs and more about why the system around those breakthroughs moves so slowly.

From More Data to Better Decisions

Colligo was designed around this gap. The platform brings together regulatory precedents, clinical registries and other sources of evidence to help teams evaluate protocol options and make development decisions with more context.

The challenge, however, is not simply collecting more data. Historical clinical and regulatory information can be fragmented, poorly structured or contradictory, and an AI system can make those problems worse if it turns uncertain information into an apparently confident answer.

AIRA Health therefore emphasizes provenance, evidence, and transparency. When sources conflict, the objective is not to quietly select the most plausible answer, but to make the uncertainty visible to the people making the decision. For a regulated industry, that distinction is critical.

The same principle applies to bias. Clinical development already contains assumptions about patient populations, endpoints, geography, and standards of care, and AI could amplify those patterns if it simply learns from historical precedent. The AIRA Health team argues that AI should instead make such assumptions easier to identify and challenge. Colligo is intended to help teams ask not only whether a protocol is efficient, but who it includes, who it excludes, and which assumptions get carried forward.

‘Trial design and protocol development need to be improved from both the biotech and the investor perspectives. Protocol development is a critical but challenging process due to its complexity, rapid changes in the regulatory and treatment environment, and the constraints biotechs are facing. I find AIRA extremely valuable because it allows teams to access and evaluate design pathways and options, understand trade-offs, and develop the regulatory submission package more accurately and quickly. AIRA can make trial design and protocol development an interactive, accurate process rather than a slow, error-prone one,’ pediatric oncologist and investor Dr Ilonna Rimm explains.

Trust Before Automation

This approach also reflects one of the biggest challenges facing AI in clinical development — trust. Sponsors and clinicians are unlikely to adopt a system simply because it can generate a sophisticated recommendation. They need to understand where that recommendation came from, which evidence supports it, what assumptions were made, and where human judgment remains necessary.

For AIRA Health, this means that AI should not be presented as a replacement for clinical expertise. The more consequential the decision, the more important it becomes to make the reasoning around it visible.

This is particularly relevant as AI moves from administrative assistance toward decisions that can influence trial design. A technically impressive recommendation that cannot be explained or defended to a regulator will have limited practical value for a sponsor.

The company therefore sees regulation not simply as a constraint on AI adoption but as part of the product environment within which it needs to work. Rather than trying to predict precisely how regulators will treat AI several years from now, AIRA Health builds around principles such as evidence, provenance, traceability, and human oversight that are unlikely to become less important.

A Team That Made Investors Break Their Own Rules

The approach attracted investors who see the founding team as an important part of the company’s potential.

‘The main reason why we chose to invest in AIRA Health is definitely the team. We’re generally not keen on the industry and even had a policy against investing in medtech and healthtech, but we made an exception here because we genuinely believe the AIRA Health team is truly outstanding,’ Nesprit’s general partner Dániel Gockler tells ITKeyMedia.

This comment puts the team ahead of the technology itself. For an early-stage company operating at the intersection of AI, healthcare, and regulated clinical development, the ability to understand both the technical opportunity and the industry’s constraints may be as important as the underlying models.

This is also where AIRA Health sees a potential advantage over large pharmaceutical companies building their own AI systems. The company doesn’t consider proprietary data alone to be a sufficient moat in a market where foundation models and AI capabilities are evolving rapidly. Instead, the company’s defensibility is intended to come from the combination of domain expertise, data infrastructure, regulatory context, workflow integration and the feedback generated through working across clinical development. Large pharmaceutical companies can build internal systems, but AIRA Health is building infrastructure around the problem across organizations.

‘Potential clients all faced similar challenges, and they clearly liked the solution. That’s why we believe AIRA has an exciting future ahead. The company solves a real problem. They’re targeting a large market while reducing costs for clients and helping them generate revenue faster. If they do it right, they can grow quickly. I usually tell founders that before they climb the mountain, they should send drones to the top. That’s the research that needs to be done, and they’ve done it,” Interactive Venture Partners,’ managing partner Laszlo Czirjak adds.

What Happens When AI Moves Faster Than Regulation?

The relationship between AI and regulation will become increasingly important as platforms like Colligo move deeper into clinical development.

AIRA Health does not see being ahead of regulation as the objective. If a technically sophisticated protocol design cannot be explained or defended to a regulator, it is not necessarily useful to the sponsor.

At the same time, regulation will inevitably evolve as much as technology. Therefore, staying ahead means building adaptability into the architecture rather than reacting to every new regulatory development. Evidence, provenance, and human oversight need to be fundamental properties of the system, not features added after the fact.

That philosophy also shapes how the company measures progress. Revenue and customer growth remain important, but a more meaningful test will be whether Colligo can change the velocity and quality of clinical development: reducing the time required to design and evaluate protocols while allowing teams to consider more evidence and alternatives.

A less quantitative milestone is also in place: if clinical development professionals begin treating Colligo as part of their decision infrastructure rather than another software tool, that would indicate that AIRA Health successfully moved beyond demonstrating what AI can do and into changing how the industry actually works.

Toward a More Personalized Clinical Development Process

Looking further ahead, AIRA Health doesn’t expect AI to make clinical trials more standardized in every respect. Instead, it locates the possibility of standardized infrastructure supporting increasingly personalized study design.

Today, the amount of human time required to evaluate different populations, endpoints, and study structures limits how many alternatives a team can realistically consider. AI could change that economics, allowing development teams to explore far more scenarios before settling on a design.

That could also make sophisticated clinical-development capabilities more accessible. The long-term opportunity would be not for the best-funded pharmaceutical companies to accumulate an even larger technological advantage, but for AI infrastructure to reduce the cost of accessing high-quality clinical-development intelligence.

‘We are building AIRA with the mindset that a new technology only matters if it leads to improved safety alongside efficiency, not just the latter. I believe every clinical development team will be using platforms like ours within the next few years. The question is no longer whether AI will reshape clinical development, but how quickly teams will adopt it,’ Mr Fazekas concludes.

For AIRA Health, the ambition therefore extends beyond automating parts of today’s workflow. Colligo is an attempt to change the architecture around clinical decisions itself: bringing more evidence into the process, increasing the velocity at which alternatives can be evaluated, and giving experts more capacity to focus on the decisions that ultimately determine whether new treatments reach patients.