hema.to is building AI-powered diagnostic infrastructure for cytometry—a specialized area of laboratory medicine analyzing immune system data to detect blood cancers like leukemia and lymphoma. Unlike radiology or pathology where AI solutions are abundant, cytometry has remained largely untouched by the AI wave, creating both opportunity and isolation for the Munich-based company. In a recent episode of BUILDERS, we sat down with Karsten Miermans, CEO at hema.to GmbH, to discuss why they're deliberately keeping sales founder-led despite having paying customers, how South America became an unexpected beachhead market, and what it actually means to build infrastructure versus point solutions in healthcare.

Topics Discussed:

From consulting project to venture-backed company: recognizing scalability in hindsight
The workflow integration problem killing healthcare AI implementations
Infrastructure versus technology: why healthcare AI isn't just about the algorithm
Learning ideal customer profile after 18 months of being "all over the place"
Why South America's governance structure enables faster adoption than the US
Resisting the urge to hire sales before achieving true repeatability
The 10-year vision: shifting from "watch and wait" to "predict and prevent" in immune disease

GTM Lessons For B2B Founders:

Pattern matching fails when you're an outsider—budget 18+ months to find your beachhead: Karsten assumed every application of their diagnostic method was the same and spent a year and a half "blue eyed" (naively optimistic) before identifying their true ICP. The outsider advantage lets you reimagine workflows insiders can't, but you'll incorrectly assume transferability across use cases. Don't expect repeatability in year one when entering regulated, workflow-dependent markets.
Infrastructure requires multi-stakeholder orchestration—resource for enterprise complexity from day one: Karsten distinguishes technology (point solutions, single users) from infrastructure (shared resources requiring data exchange and workflow integration). In healthcare, this means integration into hospital systems, databases, and electronic health records across multiple stakeholders. "Every sale becomes enterprise sales" even for individual labs because of this infrastructure requirement. Founders building horizontal platforms should model sales cycles and resource requirements as enterprise from the start, regardless of deal size.
Your ICP is cognitively overloaded—they won't understand your category innovation: Doctors are "under so much pressure that they just don't have any cognitive capacity left" to philosophically evaluate why AI might be difficult to implement or how infrastructure differs from technology. They need problems solved within their existing mental models. Skip the category education. Frame everything as workflow enhancement, not innovation. Let sophistication emerge through implementation, not pitch decks.
Revenue doesn't equal repeatability—know when you're still in discovery mode: Despite having paying customers, Karsten explicitly states "we're not at product-market fit yet" because they're "discovering and learning things with every new laboratory hospital" around data privacy, integration, and AI deployment. The PMF signal isn't customer count or revenue—it's when the process becomes predictable, customers refer others, and you stop discovering new requirements. Hiring sales before this point scales complexity, not revenue.
Regulatory friction determines market sequencing, not just market size: US governance complexity turns every deal into heavy enterprise sales with "many stakeholders," while South America proved "much more willing to move with fewer processes," making them "just much faster to adopt innovative technology." This wasn't strategy—Karsten's CTO speaks Spanish through a personal connection. But the lesson transfers: for infrastructure plays in regulated markets, test adoption velocity in lower-governance environments first to build proof points, even if TAM looks smaller on paper.
In healthcare, marketing is clinical evidence—customer success creates your GTM flywheel: Karsten spends minimal time on marketing because beyond the first 5-10 users, doctors "want to see clinical evidence, they want to see papers, they want to see maybe that a friend of theirs is using it." Marketing in healthcare isn't content or demand gen—it's peer validation and published proof. Founders should structure early customer engagements to generate this evidence, not just revenue. The "marketing sales flywheel really does kick in much more once you have product market fit" because PMF enables the evidence generation required for credibility.

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