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Strategic Communications Advisory For Visionary Founders
Actionable
Takeaways
Run parallel vertical tests with focus constraints, not sequential exploration:
Ron identified 10+ potential verticals but intentionally tested exactly five simultaneously—hospices, funeral homes, employers, and two others before life insurance emerged as the winner at position five. This parallel testing with artificial constraints forces prioritization while dramatically compressing time-to-insight. Sequential testing would have meant potentially cycling through five failed pilots before discovering their strongest market. B2B founders with horizontal platforms should pick their top 3-5 verticals and run focused pilots in parallel, accepting that this burns more resources upfront but eliminates the risk of quitting before finding your wedge.
Map the ecosystem overlap between buyer personas before choosing your wedge:
Empathy's expansion from life insurance to employers wasn't growth strategy—it was recognizing an architectural reality. Half their carriers sell group life, meaning MetLife doesn't sell to consumers at metlife.com but exclusively to employer groups. When Amanda at Paramount loses her sister (not covered by insurance), she calls Paramount HR. When her husband dies (covered by MetLife group policy), the beneficiary calls MetLife. Same end user, two different enterprise entry points into the same moment. B2B founders should map these triangular relationships before choosing their wedge vertical. The question isn't just "who has budget?" but "who else touches this user in adjacent contexts?"
Brand investment at seed stage is product strategy when fighting cognitive aversion:
Ron's insight: "The barrier to entry isn't regulatory and isn't technology. It's us humans trying really hard not to think about our own mortality." This isn't a marketing problem—it's a fundamental go-to-market blocker. The company made what most would consider Series A investments (premium domain, design system, tone/voice framework) at seed stage specifically because brand reduces psychological friction to adoption. Contrast this with Monday.com starting as "daPulse" and rebranding years into success. B2B founders addressing taboo topics (death, mental health, financial distress, relationship issues) should model brand as a core distribution lever, not post-PMF polish.
In deeply human categories, buyer's lived experience is your demo:
Enterprise buyers at Citibank, MetLife, or Google aren't experiencing crisis during the sales cycle—they're evaluating ROI in their normal workday. But as Ron noted, "Everyone we're talking to...they're humans. They have parents, they had loss, they went through probate." The most common response after seeing the product: "Damn, I wish you called me a few months ago. I needed this a year ago with my mom." This turns product demo into personal recognition. B2B founders in universal human experience categories (caregiving, bereavement, parental leave, financial stress) should structure discovery and demo to activate buyer's memory of their own experience, not just their budget authority.
Category creation is a resource-attraction strategy that trades speed for competitive exposure:
Ron explicitly acknowledged: "There's pros and cons to defining a category. It's helpful when you attract resources, talent, capital. It also creates very fertile ground for a number two sympathy.com to come along and learn from this podcast...what to go after." Category leadership accelerates recruiting and fundraising by providing narrative clarity, but it simultaneously publishes your playbook. Every hiring blog post, podcast appearance, and positioning document teaches future competitors which verticals to target and which to avoid. B2B founders should treat category creation as a conscious bet: trade competitive opacity for talent/capital velocity. If you're not ready to defend your position, stay in stealth longer.
Bridge new categories to existing budget lines through analogous benefits:
When entering new verticals beyond life insurance, Ron doesn't educate from zero. With employers, he positions bereavement care alongside caregiving solutions, fertility programs, and parental leave: "This is a life transition happening in my own intimate house. Just like a new baby. I have new duties now." This isn't metaphor—it's budget mapping. Bereavement care gets evaluated against existing family benefits spending, not created from scratch. B2B founders in new categories should identify which existing line item their solution logically extends, then structure ROI narratives around reallocation, not net-new budget creation.
Conversation
Highlights
Why This Healthcare AI Company Refuses to Scale Sales Despite Having Revenue
Karsten Miermans is breaking the conventional SaaS playbook. His AI diagnostics company, hema.to, has paying customers across Europe and South America. His CTO is closing deals. Revenue is coming in.
And he’s deliberately refusing to hire sales.
In a recent episode of BUILDERS, Karsten explained why hema.to is keeping go-to-market founder-led despite traction—and why the signal he’s waiting for has nothing to do with customer count.
From Consulting Project to Scalable Infrastructure
hema.to didn’t start with a grand vision. A large laboratory approached them to build algorithms for cytometry analysis—a specialized diagnostic method for analyzing immune system data to detect blood cancers like leukemia and lymphoma.
“Initially there was no aha,” Karsten admits. “Initially there was just a project which we sold and we forgot about it.”
The pivot came from constraint. When the company struggled, they revisited that forgotten project: “We thought, hey, wait, this is perfectly scalable. Like, every hospital needs this technology. No one’s doing it.”
They raised angel funding, entered a Munich incubator, and repositioned as venture-backed infrastructure. But turning a consulting engagement into repeatable product proved far more difficult than expected.
The Eighteen-Month ICP Discovery Problem
As outsiders to healthcare, Karsten and his team made a fatal assumption: if their AI worked for one cytometry application, it would work for all of them.
“We originally were kind of very blue eyed,” he says, using the German expression for naively optimistic. “So we thought every application of this certain diagnostic method was kind of the same, which it wasn’t.”
The result: “For one and a half years we were kind of way all over the place. I think really only after maybe two years, maybe actually even a bit later that we realized really our ideal customer profile.”
He calls the timeline “absurdly late.” But this extended discovery phase revealed something critical about why healthcare AI projects fail at scale.
Why Every Healthcare AI Sale Becomes Enterprise Sales
Karsten draws a sharp distinction between technology and infrastructure that most founders miss.
“Technology you could describe as sort of a point solution, right?” he explains. “But infrastructure is really shared technology, right? Where users have to communicate or exchange information, exchange data.”
In healthcare, this infrastructure requirement kicks in immediately. Clinicians operate within rigid workflows shaped by compliance, reimbursement incentives, and multi-stakeholder coordination across hospital systems, databases, and electronic health records.
“It very quickly kind of transforms from kind of this point solution with one user to this like multi-stakeholder, multi-user, like infrastructure,” Karsten says.
The practical implication: “Every sale becomes enterprise sales,” regardless of initial deal size. Each laboratory requires integration work, stakeholder alignment, and governance navigation that mirrors enterprise complexity.
Founders building in healthcare should resource for enterprise sales cycles from day one—even when selling to individual facilities.
The Customer Cognition Problem Nobody Talks About
Here’s where Karsten’s insight gets particularly sharp. When asked if doctors understand the infrastructure versus technology distinction, his answer challenges assumptions about customer education:
“I don’t think that doctors think about it that way, at least not in my perception. My sense actually that they’re not thinking about it that philosophically. They have a job to do and they really just need that job done quickly, on time, on budget, safely.”
Then the critical line: “My sense, they don’t kind of have the mental capacity, so to say, to kind of realize why AI may or may not be difficult for them.”
This isn’t condescension—it’s recognition that end users operating under extreme pressure lack bandwidth to evaluate category innovation. They need solutions that fit existing mental models, not education about why implementation is complex.
Category creation through customer education doesn’t work when your buyer is cognitively overloaded. Frame everything as workflow enhancement within their current paradigm.
Geographic Arbitrage Through Regulatory Friction
Market sequencing for hema.to came down to governance complexity, not market size.
“The governance is so complex in the US. You have many stakeholders. So every sale becomes enterprise sales in the US,” Karsten explains. “Whereas in South America, it does appear like they’re much more willing to move with kind of fewer of these processes, which makes them just much faster to kind of adopt innovative technology.”
South America’s emergence as a beachhead wasn’t purely strategic. “My co-founder and CTO, his girlfriend is from Colombia and he speaks Spanish fluently. So he’s right there, right this second, he’s traveling around South America and he’s our best salesperson right now.”
Personal connection opened the door. But regulatory structure made it scalable.
For infrastructure companies in regulated industries: test adoption velocity in lower-governance markets first, even if TAM appears smaller. Build proof points where friction is lowest.
The Real Product-Market Fit Signal
Which brings us to why Karsten won’t scale sales.
“I only want to grow my sales team once we’re just really there and like, it’s just a repeatable machine. We’re not at product market fit yet.”
The distinction he draws between having customers and having PMF is precise: “Right now we’re just discovering and learning things with every new laboratory hospital that we speak to, whether it’s data privacy or integration or AI. We’re kind of always discovering something new, which I think definitely means that we’re not at product market fit yet.”
Real repeatability emerges when three things happen:
- You know what the process will look like before starting
- Customers make referrals without prompting
- You stop discovering new requirements with each deal
Revenue and customer count don’t signal PMF in complex infrastructure plays. Predictability does.
Marketing as Peer-Validated Clinical Evidence
hema.to operates in cytometry, where AI solutions remain sparse. Unlike radiology or pathology—”where there’s like really a plethora of solutions”—their customers are “kind of looking for a solution to a problem they know exists. And all their colleagues have fancy tools.”
Yet Karsten spends minimal time on marketing. “Very little. My investors tell me I should think about it more, to be honest.”
His reasoning: healthcare buying behavior runs on peer validation, not marketing content.
“Going beyond six customers or eight customers to 50 or 100, you definitely need that marketing. And in healthcare, there really is evidence. Right. It’s not hype. It’s also very strongly, I think just marketing is evidence, clinical evidence, proof.”
Doctors “want to see clinical evidence, they want to see papers, they want to see maybe that a friend of theirs is using it.”
This flips the typical demand generation model. Early customer engagements must be structured to generate publishable proof and peer validation—not just revenue. Clinical evidence becomes your distribution engine.
The Kingmaker Strategy
Rather than going lab-by-lab, Karsten is pursuing a different scaling path: “Right now I’m talking to a few of these [industry players], which I’m incredibly excited about to get the endorsement, again, the scale also to not have to go from lab to lab, but really have like a big chunk of the market.”
These “kingmaker” partnerships—with laboratory networks and device manufacturers—could unlock “hospital networks or laboratory chains. Right. And these are very large organizations with really like 100 laboratories in one organization.”
But this enterprise channel strategy only becomes viable after nailing single-lab PMF: “Once we cracked basically the nut, right, when we really have product market fit in a lab or hospital, then we can really go to a hospital network.”
The sequencing matters: prove repeatability at the individual facility level, then leverage that into network-level partnerships.
The Predict and Prevent Vision
Karsten’s ten-year vision reframes the entire diagnostic paradigm: “from watch and wait to predict and prevent.”
“The big vision is that each and every one of us has a much closer relationship to our immune system and know what’s going on and can react to our immune system very quickly,” he explains. “A growing disease is monitored and discovered before it’s sort of too late.”
Today’s blood tests serve diagnostic purposes—confirming disease after symptoms appear. The future involves continuous immune monitoring through blood, saliva, urine, and stool samples, enabling intervention before disease manifests.
“If I’m being very bullish, I could say I want to make your blood speak. All the information, all the answers in your blood, we want to get them out.”
For now, that means staying disciplined about the foundational work: achieving true repeatability before scaling the go-to-market machine required to reach hospital networks with 100+ laboratories each.
It’s a patient approach in an industry obsessed with growth velocity. But for infrastructure plays navigating regulatory complexity, Karsten’s refusal to scale prematurely may prove the fastest path to sustainable enterprise penetration.
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