Founder: Catherine Del Vecchio Fitz, PhD, MSM — Founder & CEO
Location: Cumberland Center, ME (Greater Portland area)
Year founded: Conceptualized summer–fall 2025; incorporated as Savvyn, PBC in January 2026
Savvyn emerged from a tension I kept encountering: the gap between strong science and patients actually being able to access innovations.
For over 15 years, I worked in precision oncology and diagnostics—from translational research to clinical genomics to medical affairs—building platforms and helping bring new diagnostics from development to patient use. I saw the same pattern repeatedly: innovations with strong clinical evidence stalled on the path to reaching patients. The barrier wasn’t the science—it was fragmented evidence requirements, unclear expectations across stakeholders, and gaps discovered too late to address efficiently.
I was also navigating a different reality: three kids, leadership roles that demanded travel and long hours. The traditional startup playbook wasn’t going to work for me, and I had no interest in forcing it to.
When I left my last role, I was fortunate to have some space to experiment. Generative AI had reached an inflection point, and I started using it systematically—not just as a productivity tool, but as a way to fundamentally rethink how work gets structured.
That’s what led to Savvyn. It’s built on a belief that you can create serious impact without choosing between ambition and sustainability. That the right systems can multiply what one person—or a small team—can accomplish.
And perhaps most importantly, that if you’re building for healthcare innovation, patient access can’t be an afterthought. That’s why I structured Savvyn as a Public Benefit Corporation—a legal commitment that patient access and equitable evidence pathways remain core to the business model as the company grows and scales.
The earliest version of Savvyn was far less defined than what exists today.
It didn’t begin as a polished “evidence intelligence platform.” It began as a question: If I were building from scratch, with AI as a core operating layer, what would that actually look like?
In the beginning, Savvyn was broad. It focused on helping companies work smarter using AI—structuring complex data, automating workflows, generating reports, creating content more efficiently.
At that stage, Savvyn was more of an AI-enabled consulting concept than a defined product architecture. I knew the model had to be scalable, but I hadn’t yet figured out what that actually meant in practice.
Over time, through writing, reflection, and working through real problems, the focus narrowed. As a scientist, I’d always believed the path to impact was better science—more rigorous data, stronger clinical trials, clearer evidence. But what I kept encountering wasn’t a science problem. It was a coverage problem: who’s going to pay for this, and what evidence do they actually need to make that decision? That realization shifted everything.
The most meaningful opportunity wasn’t generic AI productivity. It was helping clinical, medical, and market access teams structure their evidence, understand what payers and regulators actually need, and move from clinical validation to patient access more predictably.
So the earliest version of Savvyn was intentionally iterative. It was AI-first in mindset before it was product-specific. That early experimentation shaped not just what I built, but how I approached building it.The earliest version of Savvyn was far less defined than what exists today.
So the earliest version of Savvyn was intentionally iterative. It was AI-first in mindset before it was product-specific. That early experimentation shaped not just what I built, but how I approached building it.
At its core, Savvyn is solving a translation problem.
In healthcare innovation—whether it’s diagnostics, medical devices, digital therapeutics, or new treatment approaches—scientific progress moves quickly. Data is generated. Studies are run. Performance is validated. Clinical benefit is demonstrated. But between scientific progress and real-world patient access sits a fragmented, often opaque system of evidence expectations—particularly around coverage and reimbursement.
Progress on the science side can be fast. Progress on the access side is not. For example, it can take 5+ years from clinical validation to Medicare coverage for diagnostics—and even longer for novel technologies without clear precedent. Strong clinical evidence doesn’t guarantee timely access. It doesn’t even guarantee consistent evaluation criteria across payers.
The challenge isn’t that different stakeholders want different things—it’s that critical information doesn’t flow clearly between them. Companies need revenue to keep innovating. Patients need access to new technologies but can’t pay thousands of dollars out of pocket. Doctors want to offer the best care without creating financial hardship for their patients. And insurers want to cover innovations that genuinely improve outcomes and justify the cost. Everyone actually wants the same thing: getting proven innovations to patients efficiently.
But the pathway isn’t clear. Companies are left guessing what evidence insurers need, when to generate it, and how to sequence it strategically. Evidence is scattered across different teams and formats. Gaps often aren’t discovered until it’s too late—after months of work or significant investment. What should be a structured, predictable process becomes reactive and uncertain.
Savvyn is built for healthcare innovation leaders navigating this complexity. We’re starting in oncology and diagnostics—where I have deep domain expertise and where evidence requirements are particularly complex—but the platform is designed for the broader innovation → evidence → access → impact lifecycle across healthcare.
The evidence-to-coverage pathway spans science, clinical development, regulatory strategy, reimbursement policy, health economics, and commercial execution. Each domain has its own tools and consultants. The silos are well-known, and people have been trying to bridge them for decades.
What’s been missing is a scalable way to formalize and structure the tacit knowledge that sits between those functions. Coverage decisions depend on understanding precedent, interpreting evolving policy language, and connecting clinical evidence to payer requirements in context. That expertise has historically lived in people’s heads—built over years of navigating specific therapeutic areas and payer relationships. It couldn’t easily be documented, shared, or scaled.
What’s changed is that AI can now synthesize unstructured information at scale. For the first time, it’s technically feasible to structure fragmented evidence, map it against policy precedent, and surface decision-relevant insights across the full pathway. The technology finally matches the complexity of the problem.
There’s also an economic forcing function. As evidence requirements have grown more complex—particularly around real-world data, health economics, and comparative effectiveness—the cost of getting it wrong has increased dramatically. A failed coverage submission or a multi-year delay in reimbursement can mean millions in lost revenue and thousands of patients without access. The traditional “figure it out as we go” approach is no longer economically viable, especially for early-stage companies operating on limited runway.
The companies facing this problem today also look different. Early-stage healthcare innovation companies are leaner and more capital-efficient. They often don’t have seasoned market access leadership in-house until later stages—they can’t afford to hire a VP of Market Access at Series A. But they still need to make strategic evidence decisions before Series B. That creates demand for intelligence infrastructure that simply didn’t exist when larger, better-resourced organizations dominated the space.
That’s the convergence Savvyn is building into: the right technology, at the right economic moment, for the right market structure.
Savvyn structures fragmented evidence so teams know what they have, what’s missing, and what to prioritize.
Healthcare innovation companies generate evidence across multiple functions: clinical studies, validation work, health economics analyses, policy monitoring, internal strategy documents. That evidence rarely lives in one place or speaks a common language. Teams can’t easily see the full picture, identify gaps that matter, or sequence decisions strategically.
Savvyn organizes that evidence and maps it against what stakeholders actually need across the innovation-to-impact pathway. We analyze precedent—how similar innovations succeeded or stalled. We interpret policy requirements across regulators, payers, and clinical guidelines. And we assess where your current evidence is strong and where gaps create risk.
That intelligence feeds three capabilities: structuring evidence into deliverables teams need (manuscripts, dossiers, investor materials), organizing data with full traceability across analytical and clinical validity frameworks, and visualizing readiness so leadership can make informed sequencing decisions.
The result: teams enter critical milestones—funding rounds, payer discussions, regulatory submissions—with evidence that’s organized, complete, and strategically sequenced.
Most alternatives specialize in one function. Regulatory tools manage submissions. Clinical platforms manage trials. Market access consultants produce strategy decks. Analytics systems store data.
Savvyn sits in the space between those functions—which is where the translation problem actually lives.
The differentiation isn’t simply that we use AI, it’s that we apply it to the connective logic that typically lives in people’s heads. We map evidence to precedent and stakeholder expectations so teams can see how decisions are likely to be interpreted before they become expensive.
We don’t focus on producing artifacts. We focus on sequencing decisions. Savvyn provides a persistent reasoning layer behind those documents—linking scientific evidence, precedent, and strategic timing into a single structured framework.
This changes how decisions get made. Instead of reacting to external feedback late in the process, teams can proactively sequence evidence generation, align internal stakeholders, and approach inflection points—like financing or commercialization—with a clear, defensible roadmap.
The intelligence isn’t generic either. It’s informed by 15+ years working across oncology and diagnostics—from bench science to clinical validation to medical affairs—including direct experience navigating evidence generation and payer strategy. That context allows the system to distinguish signal from noise: which precedents actually matter, which gaps are material, and which steps can wait.
Savvyn is currently in structured alpha development with early design partners. The target customer is early- to growth-stage healthcare innovation companies preparing for significant inflection points—particularly around financing, commercialization, and payer engagement. I’ve been working closely with leaders navigating the complexity of translating clinical evidence into real-world adoption and reimbursement.
We’re starting in oncology and diagnostics because that’s where my expertise runs deepest and where evidence complexity is highest, but the intelligence architecture we’re building applies across healthcare innovation more broadly.
The biggest insight: companies don’t primarily need more data or strategy. They need structure and execution.
Most organizations I’ve spoken with already have substantial evidence—clinical datasets, real-world studies, early health economics work. But it’s scattered. Evidence lives in different functions, often in formats that don’t talk to each other. What I initially assumed were “solved problems”—like knowing where all your clinical study data lives—are actually quite messy. And that’s understandable: these are early-stage companies that couldn’t justify building enterprise data systems from day one.
The real need surfaces at inflection points. A company is six months from their next funding round and needs to pull together a credible evidence story for investors—but the data isn’t organized, the narrative isn’t clear, and they don’t yet have senior leadership in place to own this. Or they’re entering payer conversations and realize their evidence isn’t structured the way those decisions actually get made.
In those moments, companies need more than strategic advice. They need someone to actually do the work: pull the evidence together, structure it properly, and create the deliverables that move them forward. The incentives align perfectly—there’s urgency, there’s a clear milestone, and there’s real value in getting it right.
I’ve also learned that relevance matters more than volume. Teams are already paying for tools that send alerts and updates, but most aren’t actionable. What they actually want to know is: What changed? Why does it matter for us specifically? What should we do about it?
And trust is non-negotiable. In evidence-intensive domains, outputs must feel authored, contextual, and explainable. Generic AI summaries don’t work. Structured reasoning and transparent logic matter more than speed alone.
In short: the problem isn’t lack of intelligence or lack of data. It’s lack of structure, lack of execution capacity, and lack of continuity across science and strategy—especially at the moments that matter most.
Early on, I was focused on AI productivity for life sciences. Ruthlessly, actually. I thought the opportunity was helping teams work faster across the board: structure data, automate reports, generate content more efficiently.
But that was too broad.
Through customer conversations, I realized companies didn’t need generic speed tools. The real pain points were practical: teams couldn’t see what evidence they had across functions, didn’t know what gaps would matter, and struggled to prioritize what to tackle when preparing for funding or commercialization.
That shifted everything. I went from “AI makes everything faster” to “AI does the assembly work so experts can focus on interpretation and strategy.”
For years, I commuted to Boston while raising three young kids. The work was meaningful, but I wanted to build something that was truly mine—where I could see all my experiences converge and create something that wouldn’t have existed otherwise.
I’m most proud that I’m actually building something on my own terms.
What feels different now is designing how the work actually gets done. And I’ve experienced firsthand what AI can do to amplify what one person can accomplish. I experimented extensively in those early months—applying AI to everything I could: writing, research, financial modeling, strategic planning. Some of it worked brilliantly. Some hit limits quickly. But that experimentation taught me where AI meaningfully extends capability versus where it just produces faster output.
Savvyn is no longer abstract. There’s a working demo. A defined thesis around evidence intelligence. A real platform that reflects how I believe complex work should be structured—and how life should work alongside it. Seeing that transition from concept to reality has been validating.
I’m also proud of the discipline behind it. Savvyn has evolved significantly over the past year—from a broad AI productivity concept to a focused evidence intelligence platform for healthcare innovation. That narrowing came from real customer conversations, deliberate iteration, and a willingness to refine the thesis rather than defend it prematurely.
I won’t pretend this hasn’t been hard. It would have been easier to take another executive role and regain immediate financial stability. And I’ll be honest: having a consistent salary again does matter. But what gives me confidence right now is that I’m building something that uses my expertise, solves a genuine problem, and is structured for the kind of life I want to lead.
Dirigo Labs provided structure at a critical inflection point—particularly around financial modeling, go-to-market sequencing, and pressure-testing core assumptions. It reinforced that disciplined iteration is part of building correctly, not a sign of uncertainty.
The program also connected me more deeply into Maine’s startup ecosystem. That community support has been invaluable—not just strategically, but in building confidence that this can be done from Maine.
The next 6–12 months are focused on disciplined execution: establishing structured beta partnerships, generating initial revenue, and refining the product around real customer workflows.
My immediate priority is validating the core system end-to-end—structuring clinical evidence, mapping it against real-world adoption requirements, and ensuring the outputs are actionable and trusted by domain experts. This work will happen directly with early partners, where real decisions drive product refinement, not hypothetical use cases.
In parallel, I’m bringing on targeted technical and scientific expertise to strengthen the underlying system and ensure it’s built on a durable foundation.
So many hats. Founder, product architect, finance lead, brand builder. Some days the breadth feels energizing. Other days it’s just overwhelming.
The hardest part is sequencing: knowing what needs attention now versus what can wait, and building the foundation correctly even when it would be faster to just ship something. I’m trying to build durable systems, not just execute tasks quickly. That means investing time upfront in architecture and structured thinking—even when the pressure is to move faster.
And I’d be lying if I said the bootstrap-versus-raise question wasn’t keeping me up at night. Bootstrapping longer might give me more leverage, but real infrastructure takes real investment—and the runway isn’t infinite.
Two things: rigorous, domain-informed pressure-testing and aligned capital.
I’m looking for market access and evidence generation operators who have navigated real coverage decisions and can challenge whether Savvyn’s structure reflects how adoption actually happens. That level of critique is essential at this stage.
I’m also seeking early-stage investment from partners who understand regulated healthcare markets and platform-scale companies. The priority is building the system correctly—with the depth and durability this space requires.
Access to technical resources, including cloud credits and development tools, would also meaningfully extend runway during this validation phase.
The Maine ecosystem has been unusually generous with time, mentorship, grant pathways, and in-kind support. That kind of high-trust environment is rare and makes early-stage company building possible.
I’m building Savvyn from Maine intentionally, and I’m committed to strengthening the ecosystem that’s made this possible. It’s early days for Savvyn, and I’m genuinely excited about where deep domain expertise, AI capability, and disciplined systems thinking can converge—especially in a space as consequential as healthcare innovation.
If you’re interested in what we’re building—or building something adjacent—let’s talk.
Best way for people to reach you: catherine@savvyn.ai