2023-2026
Daybreaker Health
I built a diagnostics-driven longevity system that turns fragmented health data into clear protocols, coaching workflows, and measurable retesting.
Built the product logic, diagnostic workflow, and service model.
Bloodwork, genetics, lifestyle data, protocols, and coaching.
Personalized protocols connect to structured review and follow-through.
What Was Built
Daybreaker Health: a diagnostics-driven longevity company that turns bloodwork, genetics, health history, lifestyle data, functional diagnostics, goals, and constraints into personalized protocols, coaching workflows, and measurable retesting loops.
The product includes a member journey, diagnostic workflow, biomarker database, protocol modules, coaching operations, retesting loops, AI-assisted review flows, and internal systems for onboarding, documentation, content, and member communication.
My Role
I founded the company and led the product and operating-system build. That included member journey design, diagnostic workflow, data structure, protocol logic, AI-assisted analysis, practitioner review flows, internal documentation, and the service model behind delivery.
The hard part was not making health data look polished. It was turning complex inputs into a plan a member could understand, trust, execute, and measure over time.
How We Measured Success
Health products fail in ways that dashboards hide, so the bars were about execution and safety rather than engagement numbers:
- Build it zero to one, front end and back end. I coded the MVP myself, which kept the product honest about what could actually be delivered rather than what demoed well.
- Handle PHI correctly from day one. HIPAA and PHI compliance were not a later phase. Getting the handling, storage, and access model right was a precondition for operating at all, not a feature.
- The protocol had to be executable, not just correct. A scientifically defensible plan a member cannot follow is a failed plan.
- Retesting had to close the loop. Biomarkers had to move measurably on follow-up diagnostics, or the protocol was unproven regardless of how good it looked on paper.
Daybreaker turned a broad longevity concept into a repeatable service model: intake, assessment, protocol design, coaching, retesting, and long-term maintenance.
The measurable layer is biomarker-driven: blood work and supporting diagnostics are used to calculate biological-age estimates and track whether protocols are moving the right markers over time.
Why It Mattered
Healthcare products have to earn trust twice. They need technical credibility, and they need operational reliability. If the dashboard is beautiful but the follow-up is inconsistent, the experience fails. If the protocol is scientifically interesting but impossible to follow, the outcome fails.
The product matters because personalized health data is only valuable when it becomes a clear next action.
What This Proves
I can build clinical-adjacent health systems that combine data, human judgment, AI assistance, service operations, and behavior change without letting the product drift into vague wellness theater.