TechPoutine: The Ten-Year Overnight Success Behind Eli Health

Summary

Recorded live at Startupfest, this episode follows Eli Health’s journey from a hormone-data problem to a full-stack health technology company. Marina Pavlović Rivas describes how the team moved from looking for off-the-shelf or academic technology to building its own lab, manufacturing, and intellectual property internally after finding no existing solution that met scientific and product requirements. She explains that Eli’s saliva-based test returns hormone results to a phone in minutes, in contrast to conventional testing that can take months, and that the company validated the approach through independent academic partnerships such as McGill. The conversation also covers the difficulty of fundraising in FemTech and hard tech, the importance of a pragmatic market-driven pitch, and the company’s strong early traction in the U.S. Marina closes with a founder mindset centered on patience, resilience, and building a future that does not yet exist.

Notes

• Eli Health measures hormones instantly from saliva.
• Results are delivered to a phone in minutes.
• The original use case was contraception.
• The product expanded from female health to broader hormonal monitoring.
• Conventional hormone testing can take months because of requisition, clinic, and lab delays.
• Hormones can vary within the same day, making delayed lab testing less useful.
• The team first tried to avoid rebuilding what already existed.
• No market solution met Eli’s requirements, so the team turned to academia.
• The company ultimately built the stack internally from scratch.
• Eli created its own lab.
• Eli also brought manufacturing in-house.
• Independent university partnerships were used to validate the technology.
• McGill helped demonstrate lab-equivalent performance.
• The company claims gold-standard laboratory-level precision.
• The company raised a large seed and a $17 million Series A.
• FemTech faces the same capital intensity as hardware and biotech.
• The investor base in FemTech is still mostly male.
• The fundraising pitch emphasized market size and proof points.
• Eli spans femtech, hardware, life science, manufacturing, and DTC.
• The company’s hybrid model made investor fit fragmented.
• Marina frames execution as patience plus resilience.
• The company’s operating rule is to move fast but diligently.
• Commercialization began in October, less than a year before the interview.
• The company has already generated several million dollars in revenue.
• About 90% of revenue comes from the United States.
• The team intentionally recruited people who can stare into the abyss and stay optimistic.
• The company needs faith in solutions even with partial information.
• They built parallel fallback technical paths.
• A small internal lab supported plan B development.
• Part-time PhD researchers handled literature reviews on alternative approaches.
• The team modeled costs for office and lab buildout in parallel.
• The pivot was presented to investors together with the implementation plan.
• Investors reacted very positively to the pivot.
• Full IP control and internal expertise were key benefits of the pivot.
• Marina says global ambitions matter more than being based in Montréal.
• The company has more than 10,000 users.
• The company has collected more than 44 million data points.
• The cortisol test launched as a stress hormone offering.
• Users report wellness, metabolic, and mental-health benefits from hormone visibility.
• The product has surfaced potential medical issues, not just wellness use cases.
• A CES award drove waitlist growth before launch.
• The waitlist exceeded 10,000 people.
• Sales doubled during CES week once the product was already on the market.
• The company already had U.S. investor exposure in pre-seed and seed.
• The U.S. market is a stronger fit for consumer health.
• Canadian customers are less accustomed to paying for health products.
• Canada strategy shifted toward employers, while the U.S. remained direct-to-consumer.
• The roadmap includes more hormones and eventually other biomarkers.
• The internal infrastructure is designed to be extensible beyond the current assay.
• The company rejected a 23andMe-style data monetization model.
• Profitability had to come from the core product, not user data.