Launching FLUXX: An AI-Powered Menopause Platform

The impact

MVP live in under 6 months. 47% engagement. 800+ quiz completions. 38% reduction in quiz abandonment. Organic search nearly tripled month over month.

Those engagement numbers sit above what is typical for early healthtech, where most education apps struggle to get past a single session. The reason is not the AI. It is that the product earned enough trust for people to come back to a topic most of them had been dismissed about for years. FLUXX went from clinical research with no delivery system to a working product with 120 active users, 30+ symptom education pages, and a phase-based AI scoring engine that a 20-member Clinician Advisory Board had actually reviewed.

The hard part wasn't the AI

When I joined, FLUXX had a founder with deep clinical expertise and a strong mission, but no product infrastructure. Ideas lived in conversations. There was no backlog, no delivery cadence, and no shared definition of "MVP."

The real constraint was sharper than "no process." Menopause education sits one wrong sentence away from medical advice. The product had to be clinically credible and emotionally safe and stay legally on the education side of a line that a diagnosis would cross. A generalist plan that optimized for shipping features fast would have shipped the company straight into that line. The work was to build a system that moved quickly because it had guardrails, not despite them.

So I started by narrowing, not building. I defined three parallel bets, a phase-aware quiz engine, clinician enablement, and monetization experiments, and put them on a now / next / later roadmap. That single framing stopped the team from chasing every good idea at once and made it obvious what we were deliberately not doing yet. We moved task management into Linear and replaced ad hoc, founder-driven decisions with weekly async rituals the team could run without me in the room.

The one structure that did three jobs

The highest-leverage decision was how to handle clinical trust. The obvious move in healthtech is a logo wall of advisors. I built the opposite: a 20-member Clinician Advisory Board, spanning OB-GYNs, nurse practitioners, urologists, naturopaths, and integrative MDs across the US and UK, designed so one structure carried three loads at once. It was the credibility signal, the live feedback loop on real content, and the validation mechanism for the AI.

That let me solve the scoring problem honestly. Designing recommendation logic across 150+ symptom types and multiple hormonal phases is not hard to build; it is hard to make safe. I built prompt-based scoring with the founder and OB-GYNs, then created a 5-step clinical validation process using GPT and NotebookLM to simulate advisory-board review on quiz outputs before any human clinician spent time on them. The simulation caught the obvious failures cheaply so the real clinicians could focus on the subtle ones. Every recommendation routed to a "What to Ask Your Doctor" prompt, which kept the product firmly in education and out of diagnosis by design, not by disclaimer.

Trust is a retention mechanism, not a feature

Menopause is personal, variable, and routinely mishandled in digital health. So the UX had to do emotional work, not just functional work. I designed trauma-aware language systems and mobile-first flows that lowered the cost of a first, vulnerable interaction, and built re-engagement on intent-based CTAs rather than guilt-driven notifications.

That distinction is why the 47% engagement number matters. It did not come from nagging people back. It came from giving them a reason to return, which is the only kind of engagement that compounds in a sensitive health product. The Clinician Advisory Board was the same bet in another form: a real feedback loop instead of a credibility prop.

What this built

• A delivery system the team can run without me. Sprint rituals, a structured backlog, and cross-functional patterns that replaced founder-driven decisions with repeatable delivery.
• A clinically validated AI scoring engine. Phase-based recommendations with traceable logic that clinicians can audit and patients can trust, kept on the right side of the education line by design.
• A monetization roadmap grounded in real data. 50+ ideas across 7 revenue categories, sequenced by phase-based kits and real willingness to pay rather than guesswork.

Sources

• Janna Bastow, "Now-Next-Later Roadmaps" via ProdPad -- roadmap framing adapted to sequence FLUXX's three parallel bets