Reducing recycling decision friction through real-time guidance

RefCycle was a four-week discovery project I led as product discovery lead with a team of five designers, working in Figma and Miro. It started from a gap between intention and behavior: people wanted to recycle correctly but often did not, and the research showed the barrier was not motivation but uncertainty. Participants were consistently unsure about hard-to-recycle materials, tripped up by inconsistent local rules, and afraid of doing it wrong, and that hesitation pushed them toward the landfill default at the moment of disposal.

I designed and ran the interviews and surveys, synthesized the findings into problem statements, and facilitated prioritization using impact-versus-effort mapping. From there I defined the core MVP, built mid- and high-fidelity prototypes, and led the usability testing. The through-line I cared about was that research drove the product decisions rather than sitting beside them as standalone insight.

Those insights reframed the product. What had looked like it might be an educational sustainability app became a real-time decision-support tool, because users wanted an answer at the moment of decision, not a lesson afterward. The MVP scoped to fast, contextual guidance: a scanner to classify materials quickly, a searchable disposal reference, a localized center locator for non-standard items, and upcycling suggestions for when recycling was not viable. Getting there meant deferring social and community features that had weak validation, cutting onboarding friction to reduce drop-off, and scoping to high-frequency scenarios for the most immediate impact.

This discovery-to-prioritization loop is the same one I now run in enterprise SaaS, and lately on my own AI tools, where evaluating the system's judgment is part of the product work.