Case study / AI product innovation
Tonguesten: an AI fitness concept built in a weekend.
A rapid research and prototyping sprint for experienced gym-goers, developed with a cross-functional team at Hack in Miami 2024.
Help experienced gym-goers optimize workouts with personalized, real-time guidance
UX design lead for research synthesis, problem definition, user flows, and prototyping
Engineers, UX designers, product leaders, and branding specialists
Weekend hackathon from initial research and concept selection through pitch
User interviews, synthesis, ideation, low-fidelity wireframes, user flows, and mid-fidelity prototyping
Second place at Hack in Miami 2024
01 / Evidence to concept
Research narrowed the opportunity.
We interviewed experienced gym-goers to understand how they track performance and where current tools fall short. The research pointed to a need for personalized, data-driven recommendations delivered during the workout rather than after it.
I helped synthesize the findings into a clear problem statement: experienced gym-goers need a way to optimize workouts with personalized recommendations in real time. That direction gave the team a practical filter for deciding what to build within the hackathon constraint.
The concept became a testable experience.
I developed low-fidelity wireframes and user flows, then partnered with engineers and branding specialists to shape a mid-fidelity prototype that was technically feasible and coherent as a product.
Tonguesten was designed to combine workout tracking, real-time feedback, and AI-generated recommendations for exercises, sets, and repetitions based on performance and fatigue. The work remained a product concept and prototype, not a production AI system.
A fast process still needed a credible story.
We presented the user need, product direction, prototype, and roadmap to a panel of investors, founders, and technology leaders. The concept received second place.
Hackathon partners