SPARKFIT
Aela School
Project Overview
As part of my Product Design Immersion Certification, the SparkFit AI project was a study case that aimed to exercise the discovery and framing methodologies. This exploration refined my approach to user research, product strategy, and the critical early stages of product development.
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Discovery Phase
The original challenge was around a fitness AI personal trainer app. During the discovery phase, in-depth user interviews were conducted to understand user needs, preferences, and pain points. This led me to pivot to a different problem: How might we help gym goers to make healthy food choices?
Full process available on Notion.
The Figma board below displays the key findings and conclusions from the discovery phase:
Framing Methodology
Based on the discovered pivot, the product framing was key to decide what would be further explored. An MLP was defined after framing. This process includes the development of key deliverables such as Value Proposition, Proto-persona, Jobs to be Done, Story Mapping and Prioritization Matrix.
See the detailed documentation on Figma.
UI Deepening
A prototype was created to bring the ideation into a visual layer. It was later tested using the Maze platform to validate the solution with real users and gather feedback for further iterations.
