Overview
Platemate is a multi-functional restaurant discovery platform for mobile and desktop. Finding somewhere to eat is surprisingly hard. Between outdated review sites, platforms that don't cater to dietary needs, and the sheer volume of options, young adults were spending more time deciding than actually dining.
This was a university team project. My role covered research, interaction design, and UI across the full process from initial brief to final prototype.
The problem
Three core challenges came out of early research: finding a restaurant takes too long, existing platforms have inaccurate or outdated information, and none of them adequately account for dietary preferences and allergies. It affects a significant number of users but gets treated as an afterthought.
We spoke to 8 participants in semi-structured interviews, ran 3 focus groups, collected 26 questionnaire responses, and conducted 8 contextual observations to understand how people actually make dining decisions.
Design process
We used Crazy 8s, reverse thinking, and a decision matrix to narrow three initial concepts down to one. From there we moved through low-fidelity sketches, wireframes, and mid-fidelity Figma mockups, testing at each stage with both users and experts using think-aloud protocols and cognitive walkthroughs.
Each round of testing surfaced something worth fixing: visual hierarchy issues, confusing icon choices, a profile page that wasn't doing enough. We iterated on each one before moving forward.
Solution
The final platform has four main sections: Home, Search, Explore, and Profile. The Search page has extensive filtering covering cuisine, price, distance, dietary requirements, and allergy flags built directly into the profile so they apply automatically.
The Explore Friends page lets users see where people they follow have been, adding a social layer that makes recommendations feel more personal than an algorithm.
Results
The final prototype scored 86 on the System Usability Scale, sitting in the "excellent" range. User feedback highlighted the filtering depth and the social features as the most useful parts. The allergy and dietary filtering in particular landed well with participants who said existing platforms had never properly accounted for their needs.