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ComplEAT

Dish by Dish

Dish level food discovery and personal food journal

UX Case Study/Mobile App Concept

Food Preparation Scene
Screenshot 2026-01-02 at 1.25.29 PM.png

Project Overview

ComplEAT is a mobile app concept designed to help frequent diners remember specific dishes they’ve tried, track personal notes, and easily rediscover meals they loved — without relying on public star ratings or restaurant-level reviews.
 

This project began as an UX exploration and later evolved into early concept work for a potential real-world product.
 

Role: UI/UX Designer (end-to-end)
Timeline: 1 week
Platform: Mobile (iOS-first)
Tools: Figma, Google Docs, remote usability testing

The Problem

Most food discovery apps prioritize restaurant-level ratings and public reviews, but frequent diners don’t think that way.

People remember:

“That spicy tonkotsu ramen”

“The poke bowl with the fresh ahi”
—not the overall restaurant rating.

Users struggle to:

Recall specific dishes they enjoyed

Remember personal preferences or modifications

Revisit meals they loved without scrolling endlessly through photos or reviews

Key Design Decision: Dish-First, Not Restaurant -First

Why this matters:

Matches how people actually remember food

Removes social pressure of public reviews

Encourages honest, personal reflection

Makes rediscovery faster and more meaningful

This shift became the foundation for all core flows.

User & Research Insights

I conducted interviews and usability testing with frequent restaurant diners who eat out multiple times per week.

Key insights:

Users want to track food for themselves, not to influence others

Dish-level memory matters more than restaurant reputation

Public ratings feel performative; private notes feel honest

Users want a lightweight “food journal,” not another review platform


“I’m not trying to hurt a business — I just want to remember what I liked.”

Core User Flows

The app centers around four simple actions:
 

Discover a dish
 

Save it to your journal
 

Add personal notes (taste, texture, price, mood)
 

Revisit favorites later
 

Each flow was designed to feel quick, personal, and low-effort.

Key Screens

Home/Feed

Screenshot 2026-01-02 at 1.25.29 PM.png

Displays dish categories and recent searches, relieving  discovery pressure

Dish Detail

Screenshot 2026-01-02 at 1.26.37 PM.png

Displays basic info such as restaurant location and menu item

Add Review

Screenshot 2026-01-02 at 3.02.12 PM.png

Allow users to log a dish quickly with optional details, keeping friction low.

Journal

Screenshot 2026-01-02 at 1.27.29 PM.png

Act as a private archive of meals, experiences, and preferences

Visual Design Approach

The visual system was designed to feel:

Warm and personal

Calm and content-first

Focused on food photography and notes

Color and typography choices intentionally stay neutral so the dish imagery and user reflections remain the focal point.

Accessibility considerations included clear hierarchy, readable type sizes, and high-contrast UI elements.

Usability Testing & Iteration

I conducted remote usability testing to validate clarity and flow.
 

Key findings:
 

Users immediately understood dish-level tracking
 

Journaling felt more natural than reviewing
 

Some labels needed clearer language to reinforce “private journal”

 

Iterations made:
 

Adjusted terminology to emphasize personal use
 

Simplified entry flow to reduce cognitive load
 

Clarified navigation between Review and Journal

Outcome & Learnings

This project reinforced the importance of:

Designing around real mental models, not industry defaults

Removing unnecessary social pressure from personal tools

Letting user behavior — not assumptions — guide product structure

ComplEAT demonstrates my ability to:

Identify meaningful product gaps

Translate research into clear design decisions

Design early-stage concepts with real-world constraints in mind

Next Steps

If this product were to move forward, next steps would include:


 

Expanded usability testing with a broader audience

Exploring camera-based dish capture

Testing AI-assisted dish recognition from menus or photos

Refining onboarding for first-time users

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