
Whatcipe Food App
Client
Whatcipe
Year
2026
Role
Product Designer, UX Resercher
Tools
Figma, FigJam, Google Form
Mobile App
What is Whatcipe?
This project focuses on designing a Food Recipe Application with AI implementation aimed primarily at students and young adults who struggle with everyday questions such as “What should I cook?” and “How do I use ingredients I already have at home?”.
My Role
In a team of 2 members. I was in charged of all phases and design process, using a combination of competitor analysis, heuristic evaluation and a user survey. Then, I followed-up with my design decisions such as sitemap, personas, user journey, information architecture and later prototyping and testing.
CONTEXT
What should I eat today?
The most common question that students and youth used to ask while doing school in university, I found out that 61–64% of Gen Z lack confidence in basic cooking and 76% of UK adults experience “decision fatigue” when planning meals.

This story became the foundation of Whatcipe
RESEARCH
Competitor Analysis
To understand the current landscape of recipe and meal-planning applications,
I conducted a competitor analysis focusing on interaction design, key features and overall usability.
Heuristic Evaluation
As part of our research we conducted a heuristic evaluation on one of the applications from our competitor analysis. We utilized Nielsen’s 10 usability heuristics as a basis for our findings. Our evaluation focused on Mealime, a meal-planning app that advertises easy-to-use features, healthy home-cooked meal options and personalized selections.
Survey
We wanted to have realistic data to create personas, user journeys that are based on the user's needs,
so we conducted an online survey to understand everyday cooking habits, pain points, and expectations around AI supported recipe tools.

KEY FINDING #1
79% cook a full meal more than three times weekly.
86% struggle with the daily question: “What should I cook?”
100% find it useful when the app suggest the recipe that they can cook with

KEY FINDING #2
How comfortable are you
with apps that uses AI to suggest recipes?

DEFINE
Whatcipe's Core Audience
We analysed the survey answers and divided similar patterns, which led to two categories

Linda William
22 years old, international student in Jönköping
Bio
Shares a small apartment with one roommate
Cooks 3–4 dinners per week, orders takeaway or eats on campus the rest
Goals & Needs
Quickly decide what to cook tonight without scrolling forever
Use up ingredients she already has so they don’t go bad
Cook simple, tasty food and affordable
Frustration
Stares at the fridge thinking “I have food but nothing to cook”
Low energy after classes, no patience for long onboarding or complex filters
Forgets what she bought, feels guilty about food waste
Traditional recipe apps feel heavy to use and not personalised
Userflow & Information Architecture
I created a sitemap that focuses on quick access to the core flows: “Cook with what I have”, "discovering new recipes”, “Photo-to-recipe”.
Which helped us to have the foundation before moving to the design process.

SOLUTION
Starting from low-fidelity sketches
These sketches helped me to turn ideas into pre-visual screens and design direction,
which are backed by research insights gathered from research phase.

Branding, Design system & Hi-fi Screens
I developed a simple brand identity and design system for the app.
The visual identity focuses on clarity, friendliness, reflecting my goal of making cooking feel less overwhelming and more enjoyable.


On Boarding Flow
This flow focuses on gathering eating habit from users
to suggest dishes based on their favorite ingredients
Cook With What You Have
With this feature users can now cook with anything they have at home
without thinking or wasting time looking for recipe by browsing on social media
Photo to Recipe
Users can save their own recipes
through capturing images of any dishes they saw at a nice restaurant or coffee shop
VALIDATION
Usability Testing
To evaluate the clarity, usability and overall experience of the Whatcipe prototype,
we conducted four usability tests with our target group


Results
Finding 1
AI is accepted conditionally
Comfort ratings 6.5 - 8/10, all caveated on accuracy

Finding 2
Discovery paths diverged
100% completion masks hesitation

Across all five participants,
the app shows strong usability,
particularly in browsing and input tasks

REFLECTION
Summary and reflection
Overall, the process highlighted the value of early testing, iterative design and grounding design decisions in user behavior rather than assumptions and started designing the UI quickly. It also created the balance between AI functionality and clear communication to ensure users feel supported by technology.

