Nutrition App

Nils is a London based Digital Design lead who focuses on functional clean interfaces and teamwork. He is currently working at Iris London as an Associate Design Director as part of a large team of talented designers. He loves subtle meaningful ui interactions and Thai food. He hates cluttered design and pickles.


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Kostantinos Frantzis
Product design manager 

Chris Georgiou
Design Director

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ZOE is a “nutritional science startup on a mission to help people eat with confidence”. Based on over 20 years of research on genetically identical twins (the largest nutritional science study in the world), ZOE’s goal is to share their research and develop a product that will help people eat the right foods for their bodies.


Problem Statement

Problem Statement

People don’t know what food is good for their bodies. Nutritionists, books, social media and news offer contradicting advice about nutrition and often blanket answers for everyone. Each person is unique, from their DNA to their gut microbiome, and they should have access to personalised nutrition.


& Ideation

Through over 80 user interviews and workshops, we started looking at trends and clustering key focus points for our MVP. Most people knew what was good or bad for them, yet they wanted a simpler way to check products and see how it would affect their body compared to others.

ZOE_Image3_cluster of 3

& testing

By continuing interviews with real users as well as testing low fidelity prototypes, we were able to refine and iterate ideas continuously, making changes and generating ideas at a very rapid pace. Through this testing we were able to start sketching out what the MVP could look like.


Design exploration

We knew early on our product would be changing very rapidly, and that we needed a very simple design system that would allow design and development to continuously adapt and morph the more we learnt.

ZOE-Design-Exp-L4 – ALT
ZOE-Design-Exp-L3 ALT

the MVP

Once we launched in the App Store and Play Store, we started getting even greater insights into our users; how they used it and which additional features would be beneficial. This qualitative data allowed us to make quicker adaptations to the design.

MVP – Image 1
MVP – Image 2 – Alt
MVP – L1
MVP – L2
MVP – R1
MVP – R2

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