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Case study · 01

NutriDash

A health-centric food delivery platform putting ingredient transparency, AI-powered personalization, and smart meal customization at the center of every order.

Mobile AppConcept ProjectSolo Designer

30

Survey respondents

56.7%

Rate healthy diet important

40%

Want AI recommendations

6

Usability tests conducted

NutriDash My Daily Nutrients ScreenNutriDash Turkey Sandwich Meal Detail Screen
01

Project Overview

NutriDash is a health-centric food delivery app concept designed to solve a critical gap in existing platforms - the lack of ingredient transparency, personalized meal recommendations, and intuitive health-focused UX. I owned the entire design process across a 6-day sprint from research to tested prototypes.

Role

Solo UI/UX Designer (End to End)

Timeline

6-Day Design Sprint

Platform

Mobile App (iOS & Android)

Tools

Figma · Notion · Google Forms

02

Problem Statement

Health-conscious individuals struggle to find convenient meal delivery options that align with their dietary goals due to a lack of ingredient transparency, personalized recommendations, and intuitive UX. This leads to frustration, uninformed choices, and real health risks.

No Ingredient Transparency

Most food delivery apps don't provide detailed ingredient breakdowns, allergen alerts, or verified sourcing. Users rely on vague labels like "healthy" which mean nothing without context.

No Personalized Recommendations

Apps don't adapt to individual dietary needs - vegan, keto, gluten-free, or allergy-specific. Users must manually filter through hundreds of options every single time.

Decision Fatigue

Users spend excessive time researching meals outside the app or avoid food delivery altogether due to uncertainty about what they're actually eating.

Health Risk for Allergy Sufferers

Consumers with allergies may unknowingly consume harmful ingredients due to missing or unclear allergen information - a serious safety issue.

03

User Research

I conducted a Google Form survey with 30 respondents to validate the problem space with real data. Every number below came from actual responses - not assumptions.

Very Important (5/5) 56.7%
Important (4/5) 16.7%
Want AI Meals 40%
Unsure on AI 50%
Feature Preferences

What Users Want Most (30 Responses)

26.7%

Customizable Meals

Users want flexibility - the ability to tailor orders to their exact dietary needs and taste preferences above all else.

20%

Ingredient Transparency

Users care deeply about knowing exactly what goes into their food - sourcing, allergens, and full ingredient lists matter more than generic labels.

16.7%

AI-Based Meal Recommendations

Strong demand for AI-driven meal options tailored to individual health goals - but awareness of the technology needs to be built alongside the feature.

13.3%

Dietary Filters + Subscription Discounts

Health-focused filtering and affordability are equally important - healthy eating needs to be accessible, not just aspirational.

"A customizable meal system with ingredient transparency and AI-based recommendations would be highly effective. The 50% 'unsure' group on AI represents an education opportunity - not a rejection."

Research Conclusion
04

User Personas

FE

Fitness Enthusiast

Active Professional · Age 28

Tracks calories and macros daily. Orders food 4-5 times per week. Follows a keto diet and needs high-protein, low-carb options without manual searching.

Goal

Find meals that fit his diet instantly - without opening a nutrition calculator or checking ingredient lists manually.

Pain Point

No macro filters in current apps. Relies on guesswork. Often gives up and eats outside his diet because it's too hard to verify meals.

AP

Allergy-Aware Parent

Parent of Two · Age 35

Orders food for the whole family. Her child has a severe nut allergy. Every order carries anxiety - even one mistake could be dangerous.

Goal

Order with complete confidence that her child's allergen needs are met - no fear, no second-guessing, no external research needed.

Pain Point

Allergen warnings are vague or missing entirely. Often calls restaurants directly to verify - a frustrating, time-consuming workaround.

05

Competitive Analysis

Existing apps fail health-conscious users in critical ways. NutriDash fills the gaps that none of them address together.

Feature Swiggy Zomato HealthifyMe NutriDash
Ingredient transparency
~
~
Real-time allergen alerts
AI meal recommendations
Customizable meals
~
~
Macro & calorie tracking
Smart meal swapping
Subscription meal plans
Gamification for health
~
06

6-Day Design Sprint

I followed a structured sprint methodology - moving from problem understanding to tested prototypes in 6 focused days.

1
Understand
2
Research
3
Redefine
4
Ideate
5
Prototype
6
Test
Day 01

Understand

Defined and framed the problem statement. Identified who is affected, what the core issue is, why it matters, and what needs to change.

Day 02

Research

Conducted a 30-person survey. Extracted key metrics across feature preferences, AI adoption rates, and dietary priorities. Validated every assumption with real data.

Day 03

Redefine

Mapped the Who/What/How/When framework. Documented the before and after impact. Sharpened the problem to a precise, actionable design brief.

Day 04

Ideate

Ran both convergent and divergent brainstorming. Identified 3 high-impact convergent solutions and 2 bold divergent concepts including a virtual nutritionist chatbot.

Day 05

Prototype

Hand-drawn sketches to validate flow before screens. Built high-fidelity mockups in Figma for 4 core user flows - Order, Nutrition, Swap, and Dashboard.

Day 06

Test

In-person usability testing with 6 real users. Collected structured feedback and documented 4 key iterations grounded in specific user pain points.

07

Brainstorming & Ideation

I used both convergent thinking (best practical solutions) and divergent thinking (bold unconventional ideas) before settling on the final feature set.

Convergent

High Impact, Practical

Ingredient Transparency Panel

  • Detailed ingredients with sourcing details
  • Nutritional breakdown - macros, calories, dietary info
  • Real-time allergen pop-up alerts
  • Auto dietary tags - vegan, gluten-free, keto

AI-Powered Recommendations

  • Smart suggestions based on preferences and past orders
  • Real-time calorie tracker synced with MyFitnessPal
  • AI flags hidden allergens automatically
  • Faster meal discovery without manual checking

Food History & Dashboard

  • Order history breakdown - calories, macros tracked
  • Weekly health report with intake insights
  • Meal recommendations based on past healthy patterns
  • Builds loyalty and health-focused ecosystem

Smart Meal Swapping

  • Pizza cheese → plant-based cheese
  • Burger bun → gluten-free bun
  • Fried chicken → grilled chicken option
  • Dynamic swaps based on user health goals
Divergent

Bold & Unconventional

Virtual Nutritionist Chatbot

  • Real-time questions about any meal's health impact
  • "Is this grilled chicken suitable for keto?"
  • Personalized advice using user health profile
  • Complex AI modeling - future phase

AR Ingredient Scanner

  • Point camera at food - identifies ingredients instantly
  • Works in restaurants, at home, or packaged food
  • Could integrate with wearables for real-time tracking
  • Hardware + ML complexity - long-term vision
08

Core Features Designed

From the brainstorming session I converged on 4 high-impact features that directly address the research findings and user pain points.

01

Ingredient Transparency Panel

A dedicated section on the meal details page showing full ingredient breakdown, sourcing details, macros, calories, real-time allergen alerts, and automatic dietary tags. Boosts user trust and allows informed choices in seconds.

02

AI-Powered Meal Recommendations

Smart meal suggestions based on saved dietary preferences, past orders, and health goals. Syncs with fitness apps for real-time calorie tracking. AI flags meals with hidden allergens automatically.

03

Smart Meal Swapping

Auto-swaps unhealthy ingredients with healthier alternatives without changing the meal type. User inputs health goals and the app dynamically suggests swaps - plant-based, gluten-free, grilled instead of fried.

04

Food History & Health Dashboard

Personalized dashboard tracking previous orders, health stats, and meal insights. Weekly health reports, calorie intake summaries, and smart re-order suggestions based on past healthy choices.

09

Final Design

High-fidelity screens designed in Figma covering the 4 core user flows. Each screen was designed after hand-drawn wireframes validated the layout and flow.

ORDER PAGE
Order Page

ORDER PAGE

NUTRITION INFO
Nutrition Info

NUTRITION INFO

INGREDIENT SWAP
Ingredient Swap

INGREDIENT SWAP

DAILY INTAKE
Daily Intake

DAILY INTAKE

AI RECOMMENDATIONS
AI Recommendations

AI RECOMMENDATIONS

DESIGN ITERATIONS
Design Iterations

DESIGN ITERATIONS

10

Usability Testing

Real in-person usability testing with 6 actual users. Not assumptions. Not surveys alone. Real people, real reactions, real problems uncovered.

Usability testing session Usability testing session Usability testing session Usability testing session

Preparation Time Issue

Restaurant owners noted that healthy meals take longer to prepare, risking delays during peak hours. Users needed realistic visibility into wait times before ordering.

Cost Barrier

Several users found healthy meal options too expensive to order regularly. Healthy eating felt aspirational but not accessible. Affordability was a real blocker.

Scheduling Needs

Users wanted to align meal delivery with their daily routines - post-gym sessions, lunch breaks, or pre-planned weekly schedules. Planning ahead was missing entirely.

Health App Integration

Strong interest in syncing with Apple Watch, Google Fit, and Fitbit to automatically track calorie intake. Users wanted food choices to flow into their existing health ecosystem.

11

Design Iterations

Based on usability testing I made 4 concrete design improvements - each directly addressing a specific user pain point discovered during testing.

01

Prep Time Badge

Introduced a Prep Time Badge on each meal card so users know what to expect before ordering. Added a "Quick and Healthy" filter for meals under 20 minutes. Restaurants can set prep-time limits during busy hours.

02

Subscription Meal Plans

Partnered with restaurants for budget-friendly healthy combos. Added weekly subscription plans with discounted pricing. Designed a Meal Credit reward system - earn points for consistent ordering.

03

Smart Delivery Scheduling

Built smart scheduling - delivery slot selection based on time of day, activity type, or calendar sync. Save routine schedules for faster future orders. Push reminders for regular meal times.

04

Gamification for Motivation

Users earn badges for consistency (e.g., "Healthy Streak: 7 Days"). Daily wellness tips and healthy eating facts. Health progress tracking integrated with Apple Health and Fitbit.

12

Future Scope

With a larger user base and more development time, these next-phase features would significantly deepen the product value.

Virtual Nutritionist Chatbot

AI chatbot answering real-time questions about any meal. Uses user health profile and app ingredient data to give personalized dietary advice.

Wearable Integration

Sync with Apple Watch, Google Fit, and Fitbit to auto-track calorie intake. App sends smart meal recommendations based on real-time health data.

Restaurant Health Score

A verified health rating system for restaurants based on ingredient quality, preparation methods, and nutritional accuracy. Builds platform-wide trust.

Corporate Wellness Program

A B2B marketplace connecting corporate wellness programs with meal prep providers. Employees get subsidized healthy meal delivery through their employer.

13

Reflection & Learnings

✦

Real research changes everything

Having actual survey data from 30 respondents changed every design decision. Numbers like 56.7% and 40% aren't estimates - they're real user voices. Every designer should start with research, not assumptions.

✦

Usability testing reveals what you miss

I didn't anticipate the prep time issue or the cost barrier until real users raised them. Testing with just 6 people uncovered 4 significant problems I had completely overlooked.

✦

Iterations show design maturity

Taking feedback and turning it into specific design changes - prep time badge, subscription plans, smart scheduling, gamification - is what separates a finished product from a concept.

✦

What I'd do differently

I'd define success metrics upfront - task completion rate on the ingredient panel, allergen alert click-through rate, repeat order frequency. Measuring impact from day one makes the design stronger.

✦

The 50% "maybe" group is the real opportunity

Half of respondents were unsure about AI recommendations - not against it. This isn't rejection, it's an education gap. The app needs to show value through onboarding and subtle UI nudges.

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Let's talk

Hiring for something complex? Tell me what your team is trying to fix. I read every message.

Mohammed Jamal

Looking for a full-time product or enterprise design role, especially one with the messy, unglamorous problems in it.

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