📉
Finding a meal that fits
Meal search and filters let people browse options by category and dietary preferences, instead of starting from a blank page.
Mobile product case study • Food & nutrition
Make every meal make sense.
Mealwise brings food discovery, nutrition information, meal planning and restaurant exploration into one mobile experience—helping people move from “what should I eat?” to a more informed next choice.
Project at a glance
One app for the everyday food journey.
Project
Mealwise Nutrition App
Category
Food & nutrition
Application
Cross-platform mobile
Framework
Flutter / FlutterFlow
Backend services
Firebase
Integrations
Maps · Subscriptions · Push
Project timeline
Not provided
Measured outcomes
Not provided
The product
From inspiration to a more informed plate.
Mealwise is a food and nutrition app whose codebase connects a set of everyday tasks: find meals, review nutrition information, plan meals ahead, scan a meal image, save favorites and explore restaurants nearby.
Rather than treating each task as a separate utility, the product brings them together in a single mobile application. The code includes account flows, meal and restaurant data, user plans and profile features that support a connected experience.
Evidence & scope
A case study grounded in the supplied app.
✓
What is documented
Implemented product areas, application source, and configured service integrations.
Implemented product areas, application source, and configured service integrations.
✓
What was not supplied
Client research, original business brief, and customer validation.
Client research, original business brief, and customer validation.
✓
How to read this case study
Product needs are observations from available features, not validated user findings.
Product needs are observations from available features, not validated user findings.
The challenge
Food decisions span more than one moment.
🧭
Making nutrition legible
Meal details and nutrition views bring calories and macros into the decision, while scanning offers another way to inspect food.
⚙️
Connecting plans to real choices
Meal plans, substitutions, saved meals, and nearby restaurant discovery support different ways to decide what to eat next.
Scope note: no supplied interviews, analytics, or business brief substantiate a specific user pain point or its prevalence.
Product objectives
A connected set of useful actions.
01 / Discover
Make meals discoverable
Search, browse, and filter meal options by category and dietary preferences.
02 / Understand
Put nutrition alongside choices
Open meal details and nutritional information as part of the discovery journey.
03 / Plan
Support planning ahead
Create and manage meal plans, with plan generation and meal swapping represented in the app.
04 / Explore
Support in-the-moment discovery
Scan a meal image or explore restaurant and venue information using location-aware features.
These objectives describe capabilities visible in the implementation; original project objectives were not supplied.
The solution
A cross-platform companion for food decisions.
Mealwise combines discovery, nutritional details, planning, image-based meal scanning, and location-aware restaurant browsing. A Flutter application connects these experiences to Firebase services and the app’s subscription, map, and notification integrations.
A product journey
Step 1 — Get started
Account registration, sign-in, and onboarding routes.
Step 2 — Discover
Search or filter meals and open a meal detail.
Step 3 — Understand, plan, or scan
Review recipe and nutrition details, add or swap planned meals, or start a meal scan.
Step 4 — Explore nearby
Browse restaurant and venue details with location features.
Product capabilities
Useful details, at the moment they matter.
🧩
Meal discovery
Search, category selection, and dietary, price, and calorie filters help people browse meals. Detail screens connect meal information with recipes, nutrition, and saving.
✨
Planning & meal swaps
Day-based plans, meal selection, and swap flows help organize meals ahead. Plan generation sends preferences and meal categories to a service, then resolves returned meal references from Firestore.
🔄
Nutrition details
Recipe and nutrition views surface information such as calories and macros alongside meal choices.
📊
Image-based meal scanning
A photo-based meal flow offers another way to inspect food in the app.
🌿
Nearby restaurants
Location-aware discovery retrieves venue candidates and filters results by distance, with restaurant and meal detail routes.
📍
Account & saved meals
Authentication, onboarding, saved favorites, plans, and profile features support a connected experience across app areas.
The app
Mealwise in action
.jpeg)
.jpeg)
.jpeg)
Technology
A Flutter app connected to managed services.
Mobile development
Flutter, Dart, and Provider power the shared iOS and Android application.
Backend & data
Firebase Auth, Cloud Firestore, Firebase Storage, and Cloud Functions.
Maps & location
Google Maps, Geolocator, and Geohash support location-aware discovery.
Product integrations
RevenueCat, OneSignal, Google Sign-In, and Sign in with Apple.
Meal-plan generation submits preferences and meal categories to a service, then resolves returned meal references from Firestore. Nearby venue lookup uses geohash neighbors followed by a distance check. Exact service boundaries and deployment topology were not supplied.
Business value
A broader foundation for food-related product journeys.
Potential product benefits implied by implemented capabilities—not verified business results.
●
Connected experience
Meal discovery, planning, nutrition details, and restaurant exploration are available in one mobile product.
Meal discovery, planning, nutrition details, and restaurant exploration are available in one mobile product.
●
Product extensibility
The Flutter client and managed Firebase services provide a base for considering further product capabilities.
The Flutter client and managed Firebase services provide a base for considering further product capabilities.
●
Personalized flows
Preferences, saved meals, plans, and account features create mechanisms that may support tailored experiences.
Preferences, saved meals, plans, and account features create mechanisms that may support tailored experiences.
The supplied project files do not include verified adoption, retention, revenue, performance, or operational metrics. No numerical outcomes or testimonials are claimed.
Next step
Have a product challenge worth solving?
This case study reflects the product capabilities visible in the supplied source and aims to explain the opportunity clearly without claiming more than the evidence supports.