CookEZ is an emerging startup whose vision is to reduce food waste through personalized meal recommendations. Through their need-finding process, CookEZ determined that consumers often lack the experience needed to maximize the use of all the groceries that they buy. Ultimately, this contributes to the whopping 30-40% of food that is wasted each year in the United States.
CookEZ hopes to help consumers reduce food waste through a mobile app that delivers:
1) Personalized meal recommendations to cater to individual dietary preferences
2) Optimized grocery lists to help consumers buy the exact amount of ingredients that they need.
Cardinal Labs was responsible for creating a functional prototype of this app that could be used for beta testing and generating investment interest before going to market.
In order to create a functional mobile app prototype, we identified the following challenges:
• Translating designs into an intuitive user interface
• Integration of a database of recipes and user information
• Intuitive search functionality
In order to best translate CookEZ’s designs into a functional prototype, we conducted weekly meetings with CookEZ in which we iterated on initial designs and pinned down key app functionalities within React Native.
We used Firebase and Algolia in order to easily integrate a cloud database and search functionalities, respectively.
React Native
Expo
Algolia
Firebase
We started from scratch to build out CookEZ’s MVP. In order to quickly produce a mobile application, we used React Native and Expo as our main development platforms. Although the application is cross platform, our main focus was on iOS.
The app features a dietary preferences quiz, a home screen with all planned meals for the week, a search page, a grocery list that updates as the user changes their recipes for the week, and a repository of their favorite meals.
We designed CookEZ’s database with efficiency in mind, storing all recipes, images, and user data. We chose to use Algolia to implement the search feature in the app, because the lightweight API allows for autocompletion and returns search results as soon as the user begins typing.
