Project by: Rupe Gopani

High Concept Statement: 

Eating recommendation system that uses artificial intelligence




Food & Drink

Need or Demand:

Growing up, I always heard my mom ask the same question: “What do you want to eat?” Day in and day out, she would ask each member of my family the same question, hoping a decision would be made so that she could start cooking dinner. However, much to her dismay, all of us would always answer, “I don’t know, you decide.” This would lead to a long, time consuming struggle on what we were going to eat that night. To this day, this still happens in my family, but it doesn’t just happen there. It happens when friends want to get together for lunch or dinner; when people are traveling, and don’t know what to eat in a new place; and even when you’re lying in bed, hungry, and you just can’t figure out what seems appetizing. You just lie there, asking yourself: What do you want to eat?

Product / Service / Solution:

Eatsy is an eating recommendation app that uses Artificial Intelligence. The app makes individual and group recommendations, and will recommend restaurants if you want to eat out, and recipes if you want to eat in. Each time you use the app, Eatsy learns more about you and the recommendations become more and more personalized. Eatsy tackles the critical question: “What do you want to eat?” When making a user account, the app collects the user’s cuisine preferences, dietary restrictions, and preferred price range. Over time, the app will also collect data on the user’s eating history. When the user wants an individual recommendation, the apps feeds their data into an algorithm and gives the user an appetizing eating recommendation. If you’re eating with a group, and want a group recommendation, the app will access the data in each group member’s user account, feed it into an algorithm, and give an optimal group recommendation.

Value Proposition:

Eatsy is tackling the problem of eating indecision by giving the user one recommendation; the optimal eating choice, rather than a list of potentially viable choices based on some filter. In addition, Eatsy also helps with group planning when it comes to eating decisions. Figuring out what to eat is often most difficult in group settings because you have to take into account each person’s cuisine preferences, eating history, price preferences, dietary restrictions, etc. To make things harder, we usually don’t know a lot of this info, so finding an optimal place becomes time consuming and difficult. Eatsy solves this problem with its group recommendation feature.


Currently, there aren’t too many services that are trying to tackle the problem of eating indecision. Services like Opentable and Seamless have filters, like a price filter or a location filter, to help you in your decision process, but you’re still left to scroll through hundreds of restaurants trying to find a place you like. One app on the market that is similar to Eatsy is Luka. Luka is a virtual assistant for eating. It will recommend restaurants, bars, and coffee shops for you to go to. Like Eatsy, Luka gives one recommendation, rather than a list, and the company has been very successful since its launch. Unlike Eatsy, however, Luka does not make group recommendations and does not recommend recipes if you want to eat at home. These two points of differentiation will help Eatsy succeed and overtake Luka in the marketplace.

Revenue Model:

Eatsy will make money in two ways. If the app recommends a restaurant, users can reserve a table at that restaurant using the app. Eatsy will collect a small fee for making that reservation; this is the same way Opentable makes money. On the other hand, if the app recommends a recipe, the user will see targeted advertisements. These advertisements will be for things that the user might need, like discounts on ingredients or cooking supplies.