All things food

Cross-functional partners
Potential investors

AI based restaurant search and recommendations engine.

Timeline
2 months

Role
Entrepreneur & designer

Company
Myself

Team
Individual

Objective

How might we find restaurant recommendations for multiple friends? 

Overview

Context

The thought for this project came up when my friends and I were deciding where to go out and eat. Usually, when we go out to eat with friends we think about where we should go. Would he/she like it? What kind of food would they prefer? Whom can I go with?

Different people have different preferences and dining together could be a game changer for many. This is where the idea of all things food came in.

Doesn’t exist

Outcome

This app shows you which friend has a similar taste as yours, restaurant recommendations, cuisines your friend or group would like, and cooking experiences you can have with locals.

Sneak peak

Before

User research

After

AI based restaurant search and recommendations engine

Before

At the time, there were no other features/apps with this technology in the consumer market. Hence, I relied heavily on user research:

1) Studying competitors in the market and how they approach the problem statement.

2) User interviews to have fluid conversations that include a set of prepared questions to understand the target user group and the difficulties they face.

My focus was on understanding the target user group for the application, Identifying the key differentiators in the app, & understanding the user's pain points, their needs, and priorities.

All Things Food offers a very unique solution and does not have direct competitors. This led us to explore two major directions: the app as a separate idea & it being incorporated as a feature in an existing app/company.

Design process

  • No direct competitors so studied food delivery and travel apps.

  • Since it was a self-sponsored project I was responsible for the user research as well.

  • Took a lot of user feedback from the target audience.

  • Presented the concept to a lot of people in the industry to get as much feedback as possible.

After

Final designs

Here are some of my iterations. This extensive project required me to analyze the entire platform experience, its post variations, and new features that needed to be incorporated.

User profile

Group chat

Similarly this shows the top cuisine for a group of friends.

Snapshot view

Ongoing.

A mobile application that is a restaurant search and recommendations engine that uses advanced machine learning and AI technology.

This allowed you to find friends who have a similar taste as you & what are the top cuisines you both might like.

This feature compares your taste with a friend & recommends cuisines/restaurants you both might like.

Results & learning

Implementation underway with growth experiments, including A/B testing.

  • Pitching it as a feature to bigger brands.

  • App development under progress.

Full case study in my portfolio presentation. ☺

Challenges

Since it was a self-sponsored project there were a couple of challenges/insights that arrived after user research and user testing.

User testing

Since it was a self-sponsored project there were a couple of challenges/insights that arrived after user research and user testing.

  • At first, the group chat was designed to be accessed from the profile but after the usability testing, we decided to have direct access to the inbox from the bottom navigation bar.

  • At first, the map was designed in a similar way as the top cuisines and ratings but after the testing, it was concluded that adding interactivity to it would it the "aha!" effect.

  • This could also be an additional feature in an app/platform existing in the market.