Yanu.AI : Creating an Ai Bartender App For a More Streamlined Ordering Experience
Yanu is a new fully autonomous Ai. powered bartender , created to minimize error, and speed up the ordering/delivery process of drinks in busy environments. I was seeking to create a fluid app from which you can order and pay for your drinks. Yanu has the potential to mix 200 drinks per hour; In order to streamline this process, and make it even more contactless, I decided to design a mobile app that would connect to this system. (This was an Independent project for a boot camp)
Client:
BootCamp Project
Role:
UX/UI Designer
Year:
2022
Overview
For my bootcamp course , I was given the prompt to create an app for an ai bartender. After some research into the few ai bartenders that existed, I stumbled across Yanu.
Yanu is a new fully autonomous Ai. powered bartender , created to minimize error, and speed up the ordering/delivery process of drinks in busy environments. I am seeking to create a fluid app from which you can order and pay for your drinks. Yanu has the potential to mix 200 drinks per hour; In order to streamline this process, and make it even more contactless, I decided to design a mobile app that would connect to this system.
Problem:
The primary concern confronting users pertains to prolonged waiting times when queuing for drink orders and payment, significantly detracting from their overall enjoyment during their social outings. Conventional bar service entails wait times ranging from 5 to 10 minutes, escalating to as much as 30 minutes in high-traffic environments.
Proposed Solution:
To address this challenge, we have devised the Ai. Bartender app, a sophisticated platform that enables users to place drink orders and execute payments directly through the AI-powered system, all from the comfort of their table. By bypassing the traditional queue, users can experience a substantial reduction in wait times, thereby optimizing their leisure time and overall satisfaction with the service.
Discovery Research
In order to better understand the users and the potential pain points they face when ordering drinks from their favorite venue , I conducted in-field user interviews with over 10 people (who stated that they go to a bar at least once a month), with varying age ranges, around Mexico City.
Some of the questions used to understand potential users were:
What are things you like / dislike about drinking / going out for drinks ?
Would you use an ordering app for drinks ? Why ?
How would you feel ordering from an ai. bartender ? Why?
When and where do you think someone would use this app/product?
What is your experience ordering at a bar like ? How does it make you feel?
Hearing the users responses to these questions gave me valuable insight into how they think and feel, which aided in shaping the personas, as well as helping me construct the app with less bias.
Synthesize and Define:
Personas: Using our research , we developed 2 personas that fit the general audience that would be utilizing this ai. bartender. I mapped out their characteristics after conducting several in-field interviews with over 10 different people with varying ages around Mexico City. We then used these personas to create User Storyboard , and User Journey maps.
Wireframing and prototyping:
Based on the research insights, I drafted out some wireframes. I then transformed my paper wireframes into a digital wireframe as well as a low-fidelity prototype. I then gathered 4 users to test out the prototypes before making iterations based on their insights
UI Design
After the low-fidelity usability studies, I chose the brands colors, font, and theme. I then began constructing the high-fidelity prototype.
I chose a blue for the primary color because of the boldness and the contrast of a 5.74:1 according to WCAG standards
The red chosen was to differentiate between the food and drink card options.