TheGo
Ordering Made for Food Events
A mobile food-ordering experience built for food trucks and pop-up vendors where long lines and limited menus slow everything down.
Product Design
UX
UI
AI
Project Overview
Client: Google UX Course Case Study
Project Type: Product Design, UX/UI
Tools: Figma, ChatGPT, Claude, Adobe Photoshop, Illustrator,
My Role: Product Designer
"Are you on Yelp?"
"No, I'm on TheGo."
CONTEXT
What You Want, Before You Want It.
Traveling food vendors often lack a lot of visibility. Trucks are always on the move, and events that host vendors aren't always easily discoverable. People need a way to discover vendors, fun events, and order ahead all in one platform.
Final Processes I designed
Event Discovery Process
Food Ordering Process
PROBLEM
Traveling Vendors Often Lack Visibility and a Method to Order Ahead.
72%
of consumers prefer ordering food digitally when the option exists.
UpMenu Food Ordering Behavior Report (2023)
30%
Long lines cause up to 30% revenue loss for vendors at high-traffic events.
Industry analysis via FoodTruckBooking (2024)
52%
of consumers won’t visit a food vendor if they can’t confirm the vendor is open or present.
Square’s “Future of Restaurants” vendor behavior survey (2025)
My Task
Develop an app focused on traveling food vendors and events that creates opportunities and saves time for both customers and businesses.
Key Painpoints
Conceptualize how AI can be integrated into this product
Develop a new brand identity
Stand out from popular apps
No team to collaborate with
RESEARCH
What's Missing From the Current Market?
From studying the current top food ordering apps and conducting 15 user interviews, I was able to uncover some key insights:
Lack of Visibility
People often don’t know of nearby vendors or local events.
"I found out on TikTok there's a weekend Thai market here. I've lived here for 5 years and never knew."
Improve vendor and event discoverability.
Make it easy for users to see who’s selling, where, and when, without relying on social media or word of mouth.
No Stock = A Wasted Trip
Vendors often sell out mid-day, leaving customers disappointed after traveling long distances.
"There were a couple of times I went home empty-handed because an item at the food truck was already sold out."
AI provided real-time stock visibility.
Users shouldn't have to waste their time on unavailable items.
Lack of Accessibility
Long lines and crowded environments prevent many people from ordering at events.
"My wife has had a lot of knee issues lately and can't stand for long periods of time."
Enable ordering without standing in line.
Let users browse and order comfortably from anywhere at the event.
Who's Our Target Audience?
User Personas
To expand my design perspective, I created personas from varied backgrounds. One was inspired by my experience as a new uncle observing the changes in my sibling's life after having a kid. I wanted to highlight the challenges of a young parent managing a child while ordering food.
Problem Statement
Penny wants a way to discover different events with food vendors that aren't too difficult to attend because she's a single mother who also needs to watch her kids.
DESIGN PROCESS
Designing the Core User Journey
Based on research, I focused on improving the end-to-end experience from event discovery → vendor browsing → ordering → pickup. Below is the evolution of this flow, from early sketches to final designs.
Quick and Revealing, Hand-Drawn Wireframes
I explored a handfull of directions and either chose one or pulled the strongest sections from each sketch and Frankensteined a stronger direction to follow.
User Flow
Making a user flow helped me map out each step the user would take in the app, from entry to final action. This helped me determine the types of screens that I would want to design for.
Problem Focus- Event Discovery
Focusing on the lack of event visibility as the first problem to solve, I began designing a discovery page to find local events.
Discover Screen
Event Profile
Usability Testing
Low-Fidelity Wireframes
The initial low-fi wireframes focused on helping users quickly see nearby events. The goal here was to ensure that the user flow was straightforward and easy to navigate.
Iteration/Design Decisions
Early feedback quickly revealed flaws.
Food names on the photo cards were too cluttered and that the distance marker also seemed out of place.
Added the names of food and the ratings below the photocards.
Users quickly noted that they couldn't go back to the last screen.
I included an icon to the top left.
Long lists were overwhelming
AI-Powered Event Recommendation
AI-Powered Event Recommendation
Many users told me a long list of events would be overwhelming. They wouldn't know which events they should actually pay attention to.
AI recommendations reduce decision fatigue and personalize discovery.
This model considers several factor's from user data to surface relevant options.
Round 1 - Low-Fiedlity Prototype
Cleaning up and designing beyond wireframes allowed testers to have a better idea of how the process flowed.
In Round 1 of testing, users:
shared that a live GPS map of the vendors would be extremely helpful.
struggled with the layout of the food vendors on the event profile screen, saying it still seemed cluttered.
didn't understand why there was no readily available info for the AI featured event.
Round 2 - High-Fidelity Prototype
Progressing to High-Fidelity Prototypes, I was able to make solutions to solve each of the insights.
A live GPS map was added to help locate all vendors in proximity.
AI featured events were added below as a written event with more info.
Vendor cards were cleaned up by improving the text hierarchy and only providing the most essential information for a quick browse.
But Wait Just A Sec…
I still had a final round of usability testing with this High-Fidelity Prototype.
Last Improvements
Adding a filter bar for the events and the discover screen and for the vendors participating at them.
Adding event ticket information and a link directing users to their page.
AI suggestions don't explain why they were suggested.
SOLUTION
Final Outcome
After multiple rounds of usability testing, these final designs solved the event and vendor visibility problem by giving users a clear way to discover.
Event Discovery In Action - Video
FINAL DESIGNS
DISCOVERY AND CHECKOUT MADE POSSIBLE
AI is a powerful tool, but one that I wanted to make sure I properly designed with properly. I decided that adding the recommendation to the home page would increase visibility for discovery.
AI Recommends Events By Considering Several Factors
Location
User's past attended events
User's frequently visted vendors
User's cuisine preferences
Popularity/trending events
Time of day likely to attend
User's accessibility needs
Distance
Peak hours (avoid crowds)
Family friendly events
Final Event Discovery Screens
Discover Page
Event Discover
GPS Map
GPS Map- Clicked
Event Profile Page
Event Profile
Vendor List
AI Improving The Checkout Process
AI improves the checkout process by proactively scanning the user’s order for allergens they’ve marked in their profile and flagging any risks before payment. It also predicts low-stock or out-of-stock items based on vendor trends so users don’t waste time ordering something that won’t be available
AI alerting users to potential allergens noted in their profile
AI alerting users to predicted stock levels of the menu items
IMPACT
Discovery Made Possible
What began as a simple gap, no app designed specifically for traveling vendors, revealed deeper issues across the entire event experience.
Upon my final user testing, 100% of users successfully completed the checkout process and comfortably navigated the discover section.
100%
of users successfully navigated the discovery section without confusion.
73%
said the event visibility solved a frustration they regularly experience.
60%
of users wished other apps included an AI allergy alert function.
86%
thought the AI stock indicators would be helpful in other ordering apps.
KEY TAKEAWAYS
Designing for Humans, With and Through AI
This project taught me how to design an experience from the ground up. One that is centered on real user needs and experiences. I had to rely on more than just my design skills that focused on aesthetics.
I learned how to use AI, including ChatGPT, as an early-stage ideation partner to broaden my perspective, validate assumptions, and explore possibilities I wouldn’t have reached alone. More importantly, I learned to design with AI in mind, not simply add it as a feature. Every AI concept I included (recommendations, stock prediction, allergen detection) was directly tied to a user pain point uncovered through research.
This helped me move from designing “interesting screens” to designing a system that intelligently supports users.
Where I Grew
Learned to use empathy as a strong tool
Became more disciplined in user research, testing, and iteration
Strengthened my ability to distill complex workflows into clear user journeys
Used AI tools to accelerate ideation, analyze feedback patterns, and refine decision-making
Sharpened my Figma skills, prototyping speed, and storytelling ability
What I'd Improve Next Time
Focus earlier on mapping user flows before committing to wireframe details
Learn to filter between helpful and distracting feedback more effectively. Not every problem needs to be solved.
Push accessibility further, especially touch targets
Explore ways to incorporate AI even more ethically and transparently
Culminating Thoughts
I’m proud of the final product and the thinking behind it. Even as a solo project, it pushed me to understand perspectives beyond my own, and to leverage AI thoughtfully as a design tool, not a shortcut. This project helped me grow into a more intentional, systems-minded product designer that can help real people.