Overview
I completed a client zero project as part of the AI for UX/UI Designers course offered by ELVTR. The course explored how AI can be applied throughout the design process from research and ideation to prototyping, testing, and delivery.
My goal in taking this course was to expand my knowledge of AI tools and learn how to integrate them more effectively into my workflow. For me, this project wasn’t just about building an AI-powered chatbot. It was about experimenting with how AI can be used in out-of-the-box ways to enhance creativity, improve efficiency, and keep the user at the center of the process.
Problem Statement
Homeowners and renters often feel overwhelmed by lawn maintenance due to lack of knowledge, time, and expenses.
Homeowners and renters often feel overwhelmed by lawn maintenance due to lack of knowledge, time, and expenses.
Solution:
An AI-powered virtual assistant app that delivers personalized, step-by-step lawn care guidance to reduce stress and build user confidence.
An AI-powered virtual assistant app that delivers personalized, step-by-step lawn care guidance to reduce stress and build user confidence.
Deliverables
• UX audit
• Qualitative Study
• Executive Insights Brief
• AI-Generated Wireframes
• AI-Driven Style Guide
• Usability Testing
• Custom UI Components (In Progress)
• Capstone (In Progress)
UX Audit
The first step was to conduct a UX audit of an existing embedded AI assistant. I chose Lemonade’s claims assistant, Jim, which guides users through filing insurance claims by asking conversational questions, gathering details, and supporting documentation uploads.
I evaluated Jim using heuristic principles such as error prevention, visibility of system status, match between system and real-world language, and consistency. The analysis showed how Lemonade designed an assistant that makes a traditionally frustrating process feel simple and even humanized. Key strengths included its clean, familiar chat interface, proactive error handling through guided flows and quick-reply chips, and strong use of personalization (e.g., remembering a pet’s name). These heuristics demonstrated how Jim reduces user effort, limits opportunities for error, and increases trust by balancing automation with empathy. View the complete audit
I evaluated Jim using heuristic principles such as error prevention, visibility of system status, match between system and real-world language, and consistency. The analysis showed how Lemonade designed an assistant that makes a traditionally frustrating process feel simple and even humanized. Key strengths included its clean, familiar chat interface, proactive error handling through guided flows and quick-reply chips, and strong use of personalization (e.g., remembering a pet’s name). These heuristics demonstrated how Jim reduces user effort, limits opportunities for error, and increases trust by balancing automation with empathy. View the complete audit
Qualitative Research &
Executive Insights Deck
Executive Insights Deck
The next step was defining a research statement to clarify what I wanted to learn. Using ChatGPT, I created 3 proto-personas to ground the work in user needs. Based on my persona needs, I identified key questions to guide my qualitative research interviews.
I tested my interview questions out with a conversational ChatGPT test before finalizing an interview guide.
I conducted 2 user interviews for qualitative insights and a survey to my classmates to gain quantitive data.
I tested my interview questions out with a conversational ChatGPT test before finalizing an interview guide.
I conducted 2 user interviews for qualitative insights and a survey to my classmates to gain quantitive data.
AI Generated Wireframes & Style Guide
Generated using Figma Make
Color palette generated using Coolors.co & type scale generated using Peppercorn Figma plugin
User Testing
I created a low-fidelity prototype using Figma Make. I imported my test to Maze and conducted 5 usability tests to evaluate how effectively users could complete a core task. I asked the users to:"Imagine you are leaving town for a week and you want to pause your lawn care schedule. Using the chatbot, try to pause all tasks for 1 week."
While all participants completed the task, completion times varied. Long pauses suggested a need for clearer labels and confirmation feedback. One user wanted custom messaging beyond the preset options. View the working prototype
✨ Capstone Coming Soon ✨
Check back soon to see the culmination of my course in the capstone project, where I’ll bring together all of the research, prototypes, and usability testing into a complete case study. This section will showcase how the project evolved end-to-end and highlight the final design decisions shaped by both AI insights and my design thinking.