Movesmart
2025
UX Designer
Figma, FigJam, Docs, Zoom
Overview
Movesmart is a AI-powered platform that match users with movers through Intelligence assistant.
Instead of endless Google searches or phone calls, users simply chat with an AI assistant that understands their needs and instantly recommends suitable movers with transparent pricing. Users book moving services in minutes, not hours.
Kick Off
Problem: Moving Shouldn't Be This Stressful
Moving consistently ranks as one of life's most anxiety-inducing experiences. For urban dwellers who frequently relocate, whether for job transitions or lease ends, finding reliable movers promptly becomes crucial.
But the current experience is broken:
Unexpected price increases that blind side users
Untrustworthy moving companies with hidden fees
Constant back-and-forth phone calls
No easy way to compare options or understand what's included
The real issue is we have no reliable, transparent way to find and book movers. They're left googling random companies or begging friends for recommendations, never knowing if they're getting a fair deal.
Understanding Moving Day Pain
I analyzed how existing services approached the moving problem:
Updater, Mover, Lugg: These platforms integrate multiple moving tasks digitally with clear pricing and service choices. Users appreciate the transparency and control.
Piece of Cake: A traditional moving company requiring phone bookings. While they focus on addressing the physical moving and storage.
Existing platforms either overwhelm users with too many options or force them through tedious phone calls. No one was creating a truly conversational, guided experience that felt easy and human.
I interviewed four participants (ages 30-65) who had recently moved or had prior moving experience. I wanted to understand their aspirations, frustrations, and what would actually make moving easier. Quote from them:
"There have been cases where costs increased unexpectedly." - Ray
"I hope I won't have to worry about anything and engage in too much communication." - GC
"I often use Google search and ask people with moving experience." - VG
"If furniture needed to be reassembled, I would expect the movers to handle that as well." - YW
I organized interview insights into themes to identify patterns:
Moving is exhausting: Both physically and mentally draining
Price anxiety is universal: Unpredictable increases create deep mistrust
Communication breakdowns are common: Late arrivals, ignored instructions, endless phone tag
Discovery is broken: Google searches and friend recommendations are the only options
Users want consolidation: A single platform handling multiple moving tasks
Personalization matters: One-size-fits-all solutions don't work for diverse moving needs
Mobile matters: Users need tracking, communication, and scheduling on their phones
Trust requires transparency: Clear pricing and service details reduce anxiety
Define
Who Am I Designing For?
I created proto-personas to focus on three primary user needs:
Quick Searching: No time for endless research
Transparent Costs: No surprise fees
Small-Scale Service: Not everyone needs a full-service moving company
How Might We?
I framed the design challenge with two questions:
How might we streamline the search process for users?
How might we provide personalized moving solutions tailored to individual needs?
Design
"At this stage, I explored how people naturally seek help in daily life. I noticed that AI assistants with simple prompts provide instant answers, dramatically reducing search time and cognitive load."
My concept
Create a user-friendly AI assistant that guides users through booking by understanding their needs in natural language, then mapping those needs to structured mover options with transparent pricing.
Core features:
AI chatbot that captures user intent through natural conversation
Personalized mover recommendations based on user input
Transparent, itemized pricing for all services
Support for small-scale, personalized moving needs
Concept to Reality
User Flows
I mapped out the complete journey from AI conversation to successful booking. This helped me understand each decision point and ensure the flow felt natural, not forced.
Low-Fidelity Wireframes
I started with quick sketches to explore the conversational interface concept. These rough wireframes let me experiment with structure without getting attached to visual details.
Once I had solid concepts, I brought my Low-Fi wireframe to my mentor and design group for critique. Their feedback helped me improve usability, remove unnecessary elements, and define the product's fundamental framework.
The guidance I received pushed me to answer: What's truly essential for booking a mover through conversation? What can we eliminate?
Validate
Testing & Refining
Low-Fidelity Usability Testing
I conducted early testing with four participants, focusing on flow navigation, information clarity, and booking process understanding. This was critical: I needed to validate the AI interaction pattern before investing in visual design.
What went wrong:
Users didn't understand the review card's purpose
No one noticed that clicking prompt cards added them to the chatbox
The homepage felt cluttered and overwhelming
The AI lacked clear direction: users felt lost
Mover cards lacked sufficient detail
Information layout on mover cards felt disorganized
Service options were too limited
These findings were gold. They showed me that users wanted guidance, but they also needed clear visual cues and structured options - pure conversation wasn't enough.
High-Fidelity Usability Testing
I refined the design based on Low-Fi feedback and conducted a second round of testing. This time, I asked users to complete four key tasks:
Enter requirements into the AI chatbot
Chat with AI and choose a mover from recommendations
Review the mover profile and select preferred services
Review booking details, confirm payment, and finalize
The results were better. Users described the experience as "interesting," "simple," and "straightforward." But testing also revealed specific interaction issues that needed fixing.
Unclear AI Prompts Interaction Patterns
Based on testing feedback, I transformed prompt buttons into radio buttons and checkboxes, making it visually obvious which options allowed multiple selections versus single choices.
I also enlarged the "Confirm" button so users clearly understood how to advance the conversation.
Mobile Input Friction (Booking Section)
Small input fields and tiny preference checkboxes for move location and details. Users accidentally tapped the wrong things, causing frustration.
I enlarged input fields and preference options, changed them to horizontal scrolling, and reduced visual congestion. For time selection, I replaced the cluttered interface with a horizontal scroll picker.
Information Overload (Booking Section)
Users found the dropdowns distracting and couldn't quickly make a decision.
I replaced dropdowns with number selectors and displayed prices directly beneath each item. The list format reduced visual clutter while making selections more direct and transparent.
Half the users suggested combining date and time selection, so I consolidated those sections.
Final Mobile Version
Final Desktop Version
Prototype
Results & Impact
The final Movesmart experience delivers on its promise: making moving feel manageable through intelligent, empathetic assistance.
The AI Conversation Flow
Users open the app and are immediately greeted by a friendly AI assistant. Instead of filling out boring forms, they simply answer questions in natural language or select from suggested prompts.
The AI asks:
Where are you moving from and to?
When do you need to move?
What items are you moving?
Do you need additional services like packing or furniture assembly?
As users provide information, the AI confirms understanding and guides them toward the next decision, creating a flow that feels more like helpful conversation than data entry.
Personalized Mover Recommendations
Based on user input, Movesmart instantly matches them with suitable movers. Each mover card displays:
Clear ratings and reviews
Transparent, itemized pricing
Available services (packing, assembly, etc.)
Availability for the requested date
Users can tap any mover to see detailed profiles, read reviews, and compare options side-by-side.
Streamlined Booking
Once users select a mover, they review and customize services with clear pricing visibility. The checkout flow confirms:
Move details (date, locations, items)
Selected services with individual costs
Total price with no hidden fees
Users complete booking in minutes with complete confidence about what they're paying for.
Reflection
Designing Movesmart fundamentally changed how I think about AI in user experiences.
My initial designs treated AI like a text interface. But users wanted something that felt more like a helpful guide - someone who understood their stress and walked them through decisions.
Conversational AI works best when it combines natural language flexibility with structured visual options. Pure text conversation is actually harder for users than guided prompts with clear choices.
Mobile-First means Thumb-First
Early designs had tiny tap targets and cluttered layouts that worked fine on my laptop but were frustrating on actual phones.
I learned design with thumbs in mind. Horizontal scrolling, larger touch targets, and reduced density are must-to-haves on mobile. They're essential for usable experiences.










