Timeline
10 Days
Role
Product Designer
Tools
Figma
Deliverables
Wireframe, High Fidelity Prototype, Design System
Overview
A common wardrobe struggle that I personally shared with many others were visualising combinations and remembering what we own. Plus, people often found that many of the outfit planner apps in the market are bulky, feature-heavy and overlook outfit repetition.
Hence, I designed Pakai, a lightweight virtual closet app that helps users rediscover unworn clothes, plan outfits visually, and encourage outfit reuse through simple and basic features.
Problem
I have clothes, but I don’t know what to wear
Users feel overwhelmed by their wardrobes and often forget or overlook items they already own.
Too much mental effort to plan outfits
Users struggle to plan their outfits ahead of time and find it hard to visualise outfits.
Existing closet apps feel overwhelming
According to online user feedback, competitor apps often overwhelm users with too many features.
The Challenge
“How might we help users plan and wear more of their wardrobe effortlessly?”
Solution
Easy sign up and login
A quick and intuitive login and sign-up flow, followed by a short intro to key features, helps new users get started with ease.
Upload clothes to your online wardrobe
Users can digitise their closet by uploading photos, with each item auto-categorized by Pakai AI and taggable by type, colour, or season for easy browsing and outfit building.
Create outfits from your closet
Once items are uploaded, users can visually combine clothing to create full outfits, helping users see how their clothes work together.
Curate themed lookbooks for any occasion
Users can group outfits into curated collections (e.g., "Work", "Vacation", "Date Night") using Lookbooks. These act as moodboards for inspiration and easy access.
Plan your outfits for the week
A visual calendar allows users to assign outfits to specific days. This supports intentional outfit planning and promotes usage of neglected items.
Smart outfit suggestions powered by Pakai AI
Using wear frequency and clothing tags, Pakai’s AI suggests old outfits and even new combinations from your closet. These looks encourage rotation and help revive underused items.
Research
Before diving into design, I wanted to make sure I was creating a solution that truly met user needs, addressed common wardrobe struggles, and offered a meaningful improvement over existing virtual closet apps.
Competitor Analysis
The competitor analysis revealed that most outfit planning apps heavily rely on AI for suggestions and automatic logging, but these AI features often feel unreliable or inconsistent (particularly with Whering’s AI). Many apps also try to do too much (combining planners, marketplaces and social features) which can make the experience feel cluttered.
However, one clear strength across leading apps is their clean and intuitive interfaces, which set the standard for user expectations.
Social Listening
By analysing user reviews and Reddit discussions, I uncovered recurring frustrations with digital closet apps. Many users reported buggy, unreliable interfaces and tedious setup processes that made it hard to stay engaged. Others felt overwhelmed by excessive features and ads that distracted from the simple goal of managing their wardrobe.
User Survey
To validate early insights, I ran a small user survey with five respondents to better understand how people manage their wardrobes.
Survey Response Highlight
All respondents struggle with disorganisation and rely on memory to keep track of clothes, often leading to underused items.
4/5 particularly showed strong interest in smart, assistive features like outfit suggestions and automatic categorisation
Most respondents are open to AI suggestions, but 2/5 showed some hesitations towards AI, especially around privacy, transparency and accuracy.
Key Insights
From combining competitor analysis, user surveys, and social listening, three clear themes emerged:
Ideate
Brainstorming
Once I had a good amount of research on the background of this project under my belt, I spent some time brainstorming solutions and must-have features.
Crazy 8's
I wanted to specifically brainstorm ideas for outfit suggestions to encourage users to wear outfits they do not use often or have not thought of. Thus, I employed the “Crazy 8s” technique and came up with 8 different ways outfits could be suggested to users.
User Flow
According to the competitor analysis and key features decided during the brainstorming session, I also mapped out a tentative user flow.
Deciding
Following the idea generation phase, I analysed each solution and ultimately chose which of the ideas were more viable to execute in terms of design (and hypothetically in the development phase). I found that the first and second ideas (from top left) was the best choice in terms of implementation while staying true to the goal of simplicity.
Storyboard exploration
I then began exploring into structuring the app overall. Hence, I sketched out low-fidelity wireframes to get a better visual of the app’s layout. I used the low-fi wireframes to put together a storyboard so that I could get a better picture of how the screens would potentially flow.
Prototyping
Very quickly, I proceeded to work on the high-fidelity prototype. But before that, I created a design system to ensure the style of the app’s interface is consistent throughout my prototyping.
Design System
Hi-fi Prototype
The following picture showcases the created prototypes that only includes the initial screens that were involved in important processes to be tested with users later on.
I would also like to mention that I initially wanted to focus on a few screens that showcased the basic and crucial features but along the way, I felt that ignoring certain processes and screens could potentially hurt the user testing phase. Hence, it took me a little longer to come up with a complete prototype of Pakai.
Testing
Once the prototype was ready, I conducted remote usability testing with 2 participants, one with no familiarity with virtual closet apps and another that has used a similar app before. For easy referencing, I named them Newbie User and Experience User. These participants completed several tasks and provided feedback on the app’s functionality and features via a Google Form with scale and open-ended questions.
Key findings
Iterations
By addressing each of the problems discovered during the testing phase and as stated above), I refined the design where I could within the short timeframe.
Problem #1: The usage of AI was still vague
Problem #2: Planner lacks clarity and control
Problem #3: Lookbook function needs clarity
Learnings
Clear research questions matter
One respondent highlighted that a question about AI trust felt too vague, making it hard to answer honestly. This emphasised the importance of crafting research questions that are specific, contextual, and easy to understand to ensure that the insights gathered are accurate and actionable.
Not everything goes as planned
From revisiting design decisions to realising my initial timeline was a bit too ambitious, this project came with its fair share of challenges. I started out picturing a perfect execution, but I quickly learned that design is rarely linear. It’s a process of exploration, iteration, and learning from missteps along the way.
Designing with awareness and openness
While making assumptions in the initial start of the process is alright, I learned how important it is to not let my own biasness take control of the design. Keeping an open mind and validating ideas with real users helped ensure the solution (and myself) stay grounded.
Future
While working on this app, I had tons of ideas that I would have loved to implement but did not have the time to do so. Hence, here are my two cents on what I could have done to further the final design of Pakai.
Better onboarding
According to my competitive analysis and the user research I carried out, I noticed alot of users felt overwhelmed by existing similar apps or have not even used a virtual closet app before. Hence, I would have loved to implement a mini tutorial when a user signs up for the first time to ensure a smoother introduction to app.
More AI-powered functions
While the current AI feature focuses on outfit suggestions based on least-worn items, there’s room to explore smarter personalisation. In future iterations, I would love to add functions such as:
Outfit recommendations based on mood or calendar events.
Smart capsule wardrobe building tailored to a user’s lifestyle.
