Making the Daily Dressing Dilemma Easier with AI: Clueless understands your preferences and uses AI to make getting dressed smarter, helping you express yourself and turning the daily dressing dilemma into something fun and personal.
Duration
2025 (3 months)
Project Role
UI Designer
METHODOLOGy
Contextual Inquiry, Competitive Analysis, Affinity Mapping, User Personas, Feature Priority Matrix, Card Sorting, Tree Test, Wireframes, High-fi Screens, First Click Test, Heat Mapping, Prototyping, Usability Testing
Tools
Figma, FigJam, Photoshop, Userberry, Proven by Users, Qualtrics, ChatGPT, Zoom, Slack, Google Workspace
Discovering the Real Problem
It began with a familiar moment, that quiet pause in front of the closet, wondering
"What do I wear today?"
A small question that somehow feels bigger than it should.
Choosing what to wear is harder than it looks. It’s a daily tug of war between comfort, confidence, and time. Most people end up reaching for the same outfits after trying on a few options, not because they lack variety but because figuring out what looks good and feels right can be unexpectedly time consuming and exhausting.
As we dug deeper, we began to see that the struggle wasn’t just about clothes. Getting dressed had quietly become a source of stress shaped by decision fatigue, confidence, and the desire to feel right in our own skin.
Most wardrobe apps focused on what was inside the closet, not what was behind the choice. That’s when it became clear that getting dressed had stopped feeling personal and started feeling like a chore.
Defining the Solution
Once we understood the problem, the path forward became clear — people didn’t need another wardrobe tracker; they needed a companion that understood them.
That’s how Clueless was born — an AI-driven personal stylist designed to make outfit decisions effortless, expressive, and empowering.
The goal was never just about choosing outfits. It was about helping people rediscover a sense of joy and confidence in getting dressed, while also encouraging mindful, sustainable habits. We wanted to turn the daily “What should I wear?” into a small moment of creativity instead of confusion.
At its heart, Clueless is built on empathy and trust. It’s about making technology feel a little more human — because great design, like great style, always begins with understanding the person behind it.
Seeing What Others Are Doing
Before building Clueless, we wanted to understand what already existed in the fashion-tech world and where users still felt unsatisfied.
We explored popular wardrobe and styling apps like Acloset and Whering, both offering digital closets and outfit planning. They did many things right, helping users organize clothing and plan looks ahead of time. But something was missing.
Features
Whering App
Acloset App
Clueless App
Wardrobe Upload & Outfit Planner
Weather Based Suggestions
Skin/Hair/Lip/Eye Color Analysis
In store scanner for New Items
Sustainability Features
Limited
Real Time Personalizations
Limited
Whering, Acloset and other apps focused heavily on what users owned, not who they were.
None of them truly captured the emotional side of dressing up, how people’s moods, weather, or self-expression influence what they wear. Personalization often felt surface-level, and AI recommendations lacked transparency. Users couldn’t tell why an outfit was suggested or how it suited them.
This gap revealed our opportunity: to design an assistant that goes beyond organization — one that understands context, emotion, and individuality. The kind of app that feels less like a machine making choices for you, and more like a stylist who gets you.
Finding Direction through Research
Before building Clueless, we wanted to understand what already existed in the fashion-tech world and where users still felt unsatisfied.
Focus Areas:
How they plan or choose outfits
What makes decision-making stressful
What they expect from AI in fashion
How they view sustainability
What we heard:
As we analyzed the interviews, stories started to connect. Each note revealed emotions, habits, and subtle frustrations that shaped how people dress.
Emerging Themes:
🧠 Decision Fatigue 💬 Trust in AI 🌱 Sustainable Use 💃 Self-Expression 📱Expected App
To turn insights into possibilities, we framed How Might We questions — not as answers, but as directions.
Each story shaped a character. We created personas to represent different mindsets, goals, and frustrations.
With our users in mind, we mapped how their ideal journey should feel — simple, guided, and flexible.
Once the flow was mapped, we needed to define what features mattered most — both for users and for the product vision.
We plotted our ideas on a Feature Priority Matrix to balance impact and effort, ensuring we focused on what truly improves the user experience.
We created feature cards with the help of AI to organize information intuitively. To validate the structure, we ran a Card Sorting study with 24 participants.
Card Sorting Results: Proven by Users
Next, we transformed those insights into a Tree Structure to test navigation clarity.
Once we ran tree test, we got some amazing results.
We brought the early wireframes to life and visually tested the flows with 10 users. We want early failures to lead to success later. We identified 4 main tasks of the app.
1) Where would you click to see what the app recommends for you from your closet?
2) Where would you click to add a new item from your phone gallery into your digital closet?
3) Where would you click first to upload or take a photo of an item and
see what outfits the app suggests?
2) Where would you clickfirst to use a previous saved outfit from
your bookmarks from particular day?
With all the research and data we collected, we can move toward a high-fidelity screen with confidence.
Designing with Intent
Before moving into the final build, we wanted to set a clear visual direction — one that reflected what we learned from users. This phase was about defining the emotion, tone, and consistency behind every interaction, ensuring the design felt human and intentional from the start.
We began by exploring how the experience should feel — calm, personal, and confident.
The moodboard helped us translate that feeling into visuals, blending minimal layouts, soft tones, and subtle movement.
To keep the design cohesive, we created a design system rooted in simplicity and usability.
It covered typography, color, spacing, and motion, ensuring that every element supported clarity and ease of use.
With the system and components ready, we started putting them together into early screen explorations.
These layouts reflected how our design language translated into real interaction moments — not the final app yet, but a clear vision of where we were headed.
Bringing Ideas to Life
We want everything to work, so we linked all the screens together to make a cohesive Prototype
Reflections & What’s Next
🧠 Key Learnings
AI personalization must stay explanatory, not authoritative — users value clarity over mystery.
Trust and transparency are the foundation of emotional comfort in AI design.
A user-centered hierarchy and balanced visuals reduce cognitive load and create calmness.
Iteration matters — every usability test revealed something that made the product more human.
🚀 What’s Next
As far as the app goes, we’re looking ahead to developing a working version that expands beyond prototyping — adding:
Enhanced personalization, integrating lifestyle and event data.
Social features, where users can share outfits or get peer feedback.
Sustainability tracking, helping users measure wardrobe reuse and conscious choices.
Accessibility & inclusion, adapting for more body types and cultural styles.
Copyright@2025
Made in Framer