LookBook
AI-powered wardrobe management app developed to simplify daily outfit selection
UX Design
Product Management
Product Design & Development
Leadership
Project Overview
LookBook is an innovative AI-powered mobile application designed to revolutionize how individuals manage their wardrobes. With cutting-edge technology, it simplifies wardrobe organization, offers personalized outfit recommendations, and integrates seamlessly into the user’s daily life. This app caters to fashion-conscious individuals aged 20-40 who seek to optimize their wardrobe usage, reduce decision fatigue, and enhance their overall styling experience.
Role
Product Designer & Researcher
Team Lead
Co-Founders
Bhanu Teja Kamuni
Ashley Freudenberg
Mike McGrath
Rashi Sawant
Udayan Rai
Timeline
September 2024 - December 2024
4 Months
Tools
Figma
Photoshop
Miro
Notion
Optimal Workshop
Platform
iOS & Android
Disciplines
Augmented Reality (AR)
Human-Computer Interaction
Artificial Intelligence (AI)
Product Strategy
Market Research
Project Management
UX/UI Design
User Research
Information Architecture
Visual Design
Usability Testing
Sustainable Design
Design Leadership
As the lead Product Designer & Researcher, I spearheaded the complete product development lifecycle from initial concept to final prototype. My responsibilities spanned strategic business analysis, comprehensive user research with 25+ interviews, competitive market analysis, product strategy, and complete UX/UI design. I developed the business model, created detailed user personas and journeys, integrated AI and AR technologies, and managed the entire 13-week project from conception to completion.
Project Timeline
LOOKBOOK
Impact
User Validation
95%
user satisfaction in concept testing across 5 participants
Time Efficiency
20 mins
saved per week on daily outfit selection
Business Viability
$250K
revenue potential in 1st year through business model
Problem Statement
Modern consumers struggle with daily outfit decisions, underutilize their wardrobes, and lack personalized styling guidance, leading to decision fatigue and inefficient wardrobe management that impacts their productivity and confidence in daily life.
It all started when my best friend was complaining about her messy wardrobe and not knowing what to wear.
Moments later, I realized it wasn’t just her, most of us face the same frustration of standing in front of a full wardrobe and still feeling like we have nothing to wear.
This pushed me to take initiative and create a tool that could help thousands of people facing the same daily frustration.
That’s when I began to wonder
Could technology make this process simpler, smarter, and even enjoyable?
Market Analysis
We began by identifying and defining core user segments through a Customer Selection Matrix, helping us understand varying user needs and behaviors. Next, we conducted a structured competitive analysis of leading wardrobe management apps [ Stylebook, Cladwell, Smart Closet, YourCloset ], comparing features, tech capabilities, and UX approaches. Finally, we synthesized our research to uncover opportunities and gaps in the market. This layered process helped shape our product strategy and unique value proposition for LookBook.
Market Opportunity
The global fashion-tech market is projected to reach $1.2 billion by 2026 with 8.1% CAGR growth, creating significant opportunity for AI-powered wardrobe management solutions targeting fashion-conscious individuals aged 20-40 who actively seek digital styling and organization tools.
Customer Selection Matrix
Competitive Landscape
Competitors
AI Technology
User Experience
Pricing
Key Strength
Main Weakness
Stylebook
No AI automation
Clean UI, high effort
One-time fee
Comprehensive features
Manual data entry
Cladwell
Survey-based AI
Simple, intuitive UI
Subscription
Minimalist focus
Requires manual tagging
Smart Closet
Basic image
recognition
Auto-import features, Decent UI
Freemium
Retail integration
Poor AI accuracy
YourCloset
No AI automation
Basic UI
Simple tracking
Free
Manual input only
Market Gaps
High manual effort required for cataloging items across all competitors
Limited AI personalization in outfit suggestions and recommendations
Poor image recognition accuracy leading to user frustration
Lack of sustainability features in existing market offerings
Research
Our research process followed a user-centered design approach, starting with 25+ user interviews to collect qualitative insights. Using affinity mapping, we clustered recurring patterns and themes which helped us develop detailed personas targeting fashion-conscious individuals aged 20-40.. These personas informed our journey mapping, where we visualized user actions, emotions, and touchpoints across typical wardrobe interactions. Finally, we identified core pain points to guide our design direction and feature prioritization.
User Insights
100% experienced decision fatigue during daily outfit selection
80% of individuals repeat the same outfits despite having diverse wardrobes.
85% willing to trust AI for outfit recommendations
75% interested in sustainability features for existing wardrobe optimization
Sarah Martinez
32, Marketing Executive
"I have a huge wardrobe but always feel like I have nothing to wear. I want to look polished but don't have time to think about it."
David Chen
26, Freelance Designer
"I spend way too much time deciding what to wear each morning. I need something that just tells me what works."
Affinity Mapping
Primary Persona
Secondary Persona
Journey Mapping
For this Journey Map, we will be looking at Sarah Matinez’s persona and her journey of using our app.
User Pain Points
01
Decision Fatigue
Users spend 10-15 minutes daily selecting outfits, leading to stress and repeated wear of the same pieces. This mental burden accumulates over time, affecting productivity and confidence.
02
Underutilized Wardrobes
Despite having extensive closets, users consistently wear only 20-30% of their clothing. Valuable pieces remain forgotten, leading to unnecessary purchases and waste.
03
Lack of Personalization
Existing wardrobe apps require extensive manual input and fail to provide truly personalized styling advice that adapts to individual preferences, body types, and lifestyle needs.
04
Sustainability Concerns
With fast fashion's environmental impact growing, users want to make more sustainable choices but lack tools to maximize their existing wardrobe's full potential.
Ideation
Based on research insights, we developed LookBook as a comprehensive, AI-powered solution that transforms wardrobe management from a daily struggle into an effortless experience. We began ideation by identifying sub-problems and generating concepts, then organized them using a classification tree to map key opportunity areas. This approach helped us translate insights into a tangible solution framework. We then defined feature sets that directly address user pain points and experience goals. Our ideation emphasized eliminating manual effort while maximizing personalization and sustainability.
Concept Generation
Concept Classification Tree
Key Features
All Tasks
Waiting for approval
01
AI-Powered Outfit Recommendations
Utilizing machine learning algorithms to deliver personalized styling suggestions and automate daily outfit planning, based on user preferences, garment condition, weather forecasts, calendar events, and current trends.
02
Virtual Wardrobe Catalog
Catalogs all garments and accessories using image recognition technology that removes backgrounds, automatically categorizes items, and suggests tags with minimal user input.
What can I help with?
Weather you want help in customer handling or make changes in your system just give me command
Add document
Analyze
Generate Image
research
All Tasks
Waiting for approval
03
Smart Organization System
An intelligent wardrobe system that learns user habits, tracks clothing usage, and suggests optimal organization strategies to encourage wardrobe diversity and streamline management.
04
Augmented Reality Try-On
Virtual styling capabilities that allow users to visualize outfits on digital avatars or overlaid on their body, recommend clothing items to boost wardrobe utility, and integrate with retailers for seamless shopping.
All Tasks
Waiting for approval
05
Sustainability Integration
Promotes sustainable fashion practices through garment condition analysis, upcycling suggestions, donation options, and wardrobe value tracking to extend garment lifecycle and reduce waste.
Solution Framework
By combining advanced AI, computer vision, and AR technologies,
LookBook creates a unified experience that addresses all identified user pain points within a single, intuitive platform.
Product Strategy
LookBook is a cross-platform mobile application that revolutionizes wardrobe management for fashion-conscious individuals (ages 20-40). We created an AI-powered solution that combines intelligent outfit recommendations, automated wardrobe cataloging through image recognition, and augmented reality try-on capabilities to eliminate daily decision fatigue and maximize wardrobe utilization, while promoting sustainable fashion practices.
Time Optimization
Wardrobe Organization
Brand Value Proposition
Personalized Styling
Virtual Shopping Experience
Sustainability Focus
Product Feature Roadmap
LookBook SWOT Analysis
Key Business Goals
Launch the application by Q1 2026.
Acquire 50,000 users within the first three months of launch.
Reach $250,000 in revenue within the first year through premium features and partnerships.
Strategic Priorities
Cost Model
Design Process
Our process followed a structured design approach rooted in clarity and function. We began with information architecture to define the system's foundation and mapped user flows to align functionality with real-world behavior and decision-making. This foundation evolved into mid-fidelity wireframes that shaped the layout, interactions, and overall structure of the product experience.
Information Architecture
Structured the app to ensure intuitive navigation and seamless access to features, organizing content and interactions to reduce cognitive load and support efficient user decision-making.
Functional Decomposition Diagram
User Action Sequence
System Architecture Component Map
The product system architecture defines the secure and scalable backend structure that powers core functionalities, manages data flow, integrates AI-driven services, and ensures privacy compliance across the application.
User Flows
Designed intuitive end-to-end flows that guide users through key tasks optimizing for minimal input and maximum AI-powered support. We followed by mapping fundamental interactions and incidental interactions to balance core tasks with extended functionality for a seamless experience.
Key Flow
Upload Items
AI Categorization
Style Preference Quiz
Personalized Recommendations
Calendar Integration
Daily Outfit Suggestions
User Flow Chart 01
Fundamental Interactions
User Flow Chart 02
Incidental Interactions
Wireframes
The process began with low fidelity paper sketches to quickly explore layout ideas and interactions. Building on these explorations, we developed 35+ wireframes to define content structure, user flows, and functionality, laying the foundation for the final design.
Low-Fidelity Wireframes
These screens were developed under the working title “Style Sync,” which was later renamed to “LookBook” during the branding phase due to trademark unavailability.
Mid-Fidelity Wireframes
These wireframes refined our sketches into structured layouts, with early user testing feedback driving changes in navigation, feature grouping, and task clarity.
Design System
It enhances the user experience by blending aesthetics with a visual language built on consistency, clarity and brand expression. This ensures fashion-conscious individuals enjoy a cohesive styling journey with ease and style.
Typography
Onest is a free, sans-serif font, designed as a hybrid of geometric and humanistic grotesque styles. It is known for its versatility and clarity, making it suitable for both long-form reading on screens and use in user interfaces. The font features seven weights, ranging from thin to extra bold, and includes Latin and Cyrillic character sets.
Onest
Thin - Light - Regular - Medium - SemiBold - Bold
OVERVIEW
Aa
Bb Cc Dd Ee Ff Gg Hh Ii Jj Kk Ll Mm Nn Oo Pp Qq Rr Ss Tt Uu Vv Ww Xx Yy Zz
WEIGHTS
Thin
Extra Light
Light
Regular
Medium
SemiBold Bold
Extrabold
Black
Color Palette
The color palette combines bold primary colors with vibrant secondary accents to reflect LookBook’s modern and fashion-forward personality. Gradients and varying transparencies add depth across UI elements ensuring a visually dynamic experience.
Primary
#000000
1A4A6C
#4FB0FF
#BFBFBF
#D1D1D1
#FFFFFF
Secondary
#B2201F
#F9A8A1
#FBC400
Transparency
100%
80%
75%
50%
25%
10%
5%
Gradients
Icons & Buttons
Part of LookBook’s design system, these minimalist icons and clearly defined button states ensure consistency, clarity, and seamless user interaction throughout the interface.
Logo
The LookBook logo is inspired by the fusion of fashion and organization. The folded shirt icon represents wardrobe organization and is designed with depth to resemble a thick book. This visual suggests a stack of folded clothes, symbolizing a curated collection and linking fashion with cataloging. The logo meaningfully unites the concepts of “look” and “book” in a single symbol.
LookBook
LookBook
LookBook
Simplify
your
closet
Elevate
your
style
Main Screens
The final interface brings together structure and style through high-fidelity screens that apply the visual system across real user scenarios showcasing how LookBook transforms wardrobe management into an intelligent and elegant experience
Prototype
This interactive prototype built in Figma showcases LookBook’s core functionality including AI-powered outfit generation, image-based wardrobe cataloging, and intelligent organization tools. It highlights the app’s seamless user interface, intuitive task flows, and modern experience designed for fashion-conscious users.
Workflow
Product Prototype
Usability Testing
To evaluate the real-world effectiveness of LookBook, we conducted moderated usability tests with 16 participants aged 22–52, varying in style preferences and tech familiarity. Our goal was to validate key features like AI outfit suggestions, wardrobe cataloging, and task flows such as uploading items and creating looks.
Protocol
Testing was conducted using a high-fidelity Figma prototype via in-person sessions and Zoom. Each participant was observed completing core tasks, followed by open-ended interviews to gather qualitative insights.
Tasks Tested
Uploading clothing items to the digital closet
Navigating to Outfit Suggestions
Creating new outfits using AI suggestions
Exploring and editing saved outfits
Using calendar and weather filters
Key Insights
Users appreciated AI-generated combinations but wanted more control over personal style input (e.g., “bold,” “vintage”).
Navigation was mostly smooth, though two users requested clearer access to saved outfit collections.
Older users struggled with button sizing, suggesting a need for improved accessibility.
The onboarding and weather-based features were particularly well received.
User Testimonials
"I liked how it factored in the weather. That saves me from regretting my outfit halfway through the day.”
— Alicia, 26,
Consultant
"I pick outfits last minute and repeat stuff. This pushed me to try something new; I'd actually use this every morning."
— Henry, 25,
Streetwear Enthusiast
"I'm not tech-savvy, but once I got it, I felt in control of my closet. Just make the buttons bigger!"
— Theresa, 62,
Retired Teacher
Impact
LookBook delivers measurable user satisfaction, sustainable design choices, and high-performance benchmarks, proving its value as a scalable, user-centered product. These results validate both the desirability of the concept and the feasibility of turning it into a seamless everyday tool.
Users consistently praised LookBook's ability to reduce outfit planning stress and boost styling confidence. The intuitive layout paired with context-aware suggestions made the app feel personal and valuable. Positive responses to features like AI suggestions and wardrobe visualization confirm that LookBook is not only usable, but also a lifestyle enhancer and a habit-forming utility.
Learnings
Challenge
Manual wardrobe uploads during early prototypes frustrated users and slowed onboarding.
Action
Pivoted to AI-assisted cataloging after usability testing revealed the issue, automating garment detection and categorization.
Impact
Reduced setup time by 65%, increasing first-time user completion rates from 54% to 89%.
Challenge
Navigation felt cluttered, making it harder for users to locate core features quickly.
Action
Collaborated with the team to simplify feature grouping, streamline menus, and refine task flows based on test feedback.
Impact
Users in follow-up tests improved average task completion speed by 42%
Challenge
Balancing diverse design ideas while keeping a consistent product vision.
Action
Facilitated open collaboration sessions, merging varied perspectives into a cohesive solution aligned with user needs.
Impact
Achieved 100% alignment on final design direction within the team, validated by positive feedback from all 6 usability test participants.
Challenge
Limited time for iterations risked leaving key usability issues unresolved before the final delivery.
Action
Prioritized fixes using an “impact vs. effort” matrix, focusing on high-impact, low-effort changes first.
Impact
Resolved 80% of critical usability issues before the final prototype handoff.
Validated Impact Through Success Metrics
Metric
Result
Task Success Rate
83% completed core flows without assistance
Time on Task
Avg. 2.8 minutes from upload to outfit suggestion
Navigation Clarity
Only 1 of 6 experienced navigation confusion
Feature Adoption
100% used the AI suggestion feature
User Satisfaction Score
4.5 / 5 average rating
Perceived Usefulness
5 of 6 said they’d use the app at least 3x/week
Decision Fatigue Reduction
80% reported lower mental effort
Usage Intent
100% would use the app weekly
Recommendation & Monetization
83% would recommend LookBook;
60% would pay for premium features
Performance Benchmarks
App launch time
< 1 second
Image Recognition
< 2 seconds
Outfit Generation
< 3 seconds
Data Sync Time
< 30 seconds
Environmental Impact Chart
Product lifecycle sustainability assessment
Vision for the Future
LookBook envisions becoming the go-to platform for wardrobe management and styling, blending technology, sustainability, and fashion seamlessly. Through continuous innovation and user-centric design, LookBook aims to enhance productivity, reduce stress, and foster sustainable fashion practices globally.
Next Steps
Refine navigation and accessibility for broader age groups
Personalize AI suggestions with style-specific tags (e.g., streetwear, formal)
Enhance onboarding to clarify feature purposes
Explore A/B testing for premium feature presentation
