Personal Styling & Digital Wardrobe
COMPANY
OVERVIEW
Add a FitCheckIt is an AI-powered personal styling and digital wardrobe platform that helps users organise their clothing and wishlist while discovering items that match their style. By learning individual preferences and analysing existing wardrobes, the platform recommends pieces that pair well with what users already own, reducing the need to browse multiple websites.
FitCheckIt delivers a personalised, streamlined shopping and outfit-planning experience designed to keep users engaged and returning whenever they shop.
PROJECT
SCOPE
Through broad ideation and concept exploration, we designed a fashion-first wardrobe and shopping app that moves beyond generic recommendations, focusing instead on immediate value, personalized styling, and seamless wardrobe integration.
THE
GOAL
Design a minimal, fashion-forward platform that:
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Delivers immediate value on first use
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Helps users organise wardrobe and wishlist
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Provides relevant, trustworthy recommendations
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Supports real-life outfit planning
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Encourages long-term engagement
THE PROBLEM
Fashion-conscious users face decision fatigue due to an overwhelming number of shopping options and disconnected fashion tools. While they need a way to discover items aligned with their style, organise their existing wardrobe, plan outfits for real-life situations, and make confident purchase decisions, current solutions fall short. Many platforms provide generic recommendations, require lengthy onboarding with limited initial value, separate shopping from wardrobe and styling features, and rely heavily on manual input.
As a result, users experience a fragmented journey where they browse extensively but struggle to make clear, confident decisions.
OPPORTUNITY
There is an opportunity to design a connected fashion ecosystem that integrates wardrobe management, personalised discovery, and AI-driven styling within a single experience. By aligning recommendations with users’ existing wardrobes and personal preferences, the platform can reduce cognitive load, support more confident decision-making, and seamlessly connect inspiration, outfit planning, and purchasing. Delivering immediate value from the first interaction can also encourage continued engagement and long-term user adoption.
HOW DO PEOPLE DECIDE THEIR PERSONAL STYLE?
RESEARCH APPROACH
To develop a clear understanding of the problem space, we used a combination of qualitative and quantitative research methods. This approach helped uncover user behaviours, identify key pain points, and validate insights that informed the product’s design direction.
Research Goals
The research aimed to:
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Understand how users currently discover and manage fashion items
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Identify pain points in existing fashion and shopping apps
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Explore how users make decisions about their personal style and purchases
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Identify opportunities for improving convenience and decision-making
Method #1
Heuristic Evaluation
We evaluated existing products using usability principles to identify systemic issues.
Key Findings
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Navigation complexity increased cognitive load
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Poor feedback reduced user confidence
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Feature-heavy interfaces created overwhelm
Insight: Simplicity and clarity would be critical differentiators.
Method # 2
User Interview + survey
Participants
We conducted 15 semi-structured interviews with participants aged 18–60, with the majority being younger users who frequently engage with fashion, shopping, and lifestyle apps.
Method
The interviews explored users’ current behaviours when discovering fashion items, managing their wardrobe, and deciding what to purchase. Participants were asked which apps they currently use, the challenges they experience with those tools, and what features would make a fashion app more convenient and useful for them.
Survey Validation
Insights from the interviews informed the design of a follow-up survey aimed at validating patterns and quantifying user preferences across a broader audience. The survey helped confirm recurring pain points and measure interest in potential features.
Outcome
This mixed-method research approach helped identify key user needs, validate design assumptions, and prioritise features that could address decision fatigue and improve the overall fashion discovery and wardrobe management experience.
KEY
WEAKNESSES
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Generic AI recommendations
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High manual input
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Disconnected user journeys
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Limited organisation tools
KEY
STRENGHTS
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Social feeds increase engagement
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Gamification improves retention
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Automation reduces effort
COMPETITIVE ANALYSIS
We analyzed four key competitors:
Alta
Acloset
Fits
Wardrobe
KEY PETTERNS
Each competitor focuses on a single
dimension:
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Discovery (feeds, inspiration)
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Wardrobe management
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Community engagement
MARKET
GAPS
1. Lack of Contextual Personalisation
Recommendations are not based on wardrobe, occasion, or behaviour
2. High User Effort
Wardrobe management requires too much manual input
3. Fragmented Experience
Shopping, styling, and planning are disconnected
USER EXPECTATIONS
WHAT WE DESIGNED
USER JOURNEY
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“ I don’t want to answer too many questions before I see value.”
Users drop off when onboarding delays value.
Design Decision
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Optional onboarding (skip anytime)
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Guest mode access
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Immediate recommendations
Impact
Users experience value within the first session, reducing friction
“I don’t know what to wear, even with many clothes.”
Wardrobe overload creates decision fatigue.
Design Decision
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Outfit creation tools
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Event-based planner
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AI suggestions based on wardrobe
Impact
Shifts focus from storage → usage
“Recommendations feel random.”
Generic AI reduces trust.
Design Decision
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Wardrobe-based recommendations
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Context awareness (style, occasion)
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Exploration options
Impact
Recommendations feel relevant and personal
“I regret buying things I don’t wear.”
Users lack confidence in purchases.
Design Decision
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Wardrobe integration in shopping
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Compatibility suggestions
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Reuse-focused recommendations
Impact
Supports better decisions, not just more purchases
“Planning outfits for events is stressful.”
Users think in real-life scenarios, not categories.
Design Decision
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Calendar-based planner
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Event-driven outfit organisation
Impact
Aligns product with real behaviour
“I want inspiration, but not endless scrolling.”
Users want curated inspiration, not noise.
Design Decision
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Controlled “Inspo” section
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Save-to-wardrobe integration
Impact
Balances exploration with action
HOW MIGHT WE ...
HOW MIGHT WE ...
Make onboarding engaging
How might we deliver immediate value to reduce onboarding drop-off?
Enable smarter
shopping
How might we create a seamless system connecting wardrobe, shopping, and planning?
Smarter wardrobe & styling
How might we enable users to make confident styling decisions?
FROM INSIGHT TO
PRODUCT REQUIREMENT
Trust & Sustainability
" Don't Sell "
Based on insights from user interviews and supporting research, we conducted affinity mapping to synthesise patterns and define key user requirements. These requirements directly informed the design of the app’s navigation and core features.
Users value transparency, control, and meaningful utility over promotional features.
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Strong privacy expectations with no intrusive tracking or advertising
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Preference for private sharing with close networks rather than public posting
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Motivation driven by wardrobe clarity, reusability, and mindful consumption rather than gamification
Design Impact
These requirements guided the information architecture and navigation design, ensuring each part of the product delivers clear value, personalised relevance, and user trust from the first interaction.
Oboarding
" How value quickly "
Users expect immediate value with minimal effort when first using the app.
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Simple, visually guided onboarding with low friction
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Fast wardrobe upload (single or batch photo) with instant sample outfit generation
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Option to explore via guest mode, supported by guided tutorials
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Early-stage preference inputs (e.g., budget or spending habits) to enable upfront personalisation
Personalisation & Relevance
" Make it feel tailored"
Users want highly relevant, context-aware recommendations that reflect their identity.
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Outfit suggestions based on existing wardrobe, personal style, body type, and location
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Recommendations must feel specific and curated, not generic
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Advanced features such as colour analysis, fit preferences (e.g., tall sizing), and seasonal curation
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Increased trust through integration with local stores, accurate sizing, and detailed material information
Based on insights from user interviews and supporting research, we conducted affinity mapping to synthesise patterns and define key user requirements. These requirements directly informed the design of the app’s navigation and core features.
MVP
To avoid overbuilding, features were prioritised based on:
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User impact
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Complexity
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Strategic value
High Impact
low Effort
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Simplified onboarding
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Unified wardrobe & wishlist
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Basic outfit creation
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AI recommendations based on wardrobe
Goal: Deliver value within first session
ONBOARDING
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Quick login with social media or as a guest
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Style Prefrences questions
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guided tutorial
WARDROBE
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Get ai recommendation from Wishlist
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Get notification from Wishlist item sale
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Stock updates
PLANNER
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Direct link to shopping wed and add item to wishlist
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my board and outfit for ability to add items and create outfit
INSPO
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follow people and follow trends
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save favourites
AI STYLIST
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Select top picks
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link to store, and get style rec from wardrobe and wishlist
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Recommendation by color season and body type
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Identify wardrobe gaps
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chat suggestion with chat history
PLANNER
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Weekly calender view
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Upcoming outfits planned by date
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Canvas with drag and drop functionality
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Ai outfit recommendation
High Impact
High Effort
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Advanced AI styling
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AR try-on
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Social features
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Behavioural analytics
Key Trade-Off
A social-first approach was considered but deprioritised:
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Higher complexity
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Lower immediate value
Focus remained on core user utility first
Solution Overview
FitCheckIt is structured around five core areas:
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Onboarding
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Wardrobe
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Inspo
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AI Stylist
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Planner
Each supports a different stage of the user journey
Core Structure
The platform is organised into five core areas: Onboarding, Wardrobe, Inspo, AI Stylist, and Planner, each aligned with a key stage of the user journey from initial setup to everyday use.
Key Sections
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Onboarding (Entry & Personalisation)
Designed to deliver immediate value with minimal friction. Users can set preferences (style, fit, brands) while accessing the app early, ensuring relevant recommendations without blocking exploration.
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Wardrobe (Core System Hub)
A centralised space for managing owned items, wishlist, and outfits. Users can organise, combine, and save looks, enabling context-aware recommendations across the platform.
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Inspo (Discovery & Social Layer)
Supports exploration through personalised feeds and social following, allowing users to discover and save styles directly into their wardrobe or wishlist.
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AI Stylist (Decision Support)
Provides context-aware outfit recommendations based on user data. Outputs are actionable—users can save, apply, and use them directly across the system.
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Planner (Real-Life Execution)
Connects digital styling to real-life use through calendar-based outfit planning, allowing users to build looks using wardrobe items and AI suggestions.
System Thinking & Connectivity
The system is designed as an interconnected ecosystem, not isolated features:
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Items flow between Wardrobe, AI Stylist, and Planner
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Inspiration feeds directly into Wardrobe and Wishlist
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AI recommendations are grounded in real user data
This creates a seamless loop:
Discover → Save → Style → Plan → Reuse
Outcome
This IA balances clear structure with strong connectivity, reducing cognitive load and aligning with user mental models. The result is an experience that is intuitive, cohesive, and practical, transforming FitCheckIt into a unified decision-support system.
INFORMATION
ARCHITECTURE
We designed FitCheckIt’s information architecture to support a seamless end-to-end experience, enabling users to move between discovery, wardrobe management, styling, and planning with minimal cognitive load.
The structure was informed by research, which showed that users think in continuous decision-making flows, not isolated features. In response, I organised the product into a connected ecosystem, aligning navigation with user goals and enabling smooth transitions
BRAND IDENTITY DEVELOPEMENT
WIREFRAME
NAVIGATION
ICON DESIGN
To give FitCheckIt a distinctive identity,
I designed custom navigation icons from scratch. Each icon was crafted for clarity, recognisability, and consistency, reflecting its feature while remaining simple and scalable across screens.
The icons align with the app’s information architecture, guiding users intuitively through Wardrobe, Inspo, Planner, and other key sections.
This approach balances usability with a polished, cohesive visual language, reinforcing both functionality and brand personality.
GRID
SYSTEM
GRID SYSTEM
FROM IDEAS
TO INTERFACE
ONBOARDING
Deliver Value From First Interaction
The onboarding flow is designed to deliver value from the first interaction while enabling personalised recommendations.
Users can:
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Create an account or continue as a guest
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Set up their profile, including name and preferences
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Define style, fit, and brand preferences
This approach ensures recommendations feel relevant from the start while keeping the process lightweight and flexible, reducing drop-off. Users can access core features immediately, allowing them to explore the app and experience value early.
USER FLOW
IDEATION
SKETCHES
TUTORIAL
The onboarding flow is designed to deliver immediate value while enabling personalisation.
Users can:
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Create an account or continue as a guest
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Set up their profile (name, preferences)
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Define style, fit, and brand preferences
This step ensures that recommendations feel relevant from the start, while remaining lightweight and flexible to reduce drop-off. Users are not blocked from accessing core features, allowing them to explore value early.
WARDROBE
Central Hub Of The Product
The Wardrobe acts as the central hub of the product, where users manage both owned and desired items.
It includes:
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Closet (owned items)
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Wishlist (saved items)
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Outfit creation and boards
Users can:
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Add, edit, and remove items
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Organise items using categories and filters
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Combine pieces into outfits
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Save complete looks for future use
By consolidating wardrobe and wishlist into a single system, the design reduces fragmentation and enables more meaningful, context-aware recommendations across the platform.
IA OF
WARDROBE
USERFLOW
OF WARDROBE
INSPO
Discovery & Social Layer
The Inspo section introduces a social and discovery-driven experience, allowing users to explore styles beyond their existing wardrobe.
Users can:
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Follow other users and profiles
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Browse trending and personalised feeds
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Save outfits and items into collections
This creates a continuous inspiration loop, where users can discover new styles and directly integrate them into their wardrobe or wishlist.
USERFLOW
OF INSPO
AI STYLIST
Decision Support System
The AI Stylist serves as a decision-support layer, providing actionable, context-aware outfit recommendations rather than abstract suggestions.
Users can:
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Receive quick outfit recommendations
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Interact via chat for tailored suggestions
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Explore recommendations based on their wardrobe and wishlist
Deep integration with other sections allows users to:
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Apply suggestions directly to their wardrobe
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Save generated outfits
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Use recommendations in planning
This design ensures AI outputs are immediately usable, personalised, and connected to the user’s ongoing styling workflow.
USERFLOW
OF AI STYLIST
PLANNER
Real Life Context & Execution
The Planner connects digital wardrobe management to real-world usage, turning styling from a passive activity into a practical, forward-looking process.
Users can:
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View a calendar of upcoming events
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Plan outfits for specific dates
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Create looks from scratch or use saved outfits
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Drag and drop items onto a planning canvas
Items can be pulled from:
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Wardrobe
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Wishlist
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AI Stylist recommendations
This design helps users prepare outfits efficiently, integrating inspiration, AI suggestions, and saved items into actionable planning for everyday life.
USERFLOW
OF PLANNER
USABILITY
TESTING
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ONBOARDING
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Strong First Impression: App feels smart, unique, and visually engaging.
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Smooth Onboarding: Clear, tailored questions; right length; useful factors (occasion & color).
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Engaging Visuals: Animations, illustrations, and tutorial icons highly appreciated.
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Guest Login: Users value the option to explore without sign-up.
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WARDROBE
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Wardrobe & Closet: Easy to add items; well-placed buttons. Users liked the organisation and felt it was tailored to their needs.
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Fits Tab: Saving outfits/collections in one place was appreciated once understood.
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Styling & AI Suggestions: Clear, engaging, and helpful for outfit inspiration.
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Shopping Integration: Web store links and adding from links/photos to wishlist were valued.
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Overall Benefit: Combines Pinterest/Instagram-style inspiration with wardrobe management, saving users time.
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INSPO
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Style Refinement: Helps users refine style without other platforms.
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Interactive Saving: Can save and remix outfits directly.
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Engagement: Recommendations and posts from other users appreciated.
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Key Feature: Inspo tab is the main feature users would return to.
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UI & Layout: Simple, clean; Trending + Following on one page works well.
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AI STYLIST
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Users find the AI stylist helpful, trustworthy, and supportive in a modern context.
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They like having both quick suggestions and the option for chat; no strong preference for one over the other.
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Outfit picker is handy and easy to use.
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Clear understanding of AI stylist capabilities;
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They find it super useful and a feature to comeback
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PLANNER
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Drag-and-drop functionality is intuitive and works well, similar to the Wardrobe tab.
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Canvas tab is fun and visually engaging; users enjoy mixing and matching outfits like a collage.
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Users like upcoming events and ready outfit
ISSUSE &
ITERATIONS
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Confusion between “Closet” vs “Wardrobe” .
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Fits” label is ambiguous; users unsure what it does.
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Styled just for you” in wishlist felt misplaced; users expected saved items first.
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Minor friction adding items for some users (clarity needed on flows).
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What still needs refinement...
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Rename labels: Closet →My Closet, Fits →Outfits.
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Reorder wishlist: show user-saved items first, then “Styled for you” recommendations.
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Add short contextual help/tooltips explaining differences between Closet/Wardrobe/Outfits.
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Keep the obvious add-item button but add microcopy or an “Add from link / photo” hint.
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NEGATIVE POINTS OF PLANNER
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Two calendars felt confusing; purpose of top calendar unclear.
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Item picker and “create outfit” actions are not discoverable (users asked for “tap to add”).
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Users expect reminders and calendar sync (Google Calendar) but it’s missing.
What still needs refinement...
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Consolidate to a single clear calendar view; remove or repurpose the extra calendar.
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Add inline labels/tooltips on canvas (e.g., “Tap to add”, “Drag item here”) and short first-use hints.
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Add calendar sync (Google/Apple) and for planned outfits.
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NEGATIVE POINTS of AI STYLIST
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Mixed preferences: quick suggestions vs conversational chat: no single clear default.
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Missing visual recommendations (images/collages) in some flows.
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Shopping links not consistently embedded;
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AR try-on missing.
What still needs refinement...
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Offer both modes: Quick Suggestions (default) + optional Chat Mode .
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Surface visual recommendations alongside text (thumbnail outfits, colour swatches).
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Embed store links for each recommended item and prioritize local Australian sources if requested.
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Roadmap AR try-on as optional enhancement (mark as future epic).
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NAGATIVE POINTS OF INSPO
Users miss social/community/chat features
What still needs refinement ...
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Prototype a lightweight community/chat feature or comment/like interactions for posts.