AI app for creatives · no coding needed
Personal wardrobe styling and fit console
Reduce daily dressing time and repeat purchase mistakes while keeping wardrobe and body data under the user's control.
Made for: People who decide what to wear and what to buy from their own wardrobe
What it does for you
The problem
Wardrobe items, outfit decisions, size information and purchase gaps live in separate apps and photos, so daily dressing and fit choices stay slow and inconsistent.
What it gives you
User-approved outfit plans, packing lists and fit recommendations linked to owned items
What you give it
Uploaded garment photosselfiesmeasurementssaved size profiles
Build your own version of I Have Nothing To Wear, Mué AI Stylist and more
One app with what these 4 AI tools do, yours to keep and change: I Have Nothing To Wear, Mué AI Stylist, Layered, Fitcheck AI.
Everything these tools do, in one app
- Digital wardrobe catalog Lets users store and organize their clothing items digitally for easy reference.Found in I Have Nothing To Wear, Mué AI Stylist, Layered
- Photo-based wardrobe digitization Builds the digital closet by analyzing user-uploaded photos of clothing or selfies.Found in Mué AI Stylist, Layered
- Personalized outfit suggestions Recommends outfits tailored to the user's style, wardrobe, and preferences.Found in I Have Nothing To Wear, Mué AI Stylist, Layered
- Mix-and-match combinations Helps users create new outfit combinations from pieces they already own.Found in I Have Nothing To Wear, Mué AI Stylist
- Daily outfit plans Provides ready-to-wear outfit plans to simplify daily dressing decisions.Found in Mué AI Stylist, Layered
- Weather-based suggestions Takes weather into account when recommending outfits.Found in I Have Nothing To Wear, Layered
- Occasion-based suggestions Suggests outfits suited to specific occasions or events.Found in I Have Nothing To Wear
- Outfit calendar planning Allows users to plan outfits in advance on a calendar.Found in I Have Nothing To Wear
- Wardrobe analytics Tracks usage and provides insights such as cost-per-wear and underused items.Found in I Have Nothing To Wear, Layered
- Wardrobe gap analysis Identifies missing pieces or gaps in the wardrobe to guide purchases.Found in I Have Nothing To Wear, Layered
- Capsule packing for trips Creates a compact packing list based on destination, weather, and luggage size.Found in Layered
- Lookbook creation Generates shareable lookbooks for new garments with cleaned-up presentation.Found in Layered
- AI stylist chatbot Provides conversational styling advice and recommendations.Found in Layered
- Home screen widget Shows tomorrow's outfit on the home screen for quick reference.Found in Layered
- Photo-based size estimation Estimates clothing size from a photo to recommend the right fit.Found in Fitcheck AI
- Manual measurement input Allows users to enter their own measurements for size recommendations.Found in Fitcheck AI
- Brand compatibility Provides size recommendations across multiple clothing brands and styles.Found in Fitcheck AI
- Saved size profiles Stores size information for future purchases and tracking.Found in Fitcheck AI
How it works, step by step
- Catalog garments with photos, tags and attributes
- Digitize wardrobe items from uploaded photos and selfies
- Suggest outfits from owned pieces by style and preference
- Build mix-and-match combinations from the catalog
- Generate daily outfit plans
- Adjust suggestions for weather
- Adjust suggestions for occasion
- Plan outfits on a calendar
- Track usage, cost-per-wear and underused items
- Identify wardrobe gaps for purchase guidance
- Build capsule packing lists by destination, weather and luggage
- Generate shareable lookbooks for new garments
- Answer styling questions in a chat panel
- Show tomorrow's outfit in a home screen widget
- Estimate clothing size from a photo
- Accept manual measurement input
- Map size recommendations across brands
- Store saved size profiles for future purchases
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned user-approved outfit plan, packing list and fit recommendation set with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Personal wardrobe styling and fit console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Personal wardrobe styling and fit console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data195 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce daily dressing time and repeat purchase mistakes while keeping wardrobe and body data under the user's control. For people who decide what to wear and what to buy from their own wardrobe, convert uploaded garment photos, selfies, measurements and saved size profiles into user-approved outfit plans, packing lists and fit recommendations linked to owned items. The benefit is a testable hypothesis, measured through accepted outfit plans per week and avoided size-related returns; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect uploaded garment photos, selfies, measurements and saved size profiles, then follow this sequence: 1. Catalog garments with photos, tags and attributes. 2. Digitize wardrobe items from uploaded photos and selfies. 3. Suggest outfits from owned pieces by style and preference. 4. Build mix-and-match combinations from the catalog. 5. Generate daily outfit plans. 6. Adjust suggestions for weather. 7. Adjust suggestions for occasion. 8. Plan outfits on a calendar. 9. Track usage, cost-per-wear and underused items. 10. Identify wardrobe gaps for purchase guidance. 11. Build capsule packing lists by destination, weather and luggage. 12. Generate shareable lookbooks for new garments. 13. Answer styling questions in a chat panel. 14. Show tomorrow's outfit in a home screen widget. 15. Estimate clothing size from a photo. 16. Accept manual measurement input. 17. Map size recommendations across brands. 18. Store saved size profiles for future purchases. Resolve uncertain cases with qualified reviewers, approve user-approved outfit plans, packing lists and fit recommendations linked to owned items, and measure accepted outfit plans per week and avoided size-related returns against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed garment taxonomy and measurement unit set; final fit and purchase decisions remain with the user. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve user voice, source attribution, measurement accuracy and usage permissions. Users approve substantive changes and sharing scope. One fixed garment taxonomy and measurement unit set; final fit and purchase decisions remain with the user. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed garment taxonomy and measurement unit set; final fit and purchase decisions remain with the user. Implement one approved input format, a bounded representative case set and the first two task modules: catalog garments with photos, tags and attributes; digitize wardrobe items from uploaded photos and selfies. Support the third module with operator review: suggest outfits from owned pieces by style and preference. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
User-owned wardrobe photos, authorized selfies and permitted measurement sources. Cloud asset storage, calendar import/export and retail size-chart destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Wardrobe library, Outfit and fit workspace, Calendar and widget preview. Use a searchable grid of garment cards, a large central outfit canvas, and a right-hand panel for weather, occasion, measurements and comments. Let users compare outfit versions side by side. Display draft, worn and archived states. Provide a shareable lookbook link with comments anchored to the relevant garment. Make the task-specific outcome user-approved outfit plans, packing lists and fit recommendations linked to owned items visible beside its evidence, review state and value baseline.
