Workshop content, facilitation, and judging by Patricia · WonderLead
WardroBro
An AI wardrobe companion for visually impaired and blind individuals · Komal Jadhav
For many visually impaired and blind individuals, something as simple as getting dressed can mean asking someone else for help — choosing an outfit, checking the weather, finding clean clothes, or shopping for something new. Over time, that dependence affects confidence, independence, and how comfortable they feel showing up in the world.
// The product
What it does
WardroBro is a voice-first AI assistant that helps visually impaired adults dress independently. Most tools identify clothes — WardroBro helps users decide what to wear.
- Wardrobe inventory — attach NFC tags, scan once, AI stores description and location
- Outfit recommendations — occasion, weather, mood, and schedule
- Laundry tracking — worn items and reminders
- Shopping assistant — describe new items, check duplicates, suggest pairings
North star metric: Independent outfit decisions made through WardroBro
End-to-end flow
Not building today: smart wardrobes, real-time camera detection, automatic closet mapping, or a full e-commerce marketplace.
Why it wins on AI-native
Voice is the primary interface — natural-language speech in, audible guidance out, with screen-reader compatibility. Recommendations combine wardrobe inventory, occasion context, weather, wear history, and user feedback scores to improve over time.
// Judging
Criteria
Problem clarity (25%) · Working product (30%) · AI-native (25%) · Demo quality (20%)
Next steps: Use ElevenLabs for richer voice, add a weather API, and make the camera scan work.
// Winner prize
Prize package
Winner receives WonderLead programmes valued at €1,496 total:
- Practitioner Programme — 12-week operator residency to upskill while using big tech methodologies when working on a real project in a cross-functional team (valued at €897)
- Career Accelerator Coaching — 3-month coaching package to accelerate your career growth and get promoted or break into tech (valued at €599)
// Build process
Tools used
Komal used Kiro to produce a full requirements spec — voice-first interaction, NFC onboarding, outfit recommendations, laundry tracking, and shopping assistant — then shipped a working prototype on Lovable in one live session.
If she had 1 more hour
Build a personalized feedback loop that learns from outfit choices, comfort levels, and compliments received — so recommendations improve based on what makes the user feel confident and comfortable.
One thing learned
Kiro helped build the app through just a few instructions — creating the exact description and workflow and saving time. The result matched the vision: a practical, accessible product for real users.