RYSA AI Wardrobe Assistant
Project Overview
In 2025, as Co-founder & Design Lead, I've been building RYSA, an AI-powered wardrobe assistant designed to reduce decision fatigue and support professionals in their daily choices. Over 6 months, I shaped the product vision, led market and user research, and built the design and AI experimentation foundation for the upcoming digital wardrobe app.
Role
Co-founder & Design Lead
Company
RYSA
Year
2025 (ongoing)
Timeline
6 months (concept → research → prototype)
Team
2 founders + early collaborators (design, tech)
Achievements
50+ user interviews and market research sessions conducted
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1 Figma MCP prototype built for wardrobe flows
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1 Cursor-based AI assistant prototyped with OpenAI API
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Extensive prompt engineering for AI image generation + recommendations
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Upcoming digital wardrobe app positioned for launch
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Core Challenges & Actions
| Challenge | Action |
|---|---|
|
1. Product Vision → MVP Turn abstract "AI wardrobe assistant" into a tangible product. |
Led discovery, concept design, and user journey mapping; translated vision into MVP flows. |
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2. Brand & Product Identity No existing brand presence or differentiation. |
Created brand identity, voice, and scalable design system aligned with product UX. |
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3. Early Growth & Go-to-Market Limited resources for marketing and growth. |
Designed referral loops, onboarding funnels, and growth-focused design assets. |
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4. Strategic Design Leadership Need to balance product execution with business building. |
Balanced roadmap design with fundraising narrative, investor materials, and strategic positioning. |
Where I Streamlined Delivery
| Challenge | Action |
|---|---|
|
1. Market Research & User Insight Uncover wardrobe pain points and validate demand. |
Conducted interviews and research to uncover wardrobe pain points and validate demand. |
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2. AI & Prompt Engineering Build reliable AI outputs for app foundation. |
Built and iterated on custom prompts for OpenAI image generation and wardrobe recommendations. |
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3. Design & Prototyping Create working prototypes for future app features. |
Designed working prototypes in Figma MCP and integrated workflows with Cursor for AI-powered UX. |
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4. Strategy & Go-to-Market Foundation Position the app within the fashion-tech market. |
Positioned the app within the fashion-tech market by mapping competitors and differentiation. |
Research & Insights
User Interviews
50+ sessions with professionals to map decision fatigue triggers.
Market Scan
Competitor analysis (Stitch Fix, Whering, AI stylists).
AI Prototyping
Tested OpenAI APIs for outfit generation and wardrobe clustering.
Design Experiments
Figma MCP prototypes for wardrobe flows, AI styling suggestions.
Reflection / Lessons Learned
Prompt Engineering as Design
Writing and iterating prompts became part of the design toolkit.
Bridging AI & UX
Success wasn't about showing AI, but making it feel seamless in daily use.
Design as Strategy
Early design experiments shaped not just flows, but how the product is positioned in the market.