fAIshion.AI
Building Trust in AI Try-On Experiences
Summary
Led the development of an AI-powered virtual try-on platform, focusing on building user trust through transparency, accuracy indicators, and educational features. Delivered MVP in 11 weeks with +18% conversion improvement.
The Challenge
AI-generated try-on images can feel uncanny or misleading. Users hesitate to trust AI recommendations for purchase decisions. The core challenge was: How do we make users confident enough in AI-generated images to make real purchasing decisions?
My Approach
- Reframed from "How do we make AI look perfect?" to "How do we make users trust imperfect AI?"
- Conducted user research to identify trust barriers in AI-generated fashion imagery
- Designed transparency features: accuracy scores, disclaimers, and comparison views
- Implemented educational onboarding to set realistic expectations
- Created feedback loops for continuous AI improvement
The Solution
Built a trust-first AI try-on platform with: (1) Accuracy indicators showing confidence levels for each generated image, (2) Side-by-side comparison views (original vs AI-generated), (3) Clear disclaimers about AI limitations, (4) Educational tooltips explaining how the AI works, (5) User feedback system to improve AI accuracy over time.
Measurable Impact
Increase in users who made purchases after using AI try-on
More users trying multiple outfits and exploring products
Post-launch user survey on AI recommendation trustworthiness
From concept to production launch with full feature set
Key Takeaways
Trust is earned through transparency, not perfection. Users accepted AI limitations when clearly communicated.
Education reduces friction. Teaching users how AI works increased adoption by 40%.
Feedback loops compound: User corrections improved AI accuracy, which increased trust, which generated more feedback.
Technical feasibility ≠ Product viability. The AI worked well technically, but required trust-building UX to drive adoption.