FitSwapr - Try On Anything, Virtually

Weak - no clear moat; relies on basic computer vision tech that's becoming commoditized

• Online fashion shoppers (18-35) who experience high return rates due to fit uncertainty
• E-commerce retailers seeking to reduce return rates (30-40% in fashion)

• No B2B offering for retailers
• Lack of size recommendation engine
• No AR mobile experience
• Missing social sharing features

• Focus on specific vertical (e.g., wedding dresses)
• Build size prediction algorithm
• Create community-driven fit database
• Offer white-label solution for retailers

Medium - visual try-on has natural sharing potential but requires excellent execution

• Heavy competition from well-funded startups
• High technical complexity for accurate simulations
• Privacy concerns with body data
• Dependence on retailer partnerships
• Low barriers to entry