Alterdd is a fit intelligence platform for fashion brands that predicts how garments will fit individual shoppers using proprietary garment measurements, body data, and observed fit outcomes. The platform integrates with Shopify to display personalized size and fit recommendations directly on product pages, helping shoppers avoid guesswork and reduce returns.
Funding
Funding not disclosed
Founders
Product
Problem
Online shoppers frequently struggle to determine how clothing will fit them, leading to high return rates, multiple size orders, and disappointment at delivery. Traditional size charts rely on generic measurements that fail to account for how a specific garment fits different body shapes, leaving customers without reliable guidance at the point of purchase.
Solution
Alterdd provides a fit intelligence platform that predicts how clothing will fit individual shoppers before purchase. The platform uses proprietary garment data, body measurements, and observed fit outcomes collected through garment testing to generate product-specific recommendations. Shoppers can either view general fit insights for each garment or complete a 30-second body scan via their phone to receive personalized size, length, and fit guidance. The Alterdd plugin integrates directly into Shopify product pages, allowing brands to deploy the technology without custom development or re-platforming.
Target Audience
Primary customers are fashion brands and e-commerce retailers using Shopify who want to reduce returns and improve the online shopping experience for their customers.
Features
- Proprietary fit engine that analyzes relationships between garments, measured bodies, and observed fit outcomes to predict size, trouser length, and fit at waist and hips
- 30-second phone-based body scan that maps shopper measurements against garment data for personalized recommendations
- Shopify plugin that drops fit recommendations directly onto product pages with no custom build required
- Product-level fit insights showing how an item typically fits shoppers matched to a particular size
- Validation methodology comparing predicted outcomes with physical garment try-ons, achieving 94.6% accuracy in 74 internal predictions
- Works without large volumes of historical sales and returns data, using garment and body-fit data instead