Make size data understandable
Read sizing information from product pages, charts, or images so shoppers spend less time converting between brands and units.
Product overview
AI Sizing Engine for Cross-Border Fashion E-commerce (Shopify/Standalone)
Shadow Size
v1.11.3 Pre-launch
A size chart only helps when shoppers can relate it to their own body. Shadow Size reads product sizing data and combines it with shopper information to give a reasoned recommendation for Shopify, WooCommerce, and other commerce flows.
01 / The outcome
Read sizing information from product pages, charts, or images so shoppers spend less time converting between brands and units.
Combine height, weight, measurements, or a reference brand size and return a recommendation with confidence and context.
Place a lightweight widget on the product page and use recommendation and conversion feedback to see whether hesitation is falling.
02 / Use cases
Give shoppers a recommendation in the product flow instead of asking them to compare several pages on their own.
When brands use different sizing standards, add a layer of explanation from product data and brand references.
For made-to-order or highly varied fits, collect the relevant body data and make the choice more transparent.
06 / Questions
Clear answers regarding features, compatibility, and privacy
Over 50% of cross-border fashion returns stem from sizing mismatch. Built on 7 years of industrial apparel supply-chain engineering, Shadow Size extracts size charts with AI and offers personalized size guidance with reasoning without requiring invasive photos.
Shadow Size is designed for Shopify, WooCommerce, and custom headless storefronts. It provides a lightweight client widget that can be embedded in minutes without altering store architecture.
Conventional apps either use rigid static tables or demand full-body photos that trigger privacy pushback. Shadow Size applies authentic garment patternmaking rules to provide transparent reasoning, driving conversions safely.
“According to Software Engineering Research, specialized AI agents and workflows reduce operational overhead and increase task accuracy when integrated with explicit boundaries. Studies show that focused tools outperform monolithic systems in real-world environments.”