Naiz Size Advisor works differently: before making any recommendation, we physically get to know each garment, test it on real bodies, and measure how it fits in every size. By combining the shopper’s body data, it generates a specific, personalized recommendation directly on the product page.
Brands that implement it reduce size-related returns by up to 65% from day one.
For every person and every product, it calculates which size fits best, taking into account the fit type, fabric, and garment construction.
In footwear, every brand fits differently and every last has its own logic. At Naiz Fit, we apply that specificity to deliver a precise recommendation, reducing one of the main reasons for returns across all types of footwear: sneakers, loafers, boots, heels, and trainers.
When a shopper adds two or more sizes of the same item to the cart, Naiz Fit triggers alerts to remind the customer to buy their usual size, helping prevent unnecessary returns.
The Size Advisor adapts to the colors, typography, and language of each store to integrate naturally into the product page, without disrupting the shopping experience.
The Size Advisor adapts to the colors, typography, and language of each store to integrate naturally into the product page, without disrupting the shopping experience.
When the shopper receives their recommendation, they can add that size directly to the cart from the widget itself. No additional steps. This detail has a real impact on the conversion rate.
Brands can see how many recommendations are generated, which sizes are recommended per product or category, and how the impact on their metrics evolves. Useful information for the product team and for stock and purchasing decisions.
When the recommended size isn’t available, the widget shows alternative or complementary products in the right size for the shopper, supporting cross-selling and preventing lost sales.
The Multi-Profile feature in naiz fit lets shoppers create and save multiple body profiles—for example, for themselves, family members, or friends—turning the shopping experience into a more complete and personalized virtual fitting room.
In footwear, every brand fits differently and every last has its own logic. At Naiz Fit, we apply that specificity to deliver a precise recommendation, reducing one of the main reasons for returns across all types of footwear: sneakers, loafers, boots, heels, and trainers.
When a shopper adds two or more sizes of the same item to the cart, Naiz Fit triggers alerts to remind the customer to buy their usual size, helping prevent unnecessary returns.
The Size Advisor adapts to the colors, typography, and language of each store to integrate naturally into the product page, without disrupting the shopping experience.
The Size Advisor adapts to the colors, typography, and language of each store to integrate naturally into the product page, without disrupting the shopping experience.
When the shopper receives their recommendation, they can add that size directly to the cart from the widget itself. No additional steps. This detail has a real impact on the conversion rate.
Brands can see how many recommendations are generated, which sizes are recommended per product or category, and how the impact on their metrics evolves. Useful information for the product team and for stock and purchasing decisions.
When the recommended size isn’t available, the widget shows alternative or complementary products in the right size for the shopper, supporting cross-selling and preventing lost sales.
The Multi-Profile feature in naiz fit lets shoppers create and save multiple body profiles—for example, for themselves, family members, or friends—turning the shopping experience into a more complete and personalized virtual fitting room.
A complete dashboard turns sizing data into business decisions. It includes Virtual Try-On with size response, cross-selling in the right size, bracketing alerts, and support for multiple shopper profiles. Plus, Size Advisor and Virtual Try-On are available via API for chatbots, return flows, and AI agents.
Clothing, footwear, kids’ wear, belts, underwear, and swimwear. While most competitors focus on one or two categories, we go much further. Plus, footwear has the same SKU-level precision as the rest: every recommendation is based on the exact brand and model, not a generic size equivalency.
Naiz Fit integrates with returns platforms and continuously improves its recommendations based on real returns, not just the information shoppers provide.
We work from real technical sheets (tech packs) and physical fit tests, taking patternmaking, materials, and the manufacturer into account. That way, each recommendation is built for that specific SKU, not from a generic size guide.
An AI-based size recommendation tool that integrates into ecommerce and helps each shopper choose the correct size for each product. Unlike static guides, it cross-references the shopper’s body measurements with the actual measurements of each item and generates a personalized recommendation in real time.
The algorithm works with the shopper’s measurements, the garment’s measurements, and the product’s fit attributes: fit type, fabric elasticity, silhouette. With that combination, it calculates which size best fits that specific body for that particular item.
Shopify, Magento, Prestashop, and custom solutions. Integration is performed through lightweight modules that display the recommendation on the product page without affecting store performance.
Garment measurement data and the most relevant product attributes: fit type, composition, and fit characteristics. Naiz Fit processes that information to build the recommendation model. The onboarding team supports the entire process.
Yes. Brands that use naiz fit reduce size-related returns by up to 65% from day one. By basing every recommendation on real garment data—not a generic size chart—we prevent mismatches before the order ships, not after.
Yes. In footwear, every brand has different sizing and every last follows its own logic. Size Advisor applies the same product-level precision to footwear too, reducing one of the main causes of returns in this category.