Fashion retailers have spent years trying to solve a deceptively difficult e-commerce problem: helping shoppers know whether a garment will actually fit before they buy it. MySize is now betting that the same data used to recommend a size can power a much broader layer of AI-driven fashion commerce. Its Naiz Fit platform has passed 500 million size recommendations and is expanding into virtual try-on, product intelligence and retail analytics as the company seeks to move from a sizing tool toward a broader AI fashion technology platform.
The next battleground in fashion e-commerce may not be another generative AI shopping assistant. It could be the data underneath the fitting room.
MySize, the Nasdaq-listed retail technology company behind Naiz Fit, says its size-and-fit platform has now generated more than 500 million size recommendations, reached more than 10 million users and connected with more than 100 million garments across more than 100 apparel brands.
That scale gives the company an increasingly valuable dataset around how consumers, body measurements, garments and fit preferences interact.
MySize is now attempting to turn that dataset into something larger.
Rather than positioning Naiz Fit simply as a digital size recommendation widget, the company is expanding into Virtual Try-On, Product Intelligence, Retail and Strategy. The broader ambition is to make size-and-fit intelligence useful across multiple parts of the fashion value chain, from online shopping to product development.
That puts Naiz Fit in a competitive category that increasingly overlaps with personalization, computer vision, AI shopping technology and fashion analytics.
The basic problem remains familiar. Online apparel purchases generate significant uncertainty because shoppers cannot physically assess a garment’s fit. That uncertainty can contribute to abandoned purchases and returns, while retailers absorb the associated logistics and inventory costs.
AI-powered sizing platforms attempt to reduce that uncertainty by combining information about shoppers and garments to make more relevant recommendations.
Naiz Fit’s evolution is notable because the company says it wants to use the resulting intelligence beyond that initial recommendation.
Its four-part strategy—E-commerce, Retail, Product and Strategy—suggests a move toward a horizontal fashion technology platform.
For e-commerce teams, the obvious application is personalization: recommending a size or fit based on the shopper’s characteristics and the garment.
For retailers, the same data could potentially inform customer understanding and product discovery.
For product teams, aggregated fit information could eventually provide signals about how garments perform across different consumer groups.
And at the strategic level, size-and-fit data could become another input into merchandising, assortment and commercial decisions.
The company is also adding a more visual component.
Naiz Fit has expanded into Virtual Try-On, with the technology already deployed by customers including Spanish menswear brand Silbon. The goal is to allow shoppers to visualize products digitally before purchasing.
Virtual try-on is becoming an increasingly crowded area of retail technology. Companies including Google, Amazon and Snap have developed computer-vision and augmented-reality capabilities aimed at helping consumers visualize products, while fashion platforms and retailers are experimenting with AI-generated models and digital fitting experiences.
The challenge is that visualizing an item is not necessarily the same as predicting fit.
A convincing virtual image can answer “How might this look?” without reliably answering “Will this fit me?”
Naiz Fit’s strategy is therefore potentially differentiated by combining the visual experience with existing size-and-fit intelligence. In theory, a shopper could receive both a visual representation and a recommendation informed by the relationship between the consumer and the specific garment.
That combination could make virtual try-on more commercially useful than a standalone visual feature.
The business case is also tied to returns.
MySize says previous customer deployments have produced results including conversion increases of up to 5.7x, average-order-value increases of up to 27% and return reductions of up to 14%, depending on implementation and use case. Those figures are company-reported and should not be interpreted as universal benchmarks.
Even modest improvements can matter in fashion, where returns can create significant costs through reverse logistics, restocking and inventory management.
The broader industry is moving toward technologies that connect customer data with product-level intelligence. Salesforce, Adobe and Shopify are investing heavily in personalization and commerce infrastructure, while Google and Amazon continue to develop AI-powered shopping experiences.
Fashion-specific platforms have another advantage: domain-specific data.
A general-purpose AI model may understand what a jacket is, but a specialized fashion platform can potentially understand how particular garment attributes, body profiles, measurements and historical fit recommendations interact.
That specialization may become increasingly important as retailers move from generic AI experimentation toward measurable commercial applications.
There is another strategic consideration for MySize.
The company already has relationships with brands including Levi’s, Desigual, Paul & Shark and Silbon. If additional products can be introduced into those existing relationships, Naiz Fit could increase the amount of technology deployed within each customer rather than relying exclusively on acquiring new brands.
That is a familiar SaaS expansion strategy: establish a foothold with one high-value use case and then broaden the platform.
The question is whether retailers will view these capabilities as a unified technology layer or as a collection of point solutions.
That will depend on integration, accuracy and measurable business outcomes.
For enterprise fashion teams, the most compelling proposition may ultimately be less about “AI try-on” and more about creating a continuous fit intelligence loop.
A shopper interacts with a garment. The platform learns from fit data. Product teams gain additional insight. Recommendations improve. Retailers potentially reduce returns and improve conversion. New products then generate more data.
If that loop works at scale, size recommendation becomes only the visible front end of a much larger data infrastructure.
MySize is betting that the future of AI in fashion will not be defined by one feature. It will be defined by how effectively retailers connect consumer, garment and behavioral data throughout the product lifecycle.
Market Landscape
AI fashion technology is developing around several overlapping categories:
- AI size recommendation: Predicting the most appropriate size or fit for an individual shopper.
- Virtual try-on: Using computer vision, augmented reality or generative AI to visualize products on consumers.
- Fashion personalization: Matching shoppers with products based on behavioral and contextual data.
- Product intelligence: Using customer and garment data to inform merchandising and product decisions.
- Returns optimization: Using fit prediction to reduce avoidable apparel returns.
The market includes technology giants such as Google, Amazon, Adobe and Salesforce, as well as specialized fashion technology providers.
For retailers, the strategic challenge is integration. A virtual fitting experience that operates separately from product data, customer profiles and commerce infrastructure may improve engagement without materially improving economics.
The more interesting architecture connects fit intelligence, product information, personalization and transaction data.
That is the direction MySize is pursuing with Naiz Fit.
The company’s reported 500 million recommendations provide scale, but the critical question for enterprise buyers will be whether that scale translates into consistently measurable improvements across different brands, categories, geographies and consumer populations.
Top Insights
- Naiz Fit has surpassed 500 million size recommendations, giving MySize a substantial dataset for expanding AI-powered personalization across fashion e-commerce and retail.
- MySize is adding Virtual Try-On to established size recommendations, attempting to combine visual product discovery with fit intelligence in one shopping experience.
- Customers including Levi’s, Desigual, Paul & Shark and Silbon demonstrate Naiz Fit’s growing reach across fashion categories, markets and consumer segments.
- The company’s strategy extends size intelligence into product, retail and strategic workflows, potentially making fit data useful beyond the checkout experience.
- For fashion enterprises, integrated fit intelligence could improve conversion while reducing returns, but accuracy and measurable customer-specific results remain critical adoption factors.
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