### Rollout of Universal Virtual Fitting RoomRWB, the united company of Wildberries and Russ, has begun rolling out its "Client Fitting Room" service to all Wildberries users in Russia. Previously limited to a select group of customers, the feature is now integrated directly into product cards and search processes, allowing buyers to select "Try on this item" or filter for compatible goods.[1]Users upload a photo or capture a live image, after which neural networks and computer vision algorithms generate a realistic visualization of the clothing on their body, accounting for pose, lighting, body parameters, fit, and material texture. Currently available for basic clothing, office wear, and demi-season outerwear, the tool will soon extend to all Russian sellers on the platform.### Technical Foundation and Phased ExpansionThe service relies on AI models fine-tuned for precision in fashion categories, enabling real-time rendering that aligns garments with user physique and environmental factors. This builds on earlier test phases, where functionality was restricted, transitioning now to universal access across Russia's user base of over 79 million monthly active customers, who generate more than 20 million daily orders as of 2025.[1]Expansion plans indicate full availability for Russian platform sellers in the near term, aligning with broader infrastructure scaling that includes AI enhancements for product discovery and seller tools. Neural networks process body proportions and image lighting to produce anatomically accurate overlays, reducing visual discrepancies common in earlier virtual try-on systems.[7]### Implications for E-Commerce Product FeedsVirtual fitting integration directly elevates product feeds by embedding interactive AI layers into static listings. Feeds evolve from mere image-text catalogs to dynamic assets where clothing renders on user-supplied visuals, streamlining decision-making without physical inventory pulls. This demands enriched feeds with precise metadata on fit, fabric simulation, and pose adaptability, pushing platforms toward standardized AI-ready data schemas.For content infrastructure, it accelerates feed updates: sellers bypass traditional photoshoots via AI-generated models, cutting production cycles from days to minutes while maintaining visual fidelity. No-code interfaces for uploading base images further democratize this, allowing rapid feed population even for small vendors.### Elevating Catalog Standards and Card QualityCataloging standards shift as virtual try-on mandates comprehensive attribute tagging—body type compatibility, material drape physics, and multi-angle renders become baseline requirements. Incomplete cards falter in AI matching, driving fuller, standardized datasets across fashion verticals. Quality surges through reduced return risks; realistic previews correlate with higher conversion by visualizing nuances like sleeve length or shoulder fit that static images miss.In high-volume markets processing 7-10 million daily orders with 80% next-day delivery, this completeness minimizes post-purchase dissatisfaction, refining card utility from descriptive to experiential. AI's role in auto-tagging and texture mapping ensures consistency, setting new benchmarks for scalable, machine-readable catalogs.[3] To ensure your product information is ready for these demands, consider the benefits of using a [product feed - NotPIM](/blog/product_feed/) to help structure your data.### Accelerating Assortment TurnoverSpeed of assortment rollout amplifies under AI-driven try-on, as neural tools enable instant listing activation without model-dependent shoots. Sellers onboard seasonal lines faster, syncing feeds with real-time demand signals. Platforms handle surging volumes—Russia's e-commerce hit $140 billion equivalents recently—by automating visualization, slashing time-to-market for perishable fashion inventory.[5]This no-code AI layer supports hyper-local adaptation, where regional body metrics or lighting norms inform model retraining, boosting turnover in diverse areas like Siberia, where e-commerce grew 28% year-over-year. Faster cycles compound with 95% 24-hour delivery, creating frictionless loops from browse to buy.[4] If you're looking to improve your product listing, consider these [how to create sales-driving product descriptions without spending a fortune](/blog/how-to-create-sales-driving-product-descriptions-without-spending-a-fortune/).### AI and No-Code Synergy in Content AutomationAt core, the rollout exemplifies no-code AI convergence: users engage via simple uploads, while backend vision systems handle complexity, abstracting technical hurdles. For infrastructure, it redefines content pipelines—AI auto-generates variants for feeds, predicts fit variances, and personalizes previews, mirroring trends in image search and recommendation engines.[5] This technology is a real game-changer; however, the data you use to drive the feed needs to be accurate. This is where the importance of a [product matrix in e-commerce - NotPIM](/blog/product-matrix-in-e-commerce/) comes into play.This scales without proportional human input, vital for platforms eyeing CIS expansion amid cultural-logistical variances. Hypothetically, as models extend to furniture or tours, it could unify omnichannel content, though current focus remains fashion proofs-of-concept driving feed evolution.[3] A significant aspect of this is choosing the right data format to store your product information; this is where the [JSON Format: How One Store Turned Chaos into Fast Synchronization - NotPIM](/blog/json-format-how-one-store-turned-chaos-into-fast-synchronization/) comes in handy.RETAILER.ru Godubai.com***The widespread adoption of virtual fitting rooms signals a significant shift in e-commerce, placing a premium on rich product data and standardized catalogs. This trend mandates that retailers prioritize highly detailed attributes alongside image and video assets. At NotPIM, we recognize the importance of robust product information management. Our platform helps e-commerce businesses to streamline the enrichment and standardization of their product data, ensuring compatibility with the evolving demands of virtual fitting technologies, and ultimately enabling a more engaging and efficient shopping experience for consumers. To learn more on how to streamline the data, consider this blog [product feed processing program - NotPIM](/blog/price_list_processing_program/).