In September 2025, Ozon, a leading online marketplace, introduced a new visual search capability that allows users to locate products by uploading or capturing photos. Previously, customers on the platform could only search for items using text queries or by scanning a barcode. The visual search function is now accessible on both Ozon’s mobile app and its website, having completed a testing phase that began in August. This rollout brings Ozon’s search ecosystem in line with a trend that has gained traction across the e-commerce sector.
Context and Functionality
The addition of photo-based search on Ozon creates a more intuitive interface for consumers. Instead of relying on keywords, which may not always match a product’s catalog description, users can simply submit an image of the product they wish to find. The algorithm analyzes the uploaded image and matches it with visually similar items from Ozon’s inventory. This can significantly reduce friction for buyers, particularly for products where model names, specifications, or language barriers complicate traditional search.
While this is a new development for Ozon, visual search has been implemented by major marketplaces in previous years. Applications of this technology are evident in the global e-commerce landscape, with companies integrating image recognition to bridge the gap between visual inspiration and product discovery. According to Retail Dive, visual search adoption has correlated with increased query volumes: Amazon reported a 70% year-over-year increase in visual search queries after introducing similar features. This suggests a growing user preference for visually facilitated shopping journeys, especially as image recognition and artificial intelligence mature to handle broader inventory sets (Retail Dive).
Significance for E-Commerce and Content Infrastructure
Impact on Product Feeds and Catalog Quality
Visual search sets higher demands for the completeness and quality of product catalogs. Each item must be represented with high-resolution imagery that adheres strictly to platform standards. Ozon, for example, requires all primary product images to be at least 1000x1000 pixels, in JPEG or PNG format, and free of text or logo overlays. These requirements are not merely cosmetic but functional; accurate image data directly improves the reliability of visual search matches. Listings that fall below quality benchmarks risk exclusion from search results, product rejection, or diminished visibility in search rankings, according to Ozon’s data and operational guidelines (SmartBuy). For more information on the specifics of product feed requirements, see our guide on How to upload product cards.
The growing importance of image quality is reinforcing the value of meticulous content management and operational standardization. Sellers must optimize images for clarity, background uniformity, and representational fidelity across all catalog entries. Failure to do so can lead to decreased click-through rates and ultimately lower sales, as the relevance and precision of visual search outputs diminish with poor-quality visuals.
Effects on Cataloguing Standards and Speed of Assortment
The integration of visual search accelerates the need for standardized cataloguing processes. It encourages marketplaces and merchants alike to adopt stricter controls over image metadata, tagging, and categorization. As a result, onboarding new SKUs requires enhanced automation in content generation and review. Product feed optimization is crucial in this automated workflow. The demand for image compliance tools—such as automatic background removal or quality assessment—has increased, enabling businesses to meet platform standards more efficiently.
Automated image tagging powered by AI can further streamline cataloguing. By extracting product attributes such as color, shape, or material directly from images, AI assists in the rapid enrichment of product data. This not only speeds up the listing of new products but also ensures that content remains consistent, structured, and easily discoverable by both users and algorithms.
Role of No-Code and AI-Driven Solutions
The convergence of no-code tools and AI has made it feasible for non-technical teams to contribute to content infrastructure workflows. Platforms increasingly offer merchants AI-driven modules for image enhancement, background removal, and automatic product classification. These capabilities reduce operational barriers, lower costs, and diminish dependency on specialized IT resources. For instance, no-code solutions allow the bulk editing or auditing of product images, while AI handling ensures search and inventory data remains accurate and up-to-date. As visual search takes hold, the broader adoption of such tools will accelerate. Automated quality checks, metadata population, and even the generation of synthetic images for catalogues are on the rise, improving search result accuracy and customer satisfaction. The net effect is a more robust, scalable content infrastructure that can adapt to evolving customer behaviors and technological standards.
Implications for Future Discovery and User Experience
The momentum behind visual search reflects a shift in consumer expectations. Shoppers increasingly seek fast, frictionless pathways from inspiration—often sourced via online content, social media, or offline encounters—to product discovery and purchase. Visual search closes the gap between offline and online commerce by converting any real-world or digital image into a potential entry point for transaction.
For content and operations teams, this trend necessitates a continuous investment in image production workflows, data consistency, and compliance monitoring. It also places new demands on analytics and monitoring tools, as platforms must now track and optimize around new types of query data and conversion funnels.
Strategic Considerations for Marketplaces and Merchants
The introduction of visual search fundamentally raises competitive benchmarks in the Russian e-commerce sector. Merchants must adapt to stricter image requirements and sophisticated content validation, and use automated solutions to manage the product data required for product feed management. From a platform perspective, investment in scalable infrastructure for AI-driven asset management is now a prerequisite to meet growing volumes and quality expectations.
Downstream, these investments are likely to stimulate further innovation in AI-powered product discovery and personalization. As visual and multimodal search capabilities proliferate, the marketplace ecosystem shifts from text-centric navigation to a richer, more context-aware customer journey.
Conclusion
Ozon’s implementation of photo-based product search is a logical evolution in response to both consumer demand and industry standards. It exemplifies the interplay between technological advancement and operational discipline in modern e-commerce. With visual search now a key customer touchpoint, the need for robust and accurate product data becomes paramount. NotPIM can help e-commerce businesses manage that data effectively, streamlining onboarding, maintaining data integrity, and ensuring a positive customer experience. This shift emphasizes the importance of automated data management tools for marketplaces and merchants, and NotPIM is well-positioned to assist with those operations.
Sources:
Retail Dive: Amazon launches suite of visual search features
SmartBuy: Ozon Image Size Specs 2025