Auto-populate product filters - sleeve type, rise, neckline, length - using AI image tagging and catalog automation. Our system extracts attributes from product images and maps them to your taxonomy so faceted navigation works out-of-the-box. The result is better on-site discovery, cleaner PLP filters, and stronger e-commerce SEO without manual tagging sprints.
Manual tagging leaves filters incomplete or inconsistent across categories. Missing attributes for sleeve type, rise, neckline, or length break faceted navigation, create ‘no results’ states, and hurt e-commerce SEO. As catalogs grow, keeping taxonomy mapping current across vendors and marketplaces becomes slow, error-prone, and expensive.
Shuru’s AI Image Tagging & Catalog Automation detects visual attributes from images (e.g., sleeve type, rise, neckline, length) and validates them against product type and size charts. We then map attributes to your taxonomy via rules and machine learning, enforce naming conventions, and apply confidence thresholds with optional human-in-the-loop review.
AI Image Tagging & Catalog Automation
Auto-tag products, standardize attributes, and structure catalogs at scale.
Faster time-to-publish by eliminating manual attribute tagging for core filters.
Higher PLP-to-PDP click-through from accurate, usable faceted navigation.
Reduced ‘no results’ and dead-end filter states through complete attribute coverage.
Lower operational costs by automating attribute extraction and taxonomy mapping.
Better e-commerce SEO via consistent attributes that strengthen crawlability and long-tail relevance.
Improved analytics fidelity with structured facets that reflect real shopper behavior.
Partner with us to design, prototype, and deploy.
