The Netcore Unbxd AI-powered product discovery platform is a suite of self-learning models that enhance ecommerce search and personalization. It interprets shopper intent, dynamically ranks products, and supports visual, conversational, measurement, and fitment search to improve relevance, discovery, and conversions.

The platform combines vector search with over 200 behavioural and contextual signals to understand what shoppers mean, not just the words they type. It interprets query context, browsing patterns, and past interactions to surface products that best match each shopper’s intent.

The platform supports advanced capabilities such as vector search, dynamic ranking, visual search from uploaded images, Shopping Agent for guided shopping, measurement search for alphanumeric queries, and fitment search to connect shoppers with compatible products.

Dynamic ranking uses self-learning algorithms and 200+ signals, such as clicks, add-to-cart events, conversions, inventory, and margins, to automatically re-order products. This ensures high-performing and high-value items are shown more prominently, increasing revenue per visit.

Visual search lets shoppers upload an image of a product they like and instantly find visually similar items in your catalog. It is especially useful for lifestyle, fashion, and home categories where shoppers are inspired by looks and styles rather than specific product names.

The platform is managed through a self-serve console where business and merchandising teams can configure models, adjust strategies, and launch experiments without heavy engineering involvement. This shortens deployment cycles, lowers maintenance effort, and frees engineers to focus on core initiatives.