ViSenze

ViSenze's Multi-Search enables customers to combine natural language text, keywords, and image uploads in a single unified query. This multimodal approach understands product intent beyond simple keyword matching, supporting AI text search, visual search, similar search, and social search modes. It significantly reduces search friction by allowing customers to search the way they naturally think about products.

The recommendation engine delivers multiple recommendation types including visually similar products, pairing suggestions for complementary items, and shop-the-look bundles that present complete outfits or styled room setups. It analyzes visual attributes, user behavior, and contextual signals to surface relevant products at optimal moments in the shopping journey, directly targeting increased average order value.

A conversational shopping assistant that lets customers describe what they are looking for in natural language and receive personalized product recommendations. Unlike static search, the assistant engages in a dialogue to understand nuanced preferences and refine suggestions, acting as a virtual personal shopper that scales across all site visitors simultaneously.

A no-code/low-code configuration tool that lets retailers customize their entire search and discovery experience without heavy developer involvement. Retailers can build site-wide text and image search interfaces, deploy conversational assistant-led search, and set up trigger-based search from any catalog image. This gives merchandising and marketing teams direct control over how product discovery works on their site.

ViSenze's social search automatically identifies products in social media posts and lifestyle imagery, linking them to purchasable items in the retailer's catalog. This turns every piece of visual content — whether from brand channels or user-generated content — into a shoppable experience with one click, capturing purchase intent at the point of inspiration rather than requiring customers to search separately.