Visual search lets a customer search with an image rather than words. They photograph a jacket, a lamp or a plant, and the store shows the same or similar items from its catalogue. It is useful when shoppers cannot name what they want.
AI models convert each product image and the shopper's photo into a numerical description of its shape, colour, pattern and style. The system then finds catalogue items whose descriptions are closest. Results can be filtered by price, size or availability so they stay relevant.
Fashion, homewares, furniture and garden retailers use it in apps and on websites, and some add a "shop the look" feature on inspiration images. It can also help staff, for example identifying a part or product from a customer's phone photo when the name is unknown.
Results are only as good as your product images and data. Poor, inconsistent photos or missing attributes produce odd matches. Check that results respect stock, that the search handles real-world photos with clutter, and that it does not replace good text search. Compare cost against the uplift in conversion before committing.
Visual search matters most for catalogues where style is hard to describe, and it often works best alongside AI-powered website search. Ask vendors for a trial using your own photos and catalogue, and track how many visual searches end in a purchase.