A mid-century buyer does not ask for a teak sideboard. They ask for a 1960s Danish teak credenza by Arne Vodder, ideally in oiled finish, with no veneer chips, between 180 and 220 cm wide. A Brutalist collector wants a Paul Evans sculpted-front cabinet and asks for the provenance trail. A vintage interiors shopper wants a Castelli Quaderna table by Superstudio and asks whether the original print laminate is intact.

SleekAI reads your WooCommerce product catalog with the era taxonomy (1920s through 1990s), the designer taxonomy, the style taxonomy across Mid-Century Modern, Brutalist, Memphis, Postmodern, and Art Deco, the dimensions postmeta with width, depth, and height, the condition_notes field, and the provenance field for documented pieces.

Generic chatbots ignore everything that matters in vintage. They cannot tell a 1960s Vodder credenza from a 2000s Vodder-inspired reproduction. They have no concept of veneer condition, joinery type, or original finish. SleekAI reads _stock_quantity and the inventory location field on every turn, so a one-of-a-kind 60-inch sideboard never gets sold twice while one is still on the showroom floor in another city.

Vintage furniture retail is a high-trust category where every detail matters and most pieces are one-of-a-kind. A generic chatbot trying to answer a Mid-Century Modern query will inevitably suggest a Restoration Hardware credenza when the buyer asked for an original Arne Vodder. The buyer leaves.

SleekAI reads era and designer as taxonomies on every turn so an originals-only shop never accidentally suggests a reproduction. Condition is the next critical dimension. Vintage pieces carry decades of life, and a buyer about to wire $8,000 wants to know exactly which veneer is chipped, whether the legs were ever repaired, and whether the original finish is intact.

SleekAI reads condition_notes and quotes them verbatim rather than summarizing into vague language. Provenance closes the trust loop. Documented single-family ownership or a known gallery acquisition trail can double the perceived value of a piece.

The bot reads provenance and offers documents on request. Finally, vintage shops operate across warehouses and ship oversized pieces nationally. The bot reads inventory_location and quotes white-glove delivery accurately so the buyer is not surprised at checkout by a $1,200 shipping line item.

The technology stays inside WordPress, the customer data stays inside your own database, and the model bills land on the API key you control.