WooCommerce Wishlists stores each list as a wishlist post type linked to a user (or session ID for guests), with each saved item as postmeta carrying product ID, added date, and the price at the time of saving. SleekAI reads that structure via the data-source wizard, so the system message can include the logged-in customer's wishlist items, the price they were saved at, and the current product price.

That changes the kind of conversation a wishlist enables. When a returning customer asks 'is the Linen Tote on sale yet' the bot compares the saved price to today's price and answers with the real delta. When they ask 'what's still in stock' the bot reads the live stock for each wishlisted product and lists what they can still buy. When they ask 'remove the smaller one' the bot identifies which saved item matches and walks them to the wishlist page that handles the edit.

Display conditions scope the bot to logged-in customers on the wishlist and account pages, multibot lets a public sales bot run on the same site without access to private wishlists, and conversation logs capture every wishlist conversation so support can tighten the prompt or fix a confusing product description.

Wishlists carry intent. A customer who saved three items two weeks ago has signalled clearly that they will probably buy when the timing is right. The friction is that they have to remember to come back, find the right page, and check whether anything changed.

A generic chatbot bolted onto a wishlist-enabled store does not help with any of that. It cannot see who is logged in, has no idea what they saved, and will cheerfully invent a discount that does not exist on a product the customer has been watching for a fortnight. Wishlist-aware AI flips the math.

SleekAI reads the same wishlist data the My Account page reads, so the bot can answer 'is anything I saved on sale yet' with a real per-item comparison. Returning customers get a single chat that summarizes what changed, asks if they want help with any specific saved item, and routes them to the wishlist page for actual edits. Combined with conversation logging, support teams see which saved items get questioned repeatedly, which usually points at a clearer product description or a stronger promo flag rather than a system-prompt tweak.

The bot never writes to the wishlist, so the plugin stays the executor for adds, removes, and shares. Display conditions keep wishlist context strictly to logged-in customers, while a separate public bot handles guests without leaking anyone's saved items. The result is a chatbot that turns wishlists into a real conversion lever instead of a static list a customer has to manage on their own.