WooCommerce Product Table (by Barn2 and similar grid-list plugins) renders a tabular catalog driven by shortcode or block configuration: columns for SKU, name, price, attributes, stock, and add-to-cart, with built-in filters by category, attribute, or custom taxonomy. The underlying data lives in standard WooCommerce post types and taxonomies. SleekAI's data-source wizard maps the catalog and the table's column configuration into named variables, so the bot knows both what is for sale and how the customer is seeing it.

That changes the answer pattern on a wholesale or large-catalog page. Asked which products match a spec, the bot filters by attribute and quotes a short list with SKUs and price tiers. Asked for the bulk-discount tier on a SKU, it reads the configured price band and quotes the per-unit price at the right quantity. Asked to start a quick order, the bot can point at the table's existing add-to-cart row instead of forcing the customer to scroll.

Display conditions scope the bot to the catalog page and to wholesale user roles, multibot lets a public retail bot run on the same store, and conversation logs inside WordPress make every catalog answer auditable. SleekAI does not duplicate the product table's render layer; it shares the same data source so answers match what the customer sees on screen.

A WooCommerce Product Table layout is built for buyers who already know roughly what they want and need fast filtering, not for shoppers exploring from scratch. That makes catalog tables great for wholesale, parts, supplements, and any business with a long SKU list, but rough on first-time customers who do not yet know which attribute to filter by. A generic chatbot does not help, because it cannot see the table, the columns, or the products.

A SleekAI bot wired to the same underlying WooCommerce data closes that gap. The buyer asks 'which throws have 100+ units in stock' and the bot answers with the right SKUs at the right quantities, because it queries the same products, variations, and stock that the table renders. Wholesale gets bulk pricing tiers quoted in chat.

Retail gets a friendlier intro to the same catalog without a parallel UI. Internal staff use a staff-scoped variant of the bot to catch data gaps (missing attribute, missing image) that would slow buyers down on the table itself. Combined with multibot, role-based display conditions, and conversation logging, the same install runs all those bots on one API key the merchant owns.

The outcome is a long catalog that finally feels short, because the questions a table layout cannot answer get a one-line answer in chat instead.