WooCommerce Min/Max Quantities stores quantity rules as postmeta on products under minimum_allowed_quantity, maximum_allowed_quantity, group_of_quantity, and allow_combination, plus a category-level meta on taxonomy terms for cart-wide caps. SleekAI exposes those keys as named variables on the bot's system message so a shopper trying to buy 8 of a product configured for groups of 6 gets a clear explanation, not a generic rejection.

That turns a confusing checkout error into a direct conversation. A shopper asks why their cart will not let them buy 8 candles, the bot reads group_of_quantity=6 and explains that this product is sold in packs of 6 and offers them 6 or 12. A wholesale buyer hits the cart-wide cap and the bot reads the category meta, confirms the daily allotment, and suggests splitting the order.

Display conditions can scope the bot to single product pages with quantity rules set and to the cart and checkout, so non-restricted products do not trigger the quantity-rule branch. Conversation logs capture each rejection-reason ask with model name and token usage, which is useful when product teams want to spot which rules are confusing real customers and which ones are working as expected.

Quantity rules are one of the most common reasons a fully-loaded WooCommerce cart fails at checkout. The shopper has added 8 of a product, the store sells it in packs of 6, the checkout validator rejects the cart with a single-line error, and the shopper has no idea what to do next. Sometimes they edit the quantity, sometimes they leave.

Min/Max Quantities is the right tool for the rule, but its error message lives on the checkout page and stops there. A chatbot scoped to the product and cart pages picks up where the error message ends. It reads the same group_of_quantity, minimum_allowed_quantity, and allow_combination values that the validator uses, and turns the rejection into a one-sentence explanation plus the nearest valid quantities.

The shopper either picks one or leaves with a clear understanding of why. The store's logs then show which rules are generating the most questions, which is the closest thing to A/B testing the wording of a rule. Generic chatbots cannot do this.

They have no idea what minimum_allowed_quantity is, let alone whether a category-wide cap exists. They will either suggest contacting support (the cart abandons) or hallucinate a rule that does not exist (the cart abandons differently). The variable mapping closes the gap.

It also lets the bot do the math correctly, which matters more than it sounds: when the rule is groups of 6 and the shopper wants 8, the right answer is 6 or 12, not "please pick a multiple of 6." A model with the actual value in its system message does that automatically, every single time, in any language the model speaks.