Shoppers don't churn because the catalog is small. They churn because the catalog is too big and nobody helps them narrow. The serious buyer who already knows the vocabulary uses the filters; everyone else opens four tabs, gets confused, and closes the browser. Cart abandonment is the visible part of the leak, but most stores leak earlier than that, at the product-discovery step, where a one-line recommendation would have closed the sale.

SleekAI runs as a shopping assistant grounded in the live WooCommerce catalog. Products, variations, stock, attributes, and any ACF fields you want flow into the prompt via the wizard. The bot asks two or three plain-language questions, recommends two SKUs with a one-line reason each, and confirms stock and shipping. For logged-in customers, the prompt includes order history meta so the bot can reference past purchases (without naming card numbers or sensitive fields) and recommend the right replenishment or upgrade.

Logs surface the queries that didn't get a clean match: the missing category, the mislabelled attribute, the product copy that doesn't match how shoppers describe the thing. That feedback loop becomes the merchandising backlog most catalog teams never had access to, and the deflection on faceted-filter friction is measurable inside the first quarter.

Cart abandonment is the visible part of the discovery problem, but most stores leak earlier than that, at the moment a shopper opens four tabs and gets overwhelmed before adding anything to the cart. Faceted filters work for the serious buyer who already knows the vocabulary; everyone else needs a conversation. A grounded shopping assistant sidesteps the filter wall because the shopper describes the goal in plain language and the bot maps it onto attributes you have already tagged.

The recommendation comes with a one-line reason, which is the comparison work shoppers otherwise outsource to a third-party blog or a long YouTube review. The operational win lands in two places. First, conversion lifts on the catalog pages where the bot loads, because the discovery friction gets resolved at the moment it appears rather than after the shopper has closed the tab.

Second, the logs become a merchandising research artefact. Every query that didn't get a clean match is a missing category, a mislabelled attribute, or a product description that doesn't match shopper language. Each fix lifts conversion on the next cohort without any further tuning.

The longer-term win is honest catalog maintenance. The questions shoppers actually ask are the proof of what the catalog is missing, and the patterns in the logs become a structured backlog for the merchandising team. The right metric is not chats handled but qualified add-to-cart from chatted-with sessions, which is the input the conversion rate actually responds to.

A bot that quotes live stock, uses order history responsibly, refuses to invent SKUs, and escalates per-order issues to support is the closest thing to a knowledgeable in-store salesperson most ecommerce sites have ever budgeted for.