Most product finders are a stack of filters that only work if the visitor already knows the words your catalog uses. A first-time buyer types "something quiet for a small apartment" into the search box and gets nothing, because your attributes say "55 dB" and "compact footprint". The mismatch is the leak, and it shows up as session bounces no analytics tool ever attributes to bad navigation.

SleekAI replaces the filter wall with a conversation grounded in the same WooCommerce or custom-post-type catalog. You map products, taxonomies, variations, stock meta, and any ACF attribute into the prompt context via the data-source wizard. The bot asks two or three qualifying questions, then names two or three SKUs with a one-line reason each. Bring your own OpenAI, Anthropic, Google, or OpenRouter key and pay the model provider directly at their rates.

Conversation logs are stored in WordPress with the query, the grounding context, the recommendation, and the page URL. Filter for queries that returned no good match and you find the missing categories, the mislabelled attributes, and the product copy that doesn't match how shoppers actually describe the thing. That backlog is the catalog merchandising work nobody could see before.

Filter-based discovery optimises for visitors who already know the vocabulary of your catalog. The serious cyclist knows what "single-wall" means; the first-time buyer doesn't. As stores expand into adjacent customer segments, that vocabulary mismatch becomes the leak, and it shows up as session bounces no analytics tool ever attributes to navigation.

A conversational interface sidesteps the problem because the shopper describes the goal in their own words and the bot maps that description onto attributes you have already tagged. The other lever is trust. A recommendation with a one-line reason beats a list of twelve unranked results because it does the comparison work the shopper would otherwise outsource to a third-party blog or YouTube review.

The ancillary win is operational. Every miss is a logged query that points at a missing category, an unclear product title, or an attribute that needs to be tagged. None of that data exists in a static facet UI.

Over a quarter, the logs become a merchandising backlog you can actually prioritise, and the questions that used to vanish into the bounce rate now show up as concrete content and tagging work. The bot also keeps merchandising honest. When a recommendation can't be grounded, the right behaviour is to say so and escalate rather than improvise, which is the difference between a finder shoppers come back to and one that loses their trust the first time it invents an SKU.