Yes. Each ski in the catalog carries a length variant list, a flex index, a waist width, and a terrain type as product meta. The bot reads these on every request and matches against the shopper's height, weight, ability, and intended terrain. The result is two or three specific in-stock models with the right length variant, not a generic chart with a sizing range to figure out alone.

Yes. EU, UK, and US shoe sizes are converted to mondopoint by the bot, and the boot catalog stores mondopoint sizes and the last width per model. A shopper with EU 43 feet and a wider forefoot gets a 27.5 boot with a 102 mm last; a narrower foot gets the 98 mm option. Both come with current stock and price, ready to add to cart or book a fitting.

Yes. Terrain type is a product attribute on every ski and snowboard in WooCommerce. A shopper asking about a park ski gets twin tipped models with a softer flex. A powder query gets wider waist widths and rocker profiles. The bot reads the terrain type on each SKU rather than guessing from the product title, so the recommendations stay accurate even with marketing-heavy names.

Yes. Snowboard length is a function of rider weight, ability, and style, and the bot reads weight range and flex index per board. A 75 kg all mountain rider gets a 155 to 158 cm recommendation with a medium flex board. A freestyle rider gets a shorter board with a softer flex. Stance width recommendations come from the binding sizing meta when the rider provides their boot mondopoint.

Yes, if your booking system exposes available slots through the WordPress REST API or a custom endpoint. The bot reads the available Saturday boot fitting slots, the weekly demo window, and the rental schedule, then offers the right slot. The actual booking is completed on your booking page, so payment and customer details stay in your existing flow.

Binding mount rules and tune intervals live in an OpenAI Files vector store. A shopper asking when to rebevel edges gets the right interval for their riding style. A question about mounting a binding on a rocker camber rocker profile gets the specific paragraph from your in-store binding handbook rather than a generic guide that ignores the profile entirely.

Conversation logs stay inside your WordPress database. The data path is browser to WordPress to your OpenAI or Anthropic key directly, with no Sleek server in between. For shops that also collect customer measurements for boot fitting or rental records, those are kept in WordPress custom tables you control, separate from the chat logs.

Generic chatbots read your help center and your sizing chart, but they have no link to the actual ski length variants, boot mondopoints, or rental schedule sitting in WooCommerce. They cannot answer a sizing question with a specific in-stock model because they cannot see the catalog. SleekAI runs inside WordPress, reads the same product meta WooCommerce reads, and runs on your own AI provider key, so the bot acts like a real counter assistant.