The default apparel size guide is a modal table of chest, waist, and hip measurements in centimeters, plus a note saying 'between sizes? Size up.' Most online shoppers do not measure themselves, do not own a tape measure, and shop on a phone. The static chart is information, not a recommendation, and the result is returns - by some industry measures the leading return reason in apparel ecommerce.

SleekAI replaces the modal with a guided fit conversation. The bot reads the current product (style, fabric, brand-specific cut) from the WooCommerce product meta and asks two or three quick questions: height, weight, what size the customer usually wears in a known reference brand, and how they like their clothes to fit. From those answers it recommends a size with a brief confidence note - 'I'd go with size M, but L is safer if you prefer extra room around the chest.'

For repeat customers it skips most of the questions because past purchase data lives in their order history. A returning customer who bought a Medium tee three months ago and is now looking at a relaxed-fit hoodie gets recommended a Medium (or a Large if the new style runs small), without re-answering every question. The bot also remembers preferences - 'fitted at chest, loose at hips' - so subsequent recommendations get sharper over time.

Apparel return rates run two to three times higher than other ecommerce categories, and fit issues are the dominant return reason in almost every brand's data. A static size chart was never going to solve this - the chart asks customers to do work they will not do (measure themselves, convert cm, interpret 'between sizes' notes) and gives them no opinion when they need one. A chatbot reverses the model.

It does the translation, makes a real recommendation, and names the trade-off when a perfect answer does not exist. Returns drop because customers buy the right size more often; first-time conversion climbs because a confident pick reduces abandonment at the size selector. The pricing argument is unusually concrete for apparel.

Per-recommendation SaaS chatbots charge precisely when traffic spikes - new collection launches, sale events, gifting seasons - and brands eat that cost on top of higher fulfilment costs during the same windows. A WordPress plugin with bring-your-own API key turns those spikes into a few extra dollars of OpenAI or Anthropic tokens at provider rates, while the return savings scale with volume in the right direction. And because the bot reads from WooCommerce product meta and order history, the same conversation gets sharper every time the same customer comes back - the first purchase informs the second, the second informs the third, and the brand learns the customer's body without ever asking them for a measurement.