Kids clothing store: AI size assistant

An online kids clothing store with about 60 thousand visits a month and a wide range from several dozen brands. Sales run mostly through messengers, and in the evening, when parents are free after work, the flow of requests grew several times over. Five operators worked in shifts but could not keep up with the peak: in the busiest hours a customer waited up to 40 minutes for a first reply, and some buyers simply closed the chat.

We reviewed a month and a half of correspondence and saw that more than half of the messages were the same question: will the size fit the child by height and age. Parents got confused because size grids differ between brands, and they asked again and again. A noticeable share of working time also went to manually copying contacts and orders into the CRM, and because of the long wait some warm clients drifted to competitors.

Simple button bots only irritated people here: a parent needs advice from a real seller, not a menu of options. The task was to teach the assistant to understand free-form wording like my son is five, height one meter ten, what should I take for autumn and reply with a specific model rather than generic words. At the same time it has to keep brand size grids in mind, see stock levels and never invent availability that does not exist.

We built an AI assistant in WhatsApp that understands questions in plain language and recommends specific models with a little room to grow, checking against the size database and current stock. It advises the size by brand, offers alternatives when an item is missing, and files the assembled order into the CRM itself with no manual copying. The tone is set by examples from the store real chats, so answers sound human. In hard cases, such as a refund, the dialog moves to an operator gently.

The launch took about five weeks. First we exported the dialog history and gathered the frequent questions and parent phrasings from it. Then we built a size matching database for each brand and linked it to warehouse stock. Separately we worked out the boundaries: where the assistant answers on its own and where it must call a human. After that we ran the wording on part of the traffic, tuned the tone from real dialogs and only then rolled the assistant out to the whole flow of requests.

Now the bot closes about 60 percent of routine size requests on its own, and the first reply arrives in seconds instead of the old forty minutes. Manual copying of orders into the CRM is gone: the client card is assembled automatically. Operators stopped answering identical questions and switched to complex cases and upsells. Chat to cart conversion grew by roughly 15 percent, because the buyer gets advice right away, while the urge to buy is still warm.