Coffee chain: B2B catering intake

A city chain of around a dozen and a half coffee shops with its own bakery. Beyond retail, the company actively sells office catering, corporate breakfasts and custom cakes, and it is exactly these B2B orders that bring the largest tickets. Requests for them arrived mixed across every channel at once: email, Instagram Direct, WhatsApp and calls to a shared number. One manager effectively sorted them by hand, and on a busy day the first reply to a client could take up to three hours.

We pulled a month and a half of chats and the call log and broke down where the time went. More than a third of the manager working day was eaten by manually sending the same menu presentations and delivery terms. On holidays, when there are three times as many requests, the line overloaded: some calls dropped and requests from different channels got lost, because there simply was no single list. Every missed catering request is not a trifle but a large corporate ticket that walked to a competitor.

The task was not to hang up a chatbot for show, but to close real qualification. The assistant should clarify the date, event format, number of guests and budget itself, check it against the real kitchen load and immediately assemble a draft estimate, while sounding human and on brand. Ideally the manager is left only to check the details and confirm the booking, rather than running the entire conversation from scratch.

We built an assistant that works on Telegram and Instagram Direct as a single entry point for B2B. In the dialog it carefully collects the event parameters, queries the bakery load sheet and checks whether the team can take the order on the requested date. It then builds a personal offer with a preliminary estimate and, if the client is ready, records the request in a shared base and hands the manager a card with all the details. The tone is set by examples from the brand real correspondence, so the assistant sounds like a living employee rather than a faceless form, and it does not invent prices that are not in the price list.

The project took about six weeks. First we digitized the menu, catering rates and kitchen load rules so the bot had something to rely on. Then we connected it to the sheets and set up the preliminary estimate calculation. We spent a separate week limiting the assistant imagination: strict wording boundaries and no promises beyond the price list. After that we ran the scenario on part of the incoming requests, tuned the tone and wording from real dialogs and only then rolled the assistant out to the full flow.

After stabilization the assistant handles the first pass of almost all incoming B2B requests itself. The time from a request to a ready estimate dropped from several hours to a couple of minutes, and the share of lost requests on peak days fell by about three times, because everything now lands in a single list instead of dissolving between channels. The manager stopped being an inbox sorter and does what actually brings money: running warm deals, agreeing large orders and growing the partner network. The average catering ticket, meanwhile, did not drop.