Cleaning company: intake and area-based quote

The cleaning company worked with private flats and small offices and took requests across three channels at once: calls, WhatsApp and Instagram Direct. Requests were written down however staff could: in notes, inside chats, on paper at the dispatcher desk. When they were passed between employees and shifts, some simply got lost. Clients, meanwhile, wanted the simplest thing: to hear the price and a free time straight away, but instead they were told the team would check and call back, and waited a long time for a reply.

We walked through every channel and saw the overall picture: there was no single list of requests, so the same request could be worked by two people while another was picked up by no one. Requests were lost especially painfully in the evening and on weekends, when the dispatcher was already off the line. A lot of time went into calculating the price by hand: an employee re-asked the area and cleaning type, dug through the rates and only then named a price. Because of this delay some clients left for whoever answered faster.

The task was to bring every request into one point and remove manual recalculation. The assistant should collect a request in a clear scheme, calculate the preliminary price itself by area and cleaning type, estimate the trip by address and immediately offer a free window in the schedule. It was important not to let the bot fantasize about prices: it calculates strictly by the company rate grid and does not promise what is not in the price list. And every request from every channel should land in a shared log so nothing gets lost.

We built an assistant on WhatsApp as a single entry for requests. In the dialog it clarifies the area, the cleaning type (maintenance, deep, after renovation) and the address, estimates the travel zone on the map, calculates a preliminary price by the company rates, shows free windows and puts the visit into the schedule. It gathers every request into a shared base, so a request from Instagram or by phone also lands in the same log. The tone is human, and the bot takes the price only from the price list and does not invent it.

The project took about three weeks. First we digitized the rates and rules: how much a square meter costs for each cleaning type, how the trip is counted by district, what surcharges exist. Then we set up the calculation, the crew schedule, the collection of requests into a single base and linked the channels so that requests from the phone, WhatsApp and Instagram landed in one log. We ran the scenario on part of the requests, tuned the wording and calculation logic from real dialogs and rolled the assistant out to the full flow.

After launch, requests from all channels gather in one log, so they stopped getting lost when passed between shifts, and the share of handled requests grew noticeably. The client sees the preliminary price and a free time straight away, at any hour, and the agreement shrank from a long back-and-forth to a couple of messages. The dispatcher no longer recalculates the cost by hand or merges requests from three places: they confirm visits and deal with complex cases. The average ticket, meanwhile, did not drop, because the calculation still runs strictly by the rates.