AI agents: Hotels and guest houses

Which ready AI agents exist in “Hotels and guest houses”

Hotels and guest houses

10 released templates. Each passed a check on test data; it is switched on in the panel by the director, and a person decides on every proposal. Example results are illustrative: the names and companies in them are made up.

Room availability and rates

The problem it removes: A guest asks “any rooms this weekend?”, and the front desk digs through the room chart and replies an hour later.

More detail

What it does: Answers booking requests from the availability export: which room fits the guest’s dates and what it costs.

What a person decides: The booking and any discount.

For whom: Guest.

How to measure: Inquiries that became bookings.

Daily housekeeping plan

The problem it removes: Housekeepers clean rooms floor by floor while an early check-in guest waits in the lobby.

More detail

What it does: Sets the day’s cleaning order from check-outs and check-ins: which rooms first, which housekeeper, and why.

What a person decides: Housekeeper assignments.

For whom: Head housekeeper, front desk.

How to measure: Guests who waited for a room at check-in.

Example result: “Action: clean room 204 first — check-out at 10:00, and a guest with a child has an agreed early check-in at 12:00; then 311 and 305 for 14:00 check-ins. Leave 108 for the afternoon — it has a late check-out until 16:00. Impact: no guest has to wait. Risk: if 204 frees up late, offer the guest 207, the same room type. Fallback: if there’s a delay, the front desk calls the guest.”

Booking.com reconciliation

The problem it removes: A Booking.com reservation never makes it to the room chart, and the guest arrives to an occupied room.

More detail

What it does: Matches the booking sites’ reservation export against the hotel’s room chart: missing bookings, double sales, and cancellations still on the chart.

What a person decides: Relocating the guest and canceling bookings.

For whom: Front desk, general manager.

How to measure: Double-sold rooms.

Example result: “Situation: 2 of 14 Booking.com reservations for November don’t match. Key point: guest Li’s booking No. 4471820 for November 12–14 is on Booking.com but not on the room chart — room 207 was sold directly for those dates. Also, Akhmetov’s booking was canceled on Booking.com on October 18 but is still on the chart. Options: 1) give Mr. Li room 209, the same type; 2) contact the guest and offer a different room type. Recommendation: 1. Today: enter the booking and clear the cancellation.”

Booking and cancellation terms

The problem it removes: Guests ask about prepayment and cancellation before booking, and every front desk agent answers differently.

More detail

What it does: Explains booking terms to guests: prepayment, how to pay, and the free cancellation deadline — per hotel policy.

What a person decides: Exceptions to the cancellation policy and refunds.

For whom: Guest.

How to measure: Cancellation questions at the front desk.

Example result: “Good afternoon, Daniyar! To book, we need prepayment for the first night — by Kaspi or bank transfer. Cancellation is free if you let us know 3 days before arrival; after that, the prepayment isn’t refundable. The balance is paid at check-in. Shall I book a room for your dates?”

Occupancy and pricing

The problem it removes: Rates get changed by gut feel: rooms are pricey on empty weekdays and sell out a week ahead at regular rates on holidays.

More detail

What it does: Reviews upcoming occupancy and competitor rates from the file and suggests which dates to raise or lower prices.

What a person decides: New rates.

For whom: General manager, owner.

How to measure: Revenue per room.

Room maintenance issues

The problem it removes: The AC in one room gets fixed for the third time this month, and the room sits closed and unsold.

More detail

What it does: Reviews the engineering request log from the file: which rooms are out of order, what keeps breaking, and what’s blocking room sales.

What a person decides: Repairs, purchases, and taking rooms out of service.

For whom: General manager, maintenance engineer.

How to measure: Room-days out of order.

Example result: “Situation: 12 maintenance requests in October, 2 rooms out of order. Key point: the AC in room 306 broke 3 times — October 4, 12, and 19 — and the room hasn’t been sold since the 19th. Options: 1) replace the AC; 2) bring in a service tech for diagnostics. Recommendation: 2, with a decision within a week. Today: call the service company and get the GM a replacement quote.”

Group bookings

The problem it removes: A company asks for 15 rooms for a conference, the proposal takes three days, and the group goes to the hotel next door.

More detail

What it does: Drafts a proposal for a group or company from the request: dates, rooms, meals, meeting room — and what to confirm before pricing.

What a person decides: Group rate and payment terms.

For whom: Sales manager.

How to measure: Group inquiries that became bookings.

Example result: “Proposal for Saryarka Energo LLP: seminar November 12–14, 18 attendees. Lodging: 18 single rooms for 2 nights with breakfast. A 20-seat meeting room with a projector on November 13, 9:00 to 18:00. Coffee breaks: 2 a day. Missing for pricing: whether lunch is needed and whether any guests will share double rooms — check with Gulmira.”

Monthly report for the owner

The problem it removes: The owner sees only revenue and doesn’t understand why there’s less money at full occupancy.

More detail

What it does: Month-end results from the export: occupancy, average rate, revenue by channel, and booking site commissions — plus what to change.

What a person decides: Pricing and sales channel decisions.

For whom: Owner, general manager.

How to measure: Share of direct bookings.

Room inspections after cleaning

The problem it removes: A guest finds a hair in the bathroom and writes a review, even though the head housekeeper checked the room.

More detail

What it does: Reviews room inspection checklists from the file: what gets missed most, by which housekeeper, and what to fix.

What a person decides: Housekeeper training and corrective action.

For whom: Head housekeeper, general manager.

How to measure: Guest complaints about cleanliness.

Example result: “Situation: 40 rooms inspected this week, 9 with issues. Key point: 5 of the 9 are ‘dust on baseboards and under the bed,’ all from one shift. Options: 1) add baseboards to the cleaning checklist; 2) a short training for that shift. Recommendation: both. Today: show the shift how baseboards are checked.”

Guest check-in documents

The problem it removes: A guest arrives without the right document, and check-in drags on at the front desk.

More detail

What it does: Tells guests which documents they need at check-in — Kazakhstan citizens, foreign guests, families with children — per hotel policy.

What a person decides: Check-in without documents.

For whom: Guest.

How to measure: Check-in delays at the front desk.

Example result: “Good afternoon, Mr. Li! At check-in we need a passport for each guest. We register foreign guests ourselves on the day of arrival — nothing else is needed from you. For children under 16, please bring a birth certificate. We look forward to seeing you!”

Basis: product documentation; the platform code. This page describes what exists in this build, not what is planned.

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