Understand the request
Keep dates, party size, room preferences and the guest’s questions alongside the conversation. Staff should not have to reconstruct the enquiry from scattered messages.
THE HOTELABLE WORKSPACE
One enquiry.
The whole picture.
A developing reservations workspace that connects guest conversations, hotel knowledge, offer preparation and the person responsible for the next step.
Discuss a pilothotelableIllustrative demo
Hello! We are looking for a room for 2 adults, 12–16 June. Do you have a sea-view option?
TEAM NOTE
Dates and party size captured. Check approved availability before preparing an offer.
A person stays in control↗
Fictional guest and sample data. This demonstration sends no messages and makes no bookings.
FROM MESSAGE TO DECISION
Keep the context.
Move the enquiry forward.
Prepare a grounded offer
Use approved hotel information to prepare an offer. Show what is known, what needs checking and which conditions apply. Pricing must not be invented by AI.
Assign the next action
Make ownership visible: who replies, who checks availability and who follows up. Shift changes should preserve the guest’s context.
Confirm with the hotel
Booking confirmation stays with an authorised employee. Record the outcome and the relevant PMS reference when the hotel confirms.
ASSISTANCE, WITH BOUNDARIES
AI supports the team.
The hotel keeps control.
Our development direction includes assistance with gathering enquiry details, finding approved hotel information and drafting replies. Escalation is part of the design: uncertain prices, missing availability and exceptions belong with a person.
Current status: controlled pilot. Autonomous AI replies are disabled in the Meta channel trial. Features are validated in stages before wider deployment.
LET’S BUILD A BETTER WORKFLOW