A custom AI chatbot, built as a ChatGPT Plugin or using the Assistants API, that connects to a hotel's private availability API. It understands natural language questions from users about dates and room types, queries the live API for real-time data, and returns the availability and rates in a conversational format.
Guests browsing a boutique hotel or motel website often have one simple question: is there a room that suits us on these dates? Booking engines answer it, but only after the guest navigates date pickers and room filters, and many leave for an online travel agency instead. Reception staff also field the same availability questions by phone and email, checking the property management system and typing replies that are out of date by the time they are read.
A hotel booking chatbot connected to live availability answers those questions conversationally. A guest types 'any king rooms for two adults from the third to the sixth?' and the assistant turns that into a structured query against your property management system or channel manager API, then replies with what is actually available and the current rate, plus a link to complete the booking directly. Because the answer comes from live data rather than the model's memory, rates and room types stay accurate, and more guests are guided towards direct bookings.
A chat widget on the hotel website sends the guest's message to an LLM configured with function calling, using OpenAI's API or a comparable platform.
The LLM extracts dates, number of guests and room preferences, asking follow-up questions if any are missing or ambiguous, such as which month or how many children.
It calls a defined function that queries the property management system's REST API for live availability and rates, rather than guessing from general knowledge.
Results are returned to the LLM, which summarises options in plain language with total price, inclusions and cancellation terms pulled from the API response.
The guest gets a deep link to the booking engine with dates and room preselected, and unanswered or complex queries are passed to reception.
A user asks the hotel's website chatbot, 'Do you have any king rooms available August 3–6 for 2 adults?' The AI Assistant makes a real-time call to the hotel's property management system API and responds, 'Yes, we have the King Suite available for those dates at a rate of $350 per night.'
OpenAI Assistants API, ChatGPT Plugins, Hotel PMS API, RESTful API integration
Under the Australian Consumer Law, prices displayed should include all compulsory fees and charges that can be calculated, so make sure the assistant presents the total rather than a base rate with surprises later. Rates should be in Australian dollars and include GST where applicable. If the chatbot collects guest names or contact details, cover this in your privacy policy under the Australian Privacy Principles. Note that OpenAI's older ChatGPT plugin model has been retired, so current builds typically use function calling through the API or a custom GPT with actions; check your chosen platform's data handling and retention settings.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
The simplest and safest version checks availability and hands the guest to your booking engine with details prefilled, so payment and confirmation stay in systems built for them. Taking bookings directly in chat is possible if your property management system's API supports reservations, but it adds payment handling and testing.
Any system or channel manager that offers a documented API for availability and rates. Many cloud-based hotel systems used in Australia provide this, sometimes as a paid add-on or through a partner program. If yours does not, a channel manager connection is often the practical route to the same data.
Typically two to four weeks. Most of the effort goes into API access approvals, mapping room types and rate plans, writing the function definitions and testing awkward date requests. Launching on one page first and reviewing chat logs for a week helps catch gaps before a wider rollout.
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