HOTEL AI DEMONSTRATION

See how one AI can understand and act across hotel systems.

This demo lets you play both sides: the guest-facing AI concierge and the hotel systems behind it. Change operational data, upload content, make requests, and watch both sides stay synchronized.

How this would work for a real guest

For this demo, you will enter a guest profile manually. In a real deployment, each guest would receive a unique secure QR code or link tied to the reservation. Opening it would preload the guest name, room, stay dates, party size, preferences, and other permitted context automatically.

Change live hotel dataEdit dining inventory, activity times, service status, and transportation and then ask the AI about the change.
Turn documents into hotel knowledgeUpload a menu image, let AI extract structured data, review it, and publish it to the concierge.
Watch actions write backReservations, activity bookings, and room requests created in chat appear in the simulated hotel systems.
See what is simulated, what is real, and a recommended 5-minute walkthrough.

PERSONALIZE THE DEMO

Choose the hotel and guest we should simulate.

These values become live demo data. The concierge will know this guest context just as it would from a reservation-linked QR code or secure guest link.

Demo assumption Room 614 is used as the simulated room. In a real deployment, room number and reservation details would also be preloaded from the hotel system.

This setup only personalizes the demonstration. It does not connect to or modify any real hotel system.

FULL DEMO GUIDE

What this demonstration is designed to prove

The core idea

The guest talks to one AI, while the AI uses several hotel systems as sources of truth and action systems. The demo intentionally lets you edit those systems so you can prove the concierge is using current operational data rather than memorized answers.

How guest access would work in real life

The manual guest form on this page exists only for demonstration. A production hotel would generate a unique, expiring QR code or secure link for each reservation. The link could identify the reservation and preload approved context such as guest name, room, check-in/check-out dates, party size, language, itinerary, loyalty status, and preferences. The guest would open the link and arrive directly at a personalized concierge without re-entering this information.

Reservation / PMS→Unique secure QR or link→Preloaded guest context→Personalized AI concierge

The three demo views

1. Guest Experience

Ask natural-language questions, make dining and activity requests, report room issues, and query transportation. The AI uses the guest profile you entered here.

2. Hotel Systems

Act as hotel staff. Change restaurant inventory, upload and publish a menu, edit activities, update service requests, and change trolley information.

3. System Activity

See an auditable record of AI tool calls and writes to the simulated hotel systems. This demonstrates that the AI is orchestrating systems rather than inventing successful actions.

Recommended 5-minute walkthrough

  1. Open Dining System and make a 7:30 PM slot unavailable. Return to Guest Experience and ask for that exact time; the concierge should offer live alternatives.
  2. Book one of the alternatives in chat, then return to Dining System and verify that the reservation was written back.
  3. Open Hotel Content, upload a menu photo, review any uncertain extraction, and publish it. Ask the concierge a question that can only be answered from the newly published menu.
  4. Report an AC problem in chat. Open Guest Services and verify the new request. Change its status, then ask the concierge for an update.
  5. Change an activity time or trolley ETA in the backend, return to chat, and confirm the next answer reflects the new value.

What is real vs. simulated in this build

Real in the demo
  • OpenAI language-model reasoning and tool selection when OpenAI mode is enabled.
  • Structured menu image extraction, human review, and publication.
  • Live shared demo data, capacity calculations, reservations, activity bookings, service tickets, and audit events.
Simulated for now
  • PMS, dining, activities, guest-services, and transportation vendors are represented by local demo systems.
  • The QR/link is described but not yet generated in this local build.
  • No real hotel, guest, reservation, payment, or lock system is connected.

Why the architecture matters

The AI sees stable hotel tools such as checking restaurant availability or creating a service request. Later, the local demo implementation behind each tool can be replaced with connectors to OPERA, Mews, Cloudbeds, SevenRooms, a service-operations platform, or another vendor without redesigning the guest conversation.