A working example of conversational discovery for hospitality & travel — natural-language input, decomposed into real, bookable structure. Built and governed the way enterprise deployments require.
Traditional filters — city, dates, star rating — force a guest to translate their actual need into system fields. A conversational layer takes the need as stated ("somewhere quiet near the conference, with a late checkout") and does the translation itself, returning structured, bookable results instead of a filtered list.
This isn't a scripted chatbot flow. Each stage below is a distinct architectural layer — the same pattern used across enterprise conversational deployments.
The guest's request is taken as freeform text or speech — no dropdowns, no required fields. Intent, constraints, and preferences are extracted in one pass.
Extracted intent is mapped against live property, amenity, and availability data — turning a sentence into a query a booking system can actually execute.
Results are composed into a coherent, editable itinerary — not a list of matches, but a proposed plan the guest can adjust in the same conversation.
Before anything is booked or charged, the action passes through policy and audit checks — the same guardrail layer that governs every agent action in production.
Anyone can wire up a chatbot. What makes this deployable at enterprise scale is what sits underneath it.
Every conversation is tied to a verified identity, not an anonymous browser session — required before any action touches a real booking or account.
Anything the agent proposes to do — book, cancel, modify — passes through an approval and guardrail layer before it executes.
Every prompt, tool call, and decision is logged and retrievable — so "what did the AI actually do" is always answerable.
The chat launcher in the corner of this page is the same conversational layer described above — live, not a mockup.
Open the concierge