AI search for travel & hospitality brands.
Travelers ask an assistant before they open a booking site. "Best serviced apartments in Singapore for a long stay." "Best airline for Jakarta to Tokyo." "Where to stay in Bali with kids." The model returns a handful of names with reasons attached. The itinerary forms around them. We work on the answer itself: which properties and carriers get named, and how favorably the model describes them.
up 450% in a single quarter
Why does AI search matter more in travel than in most categories?
Because travel questions are open-ended and the answer is a recommendation. A traveler rarely asks for a page. They ask where to stay, which airline to take, or whether a place works for kids. From there the assistant builds a shortlist from whatever sources it trusts. Most travelers act on it without opening a comparison site.
A landing page that ranks well can still be absent from that shortlist. Being named is an entity, content, and authority problem, which is the discipline we run. We took DiscoverASR (The Ascott Limited) to 450% AI search audience growth in a single quarter, with favorable sentiment held at 77%, more than twenty points clear of its peer set.
Why do two assistants answer the same travel question differently?
Each one reads a different corpus. One engine leans on review platforms, booking aggregators and forum threads; another builds its answer from a handful of sources, sometimes including an operator's own site. Put the same question to both and the shortlists rarely match.
For a travel brand that means there is no single shortlist to win. A property can be the first name on one assistant and missing from another. The same split runs across markets and languages, which is why every benchmark we run is split by engine, route, and city.
The work that moves it is unglamorous. Every property, route, and brand has to resolve as a distinct entity with consistent facts across your site, your Google Business Profile, and the platforms models read. Rates, amenities, policies, and lease terms have to sit in the page as structured facts. A booking widget hides every one of them. Then the review and award signals need building, because a model checks those before putting an operator in front of a traveler. Reporting runs monthly against named competitors, the way we measure everything else.
Which travel businesses does this apply to?
Judged property by property
An assistant asked about a city shortlists individual hotels, never the chain above them. A group with forty properties is running forty separate visibility battles and usually measuring none of them.
Long-stay intent is its own market
Extended-stay answers turn on lease minimums, inclusive utilities, and monthly rates. Most operator sites bury those facts inside a booking engine where no model can reach them.
Route answers built without you
Route recommendations are assembled almost entirely from metasearch and review communities. Carriers that publish their own route, fare, and baggage facts are the only ones with a chance of being quoted from their own domain.
Cited for facts, skipped for booking
Assistants quote OTA inventory pages for prices and availability, then hand the traveler off elsewhere to book. Being the cited source and being the booking destination have become two separate problems.
Competing for itinerary slots
"What to do in [city] with kids" is an itinerary query. Attractions publishing hours, ticket tiers, and accessibility as structured facts make the list. The rest get summarized secondhand from TripAdvisor.
Whole-destination reputation
A destination's standing in AI answers is assembled from guides, forums, and news coverage. The tourism board's own site contributes only when it is built to be lifted.
From baseline to a shortlist you hold.
Prompt set & baseline
Define the planning, comparison, route, and sentiment prompts that decide bookings in each of your markets. Record what every assistant answers today, who it names, and which sources it cites.
Property & route entity graph
Bind each property, brand, and route into a clean entity with consistent facts across your site, your business profiles, and the third-party platforms models read. Ambiguity here is why an assistant describes the wrong location or omits you.
Citation-ready facts
Move rates, amenities, policies, lease terms, and destination guidance out of booking widgets and into structured, answer-first passages that a model can lift and attribute.
Review & authority signals
Build the review depth, award coverage, and third-party mentions an assistant checks before recommending an operator. This is the slowest layer and the one competitors copy last.
Monthly reporting by route & city
Citation share, mention frequency, and sentiment against named competitors, broken down by engine, route, city, and trip type. AI visibility becomes a number your commercial team can manage.
Results from travel and hospitality clients.
Common questions
Why does AI search matter specifically for travel brands?+
Do different AI assistants recommend different hotels?+
Our properties already appear in the map panel. Is that enough?+
Can you optimize for specific routes, cities, and trip types?+
Which travel and hospitality brands do you work with?+
How do you measure it?+
Find out which properties an assistant names today.
We run your brand, your properties or routes, and your named competitors through ChatGPT, Gemini, Perplexity, and Google AI Overviews on the planning and comparison prompts your travelers use. You get the answers, the sources behind each one, and where you sit in every engine.