For hotels, airlines, OTAs, destinations, and travel apps

Be the brand AI recommends when travelers ask where to stay, fly, or visit.

Travelers ask AI where to go, where to stay, where to fly. If your brand isn't in the answer, you've lost the booking before it started.

§01 Why AI search is reshaping travel

The trip-planning conversation moved off the metasearch tab.

ChatGPT now serves more than 800 million weekly users. A growing share of travel research starts with an open-ended question to an answer engine: where should I go in November, which European city for a long weekend, what is the best business-class option to Singapore. The answers come back with named brands attached.

For travel, the consequence is direct. The old planning funnel began on Google or Skyscanner with a route or a destination already chosen. The new planning funnel begins with AI choosing the route and the destination on the traveler's behalf, and naming the brands inside the answer. If your property, airline, OTA, or destination is not in that named set, the booking conversation is over before metasearch loads.

The work is engineering the source pool AI cites for travel queries, getting your brand named inside it with consistent positioning, and managing the seasonal and event-driven volatility of the answer pool on a weekly cadence.

§02 What we do for travel brands

Five lines of work, run weekly, owned by us.

We sit alongside your in-house marketing, brand, and revenue functions. Distribution, channel management, and rate strategy stay with you. The AI visibility layer is our remit, delivered on a fixed weekly cadence.

Destination prompt citation work

Travelers ask AI where to go: best beach destinations in March, where to ski in April, which Italian cities to visit on a first trip. The answer pool for each destination prompt is decided by a small set of cited guides and editorial sources. We map the destination prompts that matter for your brand and engineer presence in the answer.

Hotel and airline recommendation surfacing

Brand-direct queries (best hotels in Lisbon, best business-class airlines for Asia, top boutique hotels in Tokyo) draw from a recurring set of review sources. We engineer the citation patterns that move recommendation rate for named properties and routes, on the surfaces AI actually reads.

TripAdvisor, Booking, and aggregator source-pool positioning

Travel aggregators carry disproportionate weight in AI citations. We work the surfaces that feed into TripAdvisor reviews, Booking and Expedia property pages, and the smaller specialist aggregators that AI cites by category. The goal is consistent attribute representation across the surfaces the model trusts.

Comparison citation for versus queries

Travelers ask AI to compare properties, airlines, and destinations head to head. We engineer the comparison content and third-party validation that gets cited when a traveler runs your brand against a category peer, on the cadence the answer pool refreshes.

Weekly booking-funnel monitoring

Travel queries are seasonal and volatile. A new flight route, a hotel renovation, a destination travel advisory, all of these shift the AI answer pool within days. We monitor the curated prompt set every week and surface movement on the queries that decide bookings.

§03 The outcomes we commit to

Named results, written into the engagement letter.

We deliver results, not dashboards. The pilot pricing is built around it. You pay €500 per month for tools and APIs plus your direct media spend. We carry the team. At the end of the 90-day pilot, if we hit the goal we agreed on day one, the engagement converts on a €6,000 success fee and a €2,500 per month retainer thereafter. If we miss, you walk. No further obligation.

Brand recommendation rate on destination prompts

The metric that decides whether your property, route, or destination is in the consideration set. We commit to a measurable lift in named-brand recommendation on a fixed prompt set.

Editorial guide and aggregator citation share

Documented presence in the editorial guides and aggregators AI weighs heavily: Condé Nast Traveler, Travel + Leisure, TripAdvisor, Booking property pages, and the specialist guides for your category.

Local entity health for AI Overviews

For travel queries with local intent, the deliverable is consistent local entity surfaces feeding the AI Overviews and near me queries that decide on-trip bookings.

Comparison-query inclusion against named peers

For versus queries between named properties, routes, or destinations, the deliverable is documented citation share lift on the comparison prompts where your brand competes.

§04 Who this is for

Brand, digital, and revenue leaders at travel businesses whose direct booking conversation starts on a chat surface.

The typical engagement is a CMO, Director of Digital, or VP Marketing at a hotel group, an airline, an OTA, a destination marketing organisation, or a travel app between fifty and a thousand staff. Traditional travel marketing channels are mature. The new pressure is on the upstream surfaces where the traveler is now making the first set of choices.

You usually come to us because of one of three triggers. Direct booking growth has stalled despite stable spend on metasearch and paid. A competitor with weaker traditional positioning keeps appearing in AI recommendation answers and the team cannot trace the source. Or a new destination, route, or property is launching and needs to be inside the AI answer pool before campaign spend ramps.

§05 How we work

One framework, applied weekly. The methodology is public.

The work runs on the CITE framework. We comprehend the prompt set, influence the source pool, track citation and recommendation movement on a weekly cadence, and evolve the program as platforms shift. The research underneath is published openly.

The methodology

CITE framework

Comprehend, Influence, Track, Evolve

The cadence

How it works

Week-by-week ledger of what we ship

The research

AI Visibility Index

Open library of operator-grade briefs

§06 FAQ

The questions travel buyers ask before they engage.

How does AI decide which hotels to recommend?

Models pull from a recurring set of travel sources: the major editorial guides (Condé Nast Traveler, Travel + Leisure, The Points Guy, AFAR), the OTA property pages (Booking, Expedia, Hotels.com), the meta-review aggregators (TripAdvisor, Tripadvisor Travelers Choice lists), specialist publications by category (Mr & Mrs Smith for boutique, Forbes Travel Guide for luxury), and Reddit threads in destination subs. AI weighs review recency, named-property specificity, awards from cited guides, and the structure of cited pages. A hotel wins recommendation when it appears across multiple cited sources with consistent positioning, not when the property's own site is rebuilt.

Do Google reviews still matter for AI?

Yes, but less than most travel teams assume. Google reviews feed local entity signals that decide near me queries and AI Overviews on local intent. For broader travel queries (best hotels in a destination, best airlines for a route), AI weighs the editorial press and OTA review aggregates more heavily than Google ratings. The work is to maintain a clean local entity through Google for funnel-bottom queries, while doing the editorial and aggregator work that moves top-of-funnel recommendation.

Why does AI favour OTAs over direct booking sites?

OTAs have higher domain authority, more structured content (rooms, rates, amenities, policies in machine-readable formats), and broader review density per property than most hotel-direct sites. When AI needs a source for a property's attributes, the OTA page is usually the cleaner and more cited option. The implication for direct booking is not to compete with the OTA page; it is to ensure your direct site is the cited source for things the OTA cannot answer (loyalty benefits, suite descriptions, in-house experiences) and to make sure your OTA pages themselves are accurate.

How do destination marketing organisations compete with private brands?

Destination marketing organisations (DMOs) have a structural advantage AI weighs heavily: they are the authoritative source for destination-level information, and AI cites them readily for queries about a region, city, or country. The work for a DMO is to make sure that authority converts into citation across the supporting surfaces (regional press, partner content, travel guides that AI reads), and to engineer the destination prompts that send travelers toward the region rather than to a private competitor. Private brands compete by becoming the cited specialist inside a category the DMO does not cover at depth.

Does AI use Booking.com or Expedia data?

Yes, both heavily. OTA property pages are among the most-cited sources in AI hotel-recommendation answers because they carry structured attribute data, review density, and verified content at scale. The implication for hoteliers is that the OTA listing is not a neutral channel; it is an AI source-pool input. Accurate property descriptions, current amenity lists, correct address and category tagging, and a strong review profile on the OTAs feed directly into how AI describes your property when a traveler asks. We work the OTA surfaces as part of the source-pool program, not as a parallel commercial conversation.

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