Luxury hotel AI visibility: the definitive guide

Written by Shamus Smith, Founder, LuxDirect

All findings referenced in this article are based on structured query testing across six AI platforms using consistent prompt frameworks.

AI visibility for luxury hotels is the measurement of how independent and boutique hotels appear when guests ask AI platforms such as ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and Grok for hotel recommendations. It is not search engine optimisation, not online reputation management, and not a feature of any single booking platform. It is its own discipline, and in 2026 it is rapidly becoming the most consequential channel a luxury hotel does not yet measure.

The reason is simple. Guests are no longer typing queries into a search engine and choosing among ten blue links. They are asking an AI platform a question and receiving one answer. Phocuswright's February 2026 research found 56 percent of US leisure travellers used AI for at least one trip in the past twelve months, and a third now use generative AI platforms specifically for trip research, closing on traditional search engines. In a live web study of 25 London luxury boutique hotels conducted by LuxDirect across 9,380 AI responses, just four properties captured 64.3 percent of all AI mentions. Twelve of the twenty-five registered under one percent share of voice. Two were entirely invisible.

This guide explains what AI visibility is, how AI engines decide which hotels to recommend, the four dimensions that determine whether a hotel appears, the eight steps independent and boutique luxury hotels can take to improve, and why the early-mover window matters now.

What is AI visibility for luxury hotels?

AI visibility for luxury hotels is the likelihood that a hotel is surfaced, described accurately, and cited consistently when prospective guests ask AI platforms for recommendations. It is measured across the six major AI platforms (ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Grok) and across four dimensions: presence, accuracy, citation quality, and sentiment.

AI visibility is whether a hotel appears when a prospective guest asks an AI platform a question that should surface it. The questions are not the ones a hotel marketing team typically optimises for. They look like this:

• "best boutique hotel in Mayfair"
• "where to stay for afternoon tea near Bond Street"
• "quiet luxury hotel near Hyde Park"
• "best romantic boutique hotel in Edinburgh for a weekend"
• "five-star hotel with a Michelin-level restaurant in London"
• "best independent hotel in Manchester for couples"
• "luxury countryside hotel with a spa near London"
• "best art hotel in Wales"
• "best luxury hotel for solo travellers in London"

The AI platform might be ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, or Grok. The outcome that matters is whether the hotel is named in the AI's response, how often it is named across many variations of the question, and what the AI says about it when it is named.

This is a fundamentally different problem from search engine optimisation. SEO determines whether a hotel ranks in a list of ten blue links that a guest then chooses from. AI visibility determines whether the AI mentions the hotel at all in a single synthesised answer. There is no list to scroll. There is no second chance to be clicked. If the hotel is not in the answer, it does not exist for that guest at that moment.

It is also distinct from online reputation management. Reputation is about what is said. Visibility is about whether anything is said at all. A hotel can have a flawless reputation across review sites and still be invisible to AI, because the AI never selected it as a candidate to discuss. Conversely, a hotel can be highly visible and described in terms that miss the property's actual positioning. These are separate failures with separate fixes.

All six AI platforms matter, and they do not behave the same way. LuxDirect's CS10 London live web study found a 24.5 percentage point spread between the most and least generous platforms for the single most visible hotel in the dataset. One platform mentioned the hotel in roughly half of relevant queries; another mentioned it in roughly a quarter. For hotels lower in the ranking, the platform-level variance is often larger in proportional terms. A hotel can be reliably visible on one engine and effectively absent from another. Monitoring only one platform, the most common starting assumption, misses where the guest probably asked.

What AI visibility is not. It is not SEO software. It is not reputation management. It is not OTA optimisation. It is not generic AI marketing. It is not paid placement inside an AI platform (no such product exists for hotels at this stage of the category). AI visibility is the measurement and improvement of how AI platforms surface and describe a hotel when prospective guests ask, across all six major engines.

The four dimensions that determine whether a hotel appears, and how it is described when it does, are presence, accuracy, citation quality, and sentiment. Each is addressed in the sections below. Together they form what LuxDirect calls the Luxury Visibility Index, the measurement framework used by the studies referenced in this guide.

AEO vs GEO vs SEO: what's the difference for luxury hotels?

SEO ranks a hotel in a list of links. AEO structures content so AI platforms can extract a clean answer. GEO is the broader discipline of becoming the source AI engines cite when they generate recommendations and comparisons.

Three acronyms are circulating in the hospitality marketing conversation: SEO, AEO, and GEO. They are not interchangeable, and the difference matters for where a hotel spends time and budget.

SEO, search engine optimisation, is the discipline most luxury hotels already understand. It governs whether a hotel ranks in Google's traditional ten-blue-links results for queries like "best Mayfair hotel" or "boutique hotel London." SEO still matters for some guest journeys, particularly branded searches (where a guest already knows the hotel's name and is looking for the official website) and high-intent transactional searches that bypass AI entirely. It is also the foundation that AI engines partially draw on when they retrieve live web content. SEO is not dead. It is no longer sufficient.

AEO, answer engine optimisation, is the practice of structuring content so AI platforms can extract a clean, citable answer from it. The unit of work is the well-formed answer to a likely question. AEO covers question-and-answer page structures, schema markup (FAQPage, HowTo, Article), clear factual statements positioned near the top of each page, and the avoidance of the kind of marketing prose that AI engines struggle to parse. AEO is what makes a hotel's website machine-readable in the first place. Without it, even an outstanding property gives an AI nothing it can quote.

GEO, generative engine optimisation, is broader and more consequential for luxury hotels. GEO is the discipline of becoming the source AI engines cite when they generate recommendations, comparisons, and descriptions. It encompasses AEO as a component but extends to the question of which external sources mention the hotel, how those sources describe it, how consistently the hotel's positioning appears across the open web, and how trustworthy the constellation of references is in aggregate. GEO is not about a hotel's website alone. It is about every place on the internet that an AI engine might cross-reference when deciding whether to recommend the property.

The order of leverage in 2026, for an independent or boutique luxury hotel currently underrepresented in AI discovery environments, is GEO first, AEO second, SEO third. SEO infrastructure is mostly mature in the sector, and branded transactional searches still convert reliably on it. AEO is a relatively contained on-site project that any competent agency can deliver. GEO is the unsolved problem, the one where the largest visibility gaps are opening, and the one where being early compounds the most.

The technical floor for AI-native infrastructure remains low across the hospitality industry. Most hotels have not yet implemented the machine-readable files, schema, or structured content that AI engines prefer to read. A hotel that takes action now is not catching up. It is moving while almost everyone else is standing still.

The next section explains what that action looks like in practice, starting with how AI engines actually decide which hotels to surface.

How do AI engines select which hotels to recommend?

AI platforms do not have opinions about hotels. They have probability distributions over text. When a guest types "best boutique hotel in Mayfair" or "where to stay near the Tate Modern with a good restaurant," the AI is not retrieving an answer from a hotel database. It is generating the most statistically plausible continuation of the conversation given everything it has learned about hotels, the location, the type of property requested, and the kind of answer such a question typically receives. Understanding this is the foundation of every practical decision in this guide.

Three inputs determine the output.

The first is training data. Every large language model is trained on a frozen snapshot of web content, books, articles, and other text. That snapshot ends on a specific date and is not updated in the model itself between training runs. For luxury hotels, training data includes editorial coverage in publications the model was trained on (broadsheet travel sections, glossy magazines, online travel media), the hotel's own website if it was crawled, third-party listings and aggregators, social content where present, and the cumulative shape of how the hotel has been described over the years. A hotel that has been covered for decades by Conde Nast Traveller and The Telegraph is present in training data in a way a newer property cannot easily replicate.

The second is live web retrieval. Most major AI platforms now perform some form of real-time web search when the question warrants it. The system reformulates the user's question into a search query, retrieves a handful of results, reads them, and synthesises an answer. This is how AI platforms surface information that post-dates their training cutoff and how they handle queries about specific current events, prices, or availability. For hotel queries, live web retrieval typically pulls from a small set of authoritative editorial sources, the hotel's own website if it ranks well, and large aggregators.

The third is the synthesis layer. This is where the model decides which of the candidate hotels surfaced by training data and live retrieval to actually include in its answer, in what order, and how to describe each one. The synthesis layer weights signals across multiple retrieved sources, applies internal consistency checks, and produces what looks like a confident single answer. Behind the scenes, that answer is often the result of reciprocal rank fusion across several independent retrieval streams.

The implication for a luxury hotel is specific. Consensus matters more than depth. A hotel that appears once in a perfect website but is rarely mentioned by independent sources will lose to a hotel with a serviceable website but consistent presence across editorial, review, and third-party coverage. AI engines look for the same property to appear, described in compatible terms, across multiple independent sources. When they find that consensus, they cite it confidently. When they do not, they hedge by either listing more hotels with less detail or omitting the property entirely.

This is why LuxDirect's CS10 London live web study, conducted across 9,380 responses, found that visibility concentrates upward in live web mode rather than diversifying. The pre-study expectation was that newer editorial coverage would surface lesser-known properties as live web replaced training data. The opposite occurred. The top four hotels gained between 3.5 and 4.6 percentage points of share of voice each, while the middle of the market lost ground and the bottom remained at zero. Live web does not democratise visibility. It amplifies whatever consensus already exists across the editorial sources AI engines trust.

The same study found that across 1,854 booking-intent queries (such as "book a luxury hotel in London Mayfair this weekend" or "best five-star to reserve near Hyde Park tonight"), the AI surfaced a hotel by name in only 3.1 percent of responses. Discovery and comparison queries returned mentions far more often. Transactional queries collapsed. AI knows the hotels exist. AI ranks them. AI compares them. But when the guest signals they want to book, AI defaults to either no answer or a routing pattern that does not name the property. This is the precise commercial gap that defines why AI visibility for luxury hotels is its own discipline, separate from any predecessor.

The four dimensions in the next section explain how to measure where a hotel stands inside this system.

How is AI visibility measured? The four dimensions that matter.

The four dimensions of AI visibility: Presence (does the AI mention the hotel), Accuracy (is what it says correct), Citation Quality (what sources is it drawing on), Sentiment (does the framing match the hotel's actual positioning). Each is measured independently across all six AI platforms.

A hotel either appears in an AI response or it does not. That binary outcome is the start of measurement, not the end. A property mentioned in fifty percent of relevant queries but described with the wrong location is in a different position from a property mentioned in twenty percent of queries with consistently accurate detail. Both signals matter. So do two others. Four dimensions together describe where a hotel actually stands.

Presence is the share of relevant queries that surface the hotel by name. It is the most intuitive dimension and the easiest to measure. A hotel with high presence is a hotel the AI considers a candidate to discuss. A hotel with low presence is invisible at the candidate-selection stage, before any other dimension comes into play. Presence is platform-specific. A hotel can have strong presence on one engine and weak presence on another, and the spread between platforms is often larger than the spread between hotels on the same platform.

Accuracy is whether the AI's description of the hotel matches reality. This is the dimension most often overlooked, and it can mask serious problems behind otherwise healthy presence figures. An AI that mentions a hotel frequently but consistently misstates its location, room count, price tier, restaurant offering, or proximity to local landmarks is not building the hotel's commercial position. It is building a corrupted version of it that any prospective guest will recognise as wrong the moment they compare the AI's claim to the actual website. Accuracy failures often trace back to outdated source material the AI has learned from. Fixing the source material is the fix.

Citation quality is the strength of the sources the AI relies on when it mentions the hotel. Two hotels can have identical presence figures while drawing on entirely different citation foundations. One might be supported by national broadsheet travel coverage, leading luxury hospitality publications, and the hotel's own well-structured website. The other might rely on aggregator pages, OTA listings, and thin third-party content. The first is durable. The second is fragile, because aggregators and OTAs reframe their content frequently and AI engines reweight their trust accordingly. Citation quality predicts whether today's presence will hold in three months.

Sentiment is how the AI describes the hotel when it does mention it. This goes beyond positive-versus-negative scoring. The substantive question is whether the AI characterises the property in terms the hotel would itself use. A hotel that markets quiet refinement but is described by the AI as "lively" is suffering a sentiment misalignment, not a sentiment failure. A property described in fragmented terms across platforms (luxury on one engine, mid-market on another, boutique on a third) faces a different problem: AI engines hedge their language when their source material disagrees, and that hedging reduces conversion among guests who came to the query with a specific preference.

The four dimensions interact. A hotel can be strong on presence and weak on citation quality, which suggests a fragile position vulnerable to the next AI model update. It can be strong on accuracy but weak on sentiment, which suggests editorial alignment work rather than technical work. The diagnostic value is not in any one dimension but in the pattern across all four. LuxDirect's Luxury Visibility Index reports each of the four dimensions independently rather than collapsing them into a single score, because the route to improvement depends entirely on which dimension is weakest and why.

What can a luxury hotel do to improve AI visibility? An eight-step checklist.

The eight steps below are ordered. Each builds on the one before. A hotel that addresses them out of order will spend effort on later steps before the foundation exists to support them.

1. Audit current AI visibility across all six platforms. Before changing anything, establish a baseline. Run a structured set of queries that real guests are likely to ask, direct ("best boutique hotel in Mayfair"), indirect ("where to stay for afternoon tea near Bond Street"), location-led, occasion-led ("romantic weekend in Edinburgh"), and booking-intent, across ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and Grok. Record where the hotel appears, where it does not, what is said when it does, and which competing properties surface in its place. Tools like the LuxDirect audit run this measurement across all six engines and produce a Luxury Visibility Index reading on each of the four dimensions. Without a baseline, every later change is unmeasurable.

Outcome: a documented starting position the hotel can compare against in 90 days.

2. Implement Hotel schema and SoftwareApplication schema markup on the website. Structured data is how AI engines parse a hotel as an entity rather than as a generic webpage. The Hotel schema type includes specific fields for room count, star rating, amenities, price range, location coordinates, and acceptable payment methods. FAQPage schema marks up question-and-answer content for direct extraction. Article schema identifies editorial content with author and date attribution. HowTo schema marks up step-by-step content. Schema does not change what a guest sees on the page. It changes what the AI engine sees.

Outcome: the hotel becomes machine-readable as a structured entity, not just a series of marketing pages.

3. Deploy llms.txt and llms-full.txt with rich property descriptions. These are the AI-native equivalents of robots.txt. They tell AI engines which pages are safe to read, which content to prioritise, and provide a structured description of the property in a format optimised for machine consumption. llms.txt is the lightweight version, typically a few hundred lines listing the hotel's key pages and a description. llms-full.txt is the rich version, containing the full property positioning, amenity detail, room types, restaurant offering, and policies in plain text the AI can use directly. Industry adoption of either remains low, which means the action has outsized current return.

Outcome: the AI has a clean canonical source to cite instead of stitching together fragments from across the site.

4. Ensure factual consistency across every web property the hotel controls. AI engines penalise contradictory facts across sources. If the hotel's own website lists thirty-two rooms but the chain's group site says thirty-five, the sister-restaurant page says "boutique luxury" and the social profile says "five-star," the AI engine hedges. Hedging shows up as fewer mentions, less specific descriptions, and lower confidence in the AI's framing of the hotel. The audit work is unglamorous: every social profile, third-party listing, group brand page, sister property reference, and historical press release needs to match the canonical positioning on the main site.

Outcome: AI engines see one coherent hotel across every source they consult.

5. Build editorial backlinks from luxury hospitality publications. Citation quality is largely determined by which sources the AI sees naming the hotel. National broadsheet travel sections, leading luxury hospitality titles, and recognised editorial publications carry materially more weight than aggregator listings or generic content farms. A single piece of authoritative coverage often outperforms ten weaker mentions in shifting an AI engine's framing. This is a slow channel that compounds. Coverage from twelve months ago still influences current AI behaviour because the AI was trained on it. Editorial outreach is not a campaign. It is a sustained discipline.

Outcome: the AI's mental model of the hotel is anchored to high-trust sources that hold up under live web retrieval.

6. Monitor citation patterns weekly, not monthly. AI citation behaviour is volatile. Monthly snapshots miss the signal. A hotel can drop from prominent to invisible inside a fortnight after a model update or a shift in the editorial sources an engine prefers. Weekly monitoring catches deterioration early enough to investigate while the change is still recent. Monthly monitoring catches it after a quarter of guest queries have already routed around the hotel.

Outcome: regressions are caught and addressed before they compound into lost bookings.

7. Address fragmented neighbourhood and category signals. AI engines hedge when their source material disagrees about basic facts. A hotel described as Mayfair on one source and Marylebone on another, or as luxury on one platform and mid-market on another, creates exactly this kind of disagreement. The fix is editorial: identify every source that uses the off-position framing and either correct it directly (in cases the hotel controls) or commission updated content that establishes the canonical positioning more loudly than the older fragmented material.

Outcome: AI engines stop hedging and start citing the hotel in its actual category and location.

8. Track changes across all four dimensions over 90-day windows. Daily fluctuation in AI visibility is noise. Single-week movements often reverse themselves the following week. The signal sits in 90-day trends across the four dimensions: presence, accuracy, citation quality, sentiment. A hotel that is improving on three dimensions and flat on the fourth is in a fundamentally different position from one that is flat on three and declining on the fourth, even if both show the same headline presence number this week.

Outcome: actual progress becomes visible, and tactical decisions are made on signal rather than on noise.

Taken together, these eight steps describe a discipline rather than a campaign. The hotels that hold their AI visibility through 2026 and into 2027 will be the hotels that treat AI visibility as a continuous measurement-and-iteration practice, not as a one-time technical upgrade.

Why is AI visibility particularly important for independent luxury hotels?

The category-level findings in this guide apply to every luxury hotel, but the practical exposure is not evenly distributed. Independent and boutique luxury hotels face a structurally different AI visibility challenge from branded chains, and the gap is widening.

Branded chains arrive in AI training data with a head start. The brand name itself acts as an entity AI engines recognise. Every property under that brand inherits a portion of the brand's accumulated citations, editorial coverage, and cross-references. A new property in a chain enters the AI's mental model with hundreds of inherited associations before its first guest checks in. An independent hotel has to build that scaffolding from scratch, one editorial mention and one structured page at a time.

The exposure shows up in the data. A LuxDirect study of fifteen independent luxury hotels in a single UK city centre found that more than half of the properties tracked registered zero competitive-query visibility across five AI platforms. Branded queries surfaced the hotels by name. Competitive queries did not. When a guest asked an AI platform for the best luxury hotel in the city without naming a property, the AI returned the same handful of names every time, and the rest of the market was absent. The pattern was not about quality. Several of the invisible hotels had stronger guest reviews and more interesting positioning than the visible ones. They were missing from the list of hotels the AI considers at all.

The parallel to direct booking in 2010 is instructive. The hotels that built direct booking infrastructure in 2010 (proper websites, room-level inventory parity, channel manager discipline, loyalty programmes) captured a structural margin advantage that compounded for the next decade. The hotels that delayed found themselves paying OTA commissions on guests they could otherwise have owned. The same logic applies to AI visibility now. The cost of building the foundation today is low. The cost of building it in 2028, after the consensus tier has hardened, will be materially higher. AI engines reinforce existing consensus more aggressively than they reshape it.

The corollary is also true. Independents have a window to claim category positioning that does not yet exist. Chains compete for visibility on brand terms ("best Four Seasons in London"). Independents have the field largely to themselves on the descriptive terms that matter most to AI engines and to guests asking AI engines for recommendations ("best boutique hotel for a romantic weekend in Edinburgh," "quiet five-star with a serious restaurant in Mayfair," "best independent luxury hotel in Manchester for couples," "luxury countryside hotel with a spa near London"). These are the queries where independent and boutique properties can win disproportionate share, but only if the foundational work in the previous section is in place when an AI engine forms its answer.

The window is open. It is not infinite.

Frequently asked questions

How is AI visibility different from SEO?

SEO ranks a hotel in a list of ten links a guest then chooses from. AI visibility determines whether the AI mentions the hotel at all in a single synthesised answer. There is no list to scroll, no second-page traffic to recover, and no opportunity to be the fifth result that catches someone's eye. The hotel is either in the AI's answer or it is not. SEO still matters for branded searches and high-intent transactional traffic that bypasses AI entirely, but it is no longer sufficient on its own.

Which AI platform matters most for luxury hotels?

All six. ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, and Grok behave differently and reward different signals. LuxDirect's live web research across thousands of responses found a 24.5 percentage point spread between the most and least generous platforms for the same hotel. A property that performs well on one engine can be effectively invisible on another, and the spread is often larger for mid-tier properties than for category leaders.

How does ChatGPT recommend hotels?

ChatGPT generates hotel recommendations through a combination of training data, live web retrieval, and synthesis. When a guest asks for hotel recommendations, ChatGPT does not query a hotel database. It produces the most statistically plausible answer based on what it has learned about hotels in the relevant location, plus (in some modes) a real-time web search that surfaces current editorial coverage. The hotels that appear consistently are those mentioned across multiple authoritative sources (broadsheets, hospitality titles, the hotel's own structured website) in compatible terms.

How long does it take to improve AI visibility?

Foundational technical work (schema markup, llms.txt, llms-full.txt, factual consistency audits) shows up in AI responses within a few weeks for the platforms that retrieve from the live web. Editorial backlinks and reputational repositioning compound over months, and the durable changes often only become measurable in 90-day windows. AI visibility is a quarterly discipline, not a weekly tactic. Hotels that expect movement inside a fortnight are misreading the channel.

Can a hotel rank in AI without a big marketing budget?

Yes, and at this stage of the category, marketing budget is one of the weaker predictors. The strongest predictors are technical hygiene (which costs little), editorial alignment (which costs time, not money), and consistency across the hotel's existing web properties (which costs only the discipline to do it). Several of the highest-performing properties in LuxDirect studies are independent boutique hotels with marketing teams of one or two. Several of the lowest-performing are well-funded chain properties with the wrong technical foundation.

What is the difference between AEO and GEO?

AEO, answer engine optimisation, is the on-site discipline of structuring content so AI platforms can extract a clean citable answer. GEO, generative engine optimisation, is the broader discipline of becoming the source AI engines cite when they generate recommendations and comparisons. AEO covers a hotel's own website. GEO covers every external place on the open web that an AI engine might cross-reference. AEO is a contained project. GEO is a sustained practice.

How accurate is AI information about hotels right now?

Less accurate than most hotels assume, and the gap widens with newer properties. AI engines confidently state room counts that are out of date, attribute restaurants to hotels that closed them, place properties in adjacent neighbourhoods, and assign price tiers that no longer reflect current positioning. The errors are systematic, not random, and they trace back to outdated source material the AI was trained on or retrieved from. The fix is not asking the AI for corrections. It is correcting the upstream sources.

Does my hotel need llms.txt?

Yes, in most cases. llms.txt and its richer companion llms-full.txt are the AI-native machine-readable description files that give AI engines a clean canonical source to cite about the property. Implementation is straightforward, typically a single text file per hotel, and the action has outsized current return because adoption across the industry remains low. The downside risk is essentially zero. The upside is that an AI engine retrieves the hotel's own canonical positioning rather than stitching together fragments from third-party sources.

How does LuxDirect's measurement differ from Google Analytics or my existing reporting?

Google Analytics measures what happened on the hotel's website after a guest arrived. LuxDirect measures what happened before the guest arrived, at the moment the AI platform decided whether to name the hotel in its response. Existing reporting tools cannot see queries that did not surface the hotel. They cannot measure what an AI engine said about the property to a guest who never clicked through. AI visibility is the channel-level question that sits upstream of every conversion tool. It cannot be inferred from downstream analytics.

Get a baseline AI visibility audit

If a hotel does not know how it currently appears across the six major AI platforms, the eight-step improvement work in this guide cannot be measured. A baseline is the prerequisite for everything else.

LuxDirect runs a structured baseline audit for independent and boutique luxury hotels. The audit covers all six AI platforms (ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Grok), produces a Luxury Visibility Index reading on each of the four dimensions, and includes a written diagnosis of the largest visibility gaps and their probable upstream causes. It is delivered as a structured report rather than as a dashboard subscription, because most hotels need a clear starting position more urgently than they need ongoing telemetry.

The audit is appropriate for any independent or boutique luxury hotel that wants to know where it stands before investing in editorial, technical, or distribution changes. It is not a sales conversation. It is a diagnostic.

Related reading

• The Manchester independent luxury study — what AI sees when guests ask for the best hotel in a major UK city
• The Edinburgh luxury market study — the same methodology applied to a different category leader landscape
• How AI forms opinions about your hotel — the mechanics of training data, retrieval, and synthesis explained in plain language

About the author

This guide was written by Shamus Smith, founder of LuxDirect, the AI visibility intelligence platform for independent and boutique luxury hotels. Shamus holds an MBA from Les Roches and spent approximately eight years in luxury hospitality operations across multiple international markets before founding LuxDirect. He has led the design and execution of more than ten structured AI visibility studies across UK luxury hotel markets, including the CS7 and CS10 London studies referenced in this guide, the CS5 Edinburgh study, the CS8 Cambridge study, and the CS9 Manchester independent luxury study. He writes for Hospitality Net and is a Les Roches alumni contributor.

LuxDirect provides AI visibility intelligence for luxury hotels through proprietary measurement across the six major AI platforms. The company is registered in England and Wales (Company No. 16544799) and based in London.

Findings are based on structured query testing across six AI platforms via API, combined with live web retrieval studies.