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AI Visibility for Hotels: What Agencies Sell, What Matters, and How to Monitor It

AI visibility for hotels: what agency packages are worth, what to fix in an afternoon, and how to build a weekly monitor of what AI says about you.

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AI Visibility for Hotels: What Agencies Sell, What Matters, and How to Monitor It
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You don't need an agency to get your hotel seen by AI. Agencies are now selling "AI visibility" packages to independent hotels, but most of what's in those packages is work you can do yourself, in a few afternoons, with tools you probably already use. This is a practical guide with no secrets held back: what to check on your website, what to fix on the third-party sites ChatGPT, Gemini and Perplexity rely on, and how to build a simple weekly monitor that shows whether AI actually recommends your hotel and whether what it says is true. Every step is ready for you to do yourself.

What these services actually do, and what each part is worth

Structured data has real value. Schema markup tells machines, without ambiguity, what you are, where you are and what you offer. But it's a one-time setup with occasional edits, not a subscription.
"Auto-updating" sounds good until you count how often hotel facts change: season dates, a new room type, restaurant hours. A few times a year. A calendar reminder at the start of each season does the same job.
Crawler dashboards are where it gets slippery, because "AI crawlers" are three different families doing three different jobs.
Training crawlers (GPTBot, ClaudeBot, Meta-ExternalAgent) collect pages that may end up inside a future model. Search-index crawlers (OAI-SearchBot, Claude-SearchBot, PerplexityBot) build the indexes an assistant queries when it searches the web. OpenAI states plainly that sites blocking OAI-SearchBot won't appear in ChatGPT search answers. User-triggered fetchers (ChatGPT-User, Claude-User, Perplexity-User) visit your page because a real person just asked a question. That last family is the closest thing to a potential guest you'll ever see in a log file.
Then there's Google, which has no separate AI crawler. AI Overviews and AI Mode are Search features built on the regular Googlebot index. Google-Extended is only a robots.txt token that controls whether Gemini can train on or ground answers in your content; it never shows up as a visitor in your logs. So a "Google AI" line in a dashboard is either Googlebot relabeled or a guess.
The bigger point: a crawler visit is not a mention. GPTBot reading your rooms page says nothing about whether ChatGPT recommends you.
The urgency claim is partly true. Your footprint on third-party sites and your presence in training data do build up over time. But many AI answers about hotels are assembled live, from search indexes that refresh constantly. Starting late doesn't lock you out; wrong information on Booking.com does more damage than starting next quarter.

Step 1: Make sure AI can read you (one afternoon)

Open yoursite.com/robots.txt. If it's missing or blocks everything by default, fix that first. A minimal version that welcomes search and user-triggered agents looks like this:
# AI search and user-triggered agents
User-agent: OAI-SearchBot
Allow: /

User-agent: ChatGPT-User
Allow: /

User-agent: Claude-SearchBot
Allow: /

User-agent: Claude-User
Allow: /

User-agent: PerplexityBot
Allow: /

User-agent: Perplexity-User
Allow: /

# Training crawlers: a business decision (see below)
User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: Meta-ExternalAgent
Allow: /
Training crawlers are a choice, not a technical requirement. Allowing them gives future models a chance to learn accurate facts about your property. Blocking them keeps your content out of datasets you're not paid for. For most small hotels, whose website is essentially a brochure, allowing is the pragmatic call, but it's your call. One detail: OpenAI and Perplexity both say their user-triggered fetchers may not follow robots.txt, since a person started the request.
Check Cloudflare. If your site sits behind it (often because a web agency set it up years ago), check the AI bot settings today. Cloudflare began blocking known AI crawlers by default on new domains in July 2025. In July 2026 it replaced the single "Block AI bots" switch with three categories (Search, Agent and Training), and new defaults took effect on 15 September 2026: Training and Agent are blocked on pages that display ads, including existing free-plan sites that never saved a preference. Most hotel sites don't run ads, but look anyway. Cloudflare also warns that blocking Training can catch multi-purpose crawlers such as Googlebot.
While you're there, open AI Crawl Control, Cloudflare's free panel. It shows which AI crawlers visit, how often and what they're allowed to do. That's the dashboard agencies sell, included in your plan.
Add Schema.org markup. Use JSON-LD with the Hotel type (a subtype of LodgingBusiness). The fields that matter: name, address, geo, telephone, checkinTime and checkoutTime, amenityFeature, priceRange, numberOfRooms, and sameAs links pointing to your Google Business Profile, Booking.com, Tripadvisor, Instagram and Wikidata pages. A compact example with placeholder values:
{
  "@context": "https://schema.org",
  "@type": "Hotel",
  "name": "Example Lake Lodge",
  "url": "https://www.example.com",
  "telephone": "+00 000 000 000",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "Route 7, km 25",
    "addressLocality": "Example Town",
    "addressCountry": "CL"
  },
  "geo": { "@type": "GeoCoordinates", "latitude": -43.0, "longitude": -72.0 },
  "checkinTime": "15:00",
  "checkoutTime": "11:00",
  "numberOfRooms": 18,
  "priceRange": "$$$",
  "amenityFeature": [
    { "@type": "LocationFeatureSpecification", "name": "Restaurant", "value": true },
    { "@type": "LocationFeatureSpecification", "name": "Free Wi-Fi", "value": true }
  ],
  "sameAs": [
    "https://maps.google.com/?cid=YOUR_CID",
    "https://www.booking.com/hotel/xx/your-hotel.html",
    "https://www.tripadvisor.com/Hotel_Review-YOUR_ID",
    "https://www.instagram.com/yourhotel",
    "https://www.wikidata.org/wiki/YOUR_QID"
  ]
}
Add a FAQPage block built from questions guests actually ask by email and WhatsApp, not questions you wish they asked. Then validate both with Google's Rich Results Test and the Schema Markup Validator.
Put key facts in plain text. Season dates, how to get there, what the rate includes. If that lives inside an image or a PDF price list, an AI reading your site may simply not see it.
llms.txt is optional. It takes five minutes. But no major AI engine has confirmed it uses the file to find or cite websites, and Google says its Search systems ignore it. Do it last, if at all.

Step 2: Fix your footprint beyond your website

AI answers about hotels lean heavily on sources you don't own: Booking.com, Expedia, Tripadvisor, Google Business Profile, travel blogs, Reddit threads, Wikipedia and Wikidata. When those carry old season dates, a dead phone number or a room type you closed three years ago, the AI repeats it to travelers with complete confidence.
Run this audit once, then again at every season change:
  • Google Business Profile: hours, seasonal closures, phone, website link, photos.
  • Booking.com and Expedia: room names, policies, description, check-in times.
  • Tripadvisor: property details, and whether recent reviews have owner responses.
  • Wikidata: if an entry exists, check coordinates, official website and status.
  • Blogs and Reddit: search your hotel's name and correct what you can, politely.
This is also where the "compounding" claim is honest. Reviews and independent mentions accumulate, and assistants weigh them. No dashboard produces them for you. Guests do, when you give them a reason and then ask.

Step 3: Build your own AI visibility monitor

The stack is four tools. OpenRouter gives you one API key and one bill for many models; we plan to query one model each from OpenAI, Anthropic and Google, plus Perplexity Sonar. Make.com handles the weekly schedule and the logic. Notion stores results and holds the dashboard. Google Search Console supplies the questions.
Three design principles. We track only our own brand, not competitors, because the goal is to see how our presence changes across the searches that matter to us. The questions come from real demand, not guesses. And the prompts stay fixed week to week, so the results are comparable.

1. Build the prompt set

Export 16 months of queries from Search Console. Group them by intent: destination, activity (fly fishing, for us), route (Carretera Austral) and type of stay. Then rewrite the biggest clusters as the questions a traveler would type into a chat: "Where should I stay near Chaitén if I want to go fly fishing?"
Aim for 20 to 40 prompts. Store them in a Notion database called Prompts, with four properties: prompt, cluster, language, active.

2. Connect OpenRouter to Make

Create an OpenRouter account, add credit, generate an API key and save it as a connection in Make (the HTTP module works; so does the OpenRouter app).
The catch: a plain API call answers from training data only. A traveler using ChatGPT or Perplexity usually gets an answer built partly from a live web search. To get closer to that, run each prompt twice: once plain, once with search enabled. On OpenRouter you add ":online" to the model name or switch on the web plugin; Perplexity's Sonar models search natively. Label the two modes separately in your data, because they answer different questions: what the model already believes, and what it finds today.

3. Create the weekly Make scenario

The scenario reads active prompts from Notion, then loops over every prompt and model combination and sends each pair to OpenRouter.
Each answer then goes to a second, cheap model with one job: return structured JSON with five fields. Was the brand mentioned (yes/no)? If the answer is a list, what position? How is the hotel described? Which facts were stated, and are they correct? Which sources or URLs were cited?
Make writes one row per answer to a Notion database called Results: date, model, mode, prompt, raw answer and the parsed fields.
notion image

4. Build the Notion dashboard

Three views do most of the work. Mention rate by week, broken down by model and by cluster, shows whether you're gaining or losing ground and where. An "incorrect facts" view becomes your to-do list for Step 2. A "cited sources" view tells you which third-party sites the assistants actually rely on, so you know where to spend your correction time first.

5. Cost and effort

The math is simple: prompts × models × modes × roughly four runs a month, plus one parsing call per answer. Thirty prompts across four models in two modes comes to around 1,000 answers a month. Search adds a per-request fee: under a cent with Exa, one of OpenRouter's engines, and more with the providers' native search. Inexpensive models cost fractions of a cent per call. Our estimate, before checking your chosen models' current prices, is somewhere between $10 and $40 a month. Setup is a few focused sessions, not a project.
Two caveats. AI answers vary between runs, even with identical prompts, so read trends over weeks rather than reacting to any single answer. And the API is not the consumer app: no personalization, no location, no memory of past chats. The monitor approximates what travelers see. It doesn't replicate it.

Step 4: Track the humans who arrive from AI

In GA4, look for referral traffic from chatgpt.com, perplexity.ai, gemini.google.com and copilot.microsoft.com, plus sessions tagged utm_source=chatgpt.com. Create a custom channel group called "AI assistants" that matches those sources, and place it above Referral so it isn't absorbed there. Treat the number as a floor: some visits from AI apps arrive without a referrer and land in Direct.
These are real people, which makes this report more valuable than any crawler log. The two layers work together: the monitor tells you what AI says about you, and GA4 tells you who acts on it.

The question worth paying attention to

The useful question isn't when a bot last came by. It's whether AI recommends you, and whether what it says is true. Most of the work to get there is yours to do, in a handful of sessions.
Start this week with the 15-minute check: open your robots.txt and your Cloudflare AI settings. Something may be blocking you right now, and nobody would have told you.
Want the AI visibility checklist and a weekly monitor set up for your property? Talk to Hotel Marketing Point.

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Written by

Seba Blanco
Seba Blanco

I help independent hotels sell more effectively and operate smarter by combining hotel technology with sales and marketing.