Why technical implementation is the unsexy part that works
Most hotel operators trying to improve their search visibility focus on photography, copy, and review responses. These matter for human visitors. For AI search, they are secondary to something more fundamental: does the AI have a clean, consistent, machine-readable understanding of what your property is?
This section covers the three layers of technical implementation, in order of impact.
Layer 1: Entity consistency (do this first)
Before any technical work, audit your property’s presence across the web for consistency. The AI will not confidently recommend a property whose identity is ambiguous.
What to check:
- Your property name (exactly as it appears on your website)
- Your address (street number, street name, city, postal code, country)
- Your category (hotel, boutique hotel, bed and breakfast, vacation rental, apartment, etc.)
- Your phone number and website URL
These four data points should be identical, word for word and character for character, on: your own website, Google Business Profile, TripAdvisor, Booking.com or Airbnb, any OTA you are listed on, and any directory or editorial site that mentions you.
A single inconsistency (abbreviated street name on one platform, slightly different business name on another) weakens the AI’s confidence in your entity. Fix the inconsistencies before adding any structured data.
Layer 2: Schema.org markup (implement on your own website)
Schema.org is a vocabulary that tells search engines and AI systems what kind of thing a web page describes. For hospitality properties, the relevant schema types are:
HotelorLodgingBusinessfor hotels, B&Bs and guesthousesAccommodationfor vacation rentals and apartmentsBedAndBreakfastfor B&Bs specifically
The minimum viable schema block for a hotel website includes: the property name, address (as a PostalAddress), geographic coordinates (latitude/longitude), telephone number, URL, and star rating (if applicable).
A more complete schema block adds: amenityFeature entries for key amenities (pool, spa, parking, restaurant, etc.), numberOfRooms, priceRange, a description that matches your About page copy, and image with at least one photograph URL.
Schema.org markup lives in a <script type="application/ld+json"> block in your page’s <head>. Most property management systems and CMS platforms have plugins or settings for this. If yours doesn’t, it can be added manually in a few hours.
Layer 3: llms.txt (30 minutes, high signal)
llms.txt is a plain text file placed at the root of your website (yourdomain.com/llms.txt). It was designed to help AI systems understand what a website is and how it should be cited in AI-generated answers.
For a hotel, an llms.txt file should include: the property name, one-paragraph description in plain prose, the property category (hotel, boutique hotel, etc.), your location including neighbourhood or area, your key attributes (number of rooms, standout features, who it’s right for), and a list of the most useful pages on your site for an AI to read (About, Rooms, Location, FAQ).
AI systems like ChatGPT and Perplexity that browse the web in real time will read this file when they crawl your site. It gives them a clean, authoritative summary to use when generating recommendations. Unlike schema.org markup (which is structured data), llms.txt is written in plain language that the AI can use directly in its answers.
Layer 4: Your About and FAQ pages (written for AI, readable by humans)
The page on your site most likely to be read by an AI and cited in a recommendation is your About or Property page. It should answer, in plain prose: what kind of property is this, where exactly is it, who is it right for, what makes it distinctive, and what are the most common questions guests have.
A well-structured FAQ section on this page, with questions and answers written in plain language, gives AI systems extractable content to use when answering traveller queries. The questions should match what guests actually ask: “Is there parking?”, “How far is it from the airport?”, “Is breakfast included?”, “What is the cancellation policy?”, “Are pets allowed?”
How long does this take to work?
Entity consistency changes propagate across the web in 4-8 weeks as platforms update their records and AI systems re-crawl. Schema.org markup is visible to AI crawlers within days of publication. llms.txt is readable immediately by any AI that crawls your site.
The first change you’ll notice is an improvement in how AI describes your property when it does mention it: more accurate attributes, more specific location descriptions, better category matching. The second change is appearing in queries you were previously invisible for. This typically takes 6-12 weeks from a complete implementation.
Services
If you want this done for your property rather than doing it yourself, see Services.