How Does AI Decide Which Hotels to Recommend?

Strip away the branding and all five major AI engines do some version of the same three-step process when a traveler asks a recommendation question. First, retrieval: the engine gathers a set of candidate sources it thinks are relevant, a mix of an existing search index, live crawling, and in some cases a maps or business-listing database. Second, source fusion: it weighs and blends what those sources say, resolving disagreements (one source says 40 rooms, another says 45) and deciding which facts to state with confidence. Third, generation: it writes the actual answer, choosing which one to three hotels to name from everything it just gathered.

The five guides on this site that cover ChatGPT, Claude, Gemini, Perplexity, and AI Overviews individually each walk through one engine's version of this process in detail. This page is the cross-engine view: the same question, run through all five at once, and what actually differs between them.

Take a real, common query: "best boutique hotel in Prague for a weekend trip." A guest types or speaks this into ChatGPT, Gemini, Claude, Perplexity, or Google, expecting a short, confident answer, not a list of ten links to sift through themselves.

Behind that one sentence, each engine now goes looking for candidate hotels, and this is exactly where the paths start to diverge, because "going looking" means something different depending on which search infrastructure sits behind the assistant.

ChatGPT's web-browsing and search feature has been publicly reported to run on Bing's search index as its underlying retrieval layer, meaning a hotel's standing in Bing's index is a real, if indirect, input into what ChatGPT can find. Claude's web search tool has been publicly reported to use Brave's search API for the same purpose, a different index with its own crawl and ranking behavior.

Gemini and Google AI Overviews both draw directly on Google's own Search index, plus Google Maps and Business Profile data for real-world, location-based questions, since both products are built and operated by Google itself. Perplexity leans less on a single pre-built index and more on live retrieval at query time, fetching and reading current pages rather than relying purely on a cached index, which is a meaningful part of why it visibly cites nearly everything it states.

None of this is published by any of these companies as an official, hotel-specific ranking algorithm. It's public, reported infrastructure, which sources feed which engine, not a documented formula for which hotel wins.

Once an engine has candidate sources, it has to reconcile them. If your own website, a Booking.com listing, and a TripAdvisor page all describe your hotel slightly differently, the AI has to decide which version to trust, or which parts to combine. Consistent, structured, matching facts across sources make this step easy. Conflicting facts make it more likely the AI either states something wrong or quietly leaves your hotel out of the answer rather than risk stating something it can't verify.

This is also the stage where third-party authority tends to win by default: an OTA listing that's been indexed for years, with thousands of consistent data points behind it, is often an easier source for an AI to trust than a hotel's own site, unless that site's own signals are just as clean.

Because retrieval differs by engine, a hotel that's well-indexed in Bing but thin on Google Business Profile data might do reasonably on ChatGPT while barely showing up on Gemini, or the reverse. A hotel with strong, frequently-updated third-party citations but a site Perplexity rarely crawls might do well everywhere except Perplexity specifically.

This is the practical reason checking only one AI assistant by hand gives a skewed picture: the gap isn't always about the hotel's overall quality or content, it's often about which specific retrieval path failed for that one engine.

Golden Well Hotel, a boutique property in Prague, is one of the real, published case studies on this site, and its results make the point concretely. Across 295 real, non-branded traveler questions run through five engines in the same week, the same hotel scored 15% detection on Gemini, 11.7% on Perplexity, 8.3% on ChatGPT, and 0% on both Claude and Google AI Overviews.

That's the same website, the same reviews, the same structured data, scoring anywhere from 15% to 0% depending purely on which engine was asked. When the AI engines didn't name Golden Well, they routed travelers to Booking.com, TripAdvisor, a French-language travel blog, and a hotel-editorial site instead, a real, visible answer to "who wins when you don't."

Underneath the different retrieval paths, all five engines reward roughly the same underlying things: a site AI crawlers can actually reach, structured data that states facts plainly, name-address-phone consistency across the sources each engine pulls from, and content that looks current rather than stale. None of these five inputs are unique to one engine. They're the shared floor every engine's fusion step is working from, whichever index or crawl fed it in the first place.

That's also why a single technical fix, correcting a Google Business Profile mismatch, say, can move the number on more than one engine at once, even though the engines never talk to each other.

Checking one AI assistant by hand and calling it representative is the most common mistake this cross-engine view corrects. A hotel invisible on ChatGPT specifically might be doing fine on Gemini, and a hotel that looks fine because someone happened to test Perplexity might be completely absent from Google AI Overviews. The only way to know the real spread is to ask the same real question across all five, on a repeatable basis, not once.

It also changes how a fix gets prioritized. A hotel scoring 0% on Claude and Gemini but fine everywhere else has a narrower, more specific problem than a hotel scoring near 0% across all five, and the two situations call for different next steps, one is likely an isolated retrieval gap, the other points at something more fundamental like blocked crawlers or missing structured data across the board.

AI answers aren't static. Search indexes update, engines change how they weigh sources, and a hotel's own third-party listings shift as reviews accumulate or a directory entry goes stale. The Golden Well numbers above are a snapshot from one real scan, not a permanent score, which is exactly why a single manual check, however careful, only tells you about that one moment.

The guide on measuring hotel AI visibility, linked below, covers the three numbers worth tracking over time (detection rate, mention rank, and the per-engine breakdown shown here) and how often it's actually worth re-checking.