The headline finding: half of these real hotels scored zero
4 of the 8 hotels in this data set, exactly half, were named by exactly zero AI engines across their entire panel of non-branded traveler questions. That includes a genuine luxury property and a large international chain, not just small independents. A generic recommendation query ("best hotel in Prague," "good spa hotel near me") tends to pull AI engines toward whichever small handful of names already dominate the training and retrieval data for that destination, and most real, well-reviewed hotels simply aren't in that handful, regardless of quality.
By hotel type
The two design-forward boutique hotels in this set (Golden Well, Mosaic House) posted the two highest detection rates, 7.1% and 12.5%. The mid-range independent and the large chain hotel split oddly: the independent had the highest technical readiness score in the entire data set (92.9%) but zero detections, while the chain had one of the lowest readiness scores (54.2%) but was one of only two hotels with any detections at all. Readiness and detection clearly aren't the same measurement, a pattern covered in more depth in the GEO guide linked below.
By engine, where the data allows it
Google AI Overviews was the hardest engine to appear in: of the 7 hotels with a published per-engine breakdown, only one (Bouda Mama) had a single AI Overviews mention, everyone else scored a flat zero on that engine specifically. Gemini and ChatGPT produced the most mentions among the hotels that did get detected. Claude was the second-hardest engine, only Mosaic House had a single mention there.
One property in this set (the large chain hotel) had 3 real detections but its exact per-engine split wasn't published in the internal exercise this page draws from, so it's left out of the by-engine breakdown above rather than guessed at.
Methodology
This first edition draws on 8 real hotels from two sources: the 4 hotels published as individual case studies on this site (named, with their real numbers), and 4 hotels from an internal engineering score-calibration exercise run in July 2026, real Prague properties scanned once, never customers, and anonymized by segment here since they never agreed to public disclosure. Panel sizes range from 24 to 299 non-branded traveler-question checks per hotel, and engine coverage (2 vs. 5 engines) depends on plan tier, not property quality, so treat this as a directional early read rather than a precise industry standard. We'll expand this data set and republish as more real, disclosable scan data accumulates, no fixed schedule promised here that we can't actually keep.
What this means for your own hotel
If your own number, once you check it, lands anywhere above 0%, this data set suggests you're already ahead of half the real hotels checked here, small comfort on its own, but a useful floor to know exists. If you land at exactly 0%, you're in good, if unwanted, company: a luxury property and a large international chain both scored the same in this set. Neither technical readiness nor brand size reliably predicted who got named.
The more useful comparison isn't against this benchmark once, it's against your own number over time, since a single check (here or on your own hotel) is one snapshot, not a trend. The measuring guide linked below covers what to track and how often.
Is a sample of 8 hotels really enough for a benchmark?
It's a small, first edition, and this page says so plainly rather than implying otherwise. It's still 8 real, individually-verifiable detection rates, not an industry survey or a modeled estimate. We'll publish a larger edition as more real scan data accumulates.
Why are 4 of the 8 hotels anonymized instead of named?
Those 4 were scanned once for an internal engineering calibration exercise, never as customers, and never agreed to public disclosure. The 4 named hotels are existing, already-public case studies. We only publish real hotels by name with their knowledge.
What counts as a "good" AI visibility score for a hotel?
Based on this data set, anything above 0% already puts a hotel ahead of half the sample. There's no fixed industry-standard benchmark yet, this page is an attempt to start building one from real, verifiable numbers instead of a guess.
Why did some hotels get tracked on only 2 engines instead of 5?
Engine coverage depends on plan tier, not the hotel's importance. The entry-level Radar plan tracks ChatGPT and Google AI Overviews only; Essential and Pro track all 5. Bouda Mama, in this data set, is a Radar-tier property.