One AI answer tells you very little

AI recommendations aren't fixed search results. Ask the same assistant the same question again and the answer may change. Reword the question and different places may show up. Ask a different model entirely and the ranking can look completely different.

So we don't try to measure AI visibility from a handful of prompts. We measure patterns across repeated observations.

How we observe AI

We ask travel-related questions across ChatGPT, Claude, Gemini, and Perplexity — covering restaurants, hotels, attractions, itineraries, family travel, romantic trips, budget travel, seasonal travel, hidden gems, and more. Rather than relying on one exact prompt, we use several phrasings of similar questions, and we ask them repeatedly over time.

Each response gets analyzed to identify the destinations, businesses, attractions, experiences, and concepts the AI recommends. Repetition is what lets us tell a place that came up once apart from something AI consistently associates with a destination.

What that lets us measure

Recommendation frequency — how often a place appears across relevant observations. A restaurant recommended in 15 of 20 responses is a different signal than one that appeared once.

Ranking — where a recommended place tends to land in an answer, not just whether it's mentioned at all.

Consistency across phrasing — AI answers can be sensitive to wording. Asking the same thing several different ways lets us tell recommendations that persist apart from ones that only show up under one specific prompt.

Model agreement — ChatGPT, Claude, Gemini, and Perplexity don't always see a destination the same way. We track where they converge and where they don't.

Change over time — we keep collecting rather than treating any single response as permanent, so we can see rankings, recommendations, and themes shift as the dataset grows.

Broader destination presence — we also ask questions that aren't about one specific destination, like the best weekend trips in a state or the best food destinations in a country. That tells us whether AI thinks of a destination even when it wasn't asked about directly.

The TakeScout score

The score is a simple summary of four signals we track:

Model agreement — how often the AI assistants converge on the same top recommendation for comparable questions. Higher agreement means a more consistent view across models.

Confirmed-open rate — among highly recommended places, how many our follow-up checks confirm are still open. This is a rough proxy for how current the recommendations actually are.

Distinctiveness — how different a destination's most prominent themes are from other tracked destinations in the same country. Sound like everyone else and you score lower; stand out and you score higher.

Presence rate — how often a destination shows up in broader regional or national questions that weren't specifically about it. A recommendation counts fully; a plain mention counts as half.

If we don't have enough observations yet to calculate a metric reliably, we leave it out of that destination's score rather than counting it as zero. The overall score is an unweighted average of whichever metrics have enough data.

What the data does — and doesn't — mean

We don't claim to know what any individual traveler will see when they ask an AI assistant a question. AI output is probabilistic — results shift with wording, model, timing, context, personalization, and changes the AI providers make on their end.

That's exactly why we don't try to draw conclusions from one answer. We're looking for patterns that hold up across many, and the more we observe, the more those patterns are worth trusting.

Questions about how a score is calculated, or something that looks off? Get in touch.