Four months before the project started, Storywalking was outside the top hundred names in its market. Four months later it is the third one assistants give when someone asks who to go to for a trip to Italy.
- 3rd — among the brands assistants cite in its market, from a position of near-total absence
- 1.6% → 14.8% — assistant answers that name the brand
- 1 day — between publishing an article and its first citation
Where it started
Storywalking is an Italian tour operator that builds tailor-made cultural trips for an international audience. It competes with operators that have decades of history, in-house editorial teams and budgets a company this size cannot come close to.
In the 123 days before the project started the platform had already analysed 6,410 answers: the brand was named in 1.6% of cases, its site appeared in 0.34% of cited sources, and it did not even enter the top hundred names in its market ranking. To an AI assistant, in practice, it did not exist.
Very few pages, almost all the citations
Between the two periods the site went from 69 to 2,298 citations, and they are not evenly spread: a single page accounts for 54% of the total, and a second collected 288, appearing in 12–16% of all Gemini answers in the market. It is an article published on 2 July at 9:55, cited by an AI assistant the next day.
That is how an operator of this size overtook companies with decades of history: in an AI answer the unit being cited is the page, not the domain. You don’t need ten thousand pages, you need a few right ones. The caveat sits in the same number: visibility concentrated in one page is only as strong as that page.
Before and after, over the same span
Four months of project against the four months immediately before: 123 days each, the same questions, the same engines.
| 9 Jan – 11 May 2026 | 12 May – 11 Sep 2026 | |
|---|---|---|
| Answers naming the brand | 1.6% | 14.8% |
| Share of voice in the market | 0.2% | 2.0% |
| Position among cited brands | outside the top hundred | 3rd of 1,054 |
| Share among cited sources | 0.34% | 2.16% |
| Citations received by the site | 69 | 2,298 |
And when it appears, it appears high up: the average position in the answers that name it is 4.0.
The outline comes from what the model searches
Fan-out is the one list of queries you won’t find in any keyword research tool, because it isn’t what people type: it’s what the model searches for while it builds the answer. Refinea records them at every measurement, so they are observed rather than estimated.
The difference shows up in practice. An outline built on user queries answers the question the user asked; one built on fan-out answers the questions the model asks itself in order to answer them, which are more numerous and more specific. The headings become those questions, and the page lets itself be cited in pieces rather than whole.
One problem remains for anyone working this way: if three operators build their page on the same searches, the model cites only one. That is why the articles start from the client’s brand kit. There is not much room to differentiate on form; on substance there is.
How these numbers are measured
Real market questions, derived from the client’s first-party data, in English, Spanish and Portuguese — the languages of its audience — asked every day to four AI assistants: 6,410 answers analysed in the 123 days before the project started, 9,051 in the 123 that followed. “Answers naming the brand” is the share of answers in which the name appears; the ranking compares the brand with every other name appearing in the same answers; a source’s share is the average of its shares across the individual engines, so a model that returns longer lists does not weigh more than the others. Every comparison is between the project period and the immediately preceding period of equal length.
You don’t need to be the biggest. You need to be the one the model finds.
Refinea tells you which questions matter in your market, who gets named instead of you today, and which content changes the answer.