AI visibility audit for the Lake Balaton destination
As more travellers ask AI engines about destinations, we built a structured audit measuring how and from which sources Lake Balaton appears in generative AI answers. Using three prompt types per profile, we analysed 210 AI answers and 6,015 cited source URLs. Queried by name the destination appeared every time, but when AI recommended alternatives it dropped to 6th among lakes, behind Bled, Bohinj, Ohrid, Garda and Annecy, with coverage concentrated on the northern shore. The audit set out the GEO and AI-SEO intervention points behind that visibility gap.
When AI recommends lakes, Lake Balaton drops to 6th behind Bled, Bohinj, Ohrid, Garda and Annecy , that is the real AI-visibility gap, and that is the GEO intervention point.
Project detail
Search has changed: more and more people ask AI engines about destinations. So we built a structured AI-visibility audit for the Lake Balaton destination, measuring how and from which sources Lake Balaton appears in generative AI answers. The methodology runs three prompt types per profile (general, constraint and comparison); in a single profile we analysed 210 AI answers and 6,015 cited source URLs. Key findings: queried by name, Lake Balaton appears in every answer, but when AI recommends alternatives it drops to 6th in the lake ranking , behind Bled, Bohinj, Ohrid, Garda and Annecy; that is the real visibility gap. The Balaton image is shaped largely by external, non-official sources (Wikipedia, Reddit/forums, international sites) , a direct GEO/AI-SEO intervention point. Internal geographic coverage is heavily concentrated on the northern shore (Balatonfüred, Badacsony, Tihany, Hévíz), while the southern shore is almost invisible. The methodology is scalable (profiles × season × motivation, to a sample of tens of thousands of prompts). This project is the essence of our brand: measuring a fresh, leading-edge technology question with research precision and turning it into a marketable intervention plan.