Why AI does not cite a multinational's website, page by page, and the plan to fix it
We audited a beverage multinational's websites in one country, the corporate site and its thematic subdomains, with a verifiable EEAT scoring model, cross checked it against what ChatGPT, Gemini and Perplexity cite and delivered a phased implementation plan with success metrics.
From problem to solution
- 01 · The challenge
The earlier diagnosis had shown that AI almost never used the corporate website as a source, precisely on the topics most sensitive for the brand's reputation.
What was missing was why, page by page, and what to do first. EEAT is not a metric Google publishes: it is a qualitative framework, and it had to become something measurable, comparable and repeatable every quarter.
- 02 · What we did
We built our own scoring model that turns EEAT into observable criteria: a correct canonical, structured data, distinct meta descriptions and social tags, real FAQs, visible authorship, links from universities or public bodies and downloadable documentation. We scored the key pages of the corporate site and the two thematic subdomains, and validated the model against 90 real AI answers: pages above the 6 out of 10 threshold get cited, pages below it are ignored. We found a newsroom with nothing published for over ten months, duplicated content, titles in another language, a URL with a typo and a subdomain whose canonical pointed to an internal IP. With that we wrote the implementation plan in five blocks, each with a deadline and a metric: technical fixes in 30 days, a new page on the most sensitive topic in 45, local media, an ecosystem of thematic subdomains and quarterly measurement.
How it comes together
Inside the project

Measurable results
Services used on this project
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