The most thorough AI visibility audit we have run
211 queries across three countries against three language models, with all 633 answers read and classified by hand. No sampling, no automation: every answer reviewed by a person.
From problem to solution
- 01 · The challenge
A family business group with operations in Spain and Venezuela needed to know what language models say about them.
This is not a marketing question: when someone looks up a business family, what the AI says about them shapes negotiations, partnerships and hires. And unlike Google, there is no dashboard to check it.
- 02 · What we did
We built the full map.
211 queries split across Spain, Venezuela and the United States, run against three language models, with the resulting 633 answers analyzed manually one at a time. The output was a 47-page report with 17 sections and three appendices, plus a second 42-page comparative report tracking month-on-month change with delta tables and summary charts. The manual review is deliberate: automated analysis classifies by keyword and loses exactly the nuance that matters, which is the tone a model uses when it talks about you.
How it comes together
Inside the project
Measurable results
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