In focus
I study French businesses that appear in one language and vanish in another. The work follows how category wording, local proof, and stale directory fragments push firms up or down in comparison answers.
Ranking evidence
AI rankings move when public evidence becomes easier to order.
A hotel in Nantes can be praised everywhere and still sit below a weaker rival in an AI best-of answer. The reason is often dull and fixable: a clearer category sentence, fresher third-party wording, a stronger local distinction, or proof that travels badly from French into English. I read those traces, separate inclusion from ordering, and show which public signals need to become easier for an AI system to compare.
Latest notes — from the field
who writes this
Maël Veyrane
I am from western France and I have spent seventeen years around search audits, editorial strategy, local-business copy diagnosis and comparison-page analysis. I read rankings as arranged proof: which public signal helped one firm keep a higher seat. I separate inclusion from ordering by hand, because a business can be eligible for a list and still lose the first three seats when its evidence stays vague.
Better ordering begins with public evidence that can be compared.
Bring me the answer where your business sits too low, disappears, or loses to a rival that should not be ahead.
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