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Methodology
The same process runs on every engagement. It starts with what engines currently understand about you, isolates what blocks citation, fixes those specific things, and measures whether it worked.
What does the web currently say about you?
We gather your public content, entity presence and the queries that matter to your buyers.
How do AI engines currently understand you?
We test retrieval and citation across generative engines and score readiness by signal.
What is blocking accurate citation?
We isolate the content, structure and entity gaps that keep you out of AI answers.
What changes make you citable?
We implement content, entity and technical changes engines can read and attribute.
Did visibility actually improve?
We track citations, mentions and share of answer against a baseline over time.
What compounds the result?
We feed measurement back into the roadmap and repeat where the return is highest.
Operating principles
These constrain what we will and will not claim, and they are the reason the method is repeatable rather than improvised.
No content is written and no markup is added until we know which signal is actually failing. Most AI visibility problems are misdiagnosed as content volume problems.
Every engagement captures how engines describe and cite you before any change. Without that capture, improvement cannot be distinguished from engine drift.
Generative engines vary by query, region, account and moment. We report what we observed and under what conditions, not what we would like to claim.
A brand described incorrectly does not need more pages. It needs the contradictions between its existing descriptions removed.
The audit is the Discover and Diagnose phases run against your domain. Everything after it depends on what those phases find.