A credible AEO partner should define what it measures, preserve the underlying evidence, disclose uncertainty, and separate observed visibility from claimed causation.
If the proposal cannot do that, the terminology does not matter.
Look for defined prompts, platforms, markets, dates, mentions, citations, competitors, accuracy, and a repeatable observation method.
A score without the answer, source, date, and prompt is difficult to review or challenge.
One prompt on one platform is an observation. A baseline requires controlled repetition and clearly stated coverage.
The partner should distinguish what appeared from why it may have appeared. A visibility change does not prove that one optimization caused it.
Technical SEO, content, schema, digital PR, reviews, and entity consistency may all contribute. The proposal should explain how each activity connects to an observed answer gap.
No agency controls a model's output. Guaranteed citations, mentions, or rankings should trigger scrutiny.
Require reviewable findings, source records, limitations, prioritized actions, ownership, and a method for re-measurement.
Automation can collect and organize observations. A named analyst should be accountable for interpretation, caveats, and recommendations.
SEO work may be part of the remedy. The problem is not overlap. The problem is relabeling familiar work without connecting it to an observed AI visibility gap.
Read AEO vs. SEO →Case studies can show prior work. They still should not be presented as a guarantee that another brand, market, or model will behave the same way.
Learn how AI visibility is measured →Cited by Bnarrativ records what AI systems say, which sources they cite, which competitors appear, and where the evidence remains incomplete.
Explore Cited by Bnarrativ →