SearchestraSix Questions That Expose a Weak AI Visibility Vendor
A vendor shows you a confident dashboard. Before you sign, ask six questions about how the numbers are produced. A rigorous provider answers easily. If a vendor dodges, that dodge tells you more about the data quality than any demo ever will.
Silence is the answer
In AI visibility, the willingness to disclose predicts quality better than the confidence of the pitch. If a vendor will not explain how it turns raw AI responses into numbers, you cannot judge whether the data is decision-grade, and you are buying a black box you will have to defend later.
The six questions to ask
| Ask | Why it matters |
|---|---|
| Which engines and model versions? | Coverage shapes every number |
| How is the prompt set built? | Determines what surfaces |
| Active queries, passive panel, or native data? | Method changes results |
| How are citations and sentiment classified? | Definitions vary widely |
| How are hallucinations detected? | Accuracy is a brand-safety issue |
| How are baselines managed across updates? | Or trends are artifacts |
A vendor built to answer them
Searchestra is designed around disclosure: a versioned, brand-neutral prompt set, per-engine reporting, and honest limits flagged, not hidden. Every one of those six questions has a straight answer.
Ask six questions about how the numbers are made; a vendor that dodges is telling you the data is not decision-grade.
Frequently asked questions
What should I ask an AI visibility vendor first?
How they build the prompt set, which engines they cover, and how they define mentions and citations. These shape every number.
Why does disclosure matter so much?
Because without it you cannot tell if data is decision-grade. Undisclosed methodology is a material gap in any quality claim.
What if a vendor will not answer?
Treat the silence as a signal. Inability to explain a required item is information about the data quality.