Signal
Sample
Measure AI-search visibility without pretending AI has rankings. Build a fixed panel of customer questions, test them manually in AI answer systems, and turn the observations into repeatable evidence.
Set the sample
Customer question panel
Aim for roughly 10–20 stable questions that reflect real customer decisions. Keep the panel consistent between rounds if you want meaningful comparisons.
Record an observation
Evidence log
| Date | Round | Prompt | Surface | Brand | Cited | Competitors | Accuracy | Evidence |
|---|
How to measure AI search visibility
AI visibility tracking is difficult because ChatGPT, Gemini, Perplexity and other answer systems can return different answers across prompts, users, dates and regenerations. A single screenshot is evidence of one response, not a universal ranking.
Signal Sample is a lightweight AI search measurement framework: choose a stable panel of customer questions, manually test the same questions across the AI surfaces that matter to you, and record brand mentions, citations, competitor appearances and factual accuracy. Repeating the same panel creates a more defensible way to discuss change over time.
What does an AI visibility tracker measure?
This v0 measures presence rate, citation rate, competitor presence and accuracy problems inside your defined sample. It can also compare one measurement round with the previous round. These are observations, not estimates of total ChatGPT brand mentions or universal LLM visibility.
Why not create an AI visibility score?
A proprietary score can imply precision that the underlying observations do not support. Signal Sample keeps the components visible so stakeholders can see the sample size, method and evidence behind the result.