Step one: Define your query universe
Before you can measure AI attention, you need to know what you are measuring it for. A query universe is the set of prompts that represent how your potential customers actually ask AI about your category.
Most brands start with brand queries: prompts that explicitly mention the brand name. These are the easiest to track and the least strategically useful. A user who already knows your brand name and asks about it is not discovering you through AI. They are verifying you.
The queries that matter are category queries: prompts that describe a need, a problem, or a decision, without mentioning your brand. "What is the best platform for measuring brand visibility in AI?" "Which analytics tools do CMOs use to track attention?" "How do I know if my brand is being cited correctly in AI responses?" These are the queries where AI is acting as the first recommender, and where your presence or absence determines whether you enter the consideration set.
A complete query universe contains a minimum of 50 category queries across four decision stages: awareness (the user does not yet know what they need), research (they are learning the options), comparison (they weigh specific alternatives), and recommendation (they ask what to choose). Have the brand approve the list before measurement starts. The same list will be used again when you measure the effect.
Step two: Establish your baseline across platforms
AI responses are not uniform. The same query on ChatGPT produces a different response than the same query on Perplexity, Gemini, or Copilot. Platform-specific behavior reflects differences in training data, retrieval architecture, and response formatting.
A brand audit that covers only one platform is not a brand audit. It is a snapshot of one model's behavior on one day.
Baseline measurement requires running your query universe across a minimum of three to five platforms and recording, for each query and each platform: whether your brand appears, at what position, with what sentiment, cited from what source, and with what accuracy.
This baseline does not need to be large. Fifty queries across five platforms is 250 data points. That is enough to identify patterns, locate outliers, and establish the starting point against which future measurements will be compared.
Date the baseline before anything on your site changes. Then set aside a control group of pages you will not touch during the project. AI models update constantly, and without a control group you cannot tell whether a later improvement came from your work or from the model.
Step three: Score each appearance
Not all appearances are equal. A brand mentioned first in a direct answer to a decision query on Perplexity, cited from the brand's own domain, in a positive and accurate context, is worth more than the same brand mentioned fourth in a list at the end of an awareness query on a platform with lower intent volume.
Scoring each appearance requires four assessments:
Position quality: where in the response does the mention appear? First paragraph scores highest. Embedded in a list scores lower. Mentioned as a counterexample scores lowest.
Sentiment quality: is the context positive, neutral, or negative, and how cognitively accessible is that context to the user? A positive mention in a dense technical paragraph is worth less than a positive mention in a clear, direct sentence.
Source quality: what domain does the model cite when mentioning your brand? Your own domain is highest. Tier-one industry publications are strong. Aggregators and comparison sites are weak. Competitor domains cited as sources of information about you are a red flag.
Accuracy: does the model describe your brand, product, or capability correctly? Inaccurate positive mentions are not wins. They are liabilities that will be corrected in the next model update, often in ways you cannot predict or control.
Step four: Map your Trust Sphere
Pull the citation domains from all appearances across your query universe. Sort them into three categories: domains you control, domains you influence, and domains that are neutral or hostile to your positioning.
The ratio of controlled to uncontrolled citations is one of the most actionable numbers in a brand audit. If less than 30 percent of citations point to your own domain or content you have directly contributed to, your AI presence is built on ground you do not own. Any model update can shift it.
The Trust Sphere map also identifies your content gaps. If AI consistently cites a competitor's comparison page when mentioning you, it means the model has found that page more authoritative than anything you have published on the same topic. That is a content brief, not just a measurement finding.
Step five: Check what AI crawlers see on your own site
Your own domain should be the strongest source in your Trust Sphere. Often it is the weakest, for reasons invisible in a browser. Test every URL in your sitemap in the raw HTML that most AI crawlers read.
Check four things: whether AI crawlers can access the pages at all; whether authors, reviewers and experts are defined in structured data; whether thin or empty pages are diluting the domain; and whether your content links to your offer in HTML rather than only through JavaScript. Each finding becomes a task with a URL, a target state and an acceptance test.
Step six: Benchmark against two to three competitors
Run the same query universe for your two or three primary competitors. Record the same data points. Compare.
The comparison reveals four possible competitive positions:
Quadrant 01
You appear more and score higher
You are winning AI attention in this category. Defend and expand.
Quadrant 02
You appear more but score lower
You have volume without quality. Your presence is broad but shallow, often cited from weak sources, often in lower positions. You are vulnerable to a competitor who builds quality rather than quantity.
Quadrant 03
You appear less but score higher
You have a quality foothold without volume. You are cited less often but in better positions, from better sources, with higher accuracy. This is a strong foundation to build on.
Quadrant 04
You appear less and score lower
You are effectively absent from AI-driven consideration in this category. This is the most urgent position and the one that requires immediate content strategy intervention.
Step seven: Establish your monitoring cadence
AI models update. Citation patterns shift. A brand that was well-positioned in January may find its position eroded by March if a competitor published authoritative content that the model incorporated in its latest update.
A minimum viable monitoring cadence is monthly measurement of your core query universe across primary platforms, with quarterly deep audits that include Trust Sphere mapping and competitive benchmarking.
The monthly measurement does not need to be comprehensive. Ten to fifteen decision queries across three platforms, scored consistently, will surface material changes before they become strategic problems.
After a remediation project, re-run the same query universe and the same site tests at a fixed point (ninety days is a practical horizon) and compare changed pages with the control group. Report change against baseline rather than against targets for traffic or citations, which no brand controls. How a BAX engagement runs.
What good looks like
After completing these seven steps, you have a baseline. Good AI attention, by the BAX framework, looks like this:
Your brand appears in the first or second position for the majority of your decision queries across primary platforms. The citations underneath your mentions point predominantly to your own domain or to tier-one publications where you have contributed authoritative content. Sentiment is positive in low-cognitive-load contexts. Accuracy is high: the model describes what you actually do, not a version of what you do that reflects outdated or third-party information. Your BAX Index is trending upward across measurement periods.
If your current audit produces a different picture, you now know exactly where to intervene: in which query types, on which platforms, with which content, and in which citation domains.
That is what measurement is for.