The practical AI Visibility guide
Interpret AI visibility scores before you act on them.
A score is a summary of a particular analysis. Read the definition, model coverage, and underlying answer before turning a number into a page task.
Put it into practice
Name the measure
Confidence, recommendation, and frequency are different.
| Signal | What it summarizes | What it does not mean |
|---|---|---|
| Domain Clarity confidence | The model’s confidence in its interpretation of the business. | A verified probability, recommendation score, or guarantee that the interpretation is correct. |
| Organic score | Recommendation strength for the buyer question with the brand withheld. | Organic search traffic, click-through rate, or citation frequency. |
| Mentioned score | Assessed recommendation fit when the domain is supplied. | Independent discovery of the brand. |
| Visibility gap | Average Mentioned-minus-Organic difference for matched completed model pairs. | A count of missed customers or a causal diagnosis. |
| Competitor score | Recommendation strength of returned competing brands in Organic analysis. | Market share or a mention-based share-of-voice percentage. |
Read a score as a summary of an answer in a defined context. A model can confidently give the wrong category. A product can be a strong fit when named and still be absent from an unbranded shortlist. A useful review tests whether the explanation is accurate and relevant to the buyer.
Interpret the pattern
The same gap can hide different problems.
| Organic | Mentioned | Gap | First interpretation |
|---|---|---|---|
| 32 | 84 | +52 | Stronger when introduced. Investigate independent consideration for this workflow. |
| 8 | 57 | +49 | Both discovery and assessed fit need review. Do not treat the gap alone as the goal. |
| 75 | 88 | +13 | The model already considers the business and sees a strong fit. Preserve the evidence and inspect limits. |
| 12 | 14 | +2 | A small gap with low scores is not a strong result. Check whether the product fits the question. |
| 80 | 65 | −15 | Naming the domain lowered the assessment. Read both results; more specific information or answer variation may explain it. |
For a completed pair, 84 − 32 = 52 points. That is a point difference, not a 52% citation uplift. The table colors help you scan the size of a gap; they are not a statistical significance test or a universal quality rating. See the measurement guide before comparing periods.
Separate zero from missing
An unfinished analysis is not a negative result.
A completed Organic result can be zero because the target business is absent from the model’s recommended-brand list. That does not prove the model has never heard of the business; use Domain Clarity to inspect recognition. A pending, failed, unavailable, or locked result is missing evidence and should not be read as zero.
AEOchamp averages completed Organic and Mentioned results separately. The gap uses only models that completed both analyses. While requests are unfinished, these can be different subsets. That is why subtracting the two visible averages may not reproduce the displayed gap.
For a trend review, use a comparable model subset and stable questions. Adding models, retiring models, changing questions, or mixing training and live checks can move a summary without showing that a page improvement caused it.
Challenge the summary
Read the model answer before writing the brief.
In Domain Clarity, inspect category, audience, job, and the underlying answer together. If confidence is high but the audience is wrong, the task is to improve the explanation, not to increase confidence further. In User Intent, expand the question and inspect model-level scores and competitor results so one provider’s pattern does not disappear inside the average.
Competitor averages need coverage context too: a competing brand’s average reflects the Organic results where it appears. A strong score in one model is not the same as broad presence across models. Use the individual results to distinguish consistent consideration from one strong assessment.
OpenAI’s evaluation guidance says to combine metrics with human judgment. Applied here, the review question is concrete: does the answer identify the right product, describe a capability you actually have, and make a fair recommendation for this customer situation?
Turn the interpretation into a decision
Write the task that the evidence supports.
| Evidence | Action | Check after publishing |
|---|---|---|
| Wrong category despite high confidence | Clarify the business category and show the actual product workflow. | Live Domain Clarity interpretation of the current site. |
| Strong Mentioned, weak Organic | Investigate the buying situation and improve the relevant use-case explanation. | Comparable intent results and the underlying answers. |
| Weak scores for a capability you do not offer | Choose a better-fitting buyer question or state your limitation clearly. | Accuracy of the page and quality of enquiries. |
| A score rose after changing the question set | Treat it as a new baseline, not proven improvement. | Compare the unchanged core questions separately. |
AEOchamp makes this review practical by attaching scores to models, business descriptions, and buyer questions. You can move from “our average is low” to a focused task such as “explain how guest reviewers approve the correct version.” Keep mentions and citations separate and use the AEO checklist to finish the page work.
Your questions, answered
What does an AEOchamp Organic score mean?
It is the business’s recommendation score in the completed unbranded buyer-question analysis. If the target is absent from that result’s recommended-brand list, its Organic score is zero. It is not a Google traffic measure.
How is the User Intent visibility gap calculated?
For each model with both analyses completed, subtract its Organic score from its Mentioned score. The intent gap averages those matched differences. While work is unfinished, it may differ from subtracting the separately displayed averages.
Does a score of 80 mean I appear in 80 percent of answers?
No. A recommendation score is an assessment of recommendation strength for that analysis. An appearance rate requires counting qualifying appearances across completed answers.
What is a good AI visibility score?
There is no universal cutoff that proves commercial success. Compare relevant questions and completed model results in a stable context, then inspect the answers and customer outcomes.
From advice to a clear next step
Use the score to ask a better question.
Open the evidence behind your Domain Clarity and User Intent results. Find a positioning mismatch or a buyer workflow you can explain more clearly.
Start your free AEOchamp check