An AEO checking tool should help you inspect whether AI answers name your brand, cite your website, or recommend your business. That is a different job from tracking keyword rankings. A technical readiness score alone does not establish that an answer actually included you.
Start with Prime AI Visibility, but judge the evidence behind the result—not the headline score. Use five buyer questions across three engines, preserve each answer, and turn the findings into a short list of corrections worth making.
Three tests that should not share one score
Before comparing tools, decide which question you need answered: can a page be accessed and understood, did a brand appear in an answer, or did a visitor arrive and take action? Treat these as separate measurement jobs.
Match the checker to the question
| Test type | Evidence to request | What it establishes | What it does not establish |
|---|---|---|---|
| On-page technical readiness | URL-level findings, affected content, and the checks performed | The page’s condition against the stated technical checks | That an AI answer mentioned, cited, or recommended the brand |
| AI answer mention and citation check | Exact prompt, answer, engine, conditions, citations, and capture date | What appeared in that captured answer | Universal visibility, future inclusion, website visits, or revenue |
| AI referral attribution | Recorded referral source, landing page, and defined conversion events | The visits and actions attributed under the analytics setup | Every answer that influenced a buyer or every exposure without a click |
For Google-specific eligibility and implementation guidance, consult Google's documentation on AI search features. Keep that technical work separate from an observed-answer test.
Then connect captured visibility to your attribution and analytics work. A brand mention belongs in the answer record; an attributed visit belongs in analytics. Neither should be substituted for the other.
Evaluate Prime AI Visibility with your own questions
Use Prime as the starting point for a practical evaluation, not as a reason to lower the standard of proof. Bring your own question set and ask whether the available plan can preserve enough detail to explain each result.
Open Prime AI Visibility features to check available coverage, and review Prime's metrics before interpreting a score. Feature availability varies by plan. Confirm the engines, capture method, retained evidence, and reporting options you need rather than assuming every capability is included.
For this audit, use ChatGPT, Perplexity, and Claude as the three-engine comparison set. Confirm which checks your Prime plan supports; keep any additional direct checks clearly labeled and separate from tool-generated results. A consumer-app answer and a tool capture should not be treated as interchangeable observations without matching conditions.
A useful evaluation sequence is straightforward:
- Bring the five questions below to the product evaluation.
- Ask to inspect the underlying answer and cited destinations for a result.
- Check how a missing capture differs from a completed answer with no brand mention.
- Compare the metric definition with the raw evidence before presenting it to leadership.
These are acceptance criteria, not claims about specific buttons or screens. The buying decision is whether the available product evidence answers your measurement question.
Treat the baseline as a dated snapshot, not a promise of improvement—the same distinction emphasized in Prime's AI visibility audit guide.
Run a seven-step, same-question audit
The purpose of the first pass is not to prove market leadership. It is to establish a small, inspectable baseline that another person can repeat.
1. Define what counts before collecting answers
Use three separate labels. A mention means the answer names the brand. An own-domain citation means a linked source points to the brand's website. An explicit recommendation means the answer affirmatively suggests the brand for the buyer's stated need.
A list of vendors is not automatically an endorsement. A third-party article about your company is not an own-domain citation. Record both when relevant, but do not combine them.
2. Choose five buyer questions
Use questions a real prospect would ask before contacting you. Start with this hypothetical question set, replacing bracketed terms with your category, buyer, and constraint:
- Q1—Discovery: Which [category] providers should a [buyer type] consider?
- Q2—Fit: Which [category] providers are suitable for [specific constraint]?
- Q3—Comparison: How do [your brand] and [competitor] compare for [use case]?
- Q4—Objections: What should a buyer verify before choosing [your brand] for [use case]?
- Q5—Selection: Which [category] provider would you recommend for [detailed requirements], and why?
Keep discovery questions unbranded. For questions that name your brand, mark the mention as prompted; do not count it as evidence that the engine independently discovered you. Keep those results in a separate group.
3. Fix the conditions you can control
Run each question across ChatGPT, Perplexity, and Claude for a 15-answer baseline. Use fresh conversations and the same question wording. Record language, region, interface, account state, search mode, and model when shown. Mark unavailable details as unspecified rather than guessing.
Aim for a consistent capture window. These controls improve comparability; they do not turn the sample into a census of every answer buyers receive.
4. Preserve the answer before scoring it
Save the complete answer or an authorized capture, the exact prompt, linked destinations, and a timestamp with its time zone. Give each observation a unique record ID.
If a response fails or cannot be captured, record an unavailable observation. Do not silently convert it into a no-mention result or remove it from the denominator.
5. Score each outcome independently
Mark brand named, own-domain URL cited, and expressly recommended as separate fields. Record competing names alongside them. For a citation, preserve the destination URL rather than only the displayed source label.
Flag suspected factual errors, then compare them with a current authoritative source. Until checked, keep them as suspected errors—not confirmed hallucinations.
6. Summarize without hiding the denominator
Report the number of completed answers and the number unavailable. Keep engine-level results visible, and separate unbranded discovery from brand-seeded questions.
If you calculate mention or citation rates, state exactly which completed observations are included. Also state whether the unit is an answer containing a citation or the total number of links. Those are different counts.
7. Assign a correction and repeat the baseline
For each finding, name an owner, the affected page or external source, and the proposed correction. After changes, repeat the original questions under documented conditions and retain both sets of answers.
A changed answer is an observation, not proof that one page edit caused it. Keep the change log so the next decision rests on more than a before-and-after headline.
Copy this blank evidence grid
Hypothetical audit setup; no results are populated. Use the five questions above across the three engines. Em dashes mean not recorded, not no. Enter a timestamp and time zone in Captured date.
| Question | Engine | Brand named? | Brand URL cited? | Expressly recommended? | Error? | Captured date |
|---|---|---|---|---|---|---|
| Q1 | ChatGPT | — | — | — | — | — |
| Q1 | Perplexity | — | — | — | — | — |
| Q1 | Claude | — | — | — | — | — |
| Q2 | ChatGPT | — | — | — | — | — |
| Q2 | Perplexity | — | — | — | — | — |
| Q2 | Claude | — | — | — | — | — |
| Q3 | ChatGPT | — | — | — | — | — |
| Q3 | Perplexity | — | — | — | — | — |
| Q3 | Claude | — | — | — | — | — |
| Q4 | ChatGPT | — | — | — | — | — |
| Q4 | Perplexity | — | — | — | — | — |
| Q4 | Claude | — | — | — | — | — |
| Q5 | ChatGPT | — | — | — | — | — |
| Q5 | Perplexity | — | — | — | — | — |
| Q5 | Claude | — | — | — | — | — |
Attach the exact prompt, answer record, citation destinations, competing names, and capture conditions to each row. For Error, use not reviewed, none found, suspected, or confirmed; attach the supporting source for a confirmed correction.
KeywordIQ and Prime answer different questions
Use Percepture KeywordIQ for search-intent keyword research context. Use an answer-level audit to inspect what an AI response actually says. KeywordIQ is not an AI-answer citation meter.
Research inputs versus observed answers
| System or workflow | Role in this audit | Boundary |
|---|---|---|
| Percepture KeywordIQ | Inform the search-intent research behind buyer questions and page priorities | Do not interpret keyword research as proof of an AI mention or citation |
| Prime AI Visibility evaluation | Assess the available plan against your answer-level evidence requirements | Confirm supported coverage and inspect underlying captures before interpreting metrics |
| Website analytics | Review attributed visits, landing pages, and defined conversion events | Do not treat attributed traffic as a complete record of AI-answer exposure |
The connection is editorial: research informs the questions, captured answers reveal what needs investigation, and analytics records the attributable activity afterward. Do not compress those stages into a single visibility score.
Turn the findings into a useful work queue
A checker is most useful when its evidence leads to a specific decision. Start with accuracy and access, then address missing buyer information. Resist rewriting every page because of one absent mention.
If a relevant page cannot be accessed or its technical condition is unclear: investigate the URL through a technical SEO audit. Identify the actual access, indexing, or rendering issue before prescribing a content change.
If the answer misses an important buyer requirement: check whether your own page explains that requirement plainly and supports it. Give the content marketing team a precise gap—such as an unanswered implementation question—not an instruction to add more keywords.
If a cited third-party source contains an error: preserve the answer and source URL, document the correct fact, and request a correction through an appropriate editorial channel. Keep digital PR focused on accurate, relevant coverage rather than purchased citations or reciprocal-link promises.
If answers name the brand but do not recommend it: inspect whether the stated buyer requirements are actually met. Lack of recommendation is not automatically a content defect. Your offer may be a poor fit for that question.
Use those findings to scope generative engine optimization services around identifiable problems. Where the work crosses content, PR, landing pages, and conversion measurement, coordinate it through omnichannel marketing rather than treating the checker as a standalone growth strategy.
What to ask before buying an AEO checking tool
- Evidence: Can you inspect the exact prompt, complete answer, and citation destinations?
- Coverage: Which engines, interfaces, regions, and question volumes does the selected plan include?
- Definitions: Are mentions, own-domain citations, and explicit recommendations separate?
- Comparability: Can you retain the original question set and document changed conditions?
- Failures: Are unavailable captures distinguished from completed answers without a mention?
- Portability: Can your team retain the records it needs for review and handoff?
Choose the product that makes an important finding explainable. A score without inspectable evidence is a weak basis for assigning budget or rewriting pages.
Questions buyers ask about AEO checkers
Can a free AEO checking tool replace ongoing monitoring?
Use a free check for an initial sample. For ongoing comparison, require retained answers, repeatable prompts, capture dates, and clear coverage limits. Do not assume that a free or paid score includes all of those.
Does a high technical readiness score prove my brand appears in AI answers?
No. A technical score describes the checks performed on a page. To establish answer-level visibility, inspect a captured answer tied to a specific prompt, engine, and date.
Is a brand mention the same as a citation or recommendation?
No. A mention names the brand. An own-domain citation links to the brand’s website. An explicit recommendation affirmatively suggests the brand for the buyer’s stated need. Record each separately.
Can I compare API checks with answers in a consumer app?
Keep them in separate series unless you can document equivalent conditions. Record the interface, model when shown, search settings, and account state. A difference between the two is not, by itself, evidence that either result is wrong.
Bring the answers, not just the score
Have a baseline already? Bring your question set, captured answers, and affected URLs to a conversation about what to investigate and fix first.
About Bob Generale
Bob Generale is President of Percepture and the author of this guide.
