Our position: a checker that returns only a score gives you something to worry about but nothing defensible to fix.
What you should leave with
- Start with buyer-intent questions.
- Count recommendation roles, not text matches.
- Inspect the evidence behind the score.
- Treat a free run as directional.
What does an AI visibility checker measure?
It measures how often a brand is recommended, compared, mentioned, or cited across a defined set of buyer questions. The result is meaningful only when the checker discloses platforms, prompts, competitors, dates, location context, and classification rules.
The strongest unit is a buyer decision, not a broad keyword. ‘Best CRM for a 20-person law firm that needs conflict checks’ reveals the audience and constraint; ‘best CRM’ blends markets that the business may not serve. A useful checker designs prompts around those real choices.
Recommendation coverage, competitor share of voice, cited-domain ownership, and answer accuracy answer different questions. Keep them separate. A brand may own a cited glossary while never reaching the shortlist, or be recommended from evidence the interface does not expose.
- Recommendation and shortlist coverage
- Competitor appearances on the same questions
- Citations to owned and third-party pages
- Wrong or stale claims about the brand
Evidence used in this section
How can you tell whether a checker is trustworthy?
Look for full answer evidence, stable prompt IDs, entity review, run timestamps, disclosed weighting, repeat tests, and explicit uncertainty. Reject a tool that cannot explain which answer created a score or why a brand counted.
String matching is not enough. A product name may collide with a common word, a parent brand, or another company. The checker should review aliases and answer roles so negative statements, citations, and passing references do not become false recommendation wins.
Ask how variability is handled. High-value prompts should be repeated before a movement is called a trend, and method changes should create an annotation or new baseline. Precision in the interface cannot compensate for an unstable sample.
- Answer and source traceability
- Human-reviewed ambiguous entities
- Visible numerator, denominator, and weights
- Repeat policy for material results
Evidence used in this section
How should you run a first visibility check?
Enter the brand or website, confirm the correct entity, review the inferred market and competitors, run a directional prompt sample, and inspect the lost high-intent questions before acting on the aggregate score.
Begin broad enough to discover unexpected competitors but narrow enough to reflect a real market. Region, buyer size, service specialty, integrations, and urgency can change the recommendation set. Correct those inputs before treating absence as a visibility problem.
Open at least three complete losses. Record which competitors appear, the reasons given, the pages cited, and whether the answer contains a false fact. Repeated evidence differences are better fix candidates than one isolated omission.
- STEP 1
Identify
Confirm the website, public brand, aliases, market, and relevant region.
- STEP 2
Test
Run a buyer-intent sample across the supported answer engines.
- STEP 3
Inspect
Review full answers, recommendation roles, competitors, citations, and errors.
- STEP 4
Prioritize
Choose a repeated, valuable, and fixable gap for the full audit.
Evidence used in this section
How should you interpret the free result?
Treat it as a directional baseline that identifies where deeper investigation is justified. Compare your brand and competitors inside the same prompt set; do not compare the raw score with an unrelated company's result or a universal benchmark.
A low score can reflect weak evidence, a narrow prompt mismatch, entity ambiguity, or a small volatile sample. A high score can still hide factual errors or losses on the most valuable decisions. Read the prompt distribution before celebrating or escalating.
The next question is causal and practical: what repeated source, proof, or entity gap separates the brand from the winners, and can it be corrected? A full audit expands coverage and reviews those hypotheses rather than merely running more prompts.
| Result | What it can indicate | Next check |
|---|---|---|
| Low recommendation rate | Possible shortlist or prompt-fit gap | Inspect valuable losses and entities |
| High citations, low recommendations | Informational authority without shortlist presence | Compare decision evidence |
| Wrong facts | Stale or conflicting public evidence | Verify source and correct broadly |
Evidence used in this section
What can no AI visibility checker guarantee?
No checker can observe every personalized answer, prove private model-ranking factors, estimate total impressions reliably, or guarantee future inclusion. It can create a controlled, transparent sample and help you improve the evidence buyers and answer systems encounter.
Answer experiences change with platform access, query wording, market, time, and context. Disclose those conditions and use consistent reruns to monitor change. A checker is a research instrument, not an official rank feed from the platforms.
Avoid fixing the score with low-value content volume. Correct false facts and access problems first, then strengthen missing decision evidence and independent corroboration. Publish only when a page genuinely resolves the observed buyer question.
- No complete impression count
- No universal ranking position
- No guaranteed citation
- No secret formula for model selection
Method boundary: A free check is intentionally narrower than a decision-grade audit. Use it to choose where to investigate, not to make an unsupported market-wide claim.
Evidence used in this section
Questions that change the decision
Frequently asked questions
Is the AI visibility checker free?
The initial AnswerMentions check is free and provides a directional sample. A full paid audit expands prompt coverage, review depth, source diagnosis, and the implementation plan.
Which AI platforms should be checked?
Check ChatGPT, Gemini, Perplexity, and Google AI experiences when they matter to your market, but report each platform separately before using an aggregate.
How many questions are enough?
A small sample can reveal a lead; a defensible baseline needs enough prompt families to cover valuable buyer decisions and repeats for material findings.
Can I improve a low score?
Often, but the fix depends on the cause. Correct errors and access issues first, then address missing first-party proof, decision pages, entity consistency, and third-party source gaps.
Primary sources and research
Platform documentation supports factual statements. Where we describe an audit method or prioritization rule, that is AnswerMentions' operating judgment and is labeled as such.
- [1]OpenAI: ChatGPT searchOpenAI describes ChatGPT search as providing timely web answers with links to relevant sources and publisher content.
- [2]Perplexity Help Center: how sources workPerplexity explains that it searches the web, identifies sources, and synthesizes an answer with citations, making source inspection central to evaluation.
- [3]OpenAI Help: accuracy and citationsOpenAI warns that ChatGPT can produce incorrect facts and fabricated references, so consequential claims should be checked against reliable sources.
- [4]NIST: AI Risk Management FrameworkNIST frames AI risk work around governance, mapping, measurement, and management, a useful model for separating observations from decisions.
- [5]Google Search Central: AI features and your websiteGoogle says AI Overviews and AI Mode build on Search fundamentals and may use query fan-out to surface a wider supporting source set.
- [6]Aggarwal et al.: Generative Engine OptimizationThe KDD 2024 paper evaluates generative-engine visibility in a controlled benchmark; it is evidence that visibility can be studied, not a universal ranking recipe.
- [7]Google Search Central: spam policiesGoogle treats scaled pages made primarily to manipulate rankings as abuse, regardless of whether automation, people, or both produced them.