AI Visibility and GEO FAQs

What is AI brand visibility?

Contxt editorial

AI brand visibility describes whether, and how often, an AI assistant names your brand in answers to questions relevant to your market. It is measured against a defined set of prompts, not against every conversation taking place on an AI platform.

For example, a cordless-tool brand could test research questions such as “What should I look for in a drill?”, comparison questions about battery platforms, and purchase questions about drilling into brick. A brand can appear frequently in one group and be absent from another. That distinction is more useful than a single percentage alone.

A useful measurement records the prompt, platform, collection date and answer. It should separate a brand mention from a recommendation, a ranked position and a citation to a particular website. Those are related observations, but they are not interchangeable.

Visibility does not measure how many people saw those answers, clicked a link or bought a product. Use it to find coverage gaps and compare repeat observations. Use analytics and conversion tracking separately to measure business outcomes.

Contxt brings prompt-level answers, platform comparisons and content gaps into one workflow. Read how Contxt works or explore AI visibility features.

What is generative engine optimisation (GEO)?

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Generative engine optimisation is work intended to make a brand’s information easier to discover, understand and use in AI-generated answers. It includes improving factual accuracy, answering relevant buyer questions and making useful evidence accessible on your website.

Start with questions where recorded answers show a gap. Check the sources cited, improve the relevant page, then repeat the measurement. GEO is not a submission process that guarantees a mention, and there is no universal number of citations that makes a brand rank. See content gaps and GEO.

How is an AI visibility percentage calculated?

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For a mention-based measurement, divide the number of tested answers that name the brand by the number of eligible, successfully collected answers, then multiply by 100. Always read the denominator and collection scope alongside the percentage.

If 58 of 200 tested answers mention a brand, the measured visibility is 29%. This is an illustrative calculation, not a customer result or a claim about the brand’s share of all AI conversations. If the test used 50 prompts across four platforms, each prompt-platform answer is a separate observation.

Platform and buying-stage breakdowns can reveal what the overall score hides. A 29% aggregate could include strong coverage on one platform and little coverage on another. Similarly, a brand might appear in comparison answers but be absent from early research questions.

A failed request, an unavailable answer and an answer that genuinely does not mention the brand must not be treated as the same thing. Review collection completeness before interpreting a change. Rankings, sentiment and cited source counts are separate measures with their own definitions.

For repeat comparisons, check the prompts, platforms, dates and collection conditions. A changed prompt set can produce a changed percentage without demonstrating an improvement in the same buyer questions. See comparing visibility over time and Contxt’s reporting features.

Why do ChatGPT, Google AI Overview, Claude and Perplexity give different results?

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Platforms can use different models, retrieval systems, source selections and answer formats. Results can also change with the question, collection date and context. Google does not show an AI Overview for every search.

Read each platform separately before relying on an aggregate. A result on one assistant is not proof of coverage on another. Contxt’s public visibility workflow covers ChatGPT, Google AI Overview, Claude and Perplexity; check the recorded answer and collection status for each.

What is the difference between an AI citation and a brand mention?

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A brand mention means an answer names the brand. A citation is a source reference or link included in the answer. An answer can mention a brand without linking to its website, or cite a page without recommending the organisation that owns it.

For a tool brand, an assistant might recommend a competitor while linking to a retailer’s buying guide. That source is useful evidence about the information used in that recorded answer. It is not automatically an endorsement of the retailer or proof of why the competitor was selected.

Citation counts also need a clear unit. A domain may appear more than once across saved answers; counting those references is different from counting unique domains or pages. Counts depend on the prompt set, platforms and collection date. A larger count does not guarantee that a brand will be cited or ranked in a future answer.

Use citation analysis to inspect actual pages. Do they provide specifications, comparisons, examples or answers that your own content lacks? Check whether the evidence is relevant to your buyer questions before deciding what to publish or update.

Contxt lets you examine source observations alongside prompt and competitor results. See competitor and citation features and how to turn a visibility gap into a content brief.

How should I compare my brand with competitors in AI answers?

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Test the same buyer questions for your brand and the competitors you track. Compare mentions by platform and buying stage, then inspect the answers behind the numbers.

A competitor can be named without a ranked position being supplied. Do not invent a position when an answer does not provide one. Look for recurring competitor-only questions: they give you a specific starting point for content analysis. The competitor features explain how those comparisons fit into Contxt.

How do I turn an AI visibility gap into a content brief?

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Start with a specific buyer question where successfully collected answers do not name your brand, especially when they consistently name competitors. Record the platform, buying stage and answer so the brief is grounded in an observed gap rather than an assumed keyword opportunity.

Next, inspect the pages cited in those answers. Note which comparisons, specifications, examples or explanations they provide. Check your existing pages before creating another one: improving a relevant page may be more useful than publishing overlapping content.

A practical brief should identify the question to answer, the intended reader, the evidence needed, an appropriate page format and the information your team can contribute first-hand. For a drill buying guide, that might include suitable materials, bit compatibility, tested limitations and a direct explanation of SDS versus hammer drills. Only include claims your team can support.

Give the page a clear main heading and a direct opening answer. Use subheadings for real follow-up questions, link to relevant supporting pages and show who is responsible for the information. Avoid padding the page to reach an arbitrary word count.

After publishing, confirm that the page is accessible and repeat the same visibility prompts. A re-scan can show whether the recorded answers changed; it cannot by itself prove that the new page caused the change or generated sales. Explore Contxt’s content workflow and measurement guidance.

What content should I create to improve AI visibility?

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Choose the format that answers the missing buyer question. A comparison, specification page, practical guide or clearly explained FAQ may be appropriate, depending on what the buyer needs and what the recorded answers cite.

Prioritise useful evidence you can contribute: tested examples, accurate product information and direct explanations. No format guarantees a citation. Start with a measured gap, not a generic instruction to publish more blog posts.

Which prompts should I use to track AI visibility?

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Choose questions that reflect how buyers research your category, compare options and make a decision. A balanced prompt set should cover those stages rather than testing only questions that already contain your brand name.

For a cordless-tool brand, an awareness prompt might ask what to look for in a drill. A consideration prompt could compare battery platforms. A decision prompt might ask which drill suits brick walls. These are illustrative examples; use your customers’ actual needs, market and terminology when building your own set.

Brand-named prompts are useful for checking what an assistant says about you, but they answer a different question from unbranded discovery prompts. Keep the distinction visible in your analysis. Avoid treating your performance on brand-named questions as proof that new buyers will discover you.

Test across the supported platforms and record the exact wording. Small changes in intent or context can change the answer. Keep a stable core set for repeat comparisons, and label newly added questions so that changes in scope are not mistaken for changes in performance.

In Contxt, set up your brand and market, add competitors and organise prompts around the buying journey. Then inspect the saved answers before prioritising content work. See how the setup and scan workflow works and how visibility is calculated.

Which AI platforms does Contxt cover?

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Contxt’s public AI visibility workflow covers ChatGPT, Google AI Overview, Claude and Perplexity. Platform results should be read alongside the prompt and collection status, because the answer format and availability can differ.

An unavailable Google AI Overview is not the same as an Overview that was collected and did not mention the brand. Review the saved evidence rather than assuming every platform supplied an equivalent answer. Explore the platform comparison features.

What should an AI visibility report show?

Contxt editorial

A useful AI visibility report should make the test scope clear: the brand, market, prompts, platforms, collection dates and number of usable answers. Readers need that context to understand what the results do and do not represent.

The findings should separate overall visibility from platform and buying-stage results. Competitor comparisons should use the same questions, and citation analysis should identify the source references observed in the saved answers. A ranked position should only be reported when the answer or measurement method actually supplies one.

Each important finding should lead back to recorded evidence. For example, “No brand mentions in 40 collected awareness-stage answers” describes a measured gap. “The brand has no awareness in the market” goes beyond that evidence. Likewise, a mention percentage is not a traffic, conversion or sales percentage.

Next actions should be specific enough for a team to use: review a group of questions, inspect particular sources, update an existing page or develop a brief supported by first-hand evidence. Repeat measurements can show changes, but should disclose changes in prompt scope or collection completeness.

Contxt’s reports can be shared as PDF or HTML. Review the findings before sharing them and keep the recorded evidence and limitations attached to the interpretation. Explore reporting features or contact the team about your reporting requirements.

Can I get started with Contxt for free?

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You can create a free Contxt account to get started. Check the limits shown in your account before choosing a scan or prompt set; allowances and paid-plan availability should be confirmed from the current product, not an old FAQ answer.

A useful first step is to add your brand and market and choose buyer questions that reflect your category. Create a free account or read how Contxt works before starting.