How to audit your brand’s visibility in AI Overviews

• Audit AI Overviews by prompt, not by one search result. Track branded, category, comparison and purchase-intent prompts across your target markets.
• Separate brand mentions, recommendations and website citations. An AI answer can mention your brand without citing your site, or cite a third-party page that shapes the answer.
• Use Google AI Overviews as the starting point, then compare results with ChatGPT, Perplexity, Gemini, Copilot and other answer engines where buyers search.
• Semrush starts at $99/mo and suits teams that want SEO data and AI visibility in one workflow, but its base AI Visibility Toolkit includes 1 domain and 25 tracked prompts.
• Re-run the audit monthly if you can, or quarterly if resources are tight. AI visibility data is directional because answers vary by prompt, region, platform and model state.

Google AI Overviews have made brand visibility harder to read. Your site can rank well in traditional search, yet still be missing from the AI answer that sits above the results.

A proper audit starts with Google AI Overviews, because that is where many teams first notice the problem. It should not stop there. The same buyer may ask a similar question in ChatGPT, Perplexity, Gemini, Copilot or Google AI Mode.

The job is to find out whether your brand is mentioned, cited, recommended and described accurately. The catch is that each of those is a different outcome, and treating them as one number hides the useful work.

This guide gives you a repeatable audit workflow. It is built for marketers, SEO leads and founders who need a baseline, a gap list and a practical plan, rather than a vague “AI visibility” score.

What does an AI Overviews visibility audit measure?

An AI Overviews visibility audit measures how your brand appears in AI-generated answers for the prompts your buyers actually use. The core outputs are mentions, citations, recommendations, share of voice, sentiment, competitor presence and missing prompts.

Mentions are the simplest signal. If an AI answer names your brand, you have some visibility. The downside is that a mention without a citation may not send traffic, and it may be based on a third-party source you do not control.

Citations are separate. Your website might be cited as a source, but the answer may still recommend a competitor. The reverse can also happen: your brand is recommended, while a review site or marketplace gets the citation.

A useful audit also records the narrative around the brand. If the answer says your product is expensive, only suitable for enterprises, or missing a feature you now support, that is a visibility problem as much as a ranking problem.

Treat the audit as a baseline-and-diagnose exercise. One AI Overview result is a snapshot. A prompt set, repeated over time, is evidence.

Step 1: define the scope before you collect results

Start by choosing the platforms, markets and entities you will test. If the target is AI Overviews, Google comes first, but a defensible audit should include other answer engines if your buyers use them.

For most B2B teams, a first audit should cover Google AI Overviews, ChatGPT and Perplexity. Add Gemini, Copilot, Claude or AI Mode if they matter in your category. The limitation is cost and noise: more platforms mean more data to clean and compare.

Choose countries, regions and languages before you run prompts. AI answers can change by location, and traditional search localisation affects which sources are available to Google. Mixing markets in one sheet makes the findings harder to act on.

List every brand entity worth testing. Include company name, product names, founder or executive names, sub-brands, common abbreviations and common misspellings. If your brand has a generic name, add disambiguating terms such as the category or industry.

Define competitors before the audit starts. Include direct competitors, larger category leaders and review sites that often appear as sources. Without that benchmark, you can prove you are visible, but not whether you are visible enough.

Step 2: build prompts, not just keywords

Use prompts as the audit unit. Traditional SEO keywords still matter, but AI systems respond to full questions, comparisons and task-based requests. A keyword list alone misses that behaviour.

Build four prompt groups. Branded prompts test whether the answer understands you, such as “What is [brand]?” and “[brand] reviews”. The downside is that branded visibility is usually the easiest to win, so it can make the audit look healthier than it is.

Category prompts test whether you appear in buyer discovery. Examples include “best [category] tools”, “top [software type] for [audience]” and “which [category] platforms support [feature]”. These are harder, because you compete with established brands and third-party lists.

Comparison prompts show how AI engines position you against named alternatives. Use prompts such as “[brand] vs [competitor]”, “alternatives to [competitor]” and “is [brand] better than [competitor] for [use case]”.

Pain-point prompts catch demand before the buyer knows your category. Examples include “how to solve [problem]”, “software for [workflow]” and “best way to track [outcome]”. They are valuable, but they often cite educational content rather than product pages.

Tag every prompt by funnel stage, product line, geography and priority. This is fiddly at the start, but it makes the audit repeatable. It also stops low-value prompts from distracting the team when fixes are prioritised.

Step 3: how do you capture a reliable baseline?

A reliable baseline records the same fields for every prompt and platform. At minimum, capture the date, location, prompt, engine, answer summary, brand mention, brand recommendation, site citation, cited URL, competitors and accuracy issues.

You can do this manually for a small prompt set. Run the prompts, save the answer text, take screenshots where useful, and log the results in a sheet. The upside is control; the downside is that manual sampling becomes a slog once you add markets and engines.

Semrush is a good fit if your team wants SEO and AI visibility in one workflow. Its AI Visibility Toolkit is listed at $99/mo and includes Brand Performance for 1 domain, 300 daily queries in AI Analysis reports, 1,000 daily Prompt Research queries and 25 tracked prompts. The catch is that there is no free trial, and extra domains, users or prompt capacity may change the maths.

Profound fits teams that need broader answer-engine intelligence across platforms and regions. Its Answer Engine Insights tracks AI visibility, citations, sentiment, share of voice, positioning and platform breakdowns. The limitation is packaging: public pricing has been reported from $99/mo, but live plans should be checked before purchase.

Otterly.AI is a practical lower-entry option if you want monitoring without a larger platform commitment. Its Lite plan is listed at $29/mo and direct signup advertises a 14-day free trial. The catch is that Google AI Mode, Gemini and Claude are add-ons depending on plan, so the real cost depends on the engine set.

Keep the first baseline narrow if resources are limited. Twenty high-intent prompts across two or three platforms will teach you more than 300 untagged prompts nobody reviews.

Step 4: separate mentions, citations and recommendations

Create separate columns for brand mentioned, brand recommended, site cited, competitor mentioned and competitor cited. This is the single most important reporting choice in the audit.

A brand mention means the answer knows you exist. A recommendation means the answer includes you as a suitable option for the prompt. A citation means the answer points to a source, which may be your site or someone else’s.

Flag cases where your brand is mentioned but your site is not cited. That often means Google or another answer engine is relying on review pages, directories, media coverage or competitor content to describe you.

Also flag cases where your site is cited but a competitor is recommended. That can happen when your page explains the category well, but does not make your own positioning clear enough for the answer to lift.

Competitor-only results deserve a separate view. If three competitors appear and your brand is absent, the gap is probably not a wording issue on one page. It may point to missing source coverage, weak category content or a lack of third-party validation.

Step 5: diagnose source gaps and technical blockers

Once you know where you are missing, inspect the sources that AI systems use instead. The fastest fixes often come from understanding why a competitor or publisher is treated as the better source.

Look at cited pages for competitor prompts and category prompts. You are looking for content types, not just domains. Common gaps include comparison pages, definition pages, pricing pages, use-case pages, review pages, Q&A sections and clear feature documentation.

If AI Overviews cite third-party sites heavily in your category, owned content alone may not be enough. You may need accurate listings, current review profiles, partner pages, analyst coverage or credible editorial mentions. That takes longer, but it can shape the sources AI answers rely on.

Check technical access as part of the audit, not as a magic fix. Important pages should be crawlable, internally linked and clear about the entity, product and use case. Schema can help structure information, but it does not guarantee an AI Overview citation.

Review blocked paths, noindex tags, messy canonical tags and thin JavaScript-rendered content. These are dull checks, but they catch avoidable problems. The limitation is that fixing crawlability only helps if the content itself deserves to be cited.

For a deeper measurement model, use a separate GEO performance guide alongside the audit. Keep this audit focused on prompts, sources and actions, or it will turn into a general SEO review.

Step 6: prioritise fixes by impact, gap and effort

Prioritise fixes by business impact, prompt importance, current visibility gap and effort. The best first tasks are high-intent prompts where competitors appear, your brand is missing, and you already have a relevant page to improve.

Update existing high-authority pages before creating new ones. A page that already earns links, rankings or mentions is more likely to be trusted than a fresh page with no history. The downside is that some prompt gaps need a new page because the intent is genuinely different.

Add concise Q&A sections that answer the exact prompts buyers ask. Keep the answers specific enough to be lifted, with clear product names, use cases and limitations. Thin FAQ blocks will not fix a weak page by themselves.

Create comparison, definition and use-case content where the audit proves demand. A “[brand] vs [competitor]” page can help if buyers ask that question. It can also backfire if it reads like a hit piece, so keep claims factual and dated where needed.

Strengthen entity consistency across your site and third-party profiles. Product names, company descriptions, categories, founding details and feature claims should match. Inconsistent wording gives AI systems more ways to describe you badly.

Where third-party sources shape the answer, update the sources if you can. That may mean correcting a directory listing, refreshing a partner profile, asking for an outdated review to be amended, or earning coverage on a credible category site.

Which tools help with an AI visibility audit?

Among the tools tracked by GeoAEO, Semrush has the highest Index Score at 83. OmniSEO follows at 81, Profound sits at 78, and Otterly.AI is at 76. For this workflow, Semrush, Profound and Otterly.AI cover three different audit needs.

Semrush is the strongest fit if your team wants AI visibility, prompt tracking, SEO data and technical checks together. It tracks prompts across AI search surfaces and separates cited pages, cited sources, missing prompts and source opportunities. The limitation is the base package: $99/mo covers a defined starting set, not unlimited domains or tracked prompts.

Profound is a better fit if your priority is market-level answer-engine intelligence. It tracks visibility, citations, sentiment, share of voice, regions and platform breakdowns across a wide set of consumer AI experiences. The caveat is commercial clarity, because packaging can vary and should be confirmed against current terms.

Otterly.AI suits smaller teams, agencies and founders validating whether AI monitoring is worth operationalising. At $29/mo for Lite, it has the lowest recorded entry price among these three tools. The trade-off is prompt and engine coverage, with Lite listing 15 prompts and some engines treated as add-ons.

Do not choose a tool only by platform count. Choose based on the decisions you need to make: content fixes, technical fixes, competitor benchmarking, source outreach or ongoing reporting. A broader tool is useful if you will act on the breadth; otherwise it becomes expensive noise.

How often should you re-run the audit?

Re-run the audit monthly if the category matters to revenue. Quarterly is a sensible fallback if resources are tight. Anything less frequent makes it hard to tell whether fixes worked.

Track trends rather than treating one answer as truth. AI search is fast-changing and personalised, and results vary by prompt wording, location, platform and model state. Visibility tools are useful, but their numbers are directional.

Keep a fixed core prompt set for trend tracking. Add an experimental set for new products, campaigns and competitor changes. If you change every prompt each month, you lose the ability to compare performance.

The final audit deliverable should be boring in the best way: a baseline scorecard, a prompt-by-prompt gap list, cited-source analysis, competitor benchmarks, technical blockers and a prioritised action plan.

Start small if this is your first audit. Use the highest-intent prompts, inspect the sources carefully, fix the obvious gaps, then expand the prompt set once the process is repeatable.

Frequently asked questions

What is the fastest way to audit brand visibility in AI Overviews?

Start with 20 to 30 high-intent prompts covering branded, category, comparison and purchase-intent searches. For each prompt, record whether your brand is mentioned, recommended, cited, missing or described inaccurately. This is less complete than a tool-led audit, but it gives you a usable baseline quickly.

Is an AI Overview mention the same as a citation?

No. A mention means the AI answer names your brand. A citation means the answer points to a source, which may be your website or a third-party page. You should track mentions, recommendations and citations separately because each points to a different fix.

Which tool should I use to audit AI Overviews visibility?

Semrush is a strong fit if you want SEO and AI visibility in one workflow, with recorded pricing from $99/mo. Profound suits broader answer-engine intelligence and share-of-voice work, also recorded from $99/mo, but confirm current packaging. Otterly.AI is a lower-entry monitoring option from $29/mo, with limits and add-ons to check before buying.

How often should I run an AI visibility audit?

Monthly is the best cadence if AI search affects pipeline or brand demand. Quarterly is a reasonable fallback for smaller teams. The key is to keep a consistent core prompt set, because AI answers change by platform, location, prompt wording and model state.

Can technical SEO alone improve AI Overview citations?

Technical SEO helps if important pages are blocked, hard to crawl or unclear, but it is not a guaranteed citation lever. AI Overviews also depend on source trust, content usefulness, entity clarity and third-party references. Treat crawlability as one audit finding, not the whole plan.

Should I audit only Google AI Overviews?

Audit Google AI Overviews first if that is where your visibility problem appears. If buyers also use ChatGPT, Perplexity, Gemini, Copilot or AI Mode, include them in a second pass. A single AI Overview result is not a full picture of AI search visibility.