How Can Semrush Help Brands Improve AI Search Visibility?

A brand can have strong Google rankings, steady organic traffic, and a technically healthy website, yet still be missing from the answers buyers receive from ChatGPT, Gemini, and other AI search platforms. That does not necessarily mean the brand has lost its search visibility. It means discovery is happening in more places, and some of those places do not look like a traditional results page.

Semrush is bringing AI search into the workflow marketers already use for SEO. Its AI Visibility Toolkit can show where a brand appears in AI answers, how competitors compare, which prompts and topics are creating opportunities, and which sources AI platforms are citing. The base toolkit starts at $99 per month per domain when billed annually and includes 25 custom prompts for daily tracking.

The more important question comes after the report. If a competitor keeps appearing for a buyer question that matters to your business, knowing that gap exists is only the beginning. Someone still has to understand why it is happening, decide what deserves attention, and turn the finding into work.

Why Does AI Search Need a Different Measurement Approach?

Traditional SEO gives marketers a familiar set of numbers: rankings, organic traffic, backlinks, impressions, and clicks. Those metrics still matter, but they do not tell the whole story when a buyer asks an AI platform to research a product, compare vendors, or recommend a solution.

AI answers could cover different brands, include references from other websites, draw on information from many pages, or favor a certain company. The consumer could follow up on the reference, look for the mentioned brand later, or not visit the referenced page at all and keep going with the purchase.

This would shift the marketing department's focus. It would be useful to know which questions lead to your brand being suggested, who the competition is, what pages are being referred to, and how often your company gets mentioned across those channels.

Visibility also differs from traffic. Semrush has reported that AI-referred visitors in its research converted at an average rate 4.4 times that of traditional organic visitors. At the same time, AI search does not necessarily send the same volume of referral traffic as conventional search. That makes visibility before the click worth measuring alongside the traffic that follows.

Semrush’s AI Visibility Toolkit is built around this broader picture. Its reports cover AI visibility, mentions, citations, cited pages, competitors, prompts, and source opportunities, giving marketers a baseline before deciding where to focus.

What Can Semrush Measure in AI Search?

Semrush does not reduce AI visibility to one number. Its toolkit includes several reports that address different parts of the problem. The real value is being able to move from a broad benchmark to the prompts, competitors, sources, and brand narratives behind it.

AI Visibility

The Visibility Overview provides a high-level view of how a brand appears in AI-generated answers. Semrush’s AI Visibility Score benchmarks a brand against the median visibility of relevant competitors rather than treating it like a conventional Google ranking.

This is where the marketing team can begin. Set the benchmark, watch for changes in visibility, and analyze the factors behind them. This score is an indicator in one way or the other, but it should never be the end goal for the business.

Competitor Visibility

Competitor research becomes more useful when it shows where another brand is winning rather than simply telling you that it has a higher visibility score.

Semrush’s Competitor Research report lets marketers compare their AI visibility against up to four competitors at a time. It surfaces metrics such as mentions, citations, and topic coverage, helping teams see where competitors have an advantage.

Those gaps can lead to a more useful question: What is causing the competitor to appear there?

The answer may be a better page, stronger third-party coverage, clearer brand associations, or simply better topic coverage. The competitor report gives the team a starting point for that investigation.

Prompt Research

AI search starts with questions, so prompt research plays a role similar to keyword research in traditional SEO.

Semrush’s Prompt Research uses a database of more than 317 million AI queries and provides topic-level information such as AI volume, intent, relevance, and difficulty. Marketers can also open individual prompts to see the brands mentioned and sources cited in the responses.

That information helps put AI visibility into a buyer context. A broad informational topic may attract a lot of AI activity but have little commercial value. A smaller set of comparison or evaluation questions may matter much more to a sales team.

Don't treat AI topic volume as a direct replacement for Google keyword volume. Semrush’s AI Volume is an estimate at the topic level, not a promise that an individual prompt is searched a specific number of times.

Brand Performance

Visibility also has a qualitative side.

A company can be mentioned frequently and still be described in a way that does not match how it wants buyers to perceive the brand. Semrush’s Brand Performance reports look at share of voice, sentiment, narrative drivers, and the questions associated with brand mentions across AI platforms.

The data is updated weekly, and Semrush generates a set of branded and non-branded prompts associated with the domain and selected location. That makes the report useful for understanding how AI platforms currently frame the brand, although it should not be confused with a company’s own buyer-prompt research.

For a marketing team, the question becomes more useful than simply asking whether the brand appears:

What are AI platforms actually saying about us?

Prompt Tracking

Once a team knows which prompts matter, it can move from research into ongoing tracking.

Semrush’s Prompt Tracking monitors selected prompts daily across ChatGPT Search, Google AI Mode, and Gemini. It shows AI visibility, mentions, average position, and the domains and pages cited for those prompts. It also allows marketers to compare AI visibility with traditional search performance within the same Position Tracking workflow.

This is particularly useful when a team wants AI search reporting to sit alongside its existing SEO work, rather than becoming another separate dashboard.

What Does the Semrush AI Visibility Toolkit Cost?

The base AI Visibility Toolkit costs $99 per month per domain when billed annually. It includes AI visibility reports, 25 custom prompts for daily tracking, one domain for Brand Performance, competitor analysis, Prompt Research, and an AI-readiness Site Audit.

For large groups, the number of prompts becomes a critical factor. A small B2B campaign will do well with 25 or even 50 prompts. However, for an organization covering different products, markets, and competitors, reaching such numbers might be quicker than anticipated.

*Annual billing. Plans and limits change, so check Semrush’s pricing page for current numbers.

The prompt allowance is more than a pricing detail. It affects how broad the measurement program can be. If the goal is to track every product, market, competitor, and stage of the buying journey, prompt selection becomes part of the strategy.

How Can Marketers Use Semrush to Find AI Visibility Gaps?

The most useful starting point is usually not a generic search for every mention of the brand. Start with the questions that matter to the business.

Develop prompts based on the challenges your target audience faces, the products they compare, and the queries they ask before reaching out to sales. Once you have the answers, see where competitors are mentioned, where they are cited from, and where your brand is missing.

This is how Semrush helps: get a baseline visibility score, conduct competitor analysis, find relevant prompts, and monitor selected prompts. The AI Search Site Audit functionality may also help you spot technical SEO problems.

That technical check is worth doing early. A team can spend weeks improving content only to discover that an important page is difficult for an AI crawler to access. Semrush combines its own crawler with checks against AI crawlers such as OAI-SearchBot, and Site Audit updates AI-search findings whenever a new crawl runs.

The important part is what happens with the findings. A missing citation is a starting point for investigation, not automatically a reason to publish another article.

What Should Teams Do After Finding a Visibility Gap?

Suppose a competitor appears repeatedly when buyers ask a commercially important question, and your brand does not. Next, understand why that competitor keeps showing up.

Start with the sources. Look at the pages being cited, the publishers mentioning the competitor, and the information that appears repeatedly across the answers. Then compare that with your own content and brand coverage.

Sometimes the gap is on your website. The competing page may answer the question more directly, provide clearer evidence, or organize the information in a format that is easier for AI systems to use.

Sometimes it is not a website problem at all. If the same industry publication, review platform, or community appears repeatedly around competitor answers, the opportunity may involve third-party coverage rather than another article on your own domain.

The response could involve improving an existing page, creating a stronger resource, strengthening structured data, building supporting content, addressing third-party brand coverage, or combining several of those efforts.

That is where the distinction between finding an opportunity and running an optimization program becomes important.

Can Semrush Replace the Need for AI Search Optimization?

For some teams, Semrush can cover much of the measurement and recommendation work.

It brings AI visibility into an environment marketers may already use for keyword research, competitor analysis, technical auditing, and reporting. Its AI-search tools can identify visibility gaps, surface competitor and source opportunities, track selected prompts, and provide content recommendations. Semrush’s Prompt Research can also send relevant prompts into Prompt Tracking or into its Content Toolkit for content creation.

The argument isn't that Semrush simply tells you what is happening.

The harder question is what happens after the recommendation appears.

A platform can identify a missing citation, highlight a competing source, or recommend changes to a page. The marketing team still has to decide which opportunities matter commercially, produce and publish the work, address technical issues, develop supporting assets, and work on third-party signals where necessary.

For a team with the right people and enough time, that may fit comfortably into the internal roadmap.

For a lean team, the gap between knowing what should change and actually shipping it can be much harder to close.

Where Does Pressfit Fit Into the Workflow?

This is where Pressfit differs from the SaaS trackers covered in our comparison of Profound, Scrunch, Evertune, and Otterly.

That comparison focuses on products built to monitor AI search visibility and help teams choose the tracking approach that fits their needs. Pressfit isn't presented as a fifth SaaS column because its model centers on the optimization work that follows measurement.

For a team already using Semrush, that does not mean replacing anything. Semrush can remain part of the research and reporting workflow while Pressfit helps turn the findings into actual optimization work.

Citation Analysis of ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews is included in Pressfit’s strategy for an AI-powered search. This is a larger framework that connects citation analytics with behavioral intelligence and pipeline signals to help the team discern true visibility from superficial visibility.

There is a difference. A brand can boost citation share for queries that are irrelevant to buyers. For an effective AI search tool, it is important to know what is relevant, which competitors win these queries, and if any shifts in visibility are accompanied by buyer behavior.

That is how Pressfit would be considered relative to an existing measurement stack, not as yet another dashboard to manage, but as the specialized layer that makes use of those findings for actual content and optimization work.

What Does a Semrush and Pressfit Workflow Look Like?

The workflow does not need to be complicated. The important thing is to keep measurement connected to action.

1. Establish the baseline. Use Semrush to understand current AI visibility, competitor presence, citations, and the prompts where the brand appears or is missing.

2. Choose the questions that matter. Prioritize prompts connected to the ICP, buying journey, and commercial priorities rather than treating every AI query equally.

3. Study the winners. Look at the pages, publishers, brand mentions, and other sources that repeatedly appear in answers where competitors are winning.

4. Decide what is actually causing the gap. It may be content quality, technical accessibility, weak third-party coverage, unclear positioning, or several factors at once.

5. Ship the work. Update content, structure, schema, supporting assets, and relevant off-site signals according to the opportunity.

6. Track the same prompts again. Keep the prompt set consistent so changes in visibility can be compared over time.

7. Connect visibility with business signals. Compare AI visibility with branded search, account engagement, opportunities, and pipeline where that data is available.

This ensures that reporting remains useful while not making reporting an end in itself. The purpose of this is to identify commercially important gaps, fill them, and then determine if visibility and buying behavior are trending in the right direction.

When Should a Team Use Semrush, and When Should It Bring in Pressfit?

Semrush makes sense for teams that want to bring AI visibility into an existing SEO workflow. It combines AI visibility measurement with prompt research, competitor analysis, brand-performance reporting, prompt tracking, and technical checks.

The need for outside help usually appears further down the process.

If a team has the people and expertise to investigate visibility gaps, decide what response makes sense, implement the work, and measure the result, Semrush can feed directly into that internal roadmap.

If the team can see the gaps but does not have the capacity to keep up with the work behind them, a specialist partner can take on that execution. That is where Pressfit fits: working alongside the tools a company already uses rather than requiring the team to rebuild its marketing stack.

So Where Does That Leave You?

AI search does not make traditional SEO irrelevant. It adds another layer marketers now need to understand.

Semrush gives teams a practical way to measure that layer. It can show where a brand appears, where competitors are gaining ground, which prompts deserve attention, how AI platforms describe the brand, and how those signals change over time.

But measurement is not the end of the process. The real value comes from taking the visibility data, finding the commercially important gaps, and doing something about them.

For teams with the expertise in-house, Semrush can provide the data and recommendations needed to build that roadmap. For teams that want help with the strategy, content, technical work, competitive analysis, and ongoing optimization behind AI visibility, Pressfit can work alongside the tools they already use.

The goal is not another visibility score. It is showing up when your buyers ask the questions that influence a purchase.

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