Semrush Release Notes

The new Model Context Protocol (MCP) connector enables all Perplexity users in Computer to access Semrush’s search intelligence data natively inside the LLM platform to boost brand visibility.

Semrush, an Adobe company, the leading brand visibility platform, today announced the launch of its MCP connector with AI-powered answer engine Perplexity. The connector unlocks access to Semrush’s expansive proprietary dataset directly within Perplexity’s AI search platform and enables automated SEO workflows with Perplexity Computer. This integration gives Perplexity users more accessible ways to use trusted search intelligence to improve brand discovery and make smarter marketing decisions.

The Semrush MCP connector enables Perplexity users to access live Semrush data directly within Perplexity’s AI-native search experience and Comet browser, streamlining the connection between Semrush insights and AI-powered discovery workflows. It empowers marketers, founders and SEO teams to find answers in their search flow powered by Semrush’s trusted data, covering 28.4B keywords, 43T backlinks, 261 million LLM prompts and 808 million domain profiles.

As AI reshapes how people discover brands, Semrush's search intelligence has become essential for businesses trying to measure and grow their visibility. Integrating their trusted dataset directly into Perplexity gives marketers, founders and SEO teams a faster, more accessible way to find the answers that drive real growth.”

Dmitry Shevelenko, Chief Business Officer at Perplexity

Brand visibility in today’s fragmented era of AI search is tough for businesses to measure and even tougher to optimize, but it’s never been more important. Integrating Semrush’s search data infrastructure directly into the Perplexity search platform is pivotal for businesses to build and scale their discoverability faster and easier. You don’t need to be an expert on AI search. Just be curious about building your brand visibility, and our industry-leading search intelligence will guide you every step of the way.”

Vitalii Obishchenko, Chief Product Officer at Semrush

A Seamless Way to Bring Semrush Search Intelligence Into Perplexity’s AI Search Engine

Setup is quick and easy, with little technical knowledge required. Here’s what users can expect inside Perplexity:

• Semrush MCP connector allows users to run keyword research, competitor analysis, and SEO audits inside AI search, plus pull keyword rankings, domain analytics, and backlink data from Semrush directly into their Perplexity workflow.
• Users can run automated SEO workflows in Perplexity Computer, facilitated by the Semrush MCP connector.

Availability

From June 3, 2026, all Perplexity users in Computer have access to Semrush MCP connector, subject to regional availability. Find full details in the documentation.

Set up the Semrush MCP connector in Perplexity

New workflows shared between Enterprise SEO and AI Optimization enable teams to grow brand visibility through high-performing content for both search channels.

Semrush, an Adobe company, today announced the launch of its Unified Content Optimization capabilities, bringing together content workflows across its Enterprise SEO and AI Optimization (AIO) solutions.

Search as a whole is growing and changing. SEO remains the primary driver of scalable visibility, while AI search increasingly influences how information is surfaced and ultimately impacts user behavior and decisions.

By bringing together SEO and AIO workflows, this update enables teams to create, manage, and optimize content for both search rankings and AI responses within a single, shared experience.

Two Critical Channels, One Workflow

Efficient tooling is vital for enterprise businesses that seek every possible edge. Unified Content Optimization lets teams access the same content briefs, recommendations, and optimization tools across both Enterprise SEO and AIO, regardless of where they start their workflow.

This streamlines processes, allowing users to:

• Create and manage content briefs that are accessible across both products.
• Optimize content using data from both search rankings and AI responses.
• Apply real-time scoring and recommendations aligned to performance in both channels.
• Maintain consistency across teams, including external content professionals.

Teams are then empowered to optimize once, for both search formats, before publishing, with full confidence of strong performance in organic Google search and all leading AI models.

Why This Matters: SEO and AI Search Are Complementary But Unique

While AI search reshapes discovery and customer journeys, it relies closely on SEO signals. Google’s own documentation confirms this.

In many cases, AI-generated answers draw heavily from top-ranking organic results. For example, Google AI Mode links have an 89% domain overlap with top 10 results.

However, optimizing purely for SEO isn’t sufficient in the age of AI search. Therefore, the content brands publish and circulate must also align with how AI systems interpret, summarize, and present information.

Semrush for Enterprise’s Unified Content Optimization capabilities solve this, streamlining the process with side-by-side scoring for each channel, and remove the risk of over indexing on one channel at the expense of the other.

Breaking Down Silos Between Teams and Workflows

Our research shows that SEO and AI search efforts often operate in parallel, with only 22.5% of teams reporting complete integration of SEO and AI activities. This leads to duplicated work, slowed content publishing time, and fragmented strategies.

By unifying content workflows, Semrush for Enterprise enables:

• SEO teams to create multiple briefs simultaneously that utilize both SEO and AI search insights.
• Content teams to leverage proven insights for both channels.
• Wider marketing teams to understand the content pipeline.
• Marketing leaders to cultivate unified brand visibility strategies.

With processes aligned, teams can then operate at global scale. Then, with unlimited access for external writers, marketing teams can also scale their production across multiple markets.

Enabling Scalable, Future-Ready Content

As discovery grows and audiences are distributed across both search engines and AI platforms, brands that can unify their approach to content will gain a clear competitive edge.

Semrush for Enterprise’s Unified Content Optimization capabilities empower teams to build and optimize content for the era of AI search.

Learn more

Global coverage expansion now provides international teams with real AI search data for previously unmeasured markets, growing the Semrush prompt database to more than 261 million LLM prompts.

Semrush, an Adobe company, has expanded its AI visibility prompt database to 32 countries, adding 17 new regional markets across the AI Visibility Toolkit and Enterprise AI Optimization (AIO) solution. The expansion grows the total prompt database to 261 million prompts globally, including 126 million US prompts and 58.4 million ChatGPT prompts.

For organizations running global strategies, AI search visibility has been constrained by limited data that reflects only a fraction of the markets where their brands compete. This data expansion closes that gap, making AI search data accessible in geographies that previously had no coverage.

17 New Markets, One Unified Database

The newly available countries in Semrush’s AI visibility database are Argentina, Austria, Belgium, Chile, Colombia, Denmark, Finland, Ireland, Israel, Norway, Panama, Peru, Poland, Saudi Arabia, South Africa, Switzerland, and Uruguay.

Coverage is consistent across all Semrush surfaces, whether you’re accessing this data from Enterprise AIO or Semrush One.

What The AI Visibility Database Powers

Semrush’s AI visibility database underpins the discovery and market-level intelligence that enterprise teams rely on to research the AI search landscape beyond what they track in their own projects.

It enables teams to:

• Explore how AI models respond to queries in a given market.
• Research and benchmark brand visibility across countries and competitors.
• Identify topics, top brands, and top sources relevant to specific regional audiences.

Without market-level organic data, global teams lack the baseline intelligence needed to understand how AI systems surface brands in different geographies. The AI visibility database provides the search intelligence teams need to prioritize where to act or how to interpret local AI search performance, and boost their brand visibility everywhere users are searching.

Scaling AI Visibility For Global Enterprise Teams

The industry needs trusted search intelligence in order to make decisions rooted in data. Expanding to 32 countries reflects the operational reality facing enterprise marketing, SEO, and brand teams right now: AI search is not a single-market phenomenon.

Brands with international presence must now manage discoverability across a growing range of AI surfaces in markets where user behavior, language, and source ecosystems differ significantly.

With this data expansion, Semrush provides a more complete foundation for organizations to benchmark, track, and optimize AI visibility at a global scale.

New Reddit and Negative Sentiment Analysis capabilities give brands deeper visibility into the sources shaping their perception in AI search.

Semrush, an Adobe company, today announced the launch of two new automations in its Enterprise AI Optimization (AIO) solution: Reddit Analysis and Negative Sentiment Analysis.

As AI search grows in adoption, brand perception becomes increasingly influenced by the complex mix of publishers, reviews, editorial content, and user discussions that act as its sources. These new automations help bring structure to that complexity, enabling teams to move from manual observation to precise, source-level understanding.

Refining Brand Perception Through Source Insights

Both workflows are designed to reduce the manual effort required to analyze AI search citations, saving teams hours of investigation while improving the accuracy of insights.

• Reddit Analysis focuses on one of the most influential sources in AI responses, helping brands understand where they are missing from high-impact conversations.
• Sentiment Analysis expands this view across all cited domains, identifying sources and topics that contribute to negative brand perception.

Together, they provide a more granular understanding of how a brand is framed, which sources are driving that narrative, and what to do next.

Reddit Analysis: Finding the Conversations Impacting AI Visibility the Most

Reddit has emerged as one of AI’s most frequently cited domains, making it a critical visibility channel. However, valuable insights can be buried deep in threads, easily missable with high-level analysis.

The new Reddit Analysis workflow enables users to:

• Identify threads where competitors are cited but their brand is absent.
• Surface the discussions influencing AI answers the most in their market.
• Prioritize engagement in conversations that AI already utilizes.

Recognizing that participation in these conversations is a directional lever, this allows brands to focus efforts where they are most likely to influence downstream AI visibility.

Negative Sentiment Analysis: Understanding What’s Shaping Perception

While a brand’s overall visibility is most crucial in AI performance, ensuring accurate and positive perception comes a close second. However, discerning the levers behind a brand’s sentiment and appropriate responses, is difficult and time-consuming.

The Negative Sentiment Analysis workflow addresses this by automatically analyzing cited sources across tracked prompts to:

• Identify domains contributing most to negative brand perception.
• Pinpoint topics or concept clusters receiving unfavorable framing.
• Find opportunities for content, PR response, or outreach to rebalance narratives.

This transforms source data into a clear picture of what and why negative sentiment could be emerging in responses, helping teams prioritize corrective action where it matters most.

Supporting More Precise, Scalable AI Search Strategy

Both workflows are built to support enterprise teams managing AI visibility at scale:

• Brand and PR teams can identify reputation risks earlier and respond quickly and more strategically.
• SEO and content teams can align efforts with the sources and content that influence AI answers.
• Marketing leaders gain a more grounded view of how their brand’s perception is evolving, and the triggers behind it.

AI search is a dynamic environment where outputs shift based on model updates, source weighting, and user context. In response, these workflows provide structured guidance helping teams act with greater confidence without relying on static assumptions.

Turning Source Complexity Into Competitive Advantage

Through automated Reddit Analysis and Negative Sentiment Analysis, Semrush Enterprise gives brands a more complete view of the influences behind their AI visibility and customer perception.

The result is a more precise approach to managing AI visibility, one that enables teams to understand the underlying source material shaping AI answers.

Learn more about Enterprise AI Optimization

Semrush has introduced YouTube Gap Analyzer to its App Center, expanding the range of tools available to marketers and creators working with video.

YouTube Gap Analyzer app helps evaluate content ideas and execution before production begins. By comparing a planned video topic with top-ranking content, the app shows users what’s working and what isn’t within their niche.

Uncover Viral Video Gaps and Turn Them into a Content Advantage

YouTube is becoming a key source of visibility in AI-generated answers. According to a Semrush study analyzing 17 months of clickstream data, YouTube ranks second among domains most-referenced in ChatGPT. A separate analysis of 230K prompts over three months shows the same position in Google’s AI-powered results.

YouTube Gap Analyzer turns competitor weaknesses into inputs for effective video planning.

The app uses AI to analyze how the top five videos in a given topic perform across key factors. This includes engagement levels, audience sentiment, content freshness, and overall production quality.

What YouTube Gap Analyzer Offers

• Content and engagement analysis: Evaluates video performance across quality, engagement, and audience sentiment to show what resonates most with viewers.
• Quality gap detection: Quickly spot weaknesses in competing videos, including poor audio, basic editing, or low production value, and create stronger content.
• Content freshness insights: Identify outdated or incomplete information in existing videos and use it as an opportunity to create more relevant, up-to-date content.
• Topic opportunity discovery: See which topics are underserved and which are overcrowded, helping creators focus on ideas with the highest potential impact.
• Actionable recommendations: Get AI suggestions to improve on top competitors and turn gaps into high-ranking videos.

Designed for Video Creators of Any Kind

YouTube Gap Analyzer is intended for all types of creators:

• Content creators and video marketers: Test ideas before production, spot gaps in competitor videos, and prioritize content that is more likely to perform.
• SEO professionals and content strategists: Identify opportunities and analyze demand around what people are actually watching.
• Agencies: Manage YouTube content for clients with clearer insights, stronger justification for content choices, and a more data-driven planning process.
• Marketing teams: Improve video strategy by aligning content decisions with audience demand and competitor performance.

Know How to Rank Higher for Your Topic

YouTube Gap Analyzer, now part of the Semrush App Center, shifts video creators’ attention to pre-production research.

Instead of competing head-on with the strongest videos on YouTube, marketers can focus on overlooked angles, unmet audience needs, and untapped opportunities to create the next viral video.

Explore YouTube Gap Analyzer

Semrush has expanded the App Center offering with Voice Assist, an AI voice assistant developed by CallRail.

Built as an extension of CallRail’s conversation intelligence capabilities, Voice Assist is a standalone app in the Semrush App Center. The tool uses advanced AI to analyze calls in real time and deliver lead management guidance.

Take Calls After Hours, Qualify Leads, and Maximize Ad Spend

Although much of customer communication has gone digital, clients keep on calling.

McKinsey & Company found that live phone calls are still valued across all consumer segments, offering a personal touch that other channels lack. When asked, “How likely are you to reach out to customer care via live phone?”, all generations responded affirmatively, including 71% among Gen Z respondents and 94% of Baby Boomers.

Businesses struggle to keep up with the demand. The gap can be closed with call-based AI insight systems. A study found that after implementation, customer satisfaction increased by nearly 24%.

Voice Assist combines an automated receptionist with a lead management system in a single tool. Its AI is trained on conversation data and company-specific information to guide callers clearly.

By handling calls around the clock, Voice Assist helps businesses stay responsive and capture every incoming lead.

What Voice Assist Offers:

• 24/7 call handling: Use AI to answer every call with your chosen voice, book appointments, and route callers to the right team.
• AI-powered next steps: Get clear follow-up actions based on the call context to convert more leads with confidence.
• Lead qualification: Automatically filter out spam and qualify high-intent callers based on custom criteria.
• Call insights: Capture structured call data (transcripts, summaries, keywords) to power CRM updates and campaign optimization.
• Seamless integration: Connect directly in tools like HubSpot, Salesforce, and Google Ads.

Perfect for SMBs and Agencies

Voice Assist makes answering calls and managing leads easier for businesses of all sizes:

• Small and medium-sized businesses: Improve call handling with one simple tool. Reduce the customer support team’s manual tasks and optimize productivity.
• Marketing teams: Expertly nurture and convert inbound leads. Track your ad ROI to maximize campaign effectiveness.
• Agencies: Prove results for the leads your campaigns create. Showcase your performance on dashboards.

Drive More Value from Every Conversation

Now available in the Semrush App Center, Voice Assist provides a unified environment for call and lead management. With AI guidance and deep conversation insights, businesses can decisively act on insight.

By answering every call with Voice Assist, organizations can protect their Local Services Ads (LSA) rankings, ensure every lead is captured, and provide exceptional customer service anytime.

Find and Fix Content Gaps for Better AI Visibility

The LLM Gap Analyzer app helps marketers understand how and why their content appears in large language models, and just as importantly, why it does not.

LLM Gap Analyzer is now part of the Semrush App Center. Marketing teams can examine where competitors are being cited, where existing content is missing context, and how those patterns affect brand discoverability across AI platforms.

AI-generated answers are quickly becoming a core search layer.

According to McKinsey & Company, 44% of agentic search users already prefer AI tools over traditional search engines. A recent Semrush study shows that, on average, traffic from LLM-based search converts 4.4 times more than traditional organic search traffic.

Marketers increasingly need brand visibility in LLM results. LLM Gap Analyzer shows how AI forms answers to specific queries, where the information comes from, and why a brand’s articles are not used in responses.

What LLM Gap Analyzer Offers

Rather than treating AI answers as a black box, the tool translates them into observable signals:

• AI answer breakdown: Check how AI has built a response to a prompt and trace it back to the sources used.
• AI Overview insights: Understand why Google AI Overviews select specific pages as sources.
• Content gap detection: Compare brand articles to cited sources to know what information a particular piece of content is missing.
• Improvement guidance: Get clear suggestions to strengthen brand content and get more citations.
• Information analysis: Spot opportunities to correct gaps, inaccuracies, and missing context in AI answers.

Ideal for Teams, Agencies, and Content Strategists

LLM Gap Analyzer is built for any specialist or business prioritizing content visibility in AI:

• SEO specialists and content strategists: Boost brand visibility by appearing as a source for LLMs.
• Agencies: Show clients why their content isn’t performing across AI platforms and justify content strategies with clearer data.
• Marketing teams: Protect organic traffic as zero-click results become more common.
• Content experts and creators: Find data voids and build brand authority in LLMs through optimized content pieces.

Understand How AI Interprets Brand Content

LLM Gap Analyzer is a new diagnostic tool in the Semrush App Center, analyzing specific articles, keywords, and prompts. It maps content gaps and shows where competitor pages are consistently chosen as sources in AI systems.

AI visibility should not rely on guesswork. LLM Gap Analyzer helps to measure, identify, and shape AI search behavior instead of reacting to it.

Unlocking Targeted Insights for Bots

New bot simulation capabilities let teams see their website exactly as AI models and search engines do. Without manual configuration.

Semrush announces the launch of Crawler Profiles in Enterprise Site Intelligence. The new feature enables teams to simulate how specific AI agents and search engine crawlers access and interpret their website, giving organizations precise, bot-level visibility into their site's technical readiness for both SEO and AI discovery.

At launch, three crawler profiles are available: the Semrush crawler, ChatGPT (including ChatGPT-User, OAI-SearchBot, and GPTBot), and Googlebot. Each profile applies bot-aligned configuration settings automatically, removing the need for manual setup and ensuring realistic, accurate simulation at the project level.

Without the ability to simulate bot behavior directly, teams are left with blind spots that can silently erode discoverability in both search and AI-generated responses.

Crawler Profiles addresses this by letting users select a predefined profile and run an analysis that mirrors how a specific bot accesses their content. Two crawl types are supported:

• A standard crawler that comprehensively sweeps the entire website to find technical issues and opportunities.
• A list-based crawler for targeted validation of specific URL sets.

Crawls can be launched immediately or scheduled to run at recurring intervals, enabling continuous monitoring as sites evolve.

Designed for Technical and Marketing Teams Alike

The feature serves a range of use cases and teams, helping them to ensure that their key pages are parsable to all search bots.

SEO managers can validate crawl accessibility and prevent hidden visibility gaps. DevOps and engineering teams can confirm rendering and blocking behavior before releases. Enterprise SEO leads can maintain AI readiness across large-scale properties, while content and digital teams can ensure their content is accessible to AI systems that increasingly shape how audiences discover brands.

Expanding Site Intelligence for the AI Era

Crawler Profiles builds on Site Intelligence’s comprehensive SEO and AI search capabilities. When combined with existing capabilities such as Bot Analytics — which delivers comprehensive crawl health analysis across 30 bots — teams now have a layered approach to understanding and optimizing how automated systems interact with their websites.

Crawler Profiles for AI agents and search bots is available now in Enterprise Site Intelligence.

Semrush Enterprise AI Optimization’s (AIO) AI Shopping report

Semrush Enterprise AI Optimization’s (AIO) AI Shopping report now delivers unified merchant and product-level insights across both AI shopping experiences.

Semrush Enterprise today announces the expansion of the
AI Shopping report
in AIO, enabling brands to monitor how their products appear across
ChatGPT Shopping
and
Google AI Mode Shopping
.

As product discovery and ecommerce behavior shift to AI interfaces, brands face a growing visibility gap as their products surface differently across platforms and prompts. Variations occur in ranking, retailer representation, and positioning, yet most brands lack the ability to understand these.

Enterprise AIO addresses this challenge by delivering structured, cross-platform monitoring that turns AI shopping exposure into measurable competitive intelligence.

Unified Monitoring for ChatGPT and AI Mode

The AI Shopping report analyzes which products are recommended by ChatGPT Shopping and Google AI Mode, with a unified data structure that enables direct comparison between environments.

This dual visibility allows brands to answer critical questions:

• Which products are recommended most often
• How highly they rank
• How visibility shifts over time
• Which prompts trigger recommendations
• Which merchants list or surface those products

Teams can dive deeper to assess performance and competitive positioning across platforms by zooming into merchants or products.

Merchant view: Centered on retailer and marketplace visibility. This view highlights which merchants dominate AI shopping exposure, where products are surfaced, and how visibility is distributed across marketplaces.

Product view: Focused on product-level performance across AI shopping prompts. Teams can evaluate recommendation frequency, average position, prompt coverage, retailer presence, and trends over time.

Together, brands gain a holistic understanding of their product’s positioning in AI responses and all the factors shaping this.

Supporting Smarter Product and Content Decisions

For product marketers, category managers, and brand managers, AIO enables strategic monitoring of product visibility, competitor benchmarking, and comparing performance across both platforms.

For SEO and content teams, the insights then extend to revealing which prompts and AI models drive product visibility and aligning content messaging with high-performing shopping queries.

By delivering structured, measurable data, Semrush Enterprise enables brands to move from passive observation to active optimization. This shift is particularly important as AI platforms, and agents, become increasingly embedded in search and as decision makers.

A New Layer of Ecommerce Intelligence

AI shopping is revolutionizing traditional ecommerce channels and reshaping how discovery and comparison happen. Semrush Enterprise provides brands with the clarity needed to navigate this shift strategically.

Learn more about Enterprise AI Optimization and the new AI Shopping report

• Download as
  • PDF
  • DOCX
  • Share

New Bot and Agent Analytics capabilities give teams clearer understanding of how search engines and AI crawlers interact with their websites, unlocking new visibility opportunities.
Semrush Enterprise announces new log file analysis capabilities across two solutions: Bot Analytics in Site Intelligence and Agent Analytics in AI Optimization (AIO). Together, these features provide brands with a clearer, more accurate understanding of how search engine bots and AI search bots access and read their websites.
Bot Analytics offers depth and complete technical understanding with coverage for 30 bots (20 search engine and 10 AI) total.
Agent Analytic provides focused context for AI visibility, with targeted views for AI agent access that encourage deeper technical analysis where needed.
Log file analysis captures what simulated crawls cannot: real bot behavior at scale. By analyzing server log data, Semrush Enterprise enables teams to validate access, identify inefficiencies, and reduce visibility risk. All grounded in what bots actually do.

One Foundational Need, Two Complementary Features

While the process of log file analysis underpins both Bot Analytics and Agent Analytics, each is tailored to the use cases of each product.
Bot Analytics in Site Intelligence delivers a comprehensive understanding of website crawl health. Designed as a core capability within SI, it supports deep technical SEO use cases, including crawl efficiency, error detection, and large-scale site diagnostics. Bot Analytics provides two core reports:

• Overview: gives a snapshot of bot activity and crawl patterns
• URLs with bot visits: validates bot access and response behavior at the URL level

This reinforces Site Intelligence as the key solution when conversations focus on site health, crawl optimization, or technical performance at scale.

Agent Analytics in AIO, offers a view focused only on AI bots. This capability is designed to specifically support AI visibility use cases by helping teams understand whether AI agents can access key pages and how they interact with content. It complements, but does not replace, full technical analysis in Site Intelligence.

Both solutions ingest up to 10GB of log data per day with three months of fine-grained data retention, ensuring consistent, enterprise-ready data handling across products. Capacity can also be extended for larger or growing environments, supporting long-term scalability.

Why Log Files Matter More Than Ever

As search and discovery increasingly depend on automated agents, understanding machine-side behavior becomes crucial. Log file analysis allows teams to:

• Confirm which pages bots and agents actually access
• Identify crawl waste and inefficiencies that dilute visibility
• Detect errors or blocked content that may impact search or AI discovery
• Reduce risk by grounding decisions in real-world bot activity

By providing targeted data, Semrush Enterprise gives organizations the flexibility to apply log insights where they matter most—whether optimizing complete technical foundations or supporting AI visibility strategies.

A Unified Vision With Targeted Solutions

Visibility starts with access. If search engines and AI bots can’t access your key content, then your competitor will be showing up instead.
Semrush Enterprise provides log file analysis that’s refined to the different needs of different teams, ensuring brands can pivot between full technical depth and targeted AI context as required.

New capabilities identify the background queries made by AI models, which are run through search engines. This helps brands identify further terms to target for AI visibility and align their SEO and GEO strategies
Semrush Enterprise has announced the release of Query Fan-Out Analysis, a new feature within AI Optimization (AIO) that reveals the Google search queries that fuel AI responses in systems such as ChatGPT.
The feature help address a growing challenge for enterprise marketers. While AI discovery is an increasingly important customer touchpoint, the background queries that AI models run to formulate their answers are hidden to brands.
Query Fan-Out Analysis brings transparency to this process, revealing this new layer of fan-out queries to help teams understand how AI systems retrieve information, additional terms to target, and where SEO performance directly impacts AI visibility.
AI Answers Are Built on Layers of Queries
When users prompt AI systems, the response they see is rarely based on one search alone. Instead, large language models break prompts into multiple related terms, retrieve results from Google, and synthesize the information from these into a single answer. This is known as query fan-out.
Semrush research shows that appearing consistently across these underlying queries increases the likelihood to be cited or surfaced in AI responses, even when they are not the top-ranked result for the original prompt.
However, until now, marketers lacked visibility into what the fan-out queries were for the key prompts being tracked. As well as which of these queries matter most and which competitors were best positioned for these.
Making AI Visibility More Actionable
Query Fan-Out Analysis exposes the search behavior that shapes AI answers by revealing:

• The Google search queries AI systems rely on to generate responses
• The domains ranking for each fan-out query
• How frequently competitors appear across those queries
• Where brands are missing from high-impact SERPs
  This allows teams to better understand and act on the search signals that influence AI responses, treating these terms as keywords and utilizing the SEO tactics they already understand and control.
  Bridging AI SEO and SEO
  The release reinforces a central pillar of the search industry: AI search and SEO are not separate disciplines. Indeed, they’re intrinsically linked.
  SEO best practices have a positive influence on AI visibility. But strong rankings for a single keyword don’t guarantee visibility in AI responses.
  At the same time, brands that focus exclusively on AI prompts without strengthening search performance risk missing the underlying drivers of AI visibility.
  Query Fan-Out Analysis enables teams to better:
• Prioritize optimization based on their impact on AI responses
• Identify competitive gaps across supporting and adjacent queries
• Align content and search strategies with how AI systems retrieve information
  When used alongside Enterprise SEO automations such as keyword gap analysis, content optimization, and position tracking, the feature helps foster a single, connected digital visibility strategy.
  Built for Teams Managing Diverse Search Initiatives
  Query Fan-Out Analysis is designed for enterprise organizations or growing teams managing visibility across multiple brands, domains, and markets.
• SEO managers gain clarity on which rankings influence AI responses and where optimization will have the greatest downstream impact
• Content teams can focus creation and updates on topics that shape AI answers
• Marketing leaders gain greater confidence that AI initiatives are grounded in proven search performance
  The feature provides a practical framework for navigating AI search volatility while continuing to invest in long-term SEO foundations.
  Search Is Expanding & Strategy Must Expand With It
  As search behavior evolves, visibility is no longer earned through a single channel or tactic. Consumers move fluidly between search engines and AI, and brands must understand their performance holistically and optimize for both.
  Query Fan-Out Analysis helps organizations measure, understand, and grow visibility wherever discovery happens, empowering teams to expand upon the SEO strategies that continue to drive results, while building leadership as AI search rises.
  Learn More & Request a Demo