But the real story happens behind that instant response.

Large language models pull from what they’ve already learned about the web: how often your brand shows up, which reputable sites mention or cite you, and whether those signals align with the topic being queried.

For anyone working in search or content, that changes the rules. Backlinks still matter, but they’re no longer the primary currency of authority. Mentions, citations, semantic context, and topical consistency now help LLMs decide whether your brand is relevant—and whether it deserves to surface inside an AI-generated answer.

So the real question becomes:

How do LLMs Discover and Validate Information?

LLMs don’t crawl the web in real-time or evaluate every page for each query. Instead, they generate responses using patterns learned during training and subsequent updates. When the model builds an answer, it pulls from associations like:

• How entities relate to each other.
• Which claims are repeated across credible sources.
• What it has seen reinforced over time.

To include your brand in an answer, the model must “believe” you genuinely belong in that topical space. That belief strengthens when your name appears across authoritative sources, when third parties echo your claims, and when those signals repeat in a stable, trustworthy pattern.

Backlinks, mentions, and citations each contribute differently, but together, they help the model determine whether your brand is not only relevant but reliable enough to feature in an AI-generated response.

Understanding the Three Signals in the Age of AI Search

Backlinks, mentions, and citations each play critical roles in discovery, but LLMs learn different things from each one.

Backlinks

A respected site linking to your content used to signal authority, relevance, and usefulness. That influence hasn’t vanished, but in an LLM-driven environment, backlinks play a slightly different role.

Models reference backlinks in two main ways. First, they use them during training. If many trusted sites link to the same resource, that page becomes more influential in the model’s understanding of a topic. Second, retrieval-based tools like Perplexity or Bing Copilot may use backlinks to check if a source is trustworthy when pulling real-time information.

So backlinks still count. They just don’t carry the entire weight on their own anymore. The model treats them as one piece of evidence in a bigger pattern.

Mentions

A mention is any written or spoken reference to your brand, even without a link. That includes Reddit threads comparing tools, a LinkedIn post from a customer, or a blog article that lists your platform alongside others.

Mentions tell the model that your brand exists and that real people talk about it in natural language. That matters because users now ask questions conversationally, and generative engines respond the same way. If your brand keeps appearing across discussions, reviews, and community spaces, the model becomes more confident in associating you with the category you want to show up in.

Citations

Citations are formal records explaining your brand’s category, positioning, and identity. They usually appear in structured reference sources, such as Wikipedia, product directories, business databases, and knowledge panels.

For LLMs, citations provide clarity. If two companies share a similar name or compete in overlapping markets, citations help the model understand which one aligns with which attributes. These become especially important in prompts where the model is asked to evaluate, compare, recommend, or decide.

Related: Answer Engine Optimization Strategies: What Top Brands Do to Keep Getting Cited

Which Signal Matters Most in AI Search?

It would be convenient if one signal (links, mentions, or citations) decided whether a brand appears in AI-generated answers. The reality is more contextual. Different prompts require different kinds of evidence, and the model adjusts based on what the question implies.

​Interestingly, the signals also reinforce one another:

• A strong backlink profile helps introduce your content.
• Citations confirm who you are and where you belong.
• Mentions show that real people discuss and reference your brand in the wild.

When those signals align and repeat across trusted environments, the LLM model becomes more certain and more willing to include your brand in answers.

Tracking Discoverability Across SERPs and AI Engines

Today, you’re operating in two visibility ecosystems at once: traditional SERPs and AI-generated answers.

• On the search side, the familiar metrics still matter: rankings, rich snippets, featured results, backlink growth, and traffic trends. These signals reveal how search engines interpret your content, and they also shape the pool of credible information that retrieval-based AI models quietly draw from.
• On the AI side, you’re measuring something different: recall. Does the model mention your brand? Does it place you in the right category? Does it reference you when users ask for recommendations or best-of lists? Here, the competition is less about ranking position and more about whether you appear at all.

Generative systems also shift over time. Model updates, retrieval layers, reinforcement signals, and even changes in public discourse can affect whether a brand appears in responses. If you aren’t paying attention to how AI platforms describe you, or whether they mention you at all, visibility gaps can form quietly.

Tracking both ecosystems together gives you a fuller picture of your current discoverability and how that presence is evolving over time.

Where Keyword.com Fits In

Teams trying to measure AI visibility usually run into the same problem. The tools they use were built for a different era. Rank trackers only show how you perform in search, while social tools track conversations without showing whether they matter. Nothing connects those signals to how AI actually forms answers.

Keyword.com fills that gap.

The platform lets you see how visible your brand is across both discovery systems: search engines and generative AI. You can see when your brand shows up, how often models choose it, and the context models attach to it.

Here’s how that aligns with the three signals from earlier:

• Mentions: Keyword.com shows when AI platforms mention your brand and how they describe it. You can also spot moments when a competitor starts appearing in prompts you should own. Those shifts are often the first sign that visibility is moving away from you.
• Citations: when a model pulls information from structured sources like Wikipedia, G2, or comparison sites, Keyword.com makes those moments visible. You can see whether those references reflect your current story or whether outdated or incomplete data is influencing how you appear.
• Backlinks: Keyword.com still tracks link-based performance, but now with added context. You see which links are helping AI tools include you in their answers.

You can also learn how AI platforms are discovering your brand and how those perceptions shift over time. It also helps make the next steps clear:

• If the model recognizes your brand but doesn’t recommend it, you have a positioning problem.
• If it recommends you but relies on outdated descriptions, you have a citation problem.
• If it never mentions you at all, you have a signal strength problem.

With Keyword.com, you get a complete view of how discoverable your brand really is and where you need to strengthen your authority signals. Start tracking AI search visibility today.

​FAQs About AI Search Visibility and Brand Discoverability

A few common questions come up when teams start measuring how AI platforms reference, rank, and interpret their brand.

1. What’s the Difference Between Brand Mentions and Citations in AI Search?

Mentions indicate that real users discuss your brand across the open web, including Reddit threads, blog posts, newsletters, comparisons, and community conversations. Citations, on the other hand, are structured references from trusted databases like Wikipedia, G2, or business directories. LLMs use both signals in different ways: mentions help models understand popularity and context, while citations help them confirm identity, category, and credibility. Strong AI visibility requires both.

2. How Do I Know Whether LLMs Can Actually “See” My Brand?

The easiest way to measure visibility is to track recall: how often ChatGPT, Gemini, Perplexity, or Bing Copilot include your brand when responding to relevant prompts. If models mention you inconsistently, misclassify you, or recommend competitors instead, your signals aren’t strong enough. Keyword.com surfaces this recall data so you can see whether AI engines recognize your brand, understand what you do, and associate you with the right category.

3. Which Discoverability Metrics Matter Most for AI Search Optimization?

For AI-driven discovery, three categories of evidence matter most:

• Mentions (real-world conversations and natural language references)
• Citations (structured sources confirming who you are)
• Backlinks (trusted sites reinforcing authority and relevance)

LLMs weigh these signals together, not in isolation. Tracking how each signal evolves, and how it influences your appearance in AI responses, is now an essential part of every LLM discoverability strategy.

This shift means SEO visibility is no longer just about ranking; it’s about whether your brand appears inside AI-generated results at all.

The New Visibility Challenge (And Why Keyword.com Measures It)

Ranking for keywords doesn’t guarantee you’re cited in AI answers. You need to know:

• Which queries trigger AI-generated responses.
• Whether your brand actually appears inside those answers across different AI engines.

Keyword.com covers both sides with a two-part system:

1. AI Search Terms (Prompt Tracking)

This feature shows which keywords trigger Google AI Overviews and whether your content is referenced.

2. AI Brand Visibility Monitoring

AI Brand Visibility tracks your brand mentions across Google, ChatGPT, Gemini, Perplexity, Claude, DeepSeek, and more, so you can see how often (and how accurately) you appear in AI conversations.

Used together, these insights show not just the prompts that matter, but whether your brand is part of the answer.

What Are AI Search Terms (Prompt Tracking)?

AI search terms are the queries that trigger Google’s AI-generated summaries, known as AI Overviews.

Instead of the familiar list of blue links, Google now sometimes delivers a synthesized answer at the very top of the results page, pulling text from a small set of sources. Like this:

This shift changes the rules for visibility. A page that once dominated the #1 organic spot can be pushed out of view if it isn’t referenced inside the AI Overview. AI search term tracking lets you go beyond simply knowing where you rank organically to understanding whether you’re represented inside the AI-generated answer itself.

Why Prompt Tracking Matters

Tracking Google AI Overview’s prompts helps you:

• Explain traffic swings: when a top-ranking page suddenly loses clicks, it’s often because an AI Overview captured the query. Tracking identifies those keywords so you can tie cause and effect for stakeholders.
• Refocus optimization: if certain high-value queries always trigger AI Overviews, you can rework those pages to increase the chance of citation. Alternatively, you can shift resources toward queries where clicks still flow.
• Benchmark competitors: seeing who is cited in the Overview gives you insight into what Google is rewarding and where your content gaps lie.

​AI Overviews are clearly reshaping how searchers interact with results, and the effects show up fast in SEO reporting.

How Keyword.com Tracks AI Search Terms

Keyword.com’s AI Overview Tracker captures this new layer of search data directly inside your dashboard. When you add a keyword, the system monitors whether it triggers an AI Overview and flags it with a green robot-like icon. Clicking into that icon reveals the exact URL cited and the snippet of text Google used, so you know precisely how your content (or a competitor’s) is being represented.

Over time, the tool records patterns. You can group or tag keywords by campaign, priority, or topic and see how inclusion trends change. To ensure accuracy, each instance is backed by a third-party verified SERP snapshot from Spyglass, which shows exactly what the result looked like in a specific location, down to city or ZIP code.

For example, if you’re tracking “AI SEO tools” and Google displays an Overview, you’ll know instantly whether your site was cited. If not, you’ll also see which competitors were included. You can then either close the content gap or create new material that targets the missing perspective.

What is AI Brand Visibility Monitoring?

Brand visibility monitoring shows where and how your brand is mentioned in AI-generated answers across Google AI Overviews and major LLM engines, including which pages or passages are cited and the tone of those mentions.

Why This Matters

Users are increasingly relying on AI responses from platforms like Google and ChatGPT. If your brand isn’t present in those answers, visibility drops, even when you rank organically.

Monitoring provides two strategic benefits:

• Attribution clarity: you can explain shifts in traffic when clicks fall despite high rankings.
• Reputation control: you can spot when AI misrepresents your brand and address perception risks before they spread.

What Keyword.com Monitors for Brand Visibility

Keyword.com’s AI Visibility Tracker captures the full picture of how your brand appears in AI-generated answers:

• Cross-engine presence: track mentions across Google AI Overviews and major AI engines.
• Cited pages: see which URLs are being pulled into responses, so you know which content is driving visibility.
• Sentiment signals: detect whether AI descriptions portray your brand positively, neutrally, or negatively.
• Competitor presence: benchmark your visibility against rivals to understand relative share of voice.
• Linked vs. unlinked references: identify when engines cite you with a clickable link vs. paraphrasing without attribution.

How AI Brand Visibility Monitoring Works in Keyword.com

Keyword.com’s AI visibility tool starts by establishing a baseline. Once you turn on AI Rank Tracker and add your brand or domain, the system scans AI-generated answers across engines. From there, you can expand monitoring beyond branded queries to include category prompts, related searches, and competitor terms.

Checks can be scheduled bi-hourly, daily, or weekly, depending on how quickly answer sets shift. Each record is logged with detail: the engine used, the specific prompt, mentions of your brand, citation sources, link status, and sentiment classification. Every entry is backed by a verified SERP or answer snapshot, so you know exactly how your brand appeared to real users.

Competitor domains can be layered into the same workflow. This makes it easy to spot where competitors are cited, and if you’re absent, prioritize the gaps worth closing, and connect changes in brand mentions to downstream outcomes like sessions and conversions.

This visibility data powers practical Answer Engine Optimization (AEO). If, for example, Perplexity repeatedly cites a competitor’s article for a high-value category prompt, the tracker shows which page was chosen, how it was framed, and how often it appears. If your content is included, you’ll see the exact snippet used.

Traditional rank trackers can’t provide this context. AI brand visibility monitoring tells you not only whether you appear but also how your brand is positioned, which engines amplify you, and where competitors are getting the upper hand. That’s the data you need to shape strategy in AI-first search.

AI Search Terms vs. AI Visibility Tracking: Side-by-Side Comparison

Why Both Matter for AI Discoverability (and What Each Unlocks)

Each view covers different gaps. You might rank #2 for “AI SEO tools,” but Google fires an Overview that cites a competitor instead.

Prompt tracking shows the loss and which URL replaced yours. Brand visibility monitoring then reveals that Perplexity and Gemini already mention your comparison page, but misstate pricing. The fix becomes clear: adapt your Google page to the citation format that’s being favored, and update product details on the pages other engines rely on.

For stakeholders, prompt tracking provides a way to forecast shifts in AI-affected SERPs with precision. It proves which URLs are included or excluded and ties those outcomes directly to performance changes. Brand visibility monitoring extends that view, attributing mentions across engines, surfacing sentiment, and exposing competitor positioning that rankings alone cannot explain.

Taken together, these insights move reporting beyond isolated search metrics and give leadership a defensible, multi-engine view of brand presence.

5 Steps to Set Up AI Visibility with Keyword.com

​These steps will help you configure Keyword.com so you can monitor prompts, citations, and brand mentions in AI results.

Step 1: Turn on AI Overview Tracking and Grant Access

Enable AI Overview Tracker and the AI Search Visibility add-on with enough credits, then invite teammates who need to view or receive reports. You’re done when you can open Rank Tracker and AI Search Visibility without upgrade prompts.

Step 2: Add Keywords With Correct Targeting and Grouping

Import or create your priority keywords, set the location and device for each, and put every keyword into a clear group that matches how you’ll report (product, use case, or funnel). You’re done when each keyword shows a location, a device, and a group.

Step 3: Verify Google AI Overview Detection and Citations

Open Rank Tracker, show the SERP Features column, and confirm the AI Overview flag appears on queries that trigger it. For any flagged query, open details to see if you’re included and copy the cited URL into a simple evidence sheet with keyword, date, inclusion, and source URL. You’re done when you’ve recorded at least one example.

Step 4: Set up Cross-Engine Brand Tracking

Open AI Search Visibility, enter your brand name, key product names if distinct, and your main domains, then select engines to monitor (Google AI Overviews, ChatGPT, Perplexity, Gemini, Claude, DeepSeek, Mistral) and run the first check. Export the results to your evidence file. You’re done when the first snapshot completes, even if it shows no mentions yet.

Step 5: Schedule a Weekly Export (Optional)

Create a saved report, choose PDF or CSV, add recipients, and set a weekly send time. You’re done when the first scheduled email lands in your inbox.

After setup, follow a simple loop: improve, measure, scale. – Improve: Make pages look like the answers that win. Add a short summary at the top. Use clear steps where helpful. Tighten product facts and pricing. Keep details consistent across docs. Publish one source-of-truth page for specs and comparisons. Refresh internal links and request indexing. – Measure (weekly): Check your AI Overview inclusion, new citations across engines, and which pages were referenced. Share screenshots and cited URLs so the team sees exactly what changed. – Scale: Add more keywords to groups that are improving. Reuse winning page patterns for related queries. Keep a backlog of pages to strengthen and topics to cover, tied to visibility gains.

This Is How You Win AI Search

Winning in AI search isn’t just about rankings anymore; it’s about proving your presence inside Google AI Overviews and tracking how your brand is mentioned across ChatGPT, Perplexity, Gemini, Claude, and other generative engines. Keyword.com brings all of that into one unified AI visibility report, giving you verified citations, competitor context, and sentiment insights you can actually act on.

If you want to understand which prompts matter, where your brand appears, and how to optimize for AI discoverability, start with the tool built for it.

Try our AI Overview tracker for free and see the difference yourself.

​FAQs About AI Search Visibility and Brand Tracking

Here’s what marketers and SEO teams ask most when getting started with AI search, Google AI Overviews, and brand visibility tracking.

​1. How Does Keyword.com Track Queries That Trigger Google AI Overviews?

Keyword.com monitors your keywords in real time and flags any query that triggers a Google AI Overview. For each triggered keyword, you’ll see whether your page was cited, which URL Google pulled from, and the exact snippet used—all backed by a verified SERP snapshot. This lets you prove inclusion (or exclusion) and tie it directly to traffic changes.

2. Can Keyword.com Track My Brand Mentions Across Multiple AI Engines?

Yes, Keyword.com’s AI Rank Tracker scans Google AI Overviews, ChatGPT, Perplexity, Gemini, Claude, DeepSeek, and Mistral, then logs every mention of your brand. You’ll see where you were cited, which page was referenced, how often you appear, competitor visibility, and whether the tone was positive, neutral, or misleading.

3. How Does Prompt Tracking Help Me Optimize My Content for AI Search?

Prompt tracking reveals which queries generate AI answers and whether your content appears inside them, helping you refine pages, close citation gaps, and improve AI discoverability.

Understandably, these back-to-back launches make it hard to keep up with what’s needed for SERP success, since each AI-powered feature affects SEO in distinct ways.

The first step in making the most of AI Mode and AI Overview is to understand how they differ: what each one does, how they pull and present information, and what this means for your content strategy.

We’ll discuss all these and more in this article. Let’s dive in.

What is Google’s AI Mode?

Google’s AI Mode is a new, interactive, and conversational search feature that enables users to ask follow-up questions directly within the search interface.

It’s similar to the ChatGPT experience, but with real-time web data and the ability to pull from Google’s extensive knowledge graph.

How AI Mode Goes Beyond AI Overviews

Google AI Mode isn’t just a smarter version of Search: it’s Google’s step toward agentic search, where the engine can reason, cross-check, and keep context like a chatbot. Here’s what makes it stand out:

​1. Contextual Reasoning

AI Mode remembers the thread of your search. You can ask follow-up questions without losing context, like we did in the image below. This allows you to get more precise responses to queries requiring further exploration, comparison, or reasoning, without opening multiple search tabs.

2. Real-Time Data Awareness

AI mode uses a custom Gemini version (specifically designed for reasoning and exploration) to generate answers. It also taps into live sources such as stock market data, restaurant availability, Google’s Shopping Graph, and map data.

3. Query Fan-Out

Instead of answering one question in isolation, Google AI Mode breaks it into sub-questions or multiple related subtopics to provide a more comprehensive answer.

4. Multimodal Input

Unlike traditional search, text isn’t the only option. You can continue a search session with your voice or even an image.

Google ​AI Mode’s conversational and multimodal features are already live in 180+ countries, including the US, UK, and India. But features like Deep Search, Gemini 2.5 Pro, and agentic workflows are gated under Google AI Pro and Ultra subscriptions, hinting at where Google plans to take Search next.

What are Google’s AI Overviews?

Google’s AI Overviews are automatically generated AI summaries that appear above traditional search results for certain queries.

Think of it this way: if AI Mode is Google’s conversational side, then AI Overviews are its quick notes. Instead of chatting back and forth, Google gives you a ready-made AI summary right at the top of the results page.

How Do AI Overviews Work?

AI Overviews scan top-ranking web results, identify key themes, and generate a short, synthesized answer; designed to give you the “main takeaway” without needing to click. Like AI Mode, they use query fan-out, breaking your question into related subtopics before summarizing each in-line.

Key Traits of AI Overviews:

• Answer-first experience. Links appear, but they’re secondary; the AI summary aims to resolve your question upfront.
• No back-and-forth. You can’t ask follow-ups or refine the query; AI Overviews are static summaries, not conversational sessions.
• Runs on Gemini, but not in real time. They draw from pre-indexed data, not live sources or dynamic feeds.
• Massive rollout. Available in 200+ countries and 40+ languages, AI Overviews reach far more users than AI Mode’s limited release.

If you want to see AI Overviews more often in your searches, you can opt into the “AI Overviews and more” experiment in Google’s Search Labs.

Core Differences Between Google’s AI Mode and AI Overviews

Both AI Mode and AI Overviews enhance Google’s search experience, but in very different ways.

Google AI Mode changes how you search, turning queries into ongoing conversations that evolve with context. AI Overviews change what you see, surfacing synthesized answers directly above traditional results.

Here’s how they differ at a glance:

How AI Mode and AI Overviews Are Changing Search And What Comes Next

Google’s rollout of AI Mode and AI Overviews isn’t random; it’s a deliberate step toward reshaping how people interact with Search. According to Google, AI Mode was “born from user behavior and built with the future in mind.”

​The goal? To make people “happier with the search experience” by meeting them where their intent lives: in conversation, not keywords. But for SEO professionals, that shift also introduces a new layer of complexity: when search becomes interactive, visibility and click behavior change too.

How Google’s AI Mode Is Reshaping the Search Journey

Traditionally, SEO research meant opening multiple tabs, comparing recommendations, and manually cross-checking data. AI Mode replaces that with a dialogue. You can ask a question, refine it, and keep building context within a single session.

Instead of starting over, it allows you to ask follow-up questions that build on the previous responses.

The example below shows how AI Mode can handle follow-up questions without losing context. The second (highlighted) question, ” Which will be more affordable…” directly relates to the first question.

In another instance, it even cites current data when summarizing verified G2 reviews for the compared tools. That blend of reasoning and live data access marks a clear evolution from traditional search toward interactive exploration.

All these advanced capabilities mean:

• Users can type longer, more detailed, contextual queries (since AI Mode will fan out the query).
• Google AI Mode search sessions will be longer (since users can get all the extra info they need from follow-up questions).
• Traffic from AI Mode citations will be high-quality (since users only click when your content is exactly what they need).

Where Google AI Mode Adoption Stands Today

Many of the early predictions about AI Mode’s impact are already playing out, at least partly. Data from SEO studies and analytics platforms suggest that some behavioral shifts are measurable, while others are still emerging.

1. Longer, More Detailed Queries (Proven)

When Search Engine Land analyzed Google Ads search query reports spanning Jan 1 to June 20 of 2025, they found that impressions and clicks:

• Increased for search terms with 3-4 words.
• Held steady for search terms with 7 or more words.
• Dropped (by about 11%) for search terms with 1-2 search words.

That aligns with AI Mode’s conversational nature as users are typing fuller, more specific questions rather than short, transactional keywords.

2. Higher-Quality Traffic (Proven)

SimilarWeb’s study of over 100k AI Mode users showed that visitors arriving from AI Mode sessions spend more time on-site and view more pages per session compared to those from traditional Google results. This suggests that while overall click volume may shrink, engagement quality and conversion potential rise.

3. Longer Search Sessions (Not Yet Proven)

The same SimilarWeb study found that while Google Search has an average of 5.6 searches per session, AI Mode ranges between 2 and 3.5, even with the Mid-July usage spike (induced by feature updates).

So while AI Mode’s session length is gradually increasing, Google Search’s is still lengthier at the moment.

AI Mode’s Early Adoption Challenge

When Garrett Sussman checked Google Trends data for the term “AI Mode”, he found (as shared in the report above) that apart from search spikes during the feature update in July, “relatively few people are even searching for it”.

In addition, SimilarWeb’s study found that:

• Over 50% of users tried Google AI Mode once and didn’t return.
• Only 20% of users come back for a second session.
• Only about 9% of users used it 5+ times.

This signals that most users are either unaware of AI Mode or lack an incentive to use or continue to use it.

Where AI Mode Is Headed

AI Mode’s growth may accelerate as Google refines the experience and promotes it more aggressively. At launch, Nick Fox, Google’s Head of Knowledge, described it as “the next evolution of Search, designed to understand context and conversation, not just keywords.”

If adoption rises, AI Mode could become a primary search interface, not just an experimental feature. That would make today’s early behavioral shifts (longer queries, higher-quality clicks, more in-depth sessions) the new normal. For SEO teams, that means the goal expands. You’re no longer optimizing to rank; you’re optimizing to be cited, summarized, and clicked from within AI Mode’s conversational answers.

How AI Overviews Are Changing What People See

If a search query triggers an AI Overview, the searcher sees that AI summary first and if it satisfies their intent, they bounce away without clicking on any website.
This AI Overview effect makes it reasonable to expect organic traffic and click-through rates to continually drop as AIO’s adoption deepens.

Current Impact of AI Overviews

​While AI Mode changes how people search, AI Overviews change what they see. Instead of prompting conversation, AI Overviews summarize information directly on the results page, often satisfying intent before a user ever clicks.

1. Websites Are Losing Clicks and Traffic

Multiple expert studies have confirmed that, indeed, AI Overviews cause a significant reduction in click-through rates to websites.

This study by Amsive reveals that keywords triggering an AI Overview experienced an average CTR drop of 15.49%. Another study by Seer Interactive found there was a 70% decline (from 2.94% to 0.84%) in organic CTR when an AI Overview appears.

2. AI Overviews Are Prone to Misinformation

Since AI Overviews’ launch in May of 2024, users have pointed out numerous accuracy pitfalls, including one instance where it incorrectly claimed SEO professional Lily Ray is nine years old.

This tendency towards misinformation makes users skeptical of AI Overviews, and SEO professionals are concerned about brand misrepresentations.

As SEO expert Max Dalaney shared in this article, “Google’s AI Overviews are often so confidently wrong that I’ve lost all trust in them”.

3. Some Brands Are Seeing Impressive Traffic from AIO Citations

Even amidst users’ lack of total trust in AI Overviews, SEO agencies like Boxena Digital have been able to help some clients 28x their visibility score in AI Overviews.

With AI Overviews still accurately answering most quick facts and how-tos, if you optimize your content to gain visibility in AI Overviews, you could position your brand to win in the AI search era.

Where AI Overviews Are Headed

Like Google AI Mode, AI Overviews are likely to grow more accurate and trusted over time. As that happens, brands must accept lower CTRs as the new normal and shift focus toward mentions and citations within AI summaries.

To track and refine that visibility, use a dedicated AI monitoring tool like Keyword.com’s AI Rank Tracker, so your brand stays visible and measurable across both traditional and AI-driven search.

What This Means for SEO: Current Trends and Future Outlook

​We’re already seeing AI Overviews reshape search behavior, and, as we’ve covered in earlier analyses, generative AI engines are rewriting the rules for visibility and engagement.

The early data shows:

• Noticeable drops in organic CTR.
• Fragmented traffic patterns and disrupted referral sources.
• Shifts in SERP real estate.

But those are surface-level signals. For SEOs and marketers, the deeper implications lie in how we measure success and how we design content strategies.

1. Rethinking Attribution and Success Metrics

Before AI search, an upward traffic graph looked good in a deck and probably to leadership. It was a quantifiable and easy metric to make your case.

But with Google AI search features becoming more prominent, more SEOs and marketers are having to ditch ‘aggregate channel-level metrics’ and track ‘journey-based metrics’, or simply put, content-influenced pipeline metrics, as to what to report on.

In Daniel Cheung’s words,

“Think like a leader. If you say “SEO is down 12% QoQ and 30% YoY,” that’s the headline they leave with: SEO is failing. Not helpful. Getting granular shifts the narrative away from things you can’t influence and toward things the business cares about.”

We’re entering, or are arguably already in, an era where SEO success will hinge on user engagement, conversion source, and content influence, not just raw traffic.

This shift aligns with how AI systems now process results: Google and LLMs are evaluating content at the passage level, not the page level. So, while classic SEO reports still have value, their relevance will fade as AI-driven visibility becomes the dominant lens.

2. Evolving Content Strategy and Distribution

Topic clustering is still a valid strategy in this AI-powered dispensation. But you need to look beyond keyword-driven clustering to clustering based on information around latent intents and pain points your customers face across the buyer’s journey.

Here’s an example of how that’ll look:

This approach increases your odds of being cited in AI Overviews or retrieved through AI Mode’s passage-level fan-out, since your content will align with nuanced user intents.

Beyond structure, diversify your distribution. Share content where your audience actually consumes it, like Reddit, YouTube, LinkedIn, Substack, and niche communities. These ecosystems often inform AI models and can indirectly boost brand citations in AI-generated summaries.

Together, these shifts point to a near future where traditional rankings matter less than being referenced, reused, or summarized by AI, whether as a snippet, passage, or cited link. The only question is: who adapts fast enough to stay visible?

How Google’s AI Search Features Compare to Other LLM-Based Search Engines

​Most LLM-based search tools, like ChatGPT and Perplexity, retrieve and rank results differently from Google. SEO Manager Metehan Yesilyurt’s experiments show:

• ChatGPT ranks content using Reciprocal Rank Fusion (RRF), which blends multiple ranking models.
• Perplexity emphasizes freshness, topic coverage, and embedding similarity rather than backlinks or authority.

That means Google rankings don’t necessarily translate to visibility on other LLMs. Lexigate’s study supports this: the #1 Google result appeared in ChatGPT’s top three answers only 13% of the time.

By contrast, Google’s AI search features still rely heavily on its existing ranking and safety systems. Botify’s research found that 75% of AI Overview citations came from pages ranking in the top 12 organic results, suggesting a strong overlap with traditional SEO.

However, other studies, like Pieter Verschueren’s, reported lower overlap (just 22.8% from the top 10 results). The discrepancy likely stems from differences in research scope and industry focus.

Regardless, the pattern holds: strong traditional rankings increase your chances of being cited, even if they no longer guarantee it.

How to Prepare for Google’s AI-Powered Search Future

Google AI Mode and AI Overviews, along with upcoming experiments, are shaping the future of organic discovery. While they disrupt traditional SEO playbooks, brands that adapt early will build a visibility moat.

Start by investing in AEO (Answer Engine Optimization) and tracking your performance across both search and AI interfaces. Pair your efforts with accurate AI visibility data to measure what truly matters: where your brand is cited, summarized, and surfaced.

Keyword.com can help you track your brand appearances in Gemini AI Mode and AI Overviews. Sign up for our 14-day free trial to know where your content stands.

The brands dominating AI results have learned how to make language models see, trust, and prioritize their content. They’re no longer optimizing just for Google, but also for AI systems that decide which answers to surface.

In this guide, you’ll learn exactly how they do it: how they structure content for AI understanding, build the right credibility signals, and stay consistently cited across platforms like ChatGPT, Perplexity, and Gemini.

TL;DR: How to Get Cited in AI Search Results

AI search is changing how people discover brands, and AEO is the only way to stay visible. Instead of chasing keywords, focus on context-driven, well-structured, and multimodal content that AI can easily retrieve and cite.

To get cited in AI answers and increase your AEO visibility:

• Keep your site crawlable
• Refresh pages often, and build brand mentions both on-and off-site.
• Track how LLMs reference your brand with tools like Keyword.com, so you can steer the narrative and continue to appear in AI conversations.

What Exactly is Answer Engine Optimization (AEO)?

AEO (or GEO/LLMO, as some might call it) is the act of creating and organizing your content in a way that makes it easy for AI search systems to find, retrieve, and cite it in relevant user conversations.

Unlike SEO, where search engines rank your content based on keyword relevance, AI search systems only link to your content when it is relevant to the user’s conversation.

Is AEO the Same as SEO?

No, SEO and AEO are not the same. SEO focuses on ranking web pages in search engines like Google, while AEO focuses on getting your brand or content cited, mentioned, or summarized accurately in AI-driven platforms like ChatGPT, Perplexity, or Gemini.

In short, SEO optimizes for links and clicks; AEO optimizes for answers and visibility. Here are a few more differences between the two concepts.

Now that you know what AEO is and how it works, let’s discuss the AEO strategies that have helped — and are still helping — top brands win citations from LLMs.

9 Strategies Top Brands Use to Keep Getting Cited in AI-Powered Search Systems

Here are nine simple but effective strategies to help you increase your visibility in AI search engines like ChatGPT and Perplexity.

1. Create Content for ICP Questions, Not Just Keywords.

People search on Google but converse with AI search systems.

If you need a keyword tracking tool and decide to ask Google, you’d probably type something like “best keyword tracking tool”. However, if you switch to Claude, your request will become more detailed and nuanced, such as “I’m new to SEO. What keyword tracking tool will be easiest to use?”

Because AI tools do multi-step query research, you can add context and details you would otherwise skip using a search engine and get a richer response. This fundamental difference in AI search querying makes keyword-based content not so useful for AEO.

So, to increase your chances of being cited for context-based queries, create content that aligns with how your ICPs question AI tools. Here’s how HeyReach’s team did it.

1. Deep Audience Research

HeyReach’s success with AEO is largely due to its marketing team’s deep understanding of its ICPs and their struggles.

According to Bojana, HeyReach’s Content Lead, their messaging and content are laser-focused because “we know our ICP and we know the exact specific set of challenges they can solve with us. We talk only to them about these things through our messaging — website copy, content, newsletter, LinkedIn content”.

If your brand doesn’t have a dedicated audience research document or you’re new to the team, you should find out your ICP’s challenges by:

• Digging into sales calls transcripts to find recurring concerns.
• Reading LinkedIn posts and comments from users and prospects to get a pulse of trendy topics.
• Lurking on industry subreddits to find hidden challenges.
• Going through customer onboarding and churn reports, to know what motivates customers to sign up or close their accounts.

2. Create Contextual Content

For example, HeyReach’s audience includes outbound sales representatives who struggle with managing LinkedIn messages as they scale their outreach.

To solve this, HeyReach’s marketing team published a detailed LinkedIn client management guide.

Now, when ChatGPT was asked to recommend tools that solve this problem, HeyReach was the first tool on its list.

And, when we made a source request, ChatGPT linked straight to that guide.

If an ICP were behind this conversation, they would likely receive these recommendations and then search for HeyReach on Google or click to read the guide.

Here’s how to implement this strategy for your content:

i. Use the one-for-one technique: focus each content piece on one ICP and one product use case. For example, create an SEO acceleration guide tailored to one-person marketing teams, rather than one for all marketers.

ii. Add a descriptive title: let your title show the problem that the content piece solves and, preferably, who it solves it for. E.g., “How rank trackers can help agencies improve organic search presence”, instead of “How rank trackers work.”

iii. Use the lingo your audience understands: include specific words your audience uses, and industry mannerisms to signal authority and niche knowledge. This section of our seasonal SEO checklist blog post does just that.

Phrases like “pulling in traffic like a magnet” and painting a scenario that commonly occurs in the SEO industry signal to the reader and AI bots that we possess more than surface-level niche knowledge.

3. Explore Query Fan-Out

Query fan-out is a technique used by AI search systems to break down a query into multiple related subtopics to provide a more comprehensive answer.

This is why, when someone types “What is SEO?” into Google, AI Overview provides not only the definition, but also the benefits, main components, and key aspects.

As a result, users get a more detailed explanation. And you get more LLM citation opportunities, as those additional subtopics can be referenced in LLM results from different sources.

For example, HeyReach created multiple expert-led guides and product pages on LinkedIn outbound automation for one of its ICPs: agency owners.

This broad approach ensures that almost every AI conversation around starting or scaling LinkedIn outbound for agencies cites one HeyReach piece or the other.

For instance, when ChatGPT was asked for a particular type of LinkedIn automation tool, it mentioned HeyReach.

Then, when we followed that request up with another nuanced ask, HeyReach showed up again. This time in two different places.

This means one thing: if you want to appear more frequently in your ICPs’ AI conversations, you need to explore query fan-out and write content around the subtopics of each ICP challenge.

How to Find Related Queries for Query Fan-Out

i. Check “steps” or “show thinking” in Perplexity or Gemini, respectively: in Perplexity, after entering your search prompt, click on “steps.”

For Gemini, click on “show thinking.”

ii. Check “related topics” in Perplexity: scroll to the end of your Perplexity response.

iii. Ask ChatGPT and Claude to show Query fan-outs: after a response, ask “how did you fan out this query?” or “can you list the query fan-out for this question?”

iv. Use tools like Qforia or this search query extractor for ChatGPT.

When you enter a prompt into either, it simulates synthetic search queries the LLM used for that response and its thought process while running the search.

2. Prioritize Content Architecture

When an AI system runs a query, it checks its database first, then scours the web for context. However, it only pulls from content that’s well-structured and easy to quote. If your content isn’t scannable or built in clean blocks that are easily retrievable, you won’t even make the shortlist.

Inflow, an SEO agency, helped its client, EarthKind, secure mentions and links in AIO by restructuring their content for AI search.

Here’s how to structure your content for AI citations

Clear Formatting

LLMs can crawl and surface answers in a few seconds. Your content must be clear enough for them to understand in that short time.

Inflow made sure every content piece had clear formatting by adding:

• Clear headings and subheadings using H1 to H4 header tags, so subtopics are clear and easy to retrieve.
• Numbers or bullet points so that AI bots can scan lists quickly.
• White space to break up content blocks

Internal Linking

Internal links are like maps that guide readers and AI bots as they navigate your website. By adding internal links to your content pages, you increase their chances of being found and cited by LLMs.

When reviewing and updating your internal links for AEO, you must:

1. Prioritize content pages that tackle ICP challenges
2. Ensure there are no isolated pages (pages not linked to from any other page)

This helps create a well-connected website while ensuring the challenge-based content pieces you’ve created earlier can be easily found and cited.

Schema Markups/Structured Data

To help LLMs quickly determine the category of each content page, implement schema markups such as FAQPage, QAPage, HowToPage, Article, and Image. That way, when users ask AI tools for very specific recommendations, your structured data can quickly signal which content page would be perfect to cite from.

To add schema markup:

• Choose your schema type: go to schema.org and pick the type (e.g., Article, Product, FAQ, LocalBusiness) that fits your page.
• Generate the markup: use a tool like Google’s Structured Data Markup Helper or Merkle’s Schema Generator.
• Add it to your page: copy the generated JSON-LD code and paste it inside the section (or just before ) of your HTML.
• Validate your markup: use Google’s Rich Results Test or Schema.org validator to check for errors.
• Monitor in Google Search Console: go to “Enhancements” to confirm Google is detecting and applying your structured data.

Many types of Schema markups exist. But numerous SEO experts — and even Google — recommend the JSON-LD type because it’s simple and easy to manage.

While answering a Google office-hours hangout question, John Mueller, Search Advocate at Google, said: “We currently prefer JSON-LD markup. I think most of the new structured data that is kind of coming out is for JSON-LD first. So that’s what we prefer.”

After implementing your schema markups, regularly audit them to ensure they stay in sync with your content.

Note: While schema markups help LLMs interpret your content pages better, there is no solid data to support that they improve AI visibility. When Molly Katz studied 100 healthcare websites, she found that sites with schemas had only slightly more mentions than those without.

That said, AEO is an emerging field. You should test different strategies that can help LLMs understand and retrieve your content more effectively, as they could become vital citation factors in the future.

Content Chunking

Content chunking for AI search engines means structuring your content so that AI models (like ChatGPT, Gemini, or Perplexity) can easily identify, retrieve, and quote the right section of your page when generating answers.

To chunk your content, put the most important information upfront (the inverted pyramid technique), make your paragraphs concise, and ensure each section clearly tells the reader what, why, how, and the result.

Like this section from our guide on winning back lost SEO clients.

3. Cover Ideas Across Multiple Content Formats

AI assistants, especially newer models, crawl, retrieve, and cite multiple content formats like text, images, voice, and video. Creating content in different formats increases the chances of being cited, but also the number of times it is cited.

For example, Instantly.ai, an email outreach software, leveraged this strategy to create a comprehensive web of text, video, and image content centered on cold emailing.

So, it’s no surprise when we asked ChatGPT for resources on finding email addresses, Instantly.ai earned three citations with links to a blog post, a YouTube video, and a cold emailing course.

You can achieve the same by:

Creating multimodal content

Cover each topic with not only text-based content, but also videos, infographics, and social posts. If you already have one content format, such as blog articles, podcasts, or webinars, consider repurposing it into other formats.

Widely distributing your multimodal content

Don’t stop at brand-owned spaces like your blog, community, or newsletter. AI models learn from the entire web, not just your site. That means social media posts, Reddit threads, YouTube videos, and even podcast transcripts can all contribute to how large language models perceive your brand and expertise.

Using alt texts and Schema markups to aid AI crawling

Adding descriptive alt text helps both search engines and large language models accurately interpret your visuals. Instead of generic labels like “chart” or “screenshot,” use natural, keyword-rich descriptions that explain what the image shows and why it matters.

Then, reinforce this context with structured data such as ImageObject, VideoObject, or HowTo schema. This tells AI crawlers exactly what type of content they’re dealing with—whether it’s an instructional visual, a product demonstration, or a data visualization—and where to find supporting information (captions, transcripts, or context).

4. Invest in Commonly Cited Content Types

Not every AI query leads to a web crawl. If an AI assistant already has enough information in its internal database to answer confidently, it won’t bother fetching or citing external sources.

That means if you’re targeting topics where AI systems rarely cite anyone — like widely known definitions or basic how-tos — you’re unlikely to earn visibility. To increase your chances of being referenced, focus on queries and formats that typically trigger citations:

i. Statistics or data round-ups

ii. Original research and research-based pieces

iii. Comprehensive tool comparisons, best x pieces, and competitor alternatives pieces

iv. Coined frameworks and expert-backed industry predictions and opinions

These content types and formats are evergreen sources of AI citations — the kind that keep your brand showing up in AI results over time, much like Modash.

As Ryan Prior, Head of Marketing at Modash, explained on LinkedIn:

In our content marketing strategy, we are making big bets on original research. AI commodifies a lot of educational or ‘how-to’ content. Original research is one format that continues to be well worth investing in.

5. Make Your Site Easy to Find and Crawl

If LLMs can’t find your content or hit blocks that prevent them from crawling it, you would have zero AI visibility even if you craft the best content.

This was what Keentel Engineering was experiencing before Dexora Digital, an SEO agency, stepped in with an AI technical SEO update.

The goal of this update? Make Keentel Engineering’s content easy for AI assistants to find and crawl. Within weeks, the update paid off — their content earned citations in ChatGPT and AIO.

Here’s what you can learn from this update:

• Proper Robots.txt usage: check your Robots.txt file to confirm you aren’t blocking AI assistants you want citations from. Remove the /disallow/directive if this is the case.

• Fast website loading: optimize your Core Web Vitals and website loading speed so AI bots don’t abandon crawling when they land on your site. Do this by compressing images/other visual content and removing render-blocking CSS. Cloudflare recommends a website loading speed of not more than 2 seconds.

• HTML structure: most AI bots can’t render JavaScript (yet), so build website pages using HTML. If you already use JavaScript, consider using server-side rendering (e.g., Next.js) or dynamic rendering (e.g., Prerender.io) to convert the JavaScript into HTML, enabling AI bots to crawl.

• Mobile friendliness: mobile-friendly websites have easy navigation, fast load times, and easily accessible content, which signals to AI bots that your content is beneficial. Moreover, mobile is also where most voice searches happen. Having a mobile-friendly site puts you in the best position for voice search citations.

6. Ramp Up Off-Site Brand Mentions

Brand mentions outside your website don’t directly affect SEO rankings—but for AEO, they’re crucial. The more your brand is cited across trusted external sources, the more likely AI systems are to recognize, recall, and recommend it in responses.

When Benjamin Thornton, Head of Growth at Keyword.com, ran a simple AEO study, he found “there’s a strong correlation between the number of citations (off-site) and a higher brand mention rate in AI search outputs.”

This finding tracks with SEO agency, Passionfruit’s explanation (paraphrased). According to them, “Because AI engines care more about entity-level relevance, brand mentions are what they look at when deciding whether to cite a brand.”

This is why Keyword.com is focused on increasing the number of times our brand name is mentioned off-site to boost AI mentions. We’re doing this by:

• Showing up in industry conversations, in communities, forums, Reddit, and social media.
• Creating valuable resources, e.g, YouTube videos, podcasts, blog articles, etc., to drive shares and mentions.
• Creating thought-leadership content on Medium and so much more.

You can also ramp up your off-site brand mentions by:

• Contributing guest posts to frequently-cited industry sites.
• Running a link-building campaign targeting top industry websites.
• Hosting webinars and industry events to drive brand mentions.
• Partnering with niche influencers to write about your brand on social media.

7. Refresh Content Often

If you only hit the content refresh button when you’re facing major traffic losses, you need to rethink that strategy in the age of AI search.

According to a recent study by AirOps, over 70% of the pages cited by ChatGPT have been updated in the past 12 months, with more than one-third (35.2%) of cited pages updated in the last three months alone.

This, as Oshen Davidson, Content Engineer at AirOps, explained in the research report, means that “Brands earning citations in ChatGPT aren’t just publishing once — they’re actively maintaining and updating their pages.”

To prevent your content pieces from being overshadowed by newer competitor content, incorporate content refreshes into your SEO efforts before metrics indicate it’s necessary.

Refreshes are especially useful when your experts’ views change on brand-relevant topics. As you update, keep in mind the content structures AI answer engines prefer, so your refreshed pieces continue to earn citations.

8. Track Important AI Search Metrics

The only way to determine which of your AEO efforts is effective and which needs iteration is to track relevant AI search metrics.

After the team at Boxena Digital executed the AI content restructure we discussed earlier, they didn’t just “set it and forget it”. Instead, they conducted daily keyword tracking to monitor key metrics.

This way, they were able to “optimize and expand on every effort using feedback loops” leading to a 28x increase in visibility score for that brand.

Depending on your specific AEO goals, you can track metrics like:

• Brand mentions: the number of times your brand is mentioned in AI responses, either with a link to your content or without.
• AI linked references: the number of times AI cites your brand with a link to your content.
• AIO visibility: how often your content shows up in Google’s AIO.
• Prompts: the prompts or queries that trigger your brand mentions.
• AI share of voice: how often you are mentioned compared to your competitors.
• AI citations link destination: the aspects of your website that AI links to the most.
• Brand sentiment: how AI systems understand and perceive your brand.

It’s important to choose an LLM monitoring tool like Keyword.com that’s powerful enough to track numerous metrics across multiple AI platforms.

Keyword.com can track all the metrics above and more for Gemini, Claude, DeepSeek, ChatGPT, Mistral, Bing, etc. Sign up for a 14-day trial to test these features.

9. Monitor AI Sentiment

If you’ve done well in SEO, chances are your brand is already showing up in AI conversations — even if you’ve never optimized for it. Large language models were trained on publicly available web content, which likely includes your articles, product pages, and off-site mentions.

But here’s the catch: you don’t control how AI describes you. LLMs can misrepresent your product, mix you up with competitors, or surface outdated narratives. When that happens, it doesn’t just hurt visibility — it erodes trust. Imagine a potential customer asking ChatGPT for the best rank tracker and seeing your name used incorrectly or not at all.

That’s why monitoring AI sentiment is crucial. It helps you understand how AI systems perceive your brand — positively, negatively, or neutrally — and where those perceptions come from. Tracking this over time allows you to:

• Detect misinformation early before it spreads across platforms.
• Spot narrative gaps where your competitors are better represented.
• Shape your brand story with updated, consistent content that LLMs can learn from.

This allows any misconception or incorrect brand sentiment to be corrected, rather than going unchecked.

Win at AI Search With Keyword.com

AEO is a new but growing field, and brands like Keyword.com are accelerating the growth pace with innovative tools and multi-expert resources.

Keyword.com’s AI rank tracker serves as a compass to guide your AEO efforts by arming you with unmatched AI visibility across all major AI platforms.

This allows you to see how your AI citations shift as you implement the strategies we’ve discussed in this article.

Want to take our AI brand monitoring tool for a spin? Get started for only $24.50 per month.

Local SEO used to be simple. Google told you exactly what mattered to rank: relevance (you matched the query), proximity (you were close to the searcher), and prominence (you had strong reviews and citations).

That playbook worked for years, but discovery is changing. With Google’s AI results and generative platforms like ChatGPT, local SEO discovery in LLMs now looks very different. Large Language Models don’t just mirror Google’s traditional algorithm — they draw on broader, more nuanced signals to decide which businesses to recommend.

Now, ranking isn’t only about Google. It’s about being chosen by AI when a customer asks the question. And that depends on a new set of “hidden” signals you may not have considered: signals that can make the difference between being included in an AI answer or being invisible altogether.

1. Accessibility and Inclusive Attributes

For years, features like wheelchair access, gender-neutral bathrooms, braille menus, or pet-friendly spaces were treated as “optional extras” on your Google Business Profile. Today, they’re part of the local SEO ranking signals AI uses to decide which businesses to highlight.

You can see why. When someone asks an assistant for recommendations, they don’t just say “restaurants near me.” They ask in full detail: “Which restaurants nearby are wheelchair-accessible and kid-friendly?” LLMs break those queries down and match them against the attributes you’ve listed. If you’ve filled them in, you’re in the running. If you haven’t — even if you offer them — you’re effectively invisible.

Google itself has been clear on this. Completing every relevant attribute on your profile, from accessibility to ownership to payment options, increases your chances of being discovered. And because LLMs pull from the same data, a query like “cafés open late that take Apple Pay” or “restaurants with outdoor seating and high chairs” will filter out businesses that left those details blank.

2. Comprehensive Google Business Profile Information

AI-driven local search runs on data. And your Google Business Profile (GBP) is one of the richest data sources an LLM can tap. If the information isn’t there, the AI can’t surface it — making profile completeness non-negotiable.

Completeness goes beyond filling in the basics, though. Accurate categories, keyword-informed descriptions, service lists, opening dates, photos, and updated hours all give AI more to work with. In Google’s Search Generative Experience (SGE), for instance, local results often display star ratings, short blurbs, hours, and review snippets — stitched together directly from GBP data. If your profile is thin or outdated, you leave gaps. If it’s thorough, the AI can present your business clearly and accurately.

Details that might feel secondary to you — Q&As about parking, storefront photos, or service attributes — often matter most to searchers. They also matter to AI. Each element adds freshness, relevance, and trust. Even Google stresses that consistent NAP (name, address, phone), correct categories, and regular updates improve local visibility. The same logic now applies to AI discovery.

Think of your GBP as a structured knowledge base for your business. The more complete and current it is, the more confidently an LLM can surface your business when someone nearby asks a question that matches what you offer.

3. User Engagement Signals (Clicks, Calls, and Dwell Time)

Google doesn’t officially rank businesses based on engagement metrics, but there’s a growing body of evidence that how people interact with your listing influences visibility.

In the 2023 Whitespark Local Search Ranking Factors survey, experts still included behavioral signals (click-through rate, “clicks to call,” and direction requests) as part of the mix. They didn’t place them among the top drivers (categories, reviews, and proximity still dominate), but their consistent presence suggests they act as secondary signals.

Think of it this way: if two businesses look equally relevant and well-reviewed, the one that earns more clicks, calls, and profile engagement sends Google a clearer signal that people find it useful. Over time, that behavior becomes a tie-breaker — and in AI-driven discovery, those signals matter even more because LLMs lean on the same data to decide which business is the “safe bet” to recommend.

So what’s the move? Polish the user experience. Use high-quality photos and benefit-led descriptions to encourage clicks. Keep your hours, address, and other details accurate to avoid bounces. Post regular updates to show you’re active. And make sure your website holds up its end — fast load times and mobile-friendly design keep visitors around once they land.

4. Review Sentiment and Detail

Reviews have always mattered for local SEO. What’s changing with AI is that it’s no longer just about your star rating — the words inside those reviews now influence your brand’s visibility.

LLMs are built to read and summarize text. Instead of stopping at “4.5 stars,” they scan the words customers use, weigh the sentiment, and highlight the details people care about. That’s why Google’s AI snippets often sound like this: “Customers rave about the vegetarian options and cozy atmosphere.” The model is literally quoting the review text to justify why a business stands out.

That can work in your favor — or against you. Consistent praise for “kid-friendly menus,” “great for remote work,” or “wheelchair accessibility” becomes the selling points that AI will surface. But recurring complaints — “slow service on weekends,” “noisy rooms” — are just as likely to show up in the cons. In other words, your reviews are training data, and patterns — good or bad — get amplified.

This is where reputation management becomes strategic, not optional. Encourage satisfied customers to leave detailed, authentic feedback that naturally calls out what you’re proud of. On the flip side, respond quickly to criticism, correct misinformation, and show you’re listening.

5. Social and Third-Party Mentions

Local SEO discovery in LLMs doesn’t stop at your website or Google Business Profile. AI models scan far wider — Yelp lists, TripAdvisor rankings, Eater round-ups, Reddit threads, even neighborhood groups on Facebook or Nextdoor. These are the places where recommendations are made, and LLMs treat them as signals worth citing.

The pattern is backed by research. A 2025 analysis found AI search results disproportionately reference third-party and earned content over brand-owned sites. In practice, that means a “Best tacos in Austin” query is just as likely to surface Yelp’s Top 10 or a local food blog as it is a Google Map result. If your restaurant is in those lists, you’re in the conversation, and vice versa.

Community chatter matters too. Mentions in a Reddit thread about 24/7 vets or a Quora answer about family-friendly gyms can end up shaping AI-generated results because those models have been trained on the same data. Google’s local algorithm has always been selective about which citations count; LLMs have no such filter.

This means that your playbook has to expand. Make sure your listings on Yelp, Apple Maps, Bing Places, and TripAdvisor are complete and consistent. Look for opportunities to appear in “Top 10” or “Best of” lists in your niche, and encourage happy customers to mention you on community forums and social media.

Lastly, don’t just lurk — join the conversation. Set up alerts for your brand name on Reddit or other platforms so you can respond, clarify, or simply thank people when your business comes up.

6. Conversational Content and FAQ Optimization

Search queries now sound more like conversations: “What’s the best coworking space in Austin with hourly meeting room rentals?” or “Which family-friendly hotels in Miami have a kids’ pool and free breakfast?”

Those aren’t neat queries — they’re complex, multi-layered questions. When an AI processes them, it’s not looking for partial matches. It’s looking for businesses that tick every single box. If your content doesn’t answer those specifics clearly, you’ll be left out.

So how do you make sure the AI chooses you?

i. Add FAQs and Q&A Content

If customers are already asking, “Do I need an appointment for walk-ins at [Your Clinic]?”, give them a clear answer on your site and in your Google Business Profile Q&A. You’re making life easier for customers and giving the AI the exact snippet it needs to trust your business.

ii. Use Long-Tail Keywords and Attributes

AI favors specificity. Don’t just say you’re a restaurant — say you’re “a late-night café with a full vegan menu.” Or if you run a gym, spell out the details: “Open 24/7 with women-only classes and childcare on-site.” If these attributes are buried or implied, LLMs might never pick them up.

iii. Leverage Schema and Structured Data

Yes, it’s technical, but schema markup (LocalBusiness, FAQPage, Review, etc.) makes your content machine-readable. It tells AI exactly what you offer, your hours, your services, and your FAQs. Think of it as plating up answers for the algorithm.

iv. Format Your Content So It’s Easy to Lift

AI is more likely to “lift” well-structured content. Short paragraphs, clean headings, bullet points, and snippet-ready blocks, such as a direct answer to “What sets you apart?” — increase the odds that your content gets cited.

Basically, focus on conversational SEO (sometimes called conversational local SEO). Instead of asking, “Am I ranking for [keyword]?” start asking, “Am I being mentioned when customers ask about my services in real language?”

Tracking Your Local SEO Performance in the AI era

You’ve done the work: updated attributes, encouraged richer reviews, refreshed photos, and written content that answers real questions. But the real measure of success is simple: are you actually showing up when AI surfaces local recommendations?

That’s the gap Keyword.com closes. Instead of only tracking if you rank for “dentist near me”, it shows how often your business appears in AI-driven results — Google’s AI Overviews, Bing Chat, ChatGPT, and other LLMs. You can see, in plain terms, whether:

• Updating attributes made you show up when people asked about specific needs like extended hours or payment options.
• Reviews are being pulled into AI-generated pros and cons.
• Fresh content updates helped you appear more often than competitors who’ve gone quiet.

At the same time, Keyword.com covers the fundamentals: traditional keyword rankings, competitor tracking, and tracking performance shifts. So you’re not choosing between “classic SEO” and “AI search” — you’re getting a clear picture of both.

Another thing to remember is that AI discovery isn’t static. Answers change as models update and competitors adjust their profiles. With Keyword.com, you can spot those changes the moment they happen and connect them back to specific actions, like filling in missing schema or asking for more detailed reviews.

Want to know if AI is recommending your business? Check out Keyword.com‘s plans to see how your local SEO translates into real visibility across search engines and AI answers.

This creates both a challenge and an opportunity. In this guide, we’ve separated what’s been validated from what’s still emerging, giving you the proven Perplexity AI search ranking factors that drive visibility, plus the experimental tactics worth testing. If you want your brand featured in Perplexity answers, these insights will show you exactly where to focus your efforts.

TL;DR: What Factors Affect Your Rankings in Perplexity AI?

• Perplexity’s AI search prioritizes different ranking factors than traditional search engines.
• Validated factors include source trustworthiness, content format/semantic clarity, content relevance/helpfulness (E-E-A-T aligned), domain/topical authority, and technical SEO/crawlability.
• Source trustworthiness is key: Perplexity favors original research, expert quotes, and mentions on authoritative third-party sites and review platforms.
• Content structure matters: Well-organized, concise, factual, and conversationally toned content is more easily cited.
• Traditional SEO metrics like backlinks have less direct impact on Perplexity visibility compared to Google.
• Speculative factors include content freshness and a potential preference for PDFs (though less validated).
• Increase visibility by: securing third-party mentions, producing data-driven MOFU/BOFU content, engaging in digital PR/community forums, and monitoring your Perplexity presence.
• Tracking your Perplexity visibility is crucial to refining your strategy.

Validated Perplexity Ranking Factors

These ranking factors have shown consistent influence across multiple tests in how Perplexity cites sources:

1. Source Trustworthiness and Citations

Perplexity doesn’t index the entire web like Google. Instead, it relies on a smaller pool of trusted sources, which means authority and credibility directly impact your visibility. To increase your chances of ranking:

Publish Citable, Expert-Backed Content

Pages that include research, case studies, statistics, and expert quotes are more likely to be pulled into answers.

• Seer Interactive analyzed 10,000 Perplexity queries and found that content with relevant quotes and stats saw a 40% visibility boost.
• Keep citations diverse so your content feels natural, not overloaded.

Earn Mentions on Authoritative Sites

Perplexity favors brands mentioned on trusted third-party domains like industry outlets, review sites, and news publications.

• FirstPageSage notes that brands included in reputable “best of” lists are far more likely to be recommended.
• For local businesses, Perplexity leans on platforms like Yelp, Tripadvisor, Google reviews, and Foursquare.

Diversify Your Presence

Perplexity answers rarely rely on a single source. It often blends:

• Your own website content
• Mentions in authoritative publications
• Reviews on sites like G2 or Yelp
• Community discussions on Reddit or forums
• Social media chatter

Example: For the query “Is Keyword.com a good rank tracker?”, Perplexity pulled from G2 reviews, Keyword.com’s site, and social media posts.

Get mentioned on high authority third-party sites

Popularity fuels visibility in Perplexity search engine. Research from Kevin Indig shows a strong correlation between brand search volume and mentions in AI chatbots. The most visible brands are digital-first. They invest in SEO, reviews, social media, and paid campaigns to build recognition.

2. Content Format and Semantic Clarity

Perplexity is built on large language models (LLMs), which don’t read content like humans. Instead, they parse text into semantic units, patterns, relationships, and factual snippets they can recombine into answers. This means the way you format information directly influences how easily the model can understand, extract, and present it.

To increase your chances of getting cited in Perplexity answers:

Structure your articles for parsing

Parsing is how Perplexity AI breaks down and interprets prompts. If your content is already structured into headings, bullets, lists, and summaries, the parsing step becomes simple. The model can lift those chunks directly into an answer. But if your content is a dense wall of text, the model has to do extra work to parse it, and that increases the chance it paraphrases, shortens, or even overlooks your insights. So, use:

• Bulleted frameworks signal discrete, digestible ideas.
• Headings map semantic relationships between topics.
• Concise paragraphs reduce noise and improve extractability.

Perplexity is more likely to lift a tight, well-structured list of points than a dense block of text.

Pre-Summarize to Control the Narrative

Perplexity often defaults to your own executive summaries, TL;DRs, or key takeaways when they’re present. Instead of generating its own summary, which may dilute or reorder your insight,s it prefers to quote the tight, structured version you provide.

This gives you two major advantages:

1. Visibility: summaries provide a high-likelihood extraction point.
2. Message control: the model cites your phrasing rather than rewriting it.

3. Content Relevance and Helpfulness

Kevin Indig analysed 7,000 citations across 1,600 URLs in Chat GPT, Perplexity, and AI Overviews. He discovered that the top 10% of the pages cited in Perplexity answers had higher sentence count, word count, and Flesch Score, or readability score.

Caption – Ranking factors for different AI search engines

The same research also showed backlinks and total traffic, two traditional SEO metrics, barely affected Perplexity mentions.

To increase your chances of getting cited in Perplexity answers:

• Add a human pulse: content that aligns with Google’s E-E-A-T guidelines stands out. Include unique experience, strong opinions, customer insights, or original data in your articles. That’s the kind of content Perplexity prefers to cite.
• Target conversational queries: Perplexity’s documentation on “how to search on Perplexity” emphasizes natural language. Frame content around the way real people ask questions.
• Prioritize clarity over cleverness: don’t bury the lead. Surface useful information early so readers (and the model) can find the answer fast.

4. Traditional search rankings

A recent study by Seer Interactive highlighted that sites ranking on Google’s first page showed a strong correlation with LLM mentions.

Reddit users also strongly speculate that there’s an overlap between Perplexity answers and Google’s first page. In addition, Research from BrightEdge comparing top-ranking sites on Perplexity against Google’s AI search found a significant correlation, particularly in sectors like B2B tech.

The better you rank on Google, the better your odds of being cited in Perplexity. LLMs aren’t replacing SEO fundamentals; they’re building on them. Don’t ignore traditional SEO metrics like:

• Domain authority and backlink profile
• Page speed and technical health
• Keyword targeting and topical depth
• Content freshness and update frequency
• User engagement signals (CTR, dwell time, bounce rate)

Strong SERP visibility remains the foundation for AI search visibility.

Related: Which SEO metrics matter in the age of answer engine optimization?

5. Technical SEO and Crawlability

Visibility in Perplexity depends on more than brand authority or citations. At a basic level, the model needs to crawl and parse your site before it can ever recommend you. If your content is blocked or inaccessible, you’re invisible no matter how strong the insights are.

For example, Kevin Indig, in the research cited above, discovered that Perplexity didn’t cite everydayhealth.com in its answers because the site blocked the LLM in its robots.txt.

Think of technical SEO as table stakes for AI visibility. Even the best research or content won’t surface in Perplexity if the model can’t crawl, load, or interpret your site effectively.

To maximize your brand’s chances of being cited, your site should be:

• Crawlable by PerplexityBot: check your robots.txt and server settings to ensure you’re not unintentionally blocking AI crawlers.
• Fast-loading: models (and users) favor pages with strong Core Web Vitals, reducing friction in data extraction.
• Mobile-friendly: since most queries originate from mobile, a responsive design helps ensure content isn’t truncated or misparsed.
• Structured with clean architecture: intuitive navigation and logical URL hierarchies make it easier for the model to map topics and entities.
• Enriched with schema markup: FAQ, HowTo, and other structured data types give Perplexity explicit signals for question-answer matching.

Speculative Perplexity Ranking Factors (Less Validated)

These factors aren’t officially confirmed, but early patterns suggest they may influence how Perplexity ranks and cites sources. Based on field tests, user behaviour, and content performance trends, they’re worth watching even if the data isn’t conclusive yet.

1. Content Freshness

Perplexity’s Sonar-Reasoning-Pro model favours recently updated content. Even minor edits can reset the freshness signal, boosting citation frequency and visibility.

Kurt Fischman tested two tech news stories on Perplexity from different sites. Both pages covered a similar topic. The first article stated “updated two hours ago” at the top of the page, while the other showed the previous month’s dateline.

Kurt Fischman’s result on the impact of content freshness on Perplexity visibility

Kurt Fischman discovered that Perplexity cited the first article with the latest update 38% more often than the other article in its answers.

Content freshness might influence Perplexity rankings, especially for time-sensitive queries. But it remains speculative since there’s no consistent evidence or official guidance. Unlike Google, which has made freshness part of its ranking systems (like the QDF — Query Deserves Freshness model).

If anything, content freshness seems to act as a supporting signal rather than a core driver, making content clarity and depth more reliable priorities.

2. Preference for PDFs

In the same research report (stated above), Fischman analysed Perplexity citations of the PDF version and the HTML rendition of a similar report from the same site. He discovered that Perplexity cited the PDF version 22% more often than the HTML version.

However, Perplexity’s preference for content depth over volume makes this factor speculative. A PDF may lack the specific and concise answer required for a user’s query, which could limit its overall relevance despite a potential format preference.

3. Backlinks

SEO experts regarded backlinks as one of Google’s primary ranking factors for years. But research suggests that things might be different with AI search.

A recent analysis of 35,000 URL citations on Perplexity answers showed that 85% of the URLs had less than 50 backlinks, and only 1.17% had 500-1000 backlinks.

This chart shows a backlinks analysis of links cited in AI search engine answers:

Kevin Indig’s Perplexity citation analysis also revealed that backlinks have minimal direct effect on Perplexity citations.

4. Multi-Format Content Preference

Perplexity often cites multiple content formats, including textual content, videos (especially YouTube), and academic or specialized sources.

Research by SE Ranking shows YouTube is the top-linked website in Perplexity answers, with 11.11%. This stat shows Perplexity likes multi-format content that enriches user answers.

However, that can’t be regarded as a sole ranking factor because multi-format content doesn’t mean it satisfies search intent.

How Does Perplexity’s Source Attribution Differs from Other LLMs and Search Engines?

Not all AI search engines play by the same rules when crediting content. Perplexity stands out for how it attributes sources, often in real-time and with visible links.

Unlike ChatGPT or Gemini, which typically summarize sources without clear citations, Perplexity shows you exactly where its answers come from.

Here’s a comparison table showing how Gemini, Perplexity, Copilot (Bing), and ChatGPT handle source attribution:

How to Increase Your Chances of Getting Mentioned in Perplexity

Now that you know how Perplexity ranks content, here are some best practices to improve your brand visibility on Perplexity:

1. Create More MOFU and BOFU Content

Perplexity favors content that answers brand-specific, high-intent questions the kinds of queries where users want clarity, not general inspiration. That’s why middle- and bottom-of-the-funnel content surfaces so often in its answers.

• MOFU content (like “[Your Product] vs [Competitor]” comparisons or “Top Alternatives to [Competitor]” lists) maps neatly to evaluative prompts. Users ask Perplexity to compare options, and it relies on structured, side-by-side content to respond.
• BOFU content (like detailed how-to guides, transparent pricing pages, case studies, or ROI breakdowns) matches decision-stage queries. When someone asks, “How much does [Your Product] cost?” or “Is it good for agencies?” Perplexity can confidently cite your branded material as the definitive answer.

Perplexity also favors listicles and comparison posts. A new study from Profound looked at 177 million sources and found that this type of content makes up over 32% of all AI citations, way more than blogs or store pages.

So, if you want your brand signals to be stronger on Perplexity, focus on mentioning your brand in helpful, specific contexts on your site and creating clear, useful comparisons and list-style content that actually helps people decide.

2. Engage in Digital PR and Community Forums

Perplexity doesn’t just pull from your website. It scans a broad mix of third-party platforms to validate and contextualize your brand. The more consistently your name shows up across credible sources, the more confident the model becomes in citing you.

One of the most effective ways to build that presence is by showing up where your audience and industry peers already are:

• Expert interviews and podcasts: when you participate as a guest, your insights often get transcribed, quoted, and published on reputable sites. These mentions feed directly into the pool of third-party citations Perplexity draws from.
• Social media platforms: consistent, value-driven activity on LinkedIn, X, or niche platforms gives your brand a digital footprint that LLMs can parse for credibility signals.
• Community forums: spaces like Reddit, Quora, or specialized Slack/Discord groups are goldmines for conversational, authentic mentions. A helpful comment or detailed answer in these environments often gets surfaced when users phrase prompts in a similar style.

3. Monitor Your Visibility on Perplexity

Creating content and building authority is only half the battle. You also need to track whether your brand is actually showing up in Perplexity answers. Because the model’s data sources and ranking factors evolve, ongoing monitoring helps you spot gaps early and adapt.

To track your brand visibility in Perplexity AI:

• Use AI visibility trackers (like Keyword.com’s AI Visibility tool) to see when and where your brand is mentioned across Perplexity queries.
• Benchmark against competitors by tracking side-by-side visibility for core keywords or category prompts.
• Audit citations regularly to identify which third-party sites are driving mentions, review platforms, industry publications, or forums.

Want to get started? Sign up for our Perplexity rank tracker tool and see how your brand measures against the competition in AI search.

As an SEO agency, you’ve likely heard this question from your clients lately.

It’s a fair question, and one that’s hard to answer with traditional SEO playbooks. AI search is changing how people discover information. There’s no fixed position to rank for, no clear attribution path, and often no link back to your site. But that doesn’t mean it’s a black box.

This guide will walk you through the key metrics that matter for AI and LLM visibility, how to track your clients’ visibility in LLM results using AI brand monitoring tools like Keyword.com, and what you can actually do to boost brand discoverability in AI search engines.

What is AI Search and LLM Visibility?

AI search refers to using large language models (LLMs) like ChatGPT, Gemini, and Claude to deliver answers and recommendations instead of a list of blue links. These models generate responses based on a blend of web content, knowledge graphs, and proprietary training data.

LLM visibility is your brand’s ability to show up in those responses.

Unlike traditional SEO, where you optimize for a keyword and aim for a specific SERP position, LLM visibility is about being referenced, cited, or recommended in AI-generated answers. That could mean:

• Getting mentioned in a ChatGPT response
• Showing up in a source list on Perplexity
• Being linked in AI overviews on Google

It’s a new layer of organic discovery, one that doesn’t replace traditional search, but definitely reshapes how users find and trust information.

What is the Difference Between SEO Performance Monitoring and LLM Tracking?

The difference between SEO performance monitoring and LLM tracking is what you’re measuring and where.

SEO performance monitoring tracks how your website ranks in traditional search engines. It focuses on keyword positions, traffic, impressions, and clicks tied to specific pages.

LLM tracking measures how your brand shows up in AI-generated answers, not rankings. It tells you whether tools like ChatGPT or Perplexity mention your brand, cite your content, or recommend your products when users ask questions.

With AI search rankings:

• Mentions may replace links
• Results are personalized and vary by user
• Context matters more than keyword position

So, instead of tracking rankings, you’re monitoring how often your client’s brand appears in AI answers, whether it’s cited, and how it’s framed across these new platforms.

Here’s a quick breakdown of how traditional SEO tracking compares to LLM visibility tracking:

But here’s the bridge: LLMs still rely heavily on high-ranking, authoritative content to generate their answers. So, doing well in traditional search improves your chances of being surfaced in AI results.

In essence, rather than treating SEO and LLM visibility as separate goals, think of them as reinforcing each other. Strong SEO gives your content a better chance of being referenced by AI, and when it is, that mention can reinforce brand authority and drive indirect impact.

What are the Key Metrics for Monitoring AI Search Performance?

Since AI-generated results don’t rely on ranked lists or clicks in the same way as traditional search results, you’ll need to monitor AI-specific performance metrics to have a true picture of your visibility in LLM results.

Here is a quick rundown of the metrics to track

1. Brand Mentions and Citations in AI Outputs

One of the most important things to monitor is how often your client’s brand appears in AI-generated search results.

Mentions indicate that your client’s brand is considered relevant to a topic, even if there’s no direct link. Think of it as the LLM equivalent of impressions or share of voice. It tells you how visible your client’s brand is in AI-powered conversations.

Citations, on the other hand, are direct references or links to your website. They’re the AI-era version of backlinks, signaling authority and source credibility.

You want to track both across different LLMs like ChatGPT, Perplexity, Google’s AI Overview, and others. But here’s the challenge: you can’t exactly predict how users phrase their queries in these tools. That’s where traditional keyword research still plays a role. Use it to uncover relevant keywords and variations, then run those queries through the LLMs to see if your client’s brand shows up.

From there, you can benchmark your client’s brand’s presence against competitors to gauge your performance in AI search.

2. Referral Traffic from AI Search and LLMs

Tracking traffic from LLMs helps you understand whether citations in AI-generated answers are driving user visits.

While traffic from Google AI Overviews often blends into standard search traffic and is hard to isolate, you can measure traffic from LLM-powered tools like Perplexity and even ChatGPT because they often pass identifiable referrer URLs when users click through to your site.

Some SEOs argue that this traffic is negligible, but Ahrefs’ experiments suggest otherwise. Their tests revealed that many LLMs suppress referral data, meaning the real volume of AI-driven traffic might be underreported.

To start tracking LLM traffic in Google Analytics 4 (GA4), Dan Taylor suggests this method in his post for Search Engine Land:

1. Open GA4 → Go to the Explore section.
2. Start a new report → Choose “Blank” to create from scratch.
3. Set Dimensions → Add Session source/medium.
4. Add Metrics → Include Views, Engaged sessions, and Key events to see user behavior.
5. Create a Segment:
   1. Add a new session segment.
   2. Name it something like “LLM Traffic.”
   3. Use a regex filter like this to match known LLM tools:

​

6. Apply the segment to your report.
7. Switch to a line graph to visualize traffic trends over time (Optional).

This gives you a baseline view of how much traffic your client’s website is getting from AI tools and how those users are engaging with your content.

Beyond just the numbers, pay close attention to the source of this traffic. Knowing which tools drive visits the most can help you prioritize your LLM optimization efforts.

3. Conversions from LLMs

You might not be getting a flood of customers from LLMs yet. But it’s still worth tracking leads that come through and seeing how that number grows over time.

It’s surprisingly easy to do. Just add “AI tool (e.g., ChatGPT, Perplexity)” as an option to your “How did you hear about us?” form. It costs nothing and gives you a clearer picture of AI-driven conversions.

You can also get your client’s customer support team to ask new leads or customers casually. People are often excited to mention they found you through an AI, especially if they’re happy with your service.

4. Consistency of Brand Mentions and Citations

Getting mentioned by LLM is great, but getting mentioned consistently is even better. LLMs like ChatGPT and Perplexity are designed to surface the most relevant, trustworthy sources. If your brand keeps showing up across different queries, it signals authority, reliability, and topical depth.

Consistent brand mentions mean:

• You’re seen as a go-to source, not a one-time reference
• You’re more likely to appear across multiple stages of the buyer journey (from awareness to decision-making)
• You increase your share of voice in AI search, edging out competitors

Use an LLM citation tracker like Keyword.com to monitor how frequently your client’s brand shows up for relevant AI search queries. For example, as an SEO agency, monitor whether your brand is consistently mentioned in AI-generated answers to queries like, “What’s the best SEO agency for ecommerce?” and “Who are the top-rated SEO consultants for SaaS companies?”

Also, pay attention to how frequently your client’s brand is cited as a source. High-frequency mentions and citations signal strong topical relevance and authority.

5. Accuracy of AI References

Accuracy comes down to these key questions:

i. Do AI answers actually reflect what your client’s brand does?

ii. Are the citations current?

iii. Or is the model pulling outdated information?

LLMs often default to old data or guess when they can’t find fresh, clear details. That’s why it’s important to double-check things like your pricing, features, location, and referenced pages.

Most AI tools let you flag incorrect answers, usually with a thumbs down, and ask for feedback. Use that to report the error and supply the correct info. This feedback loop helps train the model to improve over time. Just know it may take several corrections before you see results.

6. Tone and Sentiment in AI Narratives

It’s not enough to know your brand is mentioned in AI responses. You need to know how it’s being described. A positive recommendation builds trust. A neutral or negative mention can quietly erode it. That’s why sentiment tracking matters: it helps you catch misalignment between your brand’s messaging and how LLMs present it.

Keyword.com’s AI Visibility monitoring tool includes a sentiment tracker for measuring how AI feels about your brand. That way, you can make sure it’s talking about your brand in the right way.

To reduce the risk of misinformation or “hallucinations,” ensure you publish clear, authoritative, and up-to-date content about your product. The more reliable information LLMs can find, the more likely they are to represent your client’s brand accurately.

7. Retrieved Pages that are Known to the LLMs

Not every page on your client’s site is known or “seen” by LLMs, just like not every page gets indexed by search engines. You should know which pages LLMs recognize to help you decide where to invest more time and maybe build backlinks to get more pages noticed by AI systems.’

Use a tool like Keyword.com’s AI Visibility Tracker to see which pages are being retrieved and cited in AI responses. This gives you a clear view of what content is discoverable by LLMs.

From there, you can:

• Identify high-priority pages that need better visibility
• Strengthen underperforming content with clearer copy or updated data
• Build backlinks to key pages to boost authority and retrieval likelihood
• Fill gaps with new content LLMs are more likely to reference

Knowing what LLMs can “see” helps you focus your efforts where they’re most likely to pay off.

How to Track AI Search Visibility

You need an LLM monitoring tool like Keyword.com to track your brand visibility in AI search. Once you set up our AI rank tracker, you’ll be able to monitor citations, track sentiments, see the exact URLs featured in LLM results, and get a 360-view of your brand in AI platforms like ChatGPT, Perplexity AI, and Google AI Overviews.

Here’s how to go about it:

Step 1: Add Your Website

Sign up for the AI Visibility Tracker.

Step 2: Enter the Terms You Want to Track

Inside Keyword.com, go to the “Search Terms” tab and add the AI search queries you want to track. Then choose which AI engines you want to monitor: ChatGPT, Perplexity Sonar, Gemini, and others.

You can also organize these prompts under topic groups for better reporting. If you’re unsure which terms to start with, there’s a “Find Terms” feature that recommends relevant ones based on your site and goals.

Step 3: Understand What the Metrics Mean

Once your prompts are added, Keyword.com will show high-level metrics such as:

• Visibility Score: How visible your site is in AI responses overall
• Last Position Observed: Your most recent ranking
• Sentiment Score: How positively your client’s brand is portrayed
• Average Position Over Time: Historical ranking trends
• Brand Mentions: How often your client’s brand name appears
• Detection Rate: How frequently AI systems select your content
• Citations: Actual references to your content
• Top 3 Visibility percentage: How often you show up in the top 3 AI answer spots

Step 4: Dig Into the Results

Keyword.com provides additional metrics to help you better understand your tracked website’s performance for the search terms. Click “View result” and you’ll see:

• Ranking history over time
• Citation analysis (who’s getting cited and where)
• Reference analysis to understand the content types AI pulls from
• Mention and brand comparison across competitors
• Spyglass view to see exactly how AI engines like Perplexity present results for your chosen term

Step 5: Use the AI Visibility Overview Tab for High-Level Insights

Click the “Overview” tab in the left menu. You’ll see graphs showing:

• Brand performance over time to get a full view of how your client’s brand is performing compared to competitors
• Topic performance to see which topics (if grouped) are doing best
• AI engine-specific metrics to see which platforms you’re doing well on

At the top, you can filter the graphs by AI engine, aggregation, time range, or topic.

Step 6: Analyze Competitors

Go to the “Competitors” tab to find a competitor analysis table showing:

• Who else gets cited for your tracked terms
• Their visibility scores, sentiment ratings, and citation counts

This helps you understand what your competitors are doing right and where you can beat them.

How to Increase Your Brand Visibility on LLM Platforms

Now that you’ve seen how your client’s brand performs across LLM platforms, the question is: what can you do to improve it? Here are some ideas you can start implementing:

1. Optimize for Brand Signals (External PRs, Domain Reputations)

LLMs rely heavily on trusted sources when generating answers. Strengthening your brand’s authority across the web increases the chances of being referenced.

• Secure mentions in reputable publications and third-party sites
• Contribute expert commentary or guest posts in your niche
• Maintain a consistent brand presence across high-authority domains (news sites, industry blogs, directories)

These signals help LLMs associate your brand with credibility, making it more likely to appear in relevant AI responses.

2. Prioritize Rankings in Traditional Search Engines

A Grow&Convert study found a 77 percent correlation between pages showing up in ChatGPT and Perplexity responses and those ranking highly on Google. DemandSphere, an analytics platform, also found that 75 percent of links in Google’s AI Overviews come from the top 12 organic results. These show that the higher you rank in traditional search results, the more likely your website or content gets cited in LLM responses.

Traditional SEO is still your foundation for AI visibility. Focus on:

• Ranking for high-intent, informational keywords relevant to your niche
• Keeping top-performing pages updated and well-structured
• Using schema markup to help search engines (and LLMs) understand your content

3. Embed Schema Markups in Your Content

Pages with structured data tend to be better indexed, making them more likely to be included in LLM training data or referenced during retrieval. By embedding schema markup, you give AI systems clearer signals about what your page is about, who authored it, and how trustworthy it is.

Focus on adding:

• Organization and Person schema for brand and author credibility
• Product, FAQ, and How-To schema for content that answers common user queries
• Review schema to highlight social proof

The more context you provide through structured data, the easier it is for LLMs to interpret and reference your content accurately in responses.

Teri Sun, Chief Strategy Officer at White Rhino, sums it up well:

“It’s not a question of if AI search will be based on Schema data, but rather, will Schema data impact how you show up in AI search? For me, the answer is a resounding yes. Because, even if the AI models don’t look at the Schema data directly, you’ve still done the work to understand your own content’s underlying structure. The data relationships that Schema forces us to think about empower us to make websites more meaningful to users – and that’s exactly what search algorithms want, too.”

4. Prioritize Semantic Clarity

To improve how LLMs interpret and retrieve your content, semantic clarity should be a priority. This means writing in a way that’s clear, direct, and unambiguous, so both machines and people understand it easily.

For example, the sentence “Our platform makes business easier” is vague. Easier how? For whom? What kind of business?

A clearer, more precise version would be: “Our platform automates invoice processing for small retail businesses.”

This subject-verb-object structure makes your content more machine-readable and easier to surface in LLM responses or AI search results.

5. Create Compact Topically Focused Content Units

Today, it’s better to think of content as prompts rather than just keywords. The goal is to answer real questions clearly to increase your client’s chances of showing up in LLMs.

Here’s why: when someone clicks a source in Google AIO, they’re often taken directly to the exact part of your client’s page with the answer, which is then highlighted. These small, useful pieces of content are called “fraggles.” Also, LLMs such as Perplexity use vector-based retrieval, focusing on semantic meaning rather than just keyword matching.

When you break your client’s content into tight, focused chunks, it becomes easier for these models to:

• Understand the topic of each piece
• Find the exact chunk that answers a specific query
• Display that chunk clearly in their responses (like in AI Overviews)

This shift matters for your content strategy. Instead of building broad keyword clusters, create targeted content for closely related questions that naturally arise around your client’s main topic. And write in a conversational tone that matches how people actually ask questions in LLMs.

Stay Visible in AI-Search Results With Keyword.com

The rise of AI-powered search means your traditional SEO playbook needs an upgrade. It’s no longer enough to focus solely on rankings and keywords.

You have to think about how AI models perceive your client’s brand in terms of mentions, citations, accuracy, and pages retrieved. Precision in your messaging, strong brand signals across trusted sources, and breaking down content into clear, focused pieces will help your client’s brand stand out in AI search results.

Most importantly, you need reliable data to guide these efforts. With Keyword.com’s AI monitoring tool, you can see how your client’s content and brand perform in ChatGPT, Perplexity, Google’s AI Overview, and beyond. That insight lets you make smarter decisions, optimize faster, and future-proof your SEO strategy as AI search evolves.

Learn more about our AI Visibility Checker and how it can future-proof your online presence.

People now ask AI assistants detailed questions: “Which accounting firms in Boston offer flexible startup packages and weekend consultations?” And AI models don’t just pull a list of “Boston accounting firms.” Instead, they interpret the intent (startup expertise, flexible hours), then deliver a curated answer. Often, users never see a traditional SERP.

Up to 60% of searches now end without a click because AI-generated answers provide everything a user needs. For you, this means keyword-focused rankings alone won’t deliver visibility. Your signals must be structured, specific, and machine-readable to surface in these AI answers.

AI is the New Discovery Layer for Local Search Intent

AI tools like ChatGPT, Gemini, and Perplexity now act as advisors, recommending only the businesses that best match user intent. If your online footprint doesn’t clearly communicate expertise (via service descriptions, reviews, and structured data) your brand will be excluded from this shortlist.

Queries have also become layered and conversational. A search like “IT support Dallas” has evolved into “Which IT companies in Dallas offer HIPAA-compliant support and 24/7 on-site service?” These queries combine multiple attributes—industry requirements, availability, and location.

Voice search introduced natural language queries, but AI now goes further. It merges real-time context, historical data, and review sentiment to recommend businesses that feel hand-picked for the user.

According to The Wall Street Journal, tools like ChatGPT and Perplexity now account for 5.6% of U.S. desktop search traffic, double their share from last year. Clearly, your presence in AI-generated overviews now matters as much as showing up in map packs or organic results.

To stay visible, your content must address real scenarios clearly:

• Build structured FAQs and content designed for conversational queries.
• Use customer reviews and testimonials that highlight differentiators, like “fast same-day repairs” or “great for startups.”
• Ensure service descriptions provide real detail, not generic phrases.
• Write service descriptions with specific details—cover who it’s for, exact scope, qualifiers (e.g., 24/7 or HIPAA-compliant), pricing ranges, location availability, etc.

Let’s use our IT/SaaS support in Dallas example from before to put things into perspective:

• Generic: “Managed IT support in Dallas.”
• AI-ready: “24/7 HIPAA-compliant managed IT support for multi-location clinics in Dallas–Fort Worth. On-site response under 2 hours, encrypted remote access, BAA provided. EHR integrations (Epic, Athenahealth), MFA rollout, and quarterly compliance audits. Tiered SLAs from $2,500/month.”

Simply put: AI is deciding which businesses deserve to be recommended, based not on keywords but on the depth, authority, and trust signals it can verify.

The Ripple Effect on Local SEO Strategies

AI isn’t just changing local search, it’s rewriting the rules for how visibility is won and lost.

Map Pack Visibility is No Longer Guaranteed

Previously, ranking in the top 3 of the map pack was enough to drive calls and visits. Now, AI tools like Google’s AI Overviews synthesize data from GBP profiles and display it directly in a conversational answer. If your GBP isn’t detailed—photos, Q&A, operating hours, services—you risk being replaced by competitors with richer structured data.

For example, a SaaS provider offering local onboarding could lose visibility to a competitor whose GBP highlights timelines, case studies, and dedicated support hours.

Content Must Answer, Not Just Attract

Thin local landing pages don’t stand a chance. AI prefers content that directly answers user questions and demonstrates service expertise. For multi-location businesses, this means creating pages with:

• Location-specific FAQs (e.g., “Do you offer after-hours appointments at your Austin office?”).
• Detailed service descriptions tied to customer needs, not just keywords.
• Schema markup to help AI interpret your services.

Reputation as Data Signals

AI evaluates reviews for both sentiment and context. This means that a high rating alone isn’t enough; what customers say in their reviews matters. Encourage reviews that mention specific features or services in reviews (e.g., “HIPAA-compliant,” “flexible memberships”) to give AI clear signals and identify what sets your business apart.

Multi-Location Businesses Face a Visibility Challenge

Conversational AI is harder on multi-location brands than on single-location competitors.

You see, franchisees often stumble on the basics. Their GBP profiles vary across branches, creating data gaps that confuse AI models. Their location pages use boilerplate copy that doesn’t address unique local queries. Even reviews are scattered or lack context around specific locations and services.

AI tools prioritize depth, accuracy, and context over scale. So, a smaller competitor with localized content and detailed GBP profiles can outrank a national brand in AI answers. A dental chain might dominate map packs because of brand authority, but when someone asks, “Which dentist in Houston offers same-day crown repairs with flexible payment plans?” AI will favor clinics with richer location-specific details.

To compete, treat each branch as a standalone local authority:

• Build unique location pages with staff intros, services, and customer reviews tied to that branch.
• Use structured data to highlight offers, pricing, and amenities.
• Actively manage GBP Q&A for each location.

Shift Focus From Keyword Tracking to Intent Tracking

Intent tracking focuses on monitoring how your business appears across AI-generated responses for conversational, problem-solving queries. It measures your presence when users ask multi-layered questions like:

• “What’s the best coworking space in Austin with hourly meeting room rentals?”
• “Which marketing agencies near Chicago specialize in B2B SaaS campaigns?”

These queries rarely match exact keywords. Instead, they’re built around intent signals—the deeper context of what users are trying to solve.

For SEO professionals, this means visibility reports need to evolve. Tracking only keyword rankings can give a false sense of success while AI-driven answers quietly direct customers elsewhere.

The question isn’t “Am I ranking #1?” but “Am I being mentioned in AI-curated answers?”

How to Optimize for Local AI Search in 2026

Use this step-by-step framework to keep showing up inside conversational answers:

Step 1: Identify and Group Conversational Intent (Not Just Keywords)

Start by finding the real questions people ask about your business, services, or locations.

1. Pull your current keyword list: use GBP queries, on-site search logs, support tickets, chat transcripts, and sales objections.
2. Turn them into question-style prompts: (who, what, which, where, how, can, does, is, best for, etc.) For example, “Which IT companies in Dallas offer 24/7 on-site support?”
3. Group them by problem type, service attribute, or location qualifier: e.g., “HIPAA-compliant + 24/7 support + Dallas”.
4. Map each cluster: either to a dedicated page, FAQ section, or GBP Q&A entry.

Consider feeding your priority prompts into Keyword.com’s AI rank tracker. It’ll help you see how you rank (or don’t) in AI responses.

Step 2: Analyze AI Responses in Your Category

Run your top conversational prompts in Google AI Overviews, Perplexity, Gemini, and Bing Copilot to see which competitors are being mentioned and which data points (reviews, GBP, pricing pages, FAQs, third-party directories) they draw from.

Next, look for repeated language (e.g., “HIPAA-compliant,” “same-day,” “financing available”) and identify what your content is missing—missing service details, unclear pricing, absent FAQs, weak review language.

Expert tip: track these prompts and mentions with Keyword.com to see trends and shifts.

Step 3: Rework Location Pages Around Problems, Qualifiers, and Proof

Make each location page a direct answer hub. That means, for each location page:

• Lead with the primary problem you solve. For instance, “Emergency roof repair in Dallas with same-day storm damage response” says more than “Roofing services.”
• Add location-specific FAQs pulled from Step 1 (not generic boilerplate) tied to local concerns.
• List unique attributes: pricing ranges, turnaround times, certifications, financing, amenities, languages, accessibility.
• Embed structured data (LocalBusiness + Service + FAQPage schema) to make these pages machine-readable.
• Add review snippets that use the exact language (qualifiers) your audience searches for (e.g., “same-day crown” or “great for startups”).

Step 4: Turn Your GBP Into an AI-Ready Data Source

Think of your Google Business Profile as a structured, public-facing knowledge base.

Fill every service and product field with descriptive, solution-oriented language. Add and answer common conversational questions. Also, keep your categories, hours, pricing, and photos current and location-accurate and regularly post updates or fresh content to signal activity.

Another helpful tip is standardizing UTM parameters. Use them to track how GBP data contributes to conversions and AI visibility.

Step 5: Shape Reviews to Reflect Search Intent

Reviews are training data for AI. The more your reviews echo real user queries, the more likely AI is to trust and surface your business.

Ask for reviews with specific prompts (e.g., “If we helped you with an emergency same-day roof repair, would you mention that in your review?”). Additionally, mine testimonials for repeatable phrasing that matches user prompts (“flexible financing,” “outdoor seating,” “private room”), then highlight them on your website using Review schema.

Step 6: Publish Content Blocks AI Can Lift

AI favors concise, neatly formatted content that it can extract easily. Create FAQ hubs, service comparison tables, and scenario-based pages like “Same-day crowns in Houston” or “HIPAA-compliant IT support in Dallas.”

For industries with technical jargon, a glossary page with entity-rich definitions can help AI connect terms to your services. Use schema like HowTo or FAQPage where applicable.

Step 7: Expand Schema and Fix Technical Foundations

This step is all about making your local SEO data fully machine-readable and technically accessible.

Use LocalBusiness or Organization schema for every location and Service schema for each offering. Add FAQPage, Review, Breadcrumb, Product (if applicable), or Speakable schema for voice queries. Validate your implementation with Schema.org tools and monitor coverage in Google Search Console.

Simultaneously, ensure your site is fast and crawlable. Fix performance issues like slow load times and duplicate content. Make sure XML sitemaps are current and hreflang tags are correct for multi-language sites. Remove any unnecessary parameterized URLs, too.

Step 8: Track AI Visibility and Content Gaps

Stop relying on traditional rank reports and focus on AI visibility. Tools like Keyword.com’s AI Overview Tracker show when your keywords trigger Google AI Overviews and whether your pages are cited. They also track brand mentions and prompt-level visibility across platforms like ChatGPT, Perplexity, and Gemini.

Use these metrics—such as visibility score, mention frequency, and competitor comparisons—to see where your brand appears in AI answers versus map packs. Look for missing attributes (e.g., “same-day service,” “gluten-free menu”) and identify prompts where competitors show up but you don’t, then fill those content gaps.

Step 9: Strengthen E-E-A-T and Iterate

Add author bios with credentials, referencing certifications, and consistent name, address, and phone number (NAP) data across all listings to make your brand more visible. Mark up case studies, testimonials, and success stories with schema so AI can easily recognize them as proof of your expertise.

AI results shift frequently, so it’s worth monitoring how your brand appears in conversational answers.

Use Keyword.com to track movement, spot new competitors, or find queries where your content isn’t appearing. When you see patterns, such as certain phrasing, structured data, or FAQs driving better visibility, turn those into repeatable strategies for similar scenarios, like emergency queries or “best for X” recommendations.

How Keyword.com Bridges the AI and Local Search Gap

Keyword.com’s AI rank tracker shows where your business appears in AI-curated answers the visibility that drives real decisions. It goes beyond traditional rank tracking to reveal how AI interprets your brand, which attributes it highlights, and where competitors gain visibility.

This data gives you a clear testing ground. You can refine GBP details, improve service descriptions, or enhance FAQs, then see how those updates impact AI responses and local rankings.

Sign up with Keyword.com and get the visibility and metrics to win in today’s AI-first search.