Guide to LLM Tracking and AI Search Visibility for SEO Agencies

“Our customers are finding us more on LLMs and AI search engines. How do we scale that?”

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:

Aspect	Traditional Tracking (SEO)	LLM Tracking (AI Tools)
Main Focus	Ranking + Click-through rate	Visibility + Brand mentions
Goal	Track page position in SERPs and estimate traffic	Monitor brand presence in AI-generated answers
User Action	Clicks based on rank	Citations without guaranteed clicks
Personalization	Mostly uniform for all users	Highly personalized and varies per query/user
Criteria for Visibility	Keyword match and page authority	Semantic clarity and topic association
What You Track	SERP position, CTR, organic traffic, conversion	Frequency of mentions, citation, accuracy, sentiment, traffic, conversion

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:

Open GA4 → Go to the Explore section.
Start a new report → Choose “Blank” to create from scratch.
Set Dimensions → Add Session source/medium.
Add Metrics → Include Views, Engaged sessions, and Key events to see user behavior.
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:

Apply the segment to your report.
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

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.

A Guide to LLM Tracking and AI Search Visibility for SEO Agencies