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Digital Sleuthing: How to Find and Decipher Google’s Patents, Leaks, and PR

Francine Monahan — Thu, 29 Jan 2026 12:00:00 +0000

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When a multi-billion-dollar corporation sits at the center of the entire SEO industry, it can be tough to believe everything they say. Constant press releases with talking heads giving vague quotes can throw the industry into a tailspin in an instant, but this doesn’t always have to be the case.

There’s truth in them there hills. You just have to know how to find it.

From patents and leaks to official PR, there’s a lot to filter through, but there are many ways to parse through the information Google releases. This blog will show you how to decipher the real meaning and separate fact from vague corporate untruths.

Understanding Google Patents

For years, well-known SEO researcher Bill Slawski was at the forefront of analyzing Google patents. Since his passing in 2022, the torch hasn’t so much been passed but rather divided between a number of SEOs trying to pick up the slack.

Patent watcher and leak analyzer Mike King still touts Bill’s work and recommends that anyone new to patents should read his work at SEO by the Sea and familiarize themselves with it. That way, you’ll start to understand and recognize the terminology Google uses.

“The difficulty is they’re always going to be dense writing,” Mike said of the patents. “They’re not written to be readable.”

Patents are written that way intentionally, with vague phrasing and broad descriptions to cover all of Google’s bases legally.

Mike started looking into patents years ago after hearing many engineers and Googlers using terminology that he didn’t understand at first. Then he searched through patents for those same words to learn what everyone was talking about.

Most recently, Mike did a deep dive into the AI Mode patents and how it all works.

In the SEO world, experts make discoveries and share them at conferences. Mike says that Google does the same thing with its research by sharing papers and information at conferences for engineers.

“All of these people are sharing their ideas with each other and building upon them,” Mike said.

Now that we know what to expect, let’s look into where to find patents and how to read them.

Where to Search for Patents

There are a few main locations where you can easily find patent applications and their statuses:

Google Patents: The most user-friendly interface. This is your primary hub that indexes global filings and offers the most intuitive search interface.
Google Research: Where you can find official product and research releases directly from Google.
USPTO & Espacenet: These are official government databases with the most up-to-date legal statuses.
arXiv.org e-Print archive: A directory for white papers.

Be sure to filter your search with certain key phrases. If you’re using a site that’s not Google’s official patent directory, add the search operator assignee “Google LLC” to narrow it down, for example.

You can also use CPC (Cooperative Patent Classification) codes like G06F 16/00 (Information Retrieval) to find patents grouped by technology rather than just keywords.

“I don’t browse randomly,” said Metehan Yeşilyurt of AEO Vision. “I usually start from an observed behavior in search and work backwards. My main tools are Google Scholar (citation chains matter more than single patents), arXiv to understand the research roots, and advanced Google searches like “filetype:pdf” combined with ranking or retrieval terms. I’m looking for patterns, not one document. I’m also using consensus.app, recently.”

SEO expert and consultant Marie Haynes said she doesn’t really have a solid process for finding interesting papers and patents – she just keeps her ear to the ground.

“I have an RSS reader that I vibe coded that finds me the latest Google blog posts and more,” she said. “I pay close attention to this page: https://research.google/pubs/.”

She also scours X (formerly Twitter) for interesting papers that people are talking about.

“Most of what I find simply comes from me doing research as interesting scenarios come up,” she added.

How to Read a Patent

You don’t need a law degree to be able to read a patent, but you might need a few tips to read them efficiently.

First, don’t read from top to bottom. Focus on this order instead:

The Abstract: Read this first. It’s a 150-word summary of the problem and the solution. If the Abstract mentions “improving resource relevance based on user context,” you’re likely looking at a ranking signal.
The Claims: Found at the very end. The claims define exactly what Google owns. If a specific ranking factor isn’t in the claims, they don’t legally own that specific method.
The Drawings & Flowcharts: Look for Fig. 1 or Fig. 2. Google engineers use these flowcharts to show how data moves from a user’s query to the final result.

Keep in mind that terms like “entity” or “resource” are used as catchalls for a person, place, or a brand to keep the patent’s scope broad. Remember, Google purposely keeps their patents from being too specific.

“I mentally strip legal padding and reduce everything to inputs, transformations, scoring, and constraints,” Metehan said. “Constraints (latency, decay, thresholds, fallbacks) usually matter more than features.”

A patent does not always equal a finished product either. Google patents thousands of ideas that never see the light of day as an actual product. In fact, Google frequently files “defensive patents,” which are ideas they may never use, to prevent competitors or what they refer to as “patent trolls” from using them.

We know that the language isn’t simple for everyone to understand because it’s written through the lens of information retrieval and not SEO (which is why Mike says they’re able to get away with obfuscating information), but we now have tools to help us make sense of these patents. You can upload the PDF of the patent to NotebookLM and ask it questions or generate a podcast explaining it.

Gemini has obviously indexed all of Google’s research so you can ask it questions, too. If the information hasn’t been made public yet, you can at least give it whatever information you have and it can analyze what it all might mean based on the facts.

“It’s all a lot more accessible now than it’s ever been,” Mike said.

Sometimes, Google will publish updates or expansions, so make sure you’re looking at the right date. A patent usually lists three:

Priority Date: The most important date. This is when Google first claimed the idea. It tells you exactly when they were thinking about this technology.
Filing Date: When the formal paperwork was submitted (often months or years after the Priority Date).
Publication Date: When the patent became public.

If you see a patent published in 2026, but the Priority Date is 2021, you aren’t looking at a new invention but something Google has likely already been testing for five years.

The amount of patents Google applied for has dropped 13% since last year, though, which is interesting to see. The company was granted 1,782 patents in 2025, down from 2,054 in 2024.

Aufgesang’s Head of SEO and AI Search Olaf Kopp monitors new patent publications from Google, Microsoft, and OpenAI every week and manually filters out those that could be interesting.

“My analyses focus primarily on general information such as status, countries/regions where the patents were published, claims, and detailed descriptions,” he said. “Patents that have been published for multiple regions are more exciting, as it is more likely that the methods will also be used in practice.”

Navigating Google Leaks

In May 2024, over 14,000 internal ranking features from Google’s Content Warehouse API were released by an automated bot. Mike King was one of the first to see these leaked documents after Rand Fishkin shared them with him (after Erfan Azimi shared them with Rand).

Finding these leaks is only half the battle, though. The real skill is knowing how to read them without jumping to false conclusions.

How to Verify a Leak

To ensure what you’re hearing about Google is true, you could cross-reference with official sources such as court testimony, like how the DOJ vs. Google trial confirmed the existence of Navboost and Glue before the leak even happened.

You could also look for deprecation markers. Some leaked features are marked as “deprecated,” meaning Google used to use them but doesn’t anymore, which is crucial to avoid chasing ranking factors.

“Leaks are partial snapshots, not documentation,” Metehan said. “I map internal terms, look at defaults, check pipeline order, and cross-validate against real-world behavior and older patents. I never treat leaks as a checklist.”

Metehan also warns against optimizing purely for patents or leaks.

“I look for invariant behaviors that survive multiple re-rankers, versions, and query types and test those,” he said. “I’m using Gemini APIs a lot, models, embeddings, layout parser, model garden and Vertex AI with grounding, etc. It’s a multimodal world.”

Google has been sharing pretty much everything research-wise these days, so it’s up to us to find what’s important. Because when it comes to Google’s official information releases, that’s a different story.

“Google has been far more forthcoming about how things work on the research side than they have on the PR side to the SEO community,” Mike said.

Reading Between the Lines of Google PR

Official communications from Google (via their blogs, social media, or spokespeople) typically serve a specific purpose: to encourage behavior that makes their search engine better while discouraging “gaming the system.”

To find the truth, you have to decode the Google-Speak.

Google often uses a linguistic technique where they deny a specific term but not the concept to make it “technically truthful.”

For example, multiple Googlers like John Mueller and Gary Illyes have said that Google does not use a Domain Authority score. But the 2024 API leak revealed a metric called siteAuthority.

Google was “truthful” because “Domain Authority” is a trademarked term by the SEO company Moz. They don’t use that specific tool, but they certainly use their own internal version of the same concept.

And most recently, Danny Sullivan declared that no one should be “chunking” their content – a statement that didn’t really make much sense because it’s been an SEO and UX best practice for years to break up walls of text into easy-to-read sections.

But again, Google doesn’t use the specific word “chunking” when making content recommendations so, sure, I guess they’re technically being truthful. It still seemed like a silly statement to make, though, and got a lot of people up in arms.

These are the times when it’s important to think hard about why they’re saying these things and how it all would benefit Google financially, because that’s Google’s main concern.

When reading a release about Google, ask yourself these questions:

Is the denial hyper-specific? If they say “We don’t use [Metric X] for [Task Y],” they might still use it for Task Z.
Does it contradict sworn testimony? Always prioritize information from the DOJ vs. Google trial over a tweet. In court, off-the-record chats are evidence while on X (Twitter) or Reddit, they are PR.
Are they saying to do as they say and not as they do? Google PR often tries to scare people away from manipulation and those evil Black Hat SEO methods like buying links, even when their own systems like PageRank still rely on them.

Always keep your reporter hat on when listening to Google, even if you never actually spent three days driving around town trying to get a comment from an avoidant local politician for $12 an hour like some of us did.

Now, where can we find official information about Google?

Reliable Primary Sources for Official News

To analyze the spin yourself, go directly to the source:

The Search Central Blog: For official algorithm naming and policy changes.
The “Search Off the Record” Podcast: Where engineers often let slip technical nuances that don’t make it into the formal blog posts.
Sworn Court Exhibits: The Justice.gov Antitrust Exhibits are a big source of truth considering that lying there has legal consequences.

“It’s a combination of research and evidence,” Mike said. “You also have to be mindful that when Google is speaking to the community, they’re speaking to the average person, not the experts. They’re creating something that answers the question for most people.”

Mike emphasized that there are three different things to look at to figure out the actual truth: your research, what’s in the wild, and what Google is saying.

Googling the Truth

Overall, just remember that Google is a massive corporation that is not here to help SEOs be more successful and get more clients. Google is here to continue to be massive and rich. Everything they say should be viewed through that lens, no matter how much their statements happen to align with a point you’re trying to make at that moment.

Also, none of these sources are the whole truth on their own. Look at multiple sources, official documents, legal documents, and general research to come to a conclusion.

“Patents don’t tell you what to do,” Metehan said. “They tell you what Google struggles with and cares about. That’s where the real insight is. Think from a reverse and relevance engineering perspective.”

As we move further into 2026, where still-pretty-inaccurate AI Search has become the norm, these detective skills will be more valuable than ever. Google may obfuscate the truth to protect its bottom line, but the paper trail left by their engineers and leaks will always provide a roadmap for those willing to look.

Don’t just wait for the next Helpful Content update to tell you what to do. Go beyond the headlines and find the real story yourself.

Need help becoming more visible online?

Read about Relevance Engineering

The post Digital Sleuthing: How to Find and Decipher Google’s Patents, Leaks, and PR appeared first on iPullRank.

1 Important Lesson Omar Little Taught Me About Writing Page Titles

Eric Gilley — Thu, 22 Jan 2026 12:00:00 +0000

I got my start in content by editing articles for a site that lived and died by clicks. We didn’t use the word “clickbait” (not out loud), but that’s exactly what it was. I’m not proud of headlines like “3 Frustrating Toyota Tacoma Problems That Send Drivers Into a Rage” or “Aaron Rodgers Sends Packers Fan a Clear Message With Latest Podcast Appearance”, but hey, I had to pay the bills.

And in the wise words of Omar Little from the TV show The Wire:

Clicks equaled money, and making people feel something was how you got them to click. That was the incentive. That was the game.

These days, we’re seeing a new game forming. The incentive structure is shifting.

For many types of content, visibility now comes from being useful to something that isn’t human at all; not a reader, but an amalgamation of three different LLMs and a swarm of search, retrieval, grounding systems, summarizers, re-rankers, and answer engines I just (incorrectly) lump under the catch-all: “AI.”

It doesn’t feel outrage. It doesn’t get curious. It just scans, slices, and decides in a fraction of a second whether your page contains an answer. And what it sees first, namely your title, url slug, and a short text snippet, is the first and most decisive signal it has to go on. If those don’t scream relevance, you probably don’t get cited.

This means the headline’s job isn’t to tease; it’s to plainly declare what you know and who you’re useful to. It’s not clickbait; it’s AI bait. And once again, the game is rewarding those who know how to write to the system.

How an AI "Sees" Your Page: The Snippet-First World

Contrary to what many assume, an AI doesn’t “see” your webpage in its entirety. It doesn’t load the full HTML, parse the layout, or admire your design. Instead, As Dan Petrovic explains in How GPT Sees the Web, its first interaction is with a small, ruthlessly efficient packet of data: the page title, the URL, and a short text snippet.

When an LLM is grounding an answer via search, this is the initial context it’s given to decide whether your page is worth retrieving at all.

This is the core idea behind what we at iPullRank call Relevance Engineering: intentionally shaping content so its meaning is legible at the exact moment a system is deciding whether your page is useful. Before design, before UX, before persuasion — relevance has to be established.

Think of it as a high-stakes, single-chance audition. If that preview doesn’t scream “I have the answer!” for the user’s query, the AI will logically and instantly move on to a source with a stronger, more relevant signal.

It is an automated process of elimination designed for maximum efficiency. The AI’s decision to “click” and investigate your page further is won or lost in this initial glance, making the quality of that preview paramount.

Not to mention, even when a page is retrieved, the model still isn’t consuming it all at once. It’s evaluating the content in constrained windows, pulling only the portions most likely to resolve the question at hand.

Tools like Relevance Doctor exist to diagnose this exact moment: what signal your page sends in that first interaction, and whether it’s strong enough to survive the cut.

From Human Clickbait to "AI Clickbait"

Over the years as the web evolved, headlines changed as well.

You can think of headline evolution like this:

Early SEO (Late ‘90s to late ‘00s): Keywords signaled topical relevance (“Fixing Crawl Budget Issues”)
Content marketing era (Early to mid 2010s): Curiosity and emotion drove clicks
Social era (Late ‘10s to early ‘20s): Outrage and intrigue maximized engagement
AI Search era (today): Explicitness and semantic density determine inclusion

The incentive hasn’t changed. Visibility still rewards alignment. What’s changed is who’s doing the selecting.

You can see this incentive shift play out in real traffic patterns.

Below is an anonymized traffic chart from a content site that leaned heavily on curiosity-driven, emotionally charged headlines. For a few years, that approach performed extremely well by optimizing for human behavior: intrigue, open loops, and the promise of a payoff after the click.

That strategy wasn’t wrong. It was perfectly aligned with the distribution systems of the time. But as discovery began to move upstream into AI-powered summaries, re-rankers, and answer engines, the same ambiguity that once drove clicks became harder for machines to interpret.

When the selector changes, the headline has to change with it.

That same snippet-first, elimination-driven efficiency leads to a new way of thinking about titles and snippets: a concept we’ll call AI Clickbait. But unlike its human-focused cousin, it’s about radical clarity.

Let’s look at a real-world example of an old-school title and see how it would have to change for today’s game.

To do that, we’ll pull out a certified banger from the archives, written by none other than the founder of iPullRank, 2025 Search Marketer of the Year, and my boss (please don’t be mad at me): Mike King.

This 2016 headline was a masterclass in playing to the human incentive:

“How I Sped Up My Site 68% With One Line of Code.”

Certified banger. I want to click that right now.

But that headline would for sure be ignored by an LLM. A machine isn’t curious; it’s a logician. It can’t process the intrigue. The title offers a compelling result but hides the actual subject matter, giving the AI nothing concrete to latch onto.

For today’s AI-first world, you’d have to flip the formula and close that information gap instantly:

“How to Improve Page Speed Using the rel=prerender HTML Attribute”

Definitely not as catchy. However, that’s the version you write when the reader is a summarizer, not a person. Just topic, method, and outcome.

Human-Optimized	AI-Optimized
“This One Line of Code Changed Everything”	“How to Improve Page Speed Using rel=prerender”

Here are a couple of examples from the BBC news site. Do either of these inspire you to click and find out what they’re talking about?

You’re not trying to trick the AI; you’re trying to give it the most efficient signal possible. It proves that the core of the game remains the same: you have to know your audience. The only thing that’s changing is who (or what) you’re playing to.

Is It Time to Rethink Title Tag Length?

For years, SEOs, editors, and writers were taught to treat 60 characters like gospel. Not because Google cared about brevity, but because that’s where the snippet cut off in search results. Anything beyond that risked getting truncated.

In this guide to page titles created by Screaming Frog, these are the recommended title length maximums and minimums:

Title Maximum Length – 580 pixels or 60 characters.
Title Minimum Length – 200 pixels or 30 characters.

But AI doesn’t operate under a visual pixel constraint. It’s not laying out a search results page. It’s parsing meaning. Truncation doesn’t matter as much anymore; data loss does.

As written in this article by Krishna Madhaven at Microsoft, “Your page title, description, and H1 tag (the top-level HTML heading) are important signals AI systems use to interpret purpose and scope.”

The real risk isn’t your title getting cut off. It’s your most important concepts never making it into the snippet that gets passed to the LLM.

Take an old-school headline like:

“Fixing Crawl Budget Issues” (26 characters)

It’s short and technically accurate, but semantically flat. Instead try:

“How to Fix Crawl Budget Issues That Prevent Googlebot from Indexing Your Site” (77 characters)

It’s longer, but also denser: how-to intent, crawl budget, Googlebot, indexing problems. It gives the AI more reasons to believe your page directly addresses the user’s query. Not just “this is about SEO,” but “this is about exactly the issue you asked about.”

In a small internal analysis of one site, we compared high-ranking pages that were cited by AI systems against high-ranking pages that were not. While both groups performed well in traditional search, pages that were cited tended to use longer, more semantically dense titles on average.

This doesn’t prove that longer titles guarantee more AI visibility. But it does suggest something worth testing further: when relevance is evaluated programmatically, titles that surface more context upfront may have an advantage.

The takeaway isn’t “write longer titles.” It’s to stop treating length as the constraint and start treating meaning as the variable that matters.

The goal isn’t to jam in keywords; it’s to front-load meaning.

How to Craft a Headline for an AI-First Interface

Writing an AI-legible headline is about removing ambiguity at the moment of evaluation. Screaming Frog suggests following these best practices for page titles:

Concise
Descriptive & Relevant
Unique
Includes Brand
Optimal In Length
Enticing

A practical way for marketers to approach it:

Start with the question your page answers. If you can’t phrase it as a question, the page probably isn’t answer-shaped yet.
Name the core entity and problem explicitly. Don’t assume prior context.
Front-load meaning, not modifiers. Lead with what the page is about, not how impressive the result is.
Reduce intrigue that hides subject matter. Curiosity gaps work on humans; they slow down machines.
Sanity-check for isolation. If the title were stripped of its surrounding page, would its purpose still be obvious?

As Krishna writes, “Page titles should clearly summarize what the content delivers, using natural language that aligns with search intent.”

A New Title Length Best Practice Worth Testing

This isn’t about throwing out everything you know about SEO. It’s about recognizing that the rules of visibility are evolving, and the audience is changing.

You’re no longer writing just for humans. You’re also writing for models that don’t scroll, don’t skim, and don’t guess. They parse titles, weigh snippets, and choose what to cite based on how clearly your content signals that it’s an answer.

But let’s be real: not every headline needs to be AI Clickbait. If your content is built for humans (email, social, or storytelling) then cleverness still has its place. A good curiosity gap still works where curiosity lives.

The point isn’t to kill creativity but to optimize for the interface you’re speaking into.

When the goal is LLM visibility, AI Overviews, or zero-click citations, semantic clarity wins. In those cases, your headline isn’t a hook. It’s a claim.

So experiment. A/B test longer, clearer titles. Revisit old blog posts that rank well but get ignored by AI search. Give your best content a second chance by making it unmistakably answer-shaped.

Because like Omar said, it’s all in the game. And these days, the game picks the clearest answer.

Want to learn more about optimizing for LLMs?

Read our AI Search Manual

The post 1 Important Lesson Omar Little Taught Me About Writing Page Titles appeared first on iPullRank.

Query Fan-Out in Practice: Turning One Search into an Omnimedia Content Plan

Francine Monahan — Thu, 08 Jan 2026 12:00:00 +0000

If you’re a marketer, you’ve probably invested heavily in content marketing over the years, but the Organic Search payoff isn’t delivering, and you’re questioning your SEO strategy.

For example, for those in the finance industry, your retirement planning guides might rank pretty well, your pages explaining investment strategies get decent traffic, and your calculator tools actually help people. But maybe lately, something feels off.

Your rankings are holding steady, yet Organic Search traffic keeps declining. Is this the impact of AI Search?

Maybe you’re barely showing up in AI Overviews or AI Mode results for topics that are core to your business goals, and when potential clients ask ChatGPT or Perplexity about retirement planning, your firm doesn’t get mentioned at all.

The problem might be that you have an outdated perspective on SEO. You’re familiar with optimizing for single keywords, but that’s not how it works in AI Search. AI Search Platforms are leveraging query fan-out.

When someone searches for “What’s the best way to save for retirement,” Google’s Gemini is simultaneously running dozens of related searches for AI Overviews, such as 401(k) contribution limits, Roth IRA comparisons, retirement calculators, savings benchmarks by age, and common mistakes to avoid. If your content doesn’t include passages that satisfy your search across all of these relevant sub-queries, you’re losing visibility, brand awareness, and performance metrics.

So what’s a better, more modern strategy for AI Search? Let’s approach this problem using a fictional mid-sized financial services firm as an example. I’ll walk through the complete process: map out fan-out queries, analyze what actually ranks and gets cited by AI (including which formats and sources dominate), and build an omnimedia content plan. You can’t only worry about your owned properties, like your website. You need to cover every channel that contributes to AI Search results: YouTube, Reddit, industry trades, and every other channel where your prospects are searching.

Let’s get started.

What Is Query Fan-Out and Why Does It Matter for SEO Now?

Today, your content might rank on page one for your target keyword and still be invisible in AI Search results, and a lot of that has to do with query fan-out.

Query fan-out is the process by which AI systems and modern search engines expand a single user query into related sub-queries that run simultaneously in the background. Rather than treating your search as an isolated request, these systems interpret it as a starting point for exploration.

When you ask Google a question in AI Mode, what appears to be one search is actually dozens or even hundreds of synthetic queries working in parallel. As Mike King explained in his detailed technical analysis, these include:

Related queries that are semantically adjacent to your original search.
Implicit queries that capture what you likely meant but didn’t explicitly state.
Comparative queries that help you make decisions between options.
Personalized queries tailored to your search history, location, and behavioral patterns.

Mike’s research into Google’s patent applications revealed that it uses large language models like Gemini to generate these synthetic queries through structured prompting that emphasizes intent diversity, lexical variation, and entity-based reformulations. (If this feels like too much jargon, revisit Chapter 6 of our AI Search Manual, where we discuss some of these fundamentals that influence information retrieval.)

Modern AI Search systems use query fan-out to understand and satisfy your full search intent.

This approach enables AI systems to:

Synthesize comprehensive answers by pulling all relevant information.
Build reasoning chains that connect different aspects of a topic logically.
Anticipate follow-up questions before users ask them.
Diversify information sources to avoid relying on a single perspective.

The query fan-out process helps ensure that AI-generated responses are robust, multi-dimensional, and genuinely helpful.

Helping a Financial Brand Win Retirement Searches

What’s the practical application?

Let’s say you’re working with a mid-sized financial services firm specializing in retirement planning and investment management.

Their primary business goal is to increase qualified leads from people actively researching retirement planning options. To achieve this, they need to earn more visibility in search than direct and indirect competitors.

This company’s core audience persona is a mid-career professional in their 40s, earning over $90K annually, who has been contributing to a 401(k) for years. They have a couple of kids who are in high school and parents who have retired, so now they’re thinking about their own financial future.

The core audience also:

Have accumulated some retirement savings but lack confidence in their strategy.
Feel overwhelmed by financial jargon and competing advice.
Want personalized guidance but aren’t ready to commit to a financial advisor.
Increasingly turn to AI Search tools for quick answers to complex financial questions.

This person doesn’t search for “financial advisor near me” or “best IRA provider” (at least not yet). They start with broader, more exploratory questions.

So, we’re going to ask a broad question to start our search.

The Query

For this analysis, we’ll focus on a query that represents the prospective customer’s starting point in the retirement planning journey:

“What’s the best way to save for retirement?”

This question is exploratory rather than transactional, requires synthesized information from multiple sources, and triggers dozens of related questions that the searcher hasn’t yet articulated.

A little digging around in Ahrefs showed that this question is a difficult keyword for which to rank. The more succinct “best way to save for retirement” is more popular than the question form, which isn’t surprising. People tend to ask full questions to LLMs rather than Google these days.

We can also see where this phrase appears most often, including sites for the Department of Labor, Trinity College, Fidelity, Merrill Edge, Bankrate, and Reddit (remember these sites because they’ll appear often later in our research).

But for the financial company, ranking well for this single query in traditional search isn’t enough. They need to appear across all subqueries about 401(k) contribution limits, retirement calculators, average retirement savings by age, and much more.

Now that we’ve researched the initial query, let’s get to the query fan-out.

Generating a Query Fan-Out with Qforia

Qforia is a tool developed by Mike King at iPullRank to simulate a query fan-out. Begin by visiting the Qforia site, then enter a paid Gemini API key (the free ones won’t work) and enter your query. After a few moments of thinking, your results will appear:

Qforia leverages the same Gemini model used by AI Overviews and AI Mode, so the output is still probabilistic, but the goal is accuracy, not precision. Plus, Mike’s tool bakes in a lot of useful information to the results:

The “query” column shows you other related queries.
The “type” column shows you what kind of question the query is likely asking. Is it comparative like a 401K vs. Roth IRA or personalized like “retirement savings strategies for someone in their 30s”?
The “user intent” column tells you what the searcher is looking for with a particular query. Do they need to understand the basic options? Are they seeking advice tailored to a specific life stage? These classifications help you group your queries based on who is asking and why.

Implicit and personalized queries seem to be common in this fan-out, showing that asking about the best way to save for retirement can be very specific to a certain person.

Another useful bit of data is the “format reason” column. This is a relatively new feature of Qforia that lays out what type of content would be best to answer the query, be it a guide, article, table, interactive tool, etc.

In this example, Qforia suggests a table for a side-by-side comparison of 401Ks and Roth IRAs. And for “retirement savings strategies for someone in their 30s”, it recommends a checklist with actionable steps to take. The “steps to open an IRA account” phrase is another implicit query that would benefit from a numbered or bulleted step-by-step list to follow.

The query “what is an employer 401K match” is considered to be a frequently asked question, so Qforia recommends answering it within an FAQ page about retirement benefits. An interactive tool seems to be the best way to help someone answer “how much money should I save for retirement?”

Now that we’ve performed our fan-out research, let’s take a look at the search engine results pages (SERPs) and see what we can learn.

Analyzing the SERPs for Fan-Out Queries

For this experiment, I will choose five of the fan-out queries Qforia suggested (in addition to our original query) and see what the SERPs look like for each:

Beginner’s guide to saving for retirement
What are the different types of retirement accounts?
How much money should I save for retirement?
Retirement savings strategies for someone in their 30s
Common retirement savings mistakes to avoid

Let’s start with the SERP for the initial query: What’s the best way to save for retirement?

Once you get past the litany of sponsored posts and the AI Overview, the rest of the results give a wide range of helpful resources. There are top 10 lists of tips from Merrill Edge and the U.S. Department of Labor, Reddit and Quora forum answers, YouTube videos, and articles from universities and credit unions.

Bankrate even offers a retirement calculator to estimate how much you should save, even though I didn’t even ask that yet (we’ll see if it shows up again later for that query).

Business	Media Type	Content Type	Website Type
U.S. Dept. of Labor	PDF article	Listicle	Government site
Reddit	Forum posts	Various	Forum
Trinity College	Article	Beginner’s guide	College site
Texas Hospital Association	Article	Listicle	Brand site
Bankrate	Calculator	Informational	Brand site
Space Coast Credit Union	Article	Informational	Brand site
California Credit Union	Article	Listicle	Brand site

When it comes to the AI Overview and AI Mode results, they both offer a bulleted list of strategies for saving. The AI Overview is more detailed, though, with a table showing the different types of retirement accounts and an action plan to follow:

Now let’s see the results for “Beginner’s guide to saving for retirement”:

We see many similar results to the original query, such as the “6 Essential Steps” video, the “Retirement 101” article from Trinity College, the “top 10 ways to prepare for retirement” article from the U.S. Department of Labor, and the Reddit thread on how to start saving for retirement. The AI Overview was also another list of tips and steps to take to save.

Business	Media Type	Content Type	Website Type
NerdWallet	Calculator	Interactive	Brand site
SuperGuy	Video	List	Brand account
Trinity College	Article	Informational	College site
Amazon (Author Dan Clay)	Book	Informational	Brand site
U.S. Dept. of Labor	PDF article	Informational	Government site
Bankrate	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum

The process can be applied to every fan-out sub-query, but here we want to focus on the organic rankings.

Next up is “What are the different types of retirement accounts?”:

This query is very specific, and so the results were highly specific lists of definitions. The AI Overview and AI Mode both had the same bullet lists, and the rest of the SERP featured articles from sources like Charles Schwab, Northwestern Mutual, and American Express that were all laid out in list form.

There’s also a “people also ask” section at the top of this query with even more related and highly specific questions.

Business	Media Type	Content Type	Website Type
IRS	Table of contents	Links to informative webpages on the site	Government site
Fidelity	Article	Listicle	Brand site
U.S. Dept. of Labor	Article	Informational	Government site
Thrivent	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum
Equifax	Article	Informational	Brand site
Charles Schwab	Article	Listicle	Brand site
Northwestern Mutual	Article	Listicle	Brand site
American Express	Article	Informational	Brand site

As for “How much money should I save for retirement?”:

The results consisted of:

Articles that all gave the same recommendation: save between 10 and 15% of your annual income, implying that it really is a best practice that everyone follows (or simply everyone is copying each other’s advice).
A couple of retirement calculator tools from NerdWallet and Merrill Edge but there weren’t nearly as many as I expected to see (maybe they are difficult to build or not very accurate), so perhaps our financial organization can capitalize on that with a better one.

Based on the lack of diversity in results, it would appear that Google feels this question has already been answered and nothing else is needed.

Business	Media Type	Content Type	Website Type
NerdWallet	Calculator	Interactive	Brand site
Citizens Bank	Article	Informational	Brand site
Texas Hospital Association	Article	Listicle	Brand site
The People’s Bank	Article	Informational	Brand site
Merrill Edge	Calculator	Interactive	Brand site
Brookings	Article	Informational	Brand site

The AI Overview had some recommended guidelines, a table with age-based benchmarks for saving, and a list of methods to estimate how much, including a link to the NerdWallet calculator. The same result was found in AI Mode.

The query “Retirement savings strategies for someone in their 30s” gave a bunch of highly specific results mainly consisting of articles and videos making recommendations. The results even had a “find related products and services” section, implying that this query is so bottom of the funnel that people searching this very specific query probably want to select an actual account/financial institution.

However, one thing to note is that some of the results misunderstood the question. The article from Farther Financial talks about early retirement, as in how to retire when you’re in your 30s instead of starting to plan in your 30s.

Overall, nothing linked on this SERP was a repeat of the other queries:

Business	Media Type	Content Type	Website Type
Securian Financial	Article	Informational	Brand site
Reddit	Forum posts	Multiple	Forum
Fidelity	Article	Listicle	Brand site
Bankrate	Article	Listicle	Brand site
Thrivent	Article	Informational	Brand site
Hancock Whitney	Article	Informational	Brand site
MassMutual	Article	Informational	Brand site
Farther Financial	Article	Informational	Brand site
America’s Wealth Management Show – YouTube	Video	Informational podcast	Brand account

Aside from a few videos up top, the results for “Common retirement savings mistakes to avoid” were hilariously all list articles ranging from 10 important tips to 5 mistakes to avoid. AI Mode and the AI Overview simply listed out the mistakes grabbed from many of the articles listed on the SERP:

Business	Media Type	Content Type	Website Type
T. Rowe Price – YouTube	Video	Informational	Brand account
CBS Philadelphia – YouTube	Video	News story	News station account
MeaningfulMoney – YouTube	Video	Informational	Brand account
Louisiana Office of Financial Institutions	PDF Article	Informational	Government site
Morgan Stanley	Article	Listicle	Brand site
Charles Schwab	Article	Listicle	Brand site

Next, we’re going to look at all these results together and start analyzing what it all means.

Matching Content Formats to Intent

It’s clear that format choice matters just as much as topic choice. As we’ve seen from the screenshots, the SERPs seem to favor list articles, videos, and interactive calculator tools.

One thing I noticed (and was pretty excited about) was how many reputable sources were cited. Actual financial institutions with experience in the subject matter like Fidelity, Charles Schwab, Wells Fargo, and others topped the SERPs and were cited in AI Overviews and AI Mode. I read the AI Overviews and actually believed they were telling the truth, which isn’t something that happens often.

This is great for the people doing the searching, but a challenge for our financial organization to overcome in rankings. They will have to work hard to produce the kind of content that can compete with these huge firms. But thanks to our query fan-out and SERP research, we know what type of content ranks well to give them an idea of where to focus their marketing efforts.

However, it’s also important to consider the fact that a new type of content that hasn’t been seen on the SERPs like an FAQ page or guide might capture more eyeballs than the same listicle over and over. It’s all about experimentation and seeing what works.

A successful content strategy will need to be creative, helpful, and omnimedia to focus on every search channel in order to compete on these SERPs.

Expanding Beyond Web Pages into Omnimedia

What is an omnimedia and omnichannel strategy? It focuses on everything in every channel and in every media format: videos on YouTube, text in Reddit forums, social media, review site tables, TikTok videos, literally anywhere a person can search for something.

In our finance company example, there weren’t any instances of social media or TikTok posts showing up in the SERPs, but we did see quite a few YouTube videos and Reddit and Quora forum posts.

I looked at the same queries in a few LLMs (ChatGPT, Gemini, and Perplexity) to analyze their results. Rather than inundate you with mountains of screenshots, I will just sum up the findings for a few of the queries. I started with the original query of “What’s the best way to save for retirement”:

ChatGPT’s answer was a simple list explaining the types of retirement accounts, a recommended target percentage of income to save, and some tips for smart saving. I didn’t see any links or citations in this summary.
Gemini focused on tables that could be exported to Google Sheets that showed age benchmarks and how to choose the right account type. At the bottom, there was a sources button that opened a window on the right with a list of sources including AARP, T. Rowe Price, Fidelity, and Empower.
Perplexity offered links to cited sources throughout its summary after each section, which included Guardian, Schwab, Vanguard, and even Reddit.

For the “Beginner’s guide to saving for retirement” prompt:

ChatGPT had an overly simplistic list of steps with no cited sources.
Perplexity had a 5-step list with cited sources throughout (mainly the same ones cited the last time like Schwab and Fidelity).
Gemini had a 3-step list, a table of benchmarks, and a checklist, but the most interesting part for me was it specifically recommended several companies with which to open a Roth IRA. This is important for brands to note. With enough content on this topic, financial companies could be added to this list:

For the next query of “What are the different types of retirement accounts”:

ChatGPT gave a list with brief explanations and no citations again.
Perplexity detailed what each plan was and how it worked, citing a source at each line.
Gemini did the same citing many different sources:

For the sake of brevity, let’s go straight to “Common retirement savings mistakes to avoid” since this query is a different style – a serious prompt with negative implications on a person’s life, aka. Your Money or Your Life (YMYL) prompts:

ChatGPT offered a list of mistakes with a “why this hurts” and “do this instead” line for each.
Gemini gave a much more detailed list of mistakes with sources such as Discover, LPL Financial, T. Rowe Price, and Kiplinger. These sources differed from those that Gemini cited for the other prompts.
Perplexity cited 10 sources in their brief list of mistakes to avoid, this time with a few reputable news sources like the New York Times and The Harvard Gazette.

Since this prompt opened up the citations to new sources we hadn’t seen with the other prompts, it shows that organizations like Fidelity and Schwab don’t have a monopoly on citations for this topic. That means our financial business could possibly join the citations.

It also means that this type of prompt that’s focused on YMYL issues, such as making mistakes with your retirement savings, can impact the sources cited. Therefore, depending on their status in the industry, this may not be the right prompt for our financial company to focus on.

Let’s look at the results as a whole to see how our finance firm can fit in.

Reverse-Engineering Citations to Inform Content Creation

AI citations, derived from analyzing SERP features and LLM responses, help content planning by revealing the types of sources, formats, and channels necessary to gain visibility, especially in competitive areas like financial services.

AI Overviews and LLMs draw from a diverse ecosystem of content, which can be categorized by the credibility and nature of the source and the channels they utilize:

Source Type	Examples of Channels and Sources	Insight for Content Planning
Institutional/Financial Firms	Web pages, articles, and tools from established firms like Fidelity, Charles Schwab, T. Rowe Price, Vanguard, and LPL Financial.	These sources dominate citations for core financial topics, signaling the need for high-authority, expert-level content to compete.
Official/Educational	Articles and guides from government agencies and educational institutions, such as the U.S. Department of Labor, Trinity College, and AARP.	Citations favor unbiased, fact-checked authority, especially for complex or sensitive topics like retirement planning.
Reputable Media/News	Articles from established publishers and news sources like NerdWallet, Bankrate, Kiplinger, The New York Times, and The Harvard Gazette.	Targeting queries focused on specific angles, like “Common retirement savings mistakes to avoid” (a negative prompt), can broaden citation opportunities beyond traditional financial firms into reputable media sources.
Community/ User-Generated	Posts and threads from forums like Reddit and Quora.	An omnimedia strategy should recognize and target platforms where prospective customers are having exploratory, non-transactional conversations, as these are cited in both SERPs and LLMs.

The channels cited are primarily traditional web pages/articles, but an effective content strategy must be omnimedia, focusing on all channels where people search.

The type of content that receives citations is often determined by the specific query intent and is heavily favored by formats that are easily synthesized by AI. Cited assets tend to be highly structured and actionable:

List Articles (Listicles): Many SERP results and AI Overviews for queries like “mistakes to avoid” or “types of retirement accounts” heavily feature content formatted as lists.
Tables: AI Overviews frequently use tables to detail complex information, such as age-based benchmarks for saving or comparisons of different retirement accounts.
Interactive Tools and Calculators: For questions about specific quantities, such as “How much money should I save for retirement”, interactive tools and calculators from sources like Bankrate and NerdWallet are featured prominently in results and AI Overviews. Qforia also specifically recommends interactive tools for certain queries.
Actionable Formats: Both Qforia analysis and LLM outputs favor step-by-step lists and checklists for actionable queries like retirement strategies for someone in their 30s.
Videos: Videos are visible on SERPs across multiple exploratory queries, indicating the necessity of including video production in an omnimedia strategy.

Analyzing these citation patterns transforms competitor analysis into actionable content strategy, allowing a financial firm to focus its marketing efforts:

Prioritize Format Over Topic Saturation: Since many SERPs repeat listicles, opportunity may lie in creating different asset types, such as FAQ pages, comprehensive guides, or tables, that might capture more attention than yet another listicle.
Target Implicit Transactional Intent: Although the customer starts with exploratory questions like “What’s the best way to save for retirement”, AI models like Gemini are willing to suggest specific companies for opening accounts. This creates an opportunity for a financial business to position itself to be specifically recommended by AI.
Use Specific Query Styles to Bypass Citation Monopolies: While major firms like Fidelity and Schwab often dominate general retirement citations, queries focusing on specialized or negative topics, like “mistakes to avoid” (which touches on YMYL issues), open up citations to different types of high-authority sources, including news publications. This suggests either focusing resources on specific, highly authoritative content addressing nuanced concerns, or just letting this query go.
Adopt an Omnimedia/Multi-Channel Presence: The appearance of Reddit, Quora, and YouTube content in both SERPs and LLM citations shows that high-quality content planning must extend beyond official website articles. To fully compete, content must be tailored for, and distributed across, these community and video channels.

Now that we have an idea of how these results can inform content strategy, we’ll put together a generalized omnimedia strategy for our hypothetical finance business.

Building an Omnimedia Strategy

Here’s what we do at iPullRank:

Keyword Matrix: Establishes the groundwork for topical presence in AI Search by identifying synthetic keyword gaps, potential opportunities, and valuable terms at the passage level. It reveals which areas are strengthening or weakening your AI presence, enabling you to allocate resources toward terms that create competitive differentiation.
Omnimedia Content Audit: Comprehensive assessment of content spanning all formats and channels, covering owned, earned, and shared media properties. It uncovers what’s limiting your authority while pinpointing where focused resources will generate results.
Omnimedia Content Plan: Strategic blueprint for enhancing Content Resonance and expanding coverage across discovery channels, complete with content frameworks and governance structures. It provides a defined pathway to greater prominence in AI-generated responses.
AI Search Measurement Plan: Monitor input indicators, channel data, and performance benchmarks across AI Search environments. It identifies how AI Search influences growth trajectories, allowing you to connect spending with outcomes and refine your approach based on evidence.

Given that our fan-out research gave us the keywords we need and we already analyzed their presence in searches, let’s put together a plan for our financial company. This plan leverages the query fan-out analysis for retirement planning to create resonant, structured content across multiple channels.

Query Fan-out Prompt	Recommended Asset Type & Structure	Omnimedia Objective
“What’s the best way to save for retirement” (Core)	Pillar Guide / Comprehensive List: Create a definitive, long-form guide. Structure the article so it begins with the core answer or takeaway, followed by supporting detail and nuance.	Semantic Resonance: Ensure the title, description, and internal linking structure establish this as the canonical source for all related fan-out queries.
“Beginner’s guide to saving for retirement”	Actionable Checklist/Step-by-Step Guide: Develop a simplified, numbered list of steps for beginners. Content should be clear and simple.	Passage-Level Extraction: Optimize for easy extraction of the 3 or 5 essential steps by LLMs (like ChatGPT’s simplistic lists).
“What are the different types of retirement accounts?”	Comparison Table and FAQ Page: This requires highly specific definitions. Create a dedicated FAQ page featuring a comparison chart or table detailing account types and their features.	Structured Data: Ensure the data is provided in a clean, easily digestible format (table) that AI Overviews and LLMs already favor when detailing complex information.
“How much money should I save for retirement?”	Interactive Tool/Calculator: The primary format requested for this query is an interactive tool. This must be accompanied by structured data (tables) showing age-based benchmarks for saving.	High-Value Utility: Provide functional utility (the tool) and support the quantifiable answer with authoritative data (the table), increasing the chance of citation over static articles.
“Retirement savings strategies for someone in their 30s”	Personalized Checklist/Segmented Guide: Content should be highly specific and actionable for a segmented audience. Use the product grouping concept to show a progression_from or complementary_with path for financial products relevant to this age group.	Targeted Personalization: Customize the content to fit the behavior and specific needs of this persona, making the recommendations feel personalized, which is crucial for AI agents.
“Common retirement savings mistakes to avoid”	YMYL List Article with Solutions: Create a list detailing the mistakes, each paired with a solution. Must be accurate, with citations and data sourced from trusted research.	Trust and Authority: This content relates to YMYL issues. It must be highly detailed and authoritative to compete with reputable news sources and large financial institutions (otherwise don’t bother).

Because LLMs often prioritize user-generated content, the content plan must actively generate and integrate these signals across all channels:

Channel	Content Focus based on Retirement Queries	Strategic Goal
YouTube	How-To Videos & Guides: Create short, instructional videos for the “Beginner’s guide” and “Strategies for someone in their 30s” queries. Ensure video descriptions include relationship attributes to related website guides and link to the interactive tool.	Capture Exploratory Search: Videos are crucial for user education in the finance industry. Position the firm as an authority where consumers compare accounts and learn how they work.
Community Forums (Reddit/Quora)	Monitoring and Engagement: Focus resources on monitoring discussions related to “Common retirement savings mistakes to avoid” and “Beginner’s guide”, as these forums appear frequently in SERP and LLM citations.	Influence Citation Flow: While the firm cannot directly control these posts, understanding the conversation context allows for developing content that directly answers community-driven questions with clarity and context.
LinkedIn	Thought Leadership: Utilize LinkedIn Pulse for “Retirement savings strategies for someone in their 30s”. Publish educational, semi-formal posts that bridge technology and business to capture a broader professional audience.	Professional Authority: Establish individual and corporate credibility on a channel that ranks well for thought leadership.

By implementing this omnimedia plan, the financial company treats every content touchpoint from the interactive tool on its site to a checklist shared on LinkedIn as a signal that reinforces a clear, emotionally grounded, and contextually rich narrative about retirement planning to maximize visibility in SERPs and LLMs.

But what if your financial business is enterprise-sized with a massive library of webpages? How can you scale your content strategy? And how can you easily prune old, irrelevant content that is no longer serving your site?

Scaling Query Fan-Out and Omnimedia Planning

For a single topic or article, traditional content pruning involved manual work that was often time-consuming and subjective. This process required manually pulling traffic data, checking rankings, looking at backlinks, and skimming posts to make a “best guess” about relevance.

The content pruning methodology we’ve developed at iPullRank uses a Relevance Engineering framework that blends AI-driven semantic relevance analysis with SEO and content metadata to make data-driven decisions at scale. This will help ensure all the content on your site is relevant and identify what content should be removed.

The steps for executing this workflow involve defining your strategic benchmarks, quantifying meaning using vector embeddings, integrating performance data, and applying a final decision framework.

Here are the steps we follow to perform the content pruning:

Step 1: Define Strategic Focus

The entire analysis is grounded in business reality by defining what relevance means for the organization.

Work with the client to solidify their core solution areas or strategic pillars.
For each area, develop representative keyword portfolios reflecting user intent and product capabilities.
Draft a concise business relevance statement (a short paragraph capturing the ideal focus).

Step 2: Generate Topic & Business Relevance Centroids

This step translates the strategic pillars into quantitative benchmarks.

Using the chosen embedding model, generate an embedding for each individual keyword within a topic’s portfolio (developed in Step 1).
Calculate the average of all keyword embeddings within that cluster. This averaged vector becomes the topic centroid, serving as a robust mathematical representation of the topic’s semantic space.
Generate a single embedding for the overall business relevance statement.

Step 3: Generate Article Embeddings

The meaning of every post in the library is represented numerically.

Run the extracted article content through basic Python cleaning routines (removing excess whitespace or stray HTML tags).
Combine the Title text and the cleaned main body content for each article.
Generate a single embedding for each article using the same model used in Step 2 for consistency.
Store these individual article embeddings, typically in a NumPy array paired with their corresponding URLs.

Step 4: Calculate Similarity Scores

This step objectively measures how semantically aligned each article is with the strategic targets.

Using Scikit-learn in Python, calculate the cosine similarity between each article’s embedding (from Step 3) and each of the topic centroid embeddings (plus the business relevance embedding from Step 2).
The output is a matrix added to the spreadsheet, showing scores between -1 and 1, where scores closer to 1 indicate a stronger semantic alignment.

Step 5: Layer SEO Performance & Metadata

Real-world performance and freshness data are integrated with the relevance scores.

Pull standard SEO metrics, primarily trailing 3 to 6 months of organic clicks from Google Search Console, focusing on a recent window to avoid rewarding historical performance.
Pull Publish Date and Last Modified Date from the CMS or crawl data.
Join this performance and freshness data to the master spreadsheet containing the URLs and similarity scores.

Step 6: Apply the Decision Framework (Making Informed Choices)

The combined data points are translated into actionable categories to guide pruning and optimization efforts.

Establish data thresholds to categorize each article into three main actions:
- KILL: Candidates typically have low similarity scores across all core topics, low recent GSC clicks, and are old (more than 5 years with no significant updates).
- KEEP: Articles that qualify if they have high similarity to at least one core topic or have strong recent SEO performance.
- REVIEW/REVISE: Articles that are relevant but underperforming, outdated, or highly similar to other posts (potential consolidation targets).
Conduct manual review of edge cases, confirm decisions, and identify specific actions.

Reframing SEO with Query Fan-Out

For firms competing against giants on the SERPs and in LLMs, focusing on an omnimedia query fan-out approach to content strategy is essential. The citation analysis we walked through shows that specialized queries, especially those focused on specific concerns like “mistakes to avoid” or personalized scenarios like “strategies for someone in their 30s”, open up opportunities for smaller firms to break through with unique and truly valuable content.

You can’t out-budget the big players, but you can out-execute them on format diversity, omnimedia presence, and targeting the fan-out queries they’re ignoring. And when you scale this with the Relevance Engineering framework for content pruning, you’re optimizing your entire content library to align with how AI Search works.

We know that this can be overwhelming for a limited staff to achieve, but we can handle it for you. If you want to do this for your own business, talk to iPullRank about our AI Search Strategy Program with a full Omnimedia Content Audit and Content Plan, tied directly to a Keyword Portfolio Matrix, designed to improve visibility across traditional search, AI search, and third-party platforms. Let us help you be the first thing customers see (and trust) wherever they search.

Want to learn more about how we can help you with AI visibility?

Read about our AI Search Strategy Program

The post Query Fan-Out in Practice: Turning One Search into an Omnimedia Content Plan appeared first on iPullRank.

Local Visibility in AI Search: Findings from 6.9M Citations

Francine Monahan — Tue, 16 Dec 2025 12:00:00 +0000

“The funnel as we know it is moving,” says Christian Ward, Executive VP and Chief Data Officer at Yext. Thanks to AI, it’s moving off your website and into a chatbox (or in various forum discussions online), and marketers in the local business space need to pay attention.

It’s vital to ensure that the right people find your business wherever they’re searching without having to filter through too many irrelevant results. Like with Relevance Engineering, your brand strategy must be omnimedia and span social media, YouTube, Reddit, and other places.

Although some studies point out that Google is still the top source for search right now, there’s a lot more nuance to that.

“That’s like measuring a baseball player by the number of swings at bat,” Christian said. “That’s not how you measure search. Search has to be around the time you spend on a platform because that’s where the money is made, and it’s got to be measured on outcomes.”

And while AI outcomes aren’t that great yet, they are slowly getting better.

Yext performed a citation study on 6.9M citations, asking 1.6M questions across three models (ChatGPT, Gemini, and Perplexity) to find the impacts of AI Search on local visibility. This article will go through the data and findings.

The Impact of Search Fragmentation

Search is fragmenting. People are starting their searches on LLMs, Reddit, TikTok, and other places, and often moving to Google later on in the process to fact-check or make a final purchase. AI Search is decentralizing the search process.

Christian created the below chart two years ago. It’s evolved, yet still highlights how people use a wide range of platforms to search, but Google still dominates the market and sits in the middle (especially for fact checking).

Even if TikTok is your first stop for research, eventually the platform will add an LLM of its own. And while users are starting exploration in AI but going to Google to execute, that won’t last so we need to be prepared.

“That’s gonna stop really soon as AI models add more executions,” Christian said.

The Growth of AI

In just 2 years, ChatGPT acquired as many users as the entire internet did in 13 years.

Unlike with old-school dial-up internet, when no one in the house could use the phone while you were online, there’s no cognitive burden to AI. You can just use it easily.

And with the creation of AI Mode and constant improvements to Gemini with each release, Google will clearly be focusing its attention on AI.

“I don’t see Google just continuing to do search,” said Christian.

As search continues to fragment and more partnerships pop up with AI companies, we’ll see changes in how we measure success. Direct purchases within LLMs will impact traffic and clicks.

Walmart’s deal with ChatGPT for in-app purchases contributes 20% of Walmart’s referral traffic. This partnership with OpenAI means that Walmart can’t capture all the metrics that it used to, but at this point, they don’t even care. They just want the customer to check out, regardless of whether it’s on Walmart’s site or in ChatGPT.

In fact, traffic to many sites is down, but many businesses are still getting relevant clicks because visitors to their sites likely did extensive research before arriving.

“If they play their cards right in how they are cited and the knowledge they provide, I think they’re going to have phenomenal business outcomes,” Christian said.

However, this brings up another question. Should sites develop two types of content: one for machines and one for people? Or is a combination of the two the way to go?

“There are so many strategic applications for that for business,” said Garrett Sussman, Director of Marketing at iPullRank.

There’s a lot of data we still need to see to truly understand all of AI’s impacts on search fragmentation, but that probably won’t happen, and Christian says we need to just get used to that. There will be discomfort around monitoring, strategy, and search behavior as AI Search becomes a more integrated part of the way we search. We’re not going to get all of the data that helps us determine resource allocation and return on investment.

But some changes will have a more positive impact, like improvements to memory and personalization in LLMs. Memory will be the step that solidifies where and how we search.

The Evolution of Memory

Memory and personalization will be a huge part of the future development of AI systems, according to Sam Altman of OpenAI who has said that ChatGPT-6 will be all about improving its memory.

The main issue for Google and OpenAI is that with more memory in search, there is an impact on ads and a company’s ability to show you valuable ads at the right stage in the search journey. Memory context and location will have a huge impact on how different the search results are for everyone.

“The reality is, if I can’t have the right to show you steak restaurants because you’re a vegan, that’s a problem for the available ad space,” Christian said.

Memory in AI shrinks the ad reach because there’s only a limited number of products or services that make sense. You can’t deliver broad ads because people will stop asking broad questions. At the same time, it increases the value of the ad itself because you’re further down the funnel to your target.

“It’s a better user experience for us,” Garrett added.

But how does this play out next year?

Every so often throughout the evolution of computer technology, we’ve seen inventions jump from compute to memory improvements, and OpenAI is heading toward a memory jump.

If ChatGPT can master memory, it will have a huge advantage in the market.

“If it starts getting to that level, I think you will see a very big upswing in the value proposition to the consumer,” Christian said.

The Impact of Experience Fragmentation

An AI company’s greatest risk is users not trusting them because they can switch to another LLM so easily. If you ask one of the AI models a question, and it answers it wrong, you will probably go use another model. Google’s built up brand trust over decades. They have more at stake to get it right. AI introduces a complex risk to that trust due to the probabilistic nature of generative output.

For users, there is no cognitive burden when using LLMs for search. You don’t have to learn a whole new system because they’re all pretty much the same and very easy to operate.

There’s not going to be a one-size-fits-all solution to this, but accurate citations will have an impact. And how you format your content will control your chances of being cited.

“I think AI has a trust problem,” Christian said. “If trust is the currency by which AI will be judged and scored, then citations are the receipts.”

Citations. That’s why Christian is obsessed with where they fit in the puzzle. They are critical to the LLM-to-internet ecosystem.

Citations are dependent upon: the [QUESTION], the [CONTEXT], the [LOCATION], and the [MODEL].

The above example of a query shows the various ways people can find your business due to how your company is talked about online.

Subjective qualifiers like “best” typically come from review sites.
Categories like “Indian restaurant” come from listings and directories.
Locations like “Soho” come from maps and local pages.
Objective facts like “late night dining” or “patio seating” come from websites and pages.

“Every element on this page has a different level of potential verification by a probabilistic large language model,” Christian said.

If a fact is shown only on your site and nowhere else, that makes it seem less trustworthy in the eyes of AI. But if it also appears in reviews or on other sites, the higher the probability that your business will show up in an answer.

AI typically utilizes location, context, and memory during a search. Currently, we analyze AI visibility from the brand level down, which is not typically how a consumer uses search or AI.

Christian posits that AI Search results heavily depend on location. Every search is impacted by location in some way.

A Starbucks in the suburbs of Georgia will see different business than one located in midtown Manhattan. If you own a burger restaurant in a small town, your competition likely isn’t tons of other burger joints but rather the two or three other restaurants in town.

Mentions also have an impact here. Along with structured data, these are important to the customer journey. Common page structures, such as neighborhood, region, state, city, or any geography, need a dedicated page on your site.

Christian emphasized that we need to stop thinking globally about brand and focus instead on the consumer’s perspective regarding what’s showing up and being cited. If you’re a plumber, odds are that you want more leads for bathroom remodels than 2 a.m. calls for a clogged toilet, so your content needs to reflect that in order to be properly cited.

There are some factors you can control and that is where you should focus your marketing efforts.

What Can You Control?

Understanding that local AI Searches are probabilistic, but location has an overwhelming impact, the sphere of influence is physically reduced significantly. But of those potential citations, what can you actually influence?

A number of citation source control categories can impact visibility:

Websites
Listings
Reviews and social
News and forums

You can’t really control what people say in reviews or on social media and forums such as Reddit, so Christian says you shouldn’t spend all your time worrying about those. In fact, he describes the current environment as “an existential threat” for review sites. You can see reviews of products anywhere now, from TikTok and Instagram to Reddit, so there’s no need for people to go to specific review sites anymore.

You can control what’s depicted on websites, third-party directories, and local websites.

Local websites with subdomains are a large percentage of what’s cited because AI doesn’t have to compute all the folder structures. There’s just a concise and specific page from which to gather information.

And although Google doesn’t specifically cite Google Business Profiles, that doesn’t mean it’s not a source of data; they simply don’t cite it. You should still make sure your profile is updated and accurate.

According to Yext’s data, OpenAI, Perplexity, and Gemini are not far apart when it comes to the percentage of total citations for each citation category. And for the most controllable sources, websites and listings, citations show up very often in those models. This means there is a lot of opportunity for local businesses to control how they’re cited and found by prospective customers.

Local Search Takeaways

Location is everything. The data from Yext shows that websites are leading the citation race and that lead is expanding. Online profiles and directories also serve as crucial verification points for AI models, reinforcing what you’re saying about your business across the web.

Interestingly, Yext has seen social and review citations decline from where they were just three months ago. This shift means the controllable sources (your website, listings, and structured data) matter even more than they did before. The power is back in your hands to shape how AI models understand and cite your business.

It’s all about omnimedia strategy these days. The LLMs will pick up information about your brand from all different sources such as YouTube comments, Reddit forums, TikTok reviews, etc. You need Relevance Engineering to make sure your brand is showing up in a positive way everywhere people search.

While many sites are scrambling to block crawlers because they’re worried about giving away their content, that’s the wrong move. If you block AI from accessing your information, you’re essentially removing yourself from the conversation happening in these models and opting out of future search visibility. Your local business needs to make it easy for AI to find, understand, and cite it accurately, or you’ll be left behind.

Want to learn more about omnimedia strategy?

Read about Relevance Engineering

The post Local Visibility in AI Search: Findings from 6.9M Citations appeared first on iPullRank.

Content Strategy Tips for Visibility in the Age of AI Slop

Francine Monahan — Tue, 09 Dec 2025 12:00:00 +0000

Since ChatGPT launched in November 2022, we’ve witnessed a surge in AI-generated content flooding the internet. Right now, it’s estimated that over 17% of Google search results contain AI-generated content.

According to Ahrefs, 86.5% of top-ranking pages include some form of AI assistance, leaving only 13.5% as fully human-written. By next year, projections suggest 90% of all content will be AI-generated, and Google appears to neither reward nor penalize AI-generated pages at this time.

The problem is that AI is wrong 60% of the time, according to a March 2025 Columbia Journalism study. Hallucination is still a big problem. Despite this, people still frequently use AI to write. This leaves the web susceptible to an onslaught of low-quality, high-volume content called “AI slop” that could prevent your business from being visible to prospective customers and clients.

“If we’re all generating content based on the same training data, based on the same results that are out there, at what point are you no longer providing any value?” iPullRank’s Senior Director of SEO and Data Analytics Zach Chahalis asked.

Search Engine Optimization (SEO) ensured your site was discovered, but these days, Generative Engine Optimization (GEO) ensures your site is understood.

This blog will walk you through the strategic frameworks we’ve developed at the intersection of traditional SEO and AI-first optimization. You’ll learn how generative systems evaluate content across multiple dimensions, from scope clarity and authority corroboration to freshness signals and safety filters.

We’ll also explore how to audit your content through the lens of vector embeddings, how to use your server logs as a window into AI knowledge, and why an omnimedia content strategy is vital.

AI Search Requires Multi-Dimensional Approaches

Generative search is not a single ranking contest for a single query. It is a multi-stage filtering process in which your content competes at dozens of points in a branching, multimodal retrieval plan. The fan-out indicates the system is looking for breadth as well as depth, and the synthesis step assesses your content on relevance, extractability, and readiness.

“Essentially, we need to be looking at building and developing a multimodal content strategy that supports the retrieval plan and the fan-out queries of where your customers are, and the types of content that are expected for the different queries.”

- Zach Chahalis

Routing Profiles

You need to match the routing profile of what exists, so your content needs to be in the expected form, or it might be overlooked during retrieval. Consider these tips:

Ensure multi-modal parity (text, structured data, transcripts, etc.).
Place content where the routing logic looks (e.g., API-friendly formats, transcripts for procedural content).
Align content with routing profiles to increase retrieval across fan-out branches.

If you worked in the finance industry, routing would send rate queries to financial data APIs, minimum deposit requirements to bank product pages, insurance explanations to government or educational sources, and comparison logic to personal finance editorial sites. Each of those is a different source type, with a different retrieval method and cost profile.

Selection for Synthesis

Selection is not only about relevance to the sub-query. It is also about the suitability of a chunk to be lifted, recombined, and integrated without introducing factual errors, formatting issues, or incoherence.

In effect, the system is ranking not entire pages but atomic units of information, and the scoring

criteria are tuned to synthesis needs rather than to click-through behavior.

Synthesis requires looking at:

Extractability
Evidence density
Scope clarity
Authority
Freshness
Safety

Let’s break them all down.

Extractability

If a chunk cannot be cleanly separated from its surrounding context without losing meaning, it is less valuable to the synthesis process. This is why content that is scoped and labeled clearly tends to survive selection.

Evidence Density and Signal-to-Noise Ratio

Once extractability is established, the system looks at evidence density or the proportion of meaningful, verifiable information to total tokens. A dense paragraph that gives a clear statement, followed by an immediate citation or supporting data, is more valuable than a

lengthy, anecdote-heavy section that buries the facts in storytelling.

The system wants to find verifiable information and select the most valuable information.

“They want to surface information that’s been validated and authoritatively placed in your platform in your data,” Zach said.

Scope Clarity and Applicability

Generative systems are sensitive to scope because they’re trying to assemble an answer that’s not misleading. If a chunk does not make clear the conditions under which it is true, it is harder to place it correctly in the final answer.

It needs to be organized in a way that’s clear, detailed, and verifiable.

Authority and Corroboration

The system also weighs the source’s credibility and the degree to which other retrieved chunks corroborate the information.

“Authority is not just limited to your domain-level trust, but applies at the author or publisher level, further emphasizing the value of things like EEAT and structured data, authorship, entity definition, etc.”

- Zach Chahalis

Corroboration is a subtle but important factor. If three independent, credible sources agree on a specific mileage progression, that progression is more likely to survive selection. Outlier claims may still make it in if they are well-sourced, but the system will often prefer information that has multiple points of agreement.

Freshness and Stability

Recency is another filter, especially for topics where the facts can change. A chunk that is clearly dated and shows evidence of recent review is more attractive to the model than one with no temporal markers.

This study from Siege Media says that content that shows it’s been updated or reviewed recently (within the last 6 months to 2 years) is considered much more valuable to LLMs.

Harm and Safety Filters

Selection often applies harm and safety filters. These filters can be domain-specific, drawing on both explicit policies and learned patterns from training data.

Our advice is to avoid discussing sensitive topics or just be very careful with how this type of content is structured. Instead, focus on helpful and respectful content.

Tips for Making Extractable Content

High-quality content can be excluded from synthesis if it isn’t easily extractable. Interactive designs that aren’t crawlable or long-form narratives that bury key facts risk being skipped in favor of denser, more accessible material.

You need to engineer content for the selection funnel, which requires rethinking how you structure your content. A single long page may need to be designed as a series of clearly marked, self-contained modules, each of which could stand alone if lifted into a generative answer. It also means pairing each chunk with whatever metadata, markup, and alternative formats will make it easier for a retrieval system to recognize and use.

When organizing your content on a page, write for synthesis. To ensure your content performs well in modern retrieval systems, it’s essential to structure it in a way that is both machine-readable and human-friendly. Embedding models rely on clean, well-defined “chunks” or semantic units of information to generate precise and relevant results.

Headings and subheadings should be clear, and passages should answer queries directly and succinctly. The combination of query/passage is defined as a semantic unit, and these units are used to power AI search.

And by the way, iPullRank has a free tool called Relevance Doctor to help you with this.

Our Strategic Approach: Relevance Engineering

Relevance Engineering is the process of adjusting content so it’s selected and cited by AI systems. While traditional SEO focuses on ranking full pages, this approach targets the individual passages and concepts that AI uses to construct responses.

Our strategic approach for driving visibility in AI search includes:

A content audit for AI readability and extractability
Semantic and latent intent research
Content structuring and augmentation for AI
Testing and iteration with AI simulation

In our tactical implementation, we use these five steps to understand gaps in content:

Pull rankings and landing pages (use Search Console or your favorite Rank Tracking tool)
Generate vector embeddings
Find and score relevant passages (Use Relevance Doctor)
Compare to the vector embeddings for the citations
Improve relevance across the various pages

To identify content gaps, our team extrapolates synthetic queries using our proprietary (and free) Qforia tool. This tool now also helps determine the expected types of content per term.

You can also reverse intersect the citations yourself to figure out fan-out queries (we’ve used FetchSERP and SERP API for this).

Log File Analysis

Performing an analysis of your log files can help you understand AI crawlers and bots better.

“Logs really and truly are your visibility into the AI knowledge ingestion pipeline,” Zach said. “They reveal exactly what those systems are doing when they come into your site.”

Your website server logs can show:

Which AI bots visit
How often they visit
How deep they crawl
Which pages they never reach
Where they fail to render pages

When looking through your logs, focus on:

Crawl Frequency – Are AI bots returning regularly?
Crawl Depth – Are they exploring your site structure correctly? (Ex. Ecommerce: Homepage->Category->Sub-Category->Product Detail Page)
Render Completeness – Do bots bail before JavaScript loads meaningful content? If your content is built dynamically via JavaScript, AI crawlers may never “see” key sections, especially those rendered asynchronously or hidden behind user interactions.
Coverage Gaps – Which URLs are never seen by AI bots?
Status Patterns – Do bots encounter 3XX/4XX/5XX URLs or long response times, reducing embeddings?

Optimizing for Agentic Search

As search tools integrate AI agents more often, it’s important to keep them in mind when optimizing for search. The key pillars for optimizing for agentic search are things we’ve already discussed and are familiar with, but they’re still important:

Have structured, unambiguous entities.
Leverage predictable markup and templates so agents can extract and compare data reliably.
Use actionable endpoints such as booking paths, pricing data, availability, contact APIs, etc.
Have explainable content such as comparisons, pros/cons, ideal scenarios, summaries, etc.
Use RAG-Friendly chunked content with clean sections, tables, structured lists and definitional paragraphs.
Leverage edge rendering so AI crawlers don’t time out or get a partial picture.

“Just think about any context where you have first-person data that could be valuable to the end user,” said iPullRank’s Director of Marketing, Garrett Sussman.

Machine Context Protocol (MCP) and Application Programming Interfaces (APIs) are the future of agentic optimization.

MCP allows agents to securely access your APIs, product/service data, inventory, pricing, availability, and more. Agentic systems will prefer websites with toolable, machine-readable endpoints over raw scraped HTML, so APIs and MCP will become the canonical source that agents trust and act on, making your business a first-party data provider to AI systems.

For marketers, this will need to be a major conversation for your business (and one that we can help you with).

Omnimedia Content Strategy

An omnimedia strategy requires us to think beyond text and align with the expected content formats and locations to drive visibility.

For queries involving troubleshooting, product comparisons, lived experiences, or niche use cases, AI systems often prioritize user-generated content (UGC) and forum discussions.

“The generative models value this type of content a lot more because it’s looking for that authentic, diverse, and situational insight that you can’t necessarily find on your marketing content pages,” Zach said.

Also, focus on using entity-rich, embedding-friendly language. Write with clearly defined entities, use consistent terminology, and include modifiers and descriptors to help differentiate similar entities (i.e., qualifiers like size, function, location, and purpose).

Other things to consider:

LLMs can and do use structured data as part of RAG pipelines.
Semantic triples (subject-predicate-object) can help search engines understand context better by identifying entities, establishing connections, and building a web of interconnected concepts.
Unique content or proprietary data increases the likelihood that your page is retrieved and cited as authoritative in RAG pipelines.
Clearly defined, straightforward sentences reduce embedding noise and retrieval errors.

This content assessment flow chart can help gauge whether your content is worthy of stopping the scroll:

Preparing Your Business for an AI Future

When 90% of content is AI-generated, successful businesses will be engineering content that AI systems can actually use. That means building for extractability, structuring for synthesis, and ensuring your first-party data is accessible through the channels that agentic systems trust.

The good news is that many of the principles we’ve covered aren’t entirely new. Authority, freshness, clarity, and structured data have always mattered. What’s changed is how these signals are evaluated.

If you’re feeling overwhelmed by the scope of these changes, you’re not alone. The intersection of content strategy, technical SEO, and AI optimization requires new tools, new workflows, and new expertise.

At iPullRank, we’ve built frameworks and tools specifically to help businesses navigate this transition. Whether you need help auditing your content for AI readiness, analyzing your log files for crawler behavior, or developing an omnimedia strategy that works across modalities, we’re here to help. Reach out to learn how we can help future-proof your content.

And check out the other 2 articles in this series based on our webinars with Sitebulb:

Want to learn more about AI visibility?

Read the AI Search Manual

The post Content Strategy Tips for Visibility in the Age of AI Slop appeared first on iPullRank.