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The New Local SEO Reality: AI Up Top, Trust Still Underneath

Caleb Turner on May 12, 2026

If you want the short version, here’s what I’m seeing right now. Local SEO is still very much alive, but the way people discover and choose local businesses is changing. Google is moving toward a more conversational, AI-engine experience, so it’s not enough to just rank anymore. Your business also needs to be easy for Google to understand, easy for AI systems to summarize, and easy for real people to trust.

I recently sat down with Darren Shaw from Whitespark, and the conversation reminded me why local SEO is still one of the most valuable parts of search. 

Darren has been in this space long enough to see local search evolve from simple map pack tactics into a much more sophisticated system of proximity, relevance, reviews, business data, and now AI recommendations.

That kind of perspective matters to me. A lot of people in SEO get distracted by the latest acronym or trend, but what I appreciate about Darren is that he has seen enough cycles to know what actually holds up over time. 

The main takeaway of the conversation was pretty simple. AI is changing the way people experience search, but it is not replacing strong local SEO fundamentals. If anything, it is making them even more important.

The local search interface is changing

Google maps, maps. SEO, AEO, GEO

The local search interface is changing because people are no longer limited to short keyword searches like “plumber near me” or “dentist Dallas.” They are asking longer, more specific questions. They want recommendations based on urgency, preference, trust, location, availability, and context.

That changes the job of local SEO.

For a long time, local SEO was mostly about ranking in the local pack for short keywords. I still think that matters. I just don’t think it tells the whole story anymore. In an AI search environment, the bigger question is whether the system understands your business well enough to recommend it in the right situation.

That’s why I think Google’s Ask Maps feature matters. Google is turning Maps into a Gemini-powered conversational experience where people can ask real-world questions about places and get personalized recommendations based on Maps data. To me, that’s a pretty clear signal. Maps is becoming more than a directory. It’s becoming an AI search layer that helps people make real-world decisions.

When I look at a search like finding a nearby dentist who is good with anxious patients and offers evening appointments, I see a very different kind of intent than a simple “dentist near me” search. The practice that wins that query is usually not just the one closest to the person searching. It is the one with the clearest service information, the strongest reviews, useful supporting content, and enough trust signals that Google feels confident recommending it.

That’s where local SEO is headed.

Local SEO was never just about ranking

GEO, AEO, search, maps, local SEO

I never viewed local SEO as just a rankings play. A local business does not win because it shows up. It wins when someone chooses it. Rankings give you visibility. Selection is what turns that visibility into revenue.

That’s where I see a lot of businesses get stuck. They obsess over where they appear and ignore how they look when a real customer starts comparing options. You can rank well and still lose the lead if your Google Business Profile feels thin, your reviews do not build trust, your photos look outdated, or your service details do not answer the question the buyer actually has.

That matters even more now because AI-driven local search is not just about retrieving businesses. It is comparing them. It is summarizing them. It is trying to decide which option looks most useful for the person searching.

So when I look at local visibility, I want a business to be built for both retrieval and preference. Retrieval means Google can find it and understand it. Preference means the system has enough confidence to show it as the better fit. That’s the standard I care about.

Reviews are one of the strongest trust layers in local SEO

Reviews, customers, AEO, SEO, GEO

I think reviews are one of the strongest trust layers in local SEO because they influence both rankings and conversions. Darren made that point clearly in our conversation, and I agree with him completely.

A lot of business owners still think about reviews as reputation management. That is part of it, but it’s not the whole story. Reviews are also content. They capture the real customer experience in natural language. People talk about services, staff, neighborhoods, problems, outcomes, wait times, pricing concerns, and the reasons they chose that business in the first place.

That kind of language helps customers decide, but it also helps search systems understand the business at a deeper level.

If patients keep saying a dental office is great with nervous patients, that is a meaningful signal. If homeowners keep mentioning emergency roof repair after storms, that matters too. If restaurant customers keep bringing up gluten-free options, fast service, parking, or atmosphere, that gives useful context.

I think this matters even more in AI local search because the query is getting more conversational. People are not just searching for a category anymore. They’re looking for the right fit.

Review recency matters more than most businesses realize

Reviews, AEO, SEO, GEO

Review recency matters more than most businesses realize because customers and search systems both care about what’s happening now. A business with a large review count but no recent activity can look stale. A business with fewer total reviews but consistent new reviews can look active, trusted, and relevant.

That does not mean businesses should chase reviews in a sloppy or aggressive way. It means review generation should become part of the operating process. Ask at the right time. Make it easy. Train the team. Use a simple review link or QR code when appropriate. Follow up naturally. Keep the process tied to real customer experience.

The mistake is treating reviews like a one-time campaign. Reviews should be a steady signal that the business is active, trusted, and still delivering.

That is especially important in competitive local markets where every business claims to be the best. Reviews give buyers and AI systems a stronger reason to believe one business over another.

Your Google Business Profile has to be built like a conversion asset

Restaurant, reviews, GEO, AEO

I look at a Google Business Profile as a conversion asset, not a box to check. A lot of local buyers decide who to call before they ever spend much time on a website, so the profile itself has to help you win the click, the call, or the visit.

That is one reason local SEO has been more protected from AI disruption than pure informational SEO. If someone wants a definition or a basic how-to answer, AI may handle that without sending the user anywhere. But if someone needs a dentist, lawyer, plumber, med spa, restaurant, HVAC company, or roofer, they still have to choose a real business.

And a lot of that choice happens inside Google.

Your categories, services, products, photos, reviews, posts, hours, attributes, and business description all shape that decision. Some of those elements can affect rankings. Others do more of the conversion work. I care about both.

I see too many businesses treat their Google Business Profile like a one-time setup task. They choose a category once, add a few photos, write a generic description, and move on. Then they wonder why competitors with stronger profiles keep getting the calls.

That’s not how I approach it.

If your profile is one of the main places customers compare you, it needs to be built intentionally and managed like a living visibility asset.

Categories can quietly make or break local visibility

Categories, AEO, GEO, SEO

Categories are one of those local SEO details that seem small until they start costing a business real visibility. I check them early because they tell Google what kind of business it is. If the primary category is too broad or just not aligned with the core offer, you can create a relevance problem before anything else even gets a chance to help.

I see this a lot. A law firm selects “law firm” when the better fit might be personal injury attorney, criminal justice attorney, or bankruptcy attorney. A dental practice might not reflect the services it actually wants to be found for, which means it can miss visibility for higher-intent searches tied to emergency dentistry, cosmetic dentistry, implants, or orthodontics.

This is one of the first things I look at in a local SEO audit because it’s simple, but the impact can be significant. The primary category needs to match the main thing the business wants to be found for. Then the supporting categories, services, website content, reviews, and citations all need to reinforce that same relevance. That’s how I help close the gap between what a business offers and what Google understands.

Citations are still useful, but they are not a magic shortcut

Citations, GEO, AEO, SEO

Citations still matter, sure, but I don’t look at them as the shortcut they used to be in local SEO.

I think of them as trust and legitimacy signals.

If a business only shows up on its website and Google Business Profile, that’s a pretty thin footprint. If the same business information shows up consistently across major directories, industry platforms, local sources, review sites, and other trusted third-party profiles, the business looks more established and easier to verify.

I think that matters for Google. I also think it matters even more as AI systems take a bigger role in local discovery.

What I would not do is push a business into hundreds of weak directories just to inflate the count. I would focus on building a clean, consistent presence in the places that actually matter. That usually means major business directories, relevant local sites, industry-specific platforms, review sources, and professional associations.

The right mix depends on the business. If I am working with a lawyer, I am looking at legal directories. If it is a dentist, I want healthcare-related profiles. If it is a home services company, I am thinking about home services platforms, local associations, and trade directories.

I am not after volume here. I am after confidence.

AI local search rewards complete information

AI local search, AEO, GEO, SEO

AI local search rewards complete information because conversational searches need more context.

A short keyword search might only need a service and a location. But if someone is looking for the best emergency plumber nearby who can come out tonight and has strong reviews, the system has a lot more to evaluate. It has to understand the service, the urgency, the area, the availability, the review quality, and whether the business looks trustworthy.

If your business does not communicate those details clearly, you are asking Google and AI tools to fill in the blanks. I do not like building marketing strategies around guesswork. I would rather feed the system better information.

That starts with the basics. I want the website to answer real buyer questions. I want the Google Business Profile to list clear services. I want reviews that are recent and specific. I want citations to stay consistent. I want photos that build trust. And I want the content to explain who the business helps, what it does, where it works, and why someone should choose it.

This is not about stuffing keywords into every surface. It is about making the business easier to retrieve, understand, compare, and recommend.

That is the difference I see between old local SEO and local SEO built for AI search.

Old SEO tactics are not enough by themselves

SEO tactics, GEO, AEO, search

Old SEO tactics are not enough on their own anymore, especially in local search. Google is doing more than matching words. It’s getting more interpretive. It’s trying to understand the business itself, the context around the search, the evidence behind the claims, the sentiment around the brand, and whether the fit is actually right for that customer’s need.

That does not make traditional SEO useless. I still care about the fundamentals. Technical health matters. Crawlable pages matter. Strong content matters. Internal linking, backlinks, local relevance, and clean business data all still do real work.

But now I see those pieces as support for something bigger.

I am not just asking if a business can rank for a keyword. I am asking if Google would feel confident recommending that business for a specific situation.

That shift changes how I approach local strategy.

A generic service page is not enough. A thin Google Business Profile is not enough. A few old reviews are not enough. A list of locations without real local proof is not enough.

If a business wants to win, it has to become the clearest and most credible answer for the situations it wants to own.

That’s the work.

The biggest opportunity is fixing selection, not just visibility

Visibility, AEO, GEO, SEO

The biggest opportunity is fixing selection, not just visibility. Most businesses want more rankings, but many of them are already leaking leads from the visibility they have.

If your profile gets impressions but few calls, that is a selection problem. If people click your profile but choose a competitor, that is a selection problem. If you rank locally but your reviews, photos, services, and website do not build confidence, that is a selection problem.

This is why I don’t separate SEO from conversion.

A stronger review profile can improve trust. Better photos can reduce hesitation. Clearer services can increase relevance. Better website content can support both users and AI systems. Stronger citations can reinforce legitimacy. A better Google Business Profile can turn more visibility into actual leads.

That is how local SEO should be judged.

Not just “Did rankings move?”

The better question is, “Did the business become easier to find, trust, and choose?

Local SEO is getting more conversational, more personalized, and more influenced by AI, but the goal is still the same. I want the right customers to find your business, trust what they see, and feel confident choosing you.

What has changed is the standard. If you want to show up well in AI local search, your business needs complete information, recent reviews, accurate categories, clear service details, consistent listings, and content that answers the real questions people ask before they ever contact you.

If you want a practical game plan, schedule a call with me. I’ll show you where your local visibility is leaking, what is keeping your business from being preferred, and which fixes I would prioritize first.

AEO SEO Trends | Know what matters now | SEO Rank Media

Caleb Turner on April 10, 2026

In a recent video interview, which you can find on my YouTube channel, Mitko asked me why it is that so many people say that “SEO didn’t work” for them.

A lot of businesses think search is supposed to reward them quickly just because they made changes, published content, or hired somebody to “do SEO.” That is not really how Google operates.

In fact, one of the biggest mistakes I see is people expecting fast, clean, obvious feedback from a system that is built to be cautious, suspicious of manipulation, and slow to hand out trust.

That is the frame I want people to understand.

I talk about this in the above recent YouTube video.

In the video, I talked about a Google patent that gets at this idea directly. The core point is that when changes are made to a document or a site, Google may not just cleanly move that page from old rank to new rank in a straight line. There can be a transition period. And during that transition period, the response can look delayed, negative, random, or just unexpected.

That part matters a lot.

Because what most businesses want is this: “I made the optimization, so now show me the reward.”

But what Google seems to be saying is closer to this: “You made a change. Fine. I am still going to watch it. I am still going to test it. I am still going to make sure I am not being manipulated.”

That is a completely different mindset.

And once you understand that, a lot of what confuses people starts to make more sense. 

So when I look at Google, I do not look at it like some simple machine where you press a button and get a ranking. I look at it more like a system that is trying to protect itself. It wants to separate genuine value from manipulation. It wants to avoid being gamed. It wants to see what holds up.

And if that is how the system operates, then the businesses that win are usually not the ones chasing instant movement. They are the ones doing steady, credible, useful work long enough for trust to build.

That is the foundation.

And once you understand that foundation, the next two shifts matter even more.

Why TurboQuant Matters More Than It Sounds

 

Turboquant shows AI search getting faster and efficient

Now let’s build on that.

If Google is already operating from a place of caution and trust, then the next big question is how it gets better at understanding meaning, intent, and usefulness at scale. That is where TurboQuant gets interesting.

The simple version is that TurboQuant points to AI search getting much faster and more efficient at handling vector search. In plain English, that means better semantic understanding across huge amounts of information. It means systems can process meaning more efficiently instead of relying so heavily on simple term matching and slower methods.

What gets my attention here is not just the technical side of it. It is what it suggests about where search is going.

If Google gets faster at building and using these semantic representations, then it gets better at understanding what a person is actually looking for, not just what exact words they typed. It also gets better at pulling from a much larger pool of relevant information when deciding what to surface.

That raises the standard.

Because now it is not enough to just have a page that mentions the right terms. It is not enough to sound vaguely relevant. The system is moving more toward understanding whether your page actually helps with the need behind the query.

That is a big difference.

So when I look at something like TurboQuant, I do not see it as some separate “AI thing” over here and SEO over there. I see it as part of the same direction Google has been moving in for years. Better understanding. Better retrieval. Better intent matching. Better filtering of weak, generic, copycat content.

And that brings more pressure, not less.

You do not respond to this by pumping out more empty pages. You respond by making your content more answer-ready, more credible, and more tightly aligned with what real people and AI engines are looking for. You reduce fluff. You improve structure. You make your pages easier to understand. You make the value obvious.

That is what I think a lot of people miss when they hear about new search technology. They want a trick. They want a shortcut. But most of the time, what these changes really do is increase the reward for clarity and increase the penalty for weak thinking.

Why ChatGPT Just Became a Bigger Product-Discovery Surface

ChatGPT agentic commerce

Now, here is the other shift brands need to pay attention to.

ChatGPT just became a more serious place for product discovery.

With the richer shopping experience that rolled out in late March, users can now begin to browse more visually, compare products side by side, and move through product consideration in a much more direct way inside ChatGPT itself. To me, that matters because AI visibility is not just about being cited anymore. It is increasingly about being considered.

That is a different stage of the journey.

A lot of brands are still thinking, “Do I show up?” But that is too basic now. The better question is, “When I do show up, can I be understood, compared, and chosen?”

Because that is where this is going.

If a person is using ChatGPT to explore products, compare options, and narrow decisions, then your visibility problem is no longer just a traffic problem. It is a retrieval and consideration problem. 

Can the system pull in the right information about what you sell? Can it understand what makes your product different? Can it present your offer in a way that makes sense next to alternatives?

If not, you are leaking visibility at the exact moment somebody is trying to make a decision.

And this is where I think the connection becomes really clear.

Google’s operating logic has long been about resisting manipulation and trying to reward actual value over time. 

New developments like TurboQuant suggest search systems are getting faster and better at understanding meaning and intent. 

And now ChatGPT is becoming a stronger environment for product comparison and discovery.

Put all of that together, and the pattern is obvious.

The brands that win are not going to be the ones relying on shallow tactics, inflated claims, or messy pages that make people work to understand them. The brands that win are going to be easier to retrieve, easier to understand, easier to compare, and easier to trust.

That is the real shift.

So if I am looking at a brand right now, I am asking a few simple questions:

Is the message clear?

Is the offer easy to understand?

Is the content genuinely useful?

Are the pages structured in a way that helps both humans and machines?

When somebody compares this brand to alternatives, is there a strong reason to choose it?

That is where I would put my attention.

And this is what we help brands accomplish organically.

Because search is not just a ranking environment anymore. It is becoming more of a retrieval, evaluation, and selection environment. And honestly, that has been building for a while. It is just getting harder to ignore now.

Be clearer.
Be more useful.
Be more credible.
Be easier to understand.
And stop expecting a trust-based system to behave like a vending machine.

That is the mindset shift.

And the businesses that make that shift early are going to be in a much better position than the ones still waiting for instant feedback from systems that aren’t designed to work that way.

The Two Arenas of AI Search Optimization and the Process I Use to Win Both

Caleb Turner on March 19, 2026

If you want to win in AI search, you have to win two distinct, but related arenas. Getting retrieved and getting preferred.

I don’t treat AI visibility like a vanity ranking game. I care more about whether a brand is consistently pulled into the right conversations and preferred for high intent prompts.

The research supports that approach. The 2023 GEO study introduced Generative Engine Optimization and reported visibility gains of up to 40% from the right optimization methods.

The E-GEO: A Testbed for Generative Engine Optimization in E-Commerce study found that intent match, factuality, differentiation, and scannable formatting consistently improved ranking outcomes.

I look at AI search as a two-step system.

First, your brand has to get retrieved. That means your site, brand, or page has to make it into the possible answer set. Then, once you are in the set, you have to get preferred. That second step is the re-ranking layer, where the model decides which option is the clearest fit for the user’s prompt. The 2025 E-GEO paper formalizes that same retrieval-plus-ranking framework.

That is why I don’t separate off-page SEO from on-page AI optimization. Off-page authority, mentions, citations, links, and digital PR help you get pulled in. Clear, well-structured, evidence backed content helps you get chosen once you are there.

LLM difference

I do not chase vanity rankings. Ranking for a head term like IT managed services might look good in a dashboard, but it tells me almost nothing about whether I am visible when a real buyer is actually close to choosing a provider. 

People looking to purchase are not stopping at short category phrases anymore. They are asking long, detailed, constraint-heavy buying questions like:

 “We run a 12-location healthcare group in Dallas and need a HIPAA-compliant IT provider that can manage endpoints, harden Microsoft 365, support audits, and help with compliance documentation. Who should we talk to?” 

“Which Dallas managed IT companies can provide backup and disaster recovery with a one-hour recovery time objective, a 15-minute recovery point objective, and 24/7 incident response?” 

“We need a managed cybersecurity partner for a multi-site medical practice that can handle SIEM, endpoint detection, phishing training, and policy documentation. Who actually does all of that well?” 

This shift is not a theory anymore. Amazon says shoppers are already using Rufus to type natural-language questions, compare options, and ask granular product questions, while Bain found that 42% of large language model users already use these platforms for shopping recommendations.

In other words, I care less about whether I “rank #1” for a trophy keyword and more about whether search engines and large language models consistently understand that my brand is a strong answer for the high-intent situations my market actually cares about. That is what matters.

Query journey

The process I use to increase AI visibility

My process starts with query selection, not page editing.

First, I identify the money prompts. These are the commercial, comparison-driven, bottom-of-funnel queries that reflect how people actually ask for help today. Then I expand those into related prompt variations so I can see the full intent map around the topic. 

After that, I compare my page against competing pages and measure which page is most semantically aligned to the query. If my page is weak, I do not guess why. I fix the relevance gap.

Then I optimize at the chunk level.

Modern AI systems often retrieve and reuse sections, not entire pages. That is why I built an internal workflow that pulls strong content chunks, scores them against the target query, rewrites the best chunk into cleaner formats, and then re-ranks the outputs to identify the version most likely to be extracted and preferred.

In practice, the best version is usually the one that is easiest to scan, easiest to trust, and easiest to quote.

What I like about the 2025 E-GEO study is that it pushes the conversation beyond hype.

The researchers tested more than 7,000 realistic product queries, evaluated 15 common rewriting heuristics, and found that the best results came from a stable pattern rather than a gimmick. The content that rose tended to align closely with user intent, preserve factual accuracy, clearly differentiate itself, and present information in a format that is easy for the model to process.

I have had conversations in person and even on my YouTube channel with industry leading colleagues like Nick Eubanks, Ross Simmonds, and Charles Floate, and even when the tactics vary, the same fundamentals keep coming up: build authority beyond your site, understand how people really search, and publish content that is clear enough to be reused in answers.

That is a big reason I believe AI visibility is not a trick. It is a discipline.

GEO optimization

You need distribution. You need credibility. You need answer-ready content. And you need to stop measuring success with outdated vanity metrics that were built for a different search era.

AI visibility is not about gaming one prompt or chasing one keyword. It is about increasing the probability that your brand gets retrieved for the right conversations and is preferred when the model compares options.

Parent/head terms still matter as category and entity anchors for retrieval, internal linking, and query reformulation, but they do not deserve to be your primary measurement system for AI-era revenue visibility. 

The KPI should be whether your brand is retrieved and preferred across the high-intent prompt set that real buyers actually use.

That means winning both arenas: off-page presence strong enough to get you into the set, and on-page structure strong enough to move you to the top.

That’s the process I use, and it’s the process I keep seeing validated by research, by testing, and by conversations with other people deep in this space. If you focus on those fundamentals, you stop chasing vanity and start building visibility that actually compounds. Schedule an introductory call with me today to discuss how we can do this for your brand.

What the 2025 GEO Study Reveals About Ranking in AI Answers (and How I Apply It)

Caleb Turner on January 27, 2026

TL;DR

I built a custom internal tool inspired by that study to help me (and my clients) turn messy, long-form information into “AI-ready” answer blocks that are more likely to be pulled and preferred in AI results. We’re actively expanding that tool to make it more comprehensive, and it’s already being used to guide client content.

I’ve spent the last couple of years watching the same pattern play out across industries:

  • Some brands get mentioned in AI answers… but show up as the third or fourth recommendation.
  • Others get pulled sometimes… but inconsistently.
  • And a few dominate because their content is the easiest to trust, quote, and rank.

That’s why I pay attention when researchers publish something that goes beyond opinions and actually tests what works.

The research team from MIT and Columbia University ran the study (E-GEO: A Testbed for Generative Engine Optimization in E-Commerce) to measure how answer engine rankings change based on how content is written. They used thousands of real-world shopping-style queries, rewrote product descriptions using different approaches, and tracked whether the rewritten versions moved up or down in AI rankings.

What I’m sharing below is the practical version of what matters, and how you can apply it whether you sell products or services.

The two-step reality: getting pulled vs getting preferred

RAG process

Summary of RAG process

Most brands only think about one part of the problem.

Step 1: Retrieval (getting pulled in)

This is whether your page, brand, or product even gets included as a possible answer.

Step 2: Re-ranking (getting preferred)

This is where the AI decides what’s “best,” “second best,” and so on.

The 2025 study focused heavily on that second step: once options are in the set, what makes one rise to the top?

If you’re investing in content for AI visibility, you want both:

● content that reliably gets pulled
● content that reliably gets ranked as the preferred option

What the researchers actually did (in plain English)

Reddit queries matched to Amazon Listings

Reddit queries matched to Amazon Listings

Here’s the simple version of the test:

  1. They took 7,000+ real “what should I buy?” style posts from Reddit.
  2. They matched each query to the 10 most relevant Amazon product listings using a semantic similarity method (basically, “meaning match,” not just keyword match).
  3. They rewrote product descriptions using different prompt styles.
  4. They measured whether the rewritten descriptions moved up in the AI’s ranking.
  5. They then used a second model to iteratively improve the rewrite prompts until the rewrites performed better.

The purpose wasn’t to find a trick. It was to find patterns that hold up repeatedly across many queries and products.

That’s exactly what brands need right now, repeatable rules, not hype.

The most important practical takeaway

ranking in ChatGPT

ranking in ChatGPT

AI rankings reward content that makes it easy to confidently choose and easy to quote.

That’s why the winning patterns consistently included:

1) Clear intent match

The best performing rewrites aligned tightly with what the user actually asked for, especially long, conversational queries with constraints.

Not “Knife set for kitchen.”

More like:
“Premium durable knife set with minimal upkeep needed.”

That shift matters because it directly mirrors the user’s real goal.

2) Factual grounding

One of the most consistent themes was factuality. Keep claims accurate, avoid embellishment, and preserve what can be supported.

In real life, that means:

● don’t guess
● don’t inflate benefits
● don’t claim “best” without support

3) Clear differentiation (your “why us” without fluff)

Competitive positioning mattered, especially when it was expressed as concrete differences, not generic marketing language.

Examples:

● materials, specs, certifications
● what’s included vs not included
● warranty terms
● durability, maintenance requirements
● constraints the product/service is best for (and not best for)

4) Evidence signals

When you can back something up with data or reputable references, do it. Evidence helps the AI system “trust” the content and reuse it.

For service businesses, evidence can be:

● licensing and certifications
● documented process steps
● before/after metrics
● review volume and rating
● pricing ranges and what drives them

5) Scannable formatting

This one is huge, and it matches what I’ve seen in the wild. Content that’s easy to scan is easier for AI systems to lift into answers.

Headings, bullets, short blocks, ranges, and direct definitions beat long paragraphs every time.

What didn’t work as well

GEO and AEO Heuristics that did not work well

GEO and AEO Heuristics that did not work well

A lot of “internet advice” about AI visibility leans into tone or style:

● “Write like an ad”
● “Be super persuasive”
● “Tell a story”
● “Sound authoritative”

The study showed that these approaches can be inconsistent, and in some cases can even hurt performance if they reduce factual clarity or drift away from the user’s intent.

The takeaway: style is secondary; structure and usefulness come first.

The custom tool I built (inspired by the study)

meta optimizer for GEO and AEO

meta optimizer for GEO and AEO

This study didn’t just confirm what I suspected, it gave me a structure I could build around.

So I built an internal GPT-powered re-ranking workflow inspired by the study’s re-ranking logic.

Here’s what it does in practical terms:

What my tool does today

  1. I feed it a target query (example: “How much does carpet cleaning cost in Northern Virginia?”).
  2. It ingests multiple content “chunks” pulled from top-performing pages on Google.
  3. It ranks those chunks based on which one most directly and completely answers the query.
  4. It rewrites the best chunk into multiple output formats, especially:

○ a tight “answer block”
○ a highly scannable version with headings and bullets

  1. It re-ranks the outputs and recommends the version most likely to be extractable and preferred in AI answers.

In short, it helps us consistently produce content that is accurate, aligned with intent, and formatted for extraction, which is exactly what the study suggests is repeatably effective.

How we’re expanding it

Right now we’re expanding the workflow to be more iterative, and mathematical about which content is most optimized. This will result in:

● content that more accurately reflects what AI engines reward
● stronger evidence handling
● consistency checks
● broader content formats

How we’re using it for clients

We’re already using this tool in our client work to:

● upgrade existing pages into AI-ready “answer-first” structures
● produce scannable sections that AI engines can quote cleanly
● reduce fluff while increasing proof and clarity
● align content with how people actually ask questions in AI tools

This is one of the ways we’ve been able to move faster while staying grounded and factual, because the tool forces discipline around intent, structure, and evidence.

Final thoughts

ai seo

ai seo

The 2025 MIT + Columbia E-GEO study supports something I’ve been emphasizing for a while:

If you want to rank well in AI answers, your content has to do more than “sound good.” It has to be the clearest match to the user’s intent, backed by facts, and formatted in a way that’s easy to extract and trust.

That’s why I built a tool around this, and why we’re expanding it and using it actively in client content.

If you want your site to show up more often and be preferred in AI answers, request my free AI visibility checklist. I’ll review your site and tell you:

● what’s preventing AI engines from pulling your pages
● what’s keeping you from being ranked as the “top” recommendation
● which pages to fix first for the fastest impact

You can implement the changes yourself, or my team at SEO Rank Media can handle it for you.

The llms.txt debate: what’s real, what’s hype, and how to use it (safely)

Caleb Turner on October 9, 2025

Short version:
llms.txt is a proposal, not a formal web standard. Some sites ship it; some AI tools and SEO plugins promote it. Google says you don’t need it to show up in AI Overviews or any other Google products.

But: …like any public .txt file, an llms.txt URL can be crawled and indexed, so in rare cases a bare, unstyled text page could show up in search instead of your real, designed page.

I know of someone who created a playful file called cats.txt to make a simple point: Google can index plain text files if they’re publicly accessible and discoverable. In other words, the name doesn’t matter, if it’s a .txt and reachable, it can show up in search, just like any other indexable file type.

If a text file is publicly visible on your site, search engines can list it, which you certainly don’t want. To prevent this, send X-Robots-Tag: noindex in the HTTP response (works for non-HTML files), and if you want to point search engines to the right page, add a Link: <https://your-page>; rel=”canonical” header. Here’s what a real plain-text doc looks like in a browser (the kind that could hypothetically rank if you don’t block indexing):
 https://developers.cloudflare.com/llms.txt.

Google’s documentation covers both the noindex response header for non-HTML resources and using an HTTP canonical header.

Below is a clear walk-through, first in plain English (with analogies), then the technical details and safest practices.

What is llms.txt?

llms.txt is a simple text file you put at example.com/llms.txt. The file lists or summarizes your most important pages in Markdown, so AI systems can more easily read and use your content. Think of it like a cheat-sheet menu for bots: “Here are the dishes you should try; here’s how to ingest or interpret them.” It’s inspired by robots.txt and sitemaps, but it isn’t an official protocol like those are, just a community proposal.

Today, there’s no universal adoption. A directory of live implementations shows many developer-tooling/docs sites experimenting, often paired with a bigger llms-full.txt that expands the content.

John Mueller / Google’s stance:  In a conversation with Caleb and a few other colleagues, Google’s John Mueller made it clear what side of the debate he’s on. You don’t need llms.txt; the guidance is to keep following normal SEO practices.

Why is there a debate?

John Mueller comment highlighting the llms.txt debate around SEO tools, AI search hype, and whether websites need the file.

The “we don’t need this” camp

  • Google says it won’t use llms.txt for AI search results, so for many sites this is optional at best.
  • llms.txt isn’t a standard; support and behavior differ by bot. In other words, don’t expect consistent results.

The “we’re seeing activity in the wild” camp

  • Practitioners have shared examples and logs showing Google indexing llms.txt pages, alongside surges of bot hits when large platforms roll it out, because it’s just a public text file like any other.
  • That doesn’t prove Google uses llms.txt for AI search results. It only proves that public .txt files can be crawled and indexed, which Google’s docs have said for years.

A layperson’s guide to the technical bits

  • robots.txt vs noindex
    robots.txt     is like a bouncer who tells certain crawlers not to walk certain halls. It doesn’t guarantee your URLs won’t show up in the search phone book (the index). Pages blocked in robots can still be indexed by URL if they’re linked elsewhere. If you truly don’t want a URL in the index, use noindex, that’s a separate rule delivered in HTML or HTTP headers.
  • X-Robots-Tag and canonical
    For non-HTML files like .txt, the right place to control indexing is the HTTP response header.

    • X-Robots-Tag: noindex = “don’t list this address in Google.”
    • Link: <https://example.com/page>; rel=”canonical” = “if you do need to reference this, use our public address here.” (Google supports canonical in the header for non-HTML formats.)

  • User-Agent/UA and why blocking by UA isn’t enough
    “UA” is the name badge a crawler shows at the door (e.g., Googlebot, GPTBot). You can write per-UA rules in robots.txt, and major AI vendors document their UA strings.

But name badges can be forged; to be sure a request is truly a vendor’s bot, verify by reverse-DNS/IP, not just the UA string. Cloudflare has even accused some AI crawlers of stealth crawling (changing badges and IPs).

  • “Cloaking” and dynamic rendering
    Serving different content to bots than to users is a slippery slope. Google considers cloaking a spam tactic when bots and users see materially different things. Google also deprecated dynamic rendering (bot-only HTML) as a long-term approach. If you want a bot-friendly version, keep the substance the same as what people see.

The cannibalization risk (and how to avoid it)

User reviewing a raw indexed page showing the cannibalization risk of llms.txt replacing a designed website page in search results.

Imagine your llms.txt ranks for a branded query. A searcher clicks and lands on a wall of plain text with no design, navigation or conversion paths. That’s the risk: poor UX and lost revenue. It’s not hypothetical, plain text files do get indexed, and practitioners have shown real examples of llms.txt and llms-full.txt pages in the index. We also showed an example earlier in this article.

Fix: keep llms.txt fetchable (so AI tools can read it) but non-indexable in web search with:

X-Robots-Tag: noindex

Link: <https://www.example.com/your-preferred-page>; rel=”canonical”

These are HTTP headers on the llms.txt response, not tags inside the file. This is the safest, standards-compliant way to prevent cannibalization while still letting crawlers pull your file.

If you still want to experiment: safest practices

SEO analyst reviewing safe llms.txt testing practices with analytics before experimenting with AI search visibility.

1) Treat llms.txt as optional, and experimental

Ship it only if it supports real goals. Keep expectations modest; it’s a proposal, not a protocol.

2) Prevent web-search cannibalization

Serve HTTP headers on llms.txt (and llms-full.txt if you publish one):

  • X-Robots-Tag: noindex
  • Optional: Link: <https://www.example.com/the-main-URL>; rel=”canonical”

Think: “Please don’t list this file in the phone book; if you must reference something, here’s the main storefront.”

3) If you want to block certain AI crawlers elsewhere, do it the right way

  • In robots.txt, write rules per UA (e.g., User-agent: GPTBot). Vendors like OpenAI document their bot names.
  • For high-stakes data, verify IPs (reverse-DNS) because UA strings can be faked. Google documents how to verify Googlebot; similar logic applies to others.
  • Be aware: some AI bots have been accused of ignoring robots.txt or crawling stealthily, so consider edge-level blocking if needed (WAF/CDN).

4) What to put in llms.txt (if you use it)

Link to canonical, public pages that you want AI systems to cite: FAQs, policies, product specs, pricing explainer, and key how-tos. Keep it concise; don’t dump the whole site.

5) Instrumentation & monitoring

  • A .txt file is just raw text. Web browsers don’t run code inside it, so you can’t drop a JavaScript analytics snippet (like GA/GTAG) into a .txt and expect it to fire. Browsers only execute scripts when the content is served as a script/HTML type, not text/plain.

If you still want to see who’s fetching that file, look at your server or CDN access logs. Those logs list every request (time, IP, user-agent, URL, etc.), so you can count hits to /llms.txt even without JavaScript. Examples: Apache’s access log and Cloudflare Logs.

  • Watch Search Console: if a text file starts appearing in “Indexed,” revisit your headers. Google’s docs confirm indexing can occur even without crawling the content (e.g., when discovered by links).

6) Don’t block JS/CSS for Googlebot

If your SEO defense plan includes blocking scripts to hide unique content from AI, be careful: blocking JS/CSS broadly can break rendering in Google Search. If you must, target AI bots individually, not Googlebot

The bottom line (for decision-makers)

Business decision-maker weighing the pros and cons of llms.txt for AI search strategy and safe SEO implementation.
  • Not required: Google’s AI Mode doesn’t depend on llms.txt; normal SEO still wins.
  • Not a standard: It’s a proposal with uneven support. Useful for experimenting, especially for docs-heavy products; not a silver bullet.
  • If you try it, ship it safely:

    • Put it at the root.

      Keep it short and link to your best pages.

      Send X-Robots-Tag: noindex and, if helpful, a header canonical.

      Keep content parity; avoid UA-based “special versions” that diverge.
    • If you must block certain AI bots elsewhere, use per-UA robots rules plus IP verification at the edge; be aware of stealth crawlers.

If you want a place to start, you can base your evaluation on current adoption (developer docs ecosystems, directories of live files) and any internal log evidence you have about bot hits to llms.txt. Then decide whether it’s worth maintaining a curated cheat-sheet for AI, or whether your time’s better spent doubling down on structure, internal links, and copy, the proven levers. SEO Rank Media is among the leaders in the AI search conversation. Reach out to explore how we can set your brand up for the future.

The AI Era: Why Search Engines Aren’t Going Anywhere

Caleb Turner on September 22, 2025

There’s a common misunderstanding that large language models (LLMs) like ChatGPT or Gemini are replacing search engines. They aren’t. LLMs change how results are presented and explained, but the heavy lifting of finding, organizing, and ranking the web still belongs to search engines. In plain English: LLMs are the brainy librarians inside of a giant library; search engines are the library’s cataloging system that keeps track of every book, page, and shelf.

Below is a clear look at what each does, why they’re different, and why search is not only sticking around but also growing.

What search engines actually do (and why that matters)

Search engines run a huge, ongoing pipeline that works like this:

  1. Crawl: Automated bots (“crawlers”) visit web pages and take notes on what they find.
  2. Index: Those notes are stored in a gigantic, constantly updated catalog (the “index”).
  3. Rank & Serve: When you search, the engine looks up the most relevant pages in that index and ranks them using complex algorithms.

Google’s own documentation lays out this crawl → index → rank process in detail. If you’ve never read it, it’s surprisingly readable and shows the scope and complexity behind what looks like a simple search box. directly;

You can’t browse Google’s index directly, it’s proprietary and unimaginably large. You query it. If you own a website, you can see your slice of the index in Google Search Console’s Page indexing report, which shows which of your pages are in or out and why. Microsoft offers similar visibility in Bing Webmaster Tools, including a Sitemap Index Coverage report that flags reasons URLs are excluded.

This is the invisible machinery of the open web. It’s what makes it possible to find new content minutes after it’s published and to keep billions of pages ordered enough to be useful.

What LLMs actually do (and what they don’t)

LLMs are trained to predict and compose text. They’re excellent at summarizing, explaining, reformatting, and reasoning over information they’re given. But there are two common misunderstandings:

  • LLMs do not maintain a live, internet-wide search index. The model itself isn’t crawling the web in real time or keeping a searchable catalog of every page like a search engine does. When LLMs need fresh facts, they typically consult a search engine index. Meaning they call a search engine service (UI or API). The search engine then queries its own index, returns ranked results, and the LLM fetches a few of those pages and combines them into the answer it generates for the user. Google literally calls this “grounding with Google Search.”
  • “Browsing” ≠ “crawling.” What we just described is called retrieval and summarization, not operating a global crawler and index. OpenAI’s newer “deep research” mode, for example, plans multi-step lookups and shows sources. Again: retrieval plus synthesis, not running its own universal web index.

This distinction matters because it explains why LLM answers can be hallucinatory. Without a high-quality retrieval step (i.e., search), an LLM is just “guessing” based on training data that could be outdated or incomplete.

That said, ChatGPT (the product of OpenAI) now runs a real web crawler called OAI-SearchBot and maintains OpenAI’s own web index so it can discover pages and show them as cited sources in ChatGPT Search. Which again proves this article’s point: you still need search infrastructure under the LLM.

The winning combo: grounding LLMs with search

The industry term for blending search with generation is Retrieval-Augmented Generation (RAG). In RAG, the system first retrieves relevant documents from a trusted source (like a search index or an enterprise knowledge base) and then generates an answer that cites those sources. Requiring the AI search engine to cite its sources can also dramatically reduce hallucinations. The original RAG research popularized this approach in 2020, and it’s now widely used.

You’ll see this philosophy in multiple places:

  • Google Gemini / AI Overviews: “Grounding with Google Search” pipes real-time search results into the model and returns answers with citations.
  • Vertex AI: Google Cloud’s guidance explicitly recommends grounding model outputs in verifiable data, via Search, RAG, Maps, and more, to reduce hallucinations.

The big picture: LLMs are the presentation and reasoning layer; search is the fact-finding and verification layer. You need both.

The library and the librarian

Think of the web as a giant library:

  • The search engine builds and maintains the card catalog (the index). It constantly scans new “books” (web pages), decides where they belong, and keeps the catalog current.
  • The LLM is the librarian who reads the relevant pages you point to and then explains them in friendly language, weaving them into a clear, direct answer. If the librarian is allowed to cite the exact books and page numbers, you can check the work.

When the librarian doesn’t check the catalog first and just “remembers” what books might say, mistakes happen. That’s why modern AI features emphasize grounding and citations.

“But aren’t people just using AI instead of Google now?”

Short answer: no. AI usage is up, and Google Search remains massive and growing.

  • Alphabet’s earnings releases and CEO remarks throughout 2025 show double-digit growth in Search revenue and healthy overall query growth, including a 70% year-over-year jump in Google Lens searches, much of which is incremental (i.e., additional to traditional text queries). That’s expansion, not replacement.
  • Independent financial reporting backs this up: multiple quarters in 2025 attribute Alphabet’s outperformance partly to strength in core search, even as AI features roll out alongside it.

It’s also useful to separate revenue from queries. Revenue grows when users stay engaged and ads remain effective; queries grow when people search more, in more ways. Google has repeatedly highlighted growth in newer, multimodal behavior, like searching with your camera (Lens) or combined gestures, showing search is evolving rather than shrinking.

Why LLMs don’t (and shouldn’t try to) be search engines

  1. Freshness at web scale: The public web adds and changes billions of pages. Keeping a comprehensive, deduplicated, spam-resistant, and continuously updated index is a specialized, infrastructure-heavy job. It’s what search engines were built for.
  2. Transparency and provenance: When an LLM is required to cite sources, users can click and verify. This is standard in grounded systems like Gemini’s “Search grounding” and Vertex’s guidance. Purely generative answers can’t offer the same audit trail.
  3. Governance and site control: Website owners monitor their presence in the index through Google Search Console and Bing Webmaster Tools, diagnosing why pages are in or out. That visibility is essential for a healthy open web and isn’t replaced by a model’s internal training data.
  4. Commercial ecosystems: Search drives measurable, intent-rich traffic that businesses can analyze and optimize. That incentive structure sustains publishing and commerce broadly. The earnings results we’ve seen suggest these dynamics are holding, even as AI features appear in the interface.

What this means for everyday users

  • You’ll see more answers. AI summaries sit on top of search results and often include citations so you can dive deeper. Expect more multimodal options (speak, snap a photo, or draw a circle on your screen) that kick off a search behind the scenes.
  • Quality still wins. If you publish online, the fundamentals matter even more: sitemaps, clean site architecture, crawlability, canonical tags, structured data, and helpful content. Search engines need to index and rank your pages before an LLM can confidently cite them.
  • Trust but verify. AI answers can be great for speed and clarity, but when it counts, click through the citations. Even OpenAI’s more advanced research features emphasize sources precisely because models can still overstate or hallucinate details.

What this means for businesses and publishers

  • Search is still the discovery backbone. Alphabet’s 2025 results show search’s resilience and growth as AI features roll out; the pie is getting bigger, not smaller.
  • Optimize for being cited. When LLMs ground answers, they look for trustworthy, well-structured, crawlable sources. Make sure your pages are indexable and well-labeled so they’re retrieved and cited instead of a forum thread summarizing your work.
  • Expect new query types. Visual and voice-led searches are growing fast, often incrementally—meaning they’re additions to classic typed searches, not replacements. Prepare your content and product data (images, alt text, schema) to be useful in those contexts.

Quick FAQ

Do LLMs “crawl the web”?
No. The applications around LLMs may fetch pages when you ask a question, often via a search partner, but the models themselves don’t operate a global crawler and index like a search engine. Google’s own AI stack explicitly “grounds with Google Search.”

Can I see the web index somewhere?
Not directly. You can query it (e.g., with Google or Bing), and if you own a site, you can inspect your pages’ status in Google Search Console or Bing Webmaster Tools.

Isn’t AI going to reduce searches
Evidence to date suggests the opposite: search usage and revenue are growing while AI features roll out, and newer behaviors like Lens are expanding the pie.

So what’s the right mental model?
Search engines find and rank facts at web scale. LLMs present and reason over those facts. Together, they produce faster, clearer answers, with links you can check.

The bottom line

LLMs have not replaced search; they’ve changed its surface. Underneath any polished AI answer, the classic information-retrieval pipeline, crawling, indexing, retrieval, and ranking, is still doing the heavy lifting. Modern systems combine them: search grounds the answer; the LLM explains it. And if you look at 2025’s numbers and usage patterns, search isn’t going anywhere. It’s evolving, growing, and quietly powering the AI experiences we’re all watching unfold before our eyes. Reach out to SEO Rank Media if you want a partner who understands the direction search is headed and how to position your business to be at the forefront of the evolution.

Caleb Spoke With SEMrush, Here’s What Happened

Caleb Turner on August 25, 2025

I recently compared notes with my good friend Nick Eubanks, VP of Owned Media at SEMrush. We aligned on two truths: 1) classic SEO is table stakes; 2) GEO/AEO is now where growth compounds. Nick also shared what his team is seeing on the ground: traffic from LLM-assisted journeys tends to convert better because people use assistants to research and shortlist—so by the time they hit your site, they’re warmer. In one study, conversion rates were ~4.4× higher from LLM traffic vs. traditional organic. Combined with our own client data, the takeaway is clear: being present in AI answers isn’t a vanity metric—it’s a revenue lever. You can view the full video here.

What actually changed (and why you should care)

Under the hood, AI platforms(like Google’s AI Mode) follow a similar pipeline when they search the web: your content is chunked into passages, embedded as vectors (to capture meaning), fetched by high-speed retrieval, filtered by hybrid re-rankers, and then woven into a natural-language answer. If your passages aren’t crystal-clear semantically, they’re less likely to be pulled into that answer layer. Think “optimize for meaning and entities,” not just keywords.

Google has also published sensible basics for the AI era: keep your site crawlable, avoid blocking AI crawlers (Google-Extended, GeminiBot, GPTBot), use clear headings and schema, and ensure what’s in your structured data actually matches what users see. Over-restrictive snippet settings can remove you from summaries entirely.

No, SEO isn’t dead—it’s your eligibility layer

No, SEO isn’t dead—it’s your eligibility layer

All of the fundamentals still determine whether you’re “in the pool”:

  • Technical hygiene: sane robots.txt and open sitemaps; don’t block JS/CSS required to render full pages; allow Google-Extended, GeminiBot, GPTBot.
  • On-page semantics: precise H1–H3 hierarchy, answer-like formatting, and schema (FAQ/HowTo/Product/Organization). Write in short, extractable passages with concrete stats and full dates.
  • Internal links & authority: interlink topic clusters with descriptive anchors; keep building high-quality links and local citations (they’re still a ranking and trust signal and they feed AI answers).

Think of this as your eligibility layer. Without it, GEO/AEO can’t help you.

GEO/AEO: the layer that gets you retrieved and quoted

Answer engines select passages, not just pages. That means writing “atomic” content—one idea per paragraph, tight sentences, tables or lists where they help, and entity-rich language (use exact names for people, places, and products, minimize pronouns). Each paragraph should stand alone as a mini-answer that a model can lift verbatim.

Two GEO/AEO practices our clients find immediately useful:

  1. Vector validation: Before publishing, embed your draft passages and check similarity against your target questions. Iterate until the cosine similarity is strong (we treat ≥0.85 as a healthy threshold). It’s a quick way to pre-test “retrievability.”
  2. Monitor AI citations: Track when your URLs surface inside Google AI Mode, Perplexity, and ChatGPT. If citations dip, refresh and tighten the affected passages. This is your “share of voice” in AI.

Local & service businesses

Local & service businesses

Local SEO is currently less affected by AI search engines. When people search in their local area for services, they almost always search into Google, and more specifically the map pack.

People have evolved to use LLMs to research about and compare local businesses, so think of AI search optimization as complimentary, not primary for local service based businesses.

Local also remains a smart place to invest because the evidence LLMs prefer—real reviews, practitioner bios, photos, and third-party citations—maps cleanly to what local SEO already does well. 

Assistants won’t just list options; they’ll recommend one provider and explain why. Help them help you:

  • Fortify proof: Showcase review excerpts, credentials, and outcomes as short, quotable blocks.
  • Citations & consistency: Keep your NAP data clean and expand high-relevance local citations; it improves Maps visibility and gives answer engines more reliable signals to quote.

Watch GA4 and Search Console, but with added context: impressions and AI citations often lead the way; raw CTR can be misleading when assistants satisfy the query in-place.

What to do next (simple, high-impact moves)

You don’t need a full overhaul to benefit from GEO/AEO. Start here:

  1. Open the gates: sanity-check robots.txt and server logs; allow Google-Extended, GeminiBot, and GPTBot; keep XML/RSS feeds current.
  2. Make content “answer-able”: give each H2 a direct ≤80-word answer, then details. Use tables/lists for facts and include full dates.
  3. Strengthen semantics: ensure one topic per page, clean H1–H3, and add relevant schema (FAQ/HowTo/Product/Organization).
  4. Validate vectors: quickly test your top five money pages for cosine similarity against priority questions; revise ambiguous phrasing.
  5. Distribute beyond your site: publish the same answers (adapted) across formats—web, video, and communities—because LLMs cite the whole web, not just your domain. For example, studies show that Reddit is a place that AI search engines love to quote from.

Will traditional SEO fade away?

Not in the next five, or ten, years. People will always compare, verify, and buy; what’s changed is the quality bar and the interface. In our clients’ data and in my conversation with Nick, the brands winning AI placements combine (a) clean technical SEO and authoritative content with (b) GEO/AEO practices that make them more likely to be cited in AI searches. That’s the new landscape for 2025-2026.

How AI Mention Trackers Work: A Clear Guide to Understanding Visibility in Large Language Models

Caleb Turner on July 14, 2025

Artificial intelligence is becoming more deeply embedded in the way users search for, engage with, and consume information online. Businesses are now facing a new visibility frontier: large language models (LLMs) like ChatGPT, Claude, Google’s Gemini, and Perplexity. These AI tools are rapidly shifting how people discover brands and products, but there has long been a missing piece for marketers: how can you measure your brand’s presence across these tools?

Enter AI mention trackers—tools like Profound, Peec AI, and others that are helping brands figure out how often they appear in AI-generated answers. Think of them as the modern-day equivalent of media monitoring tools, but instead of scanning newspapers or websites, they scan what the AIs are “saying” about you. Let’s walk through exactly how these tools work, step by step, in simple and clear terms.

Step 1: Feeding Questions to AI Models

The first thing an AI mention tracker does is simulate real-world user queries. For example, if you sell coffee, it might generate prompts like:

  • “What are the best coffee brands for home brewing?”
  • “Which companies sell sustainable coffee beans?”

These questions are either preloaded by the tool or customized by the user. Then, the tool asks these questions to various AI platforms—ChatGPT, Claude, Perplexity, and others. These queries are sent using APIs or simulated browser sessions, mimicking the behavior of a real user.

To make the results more robust, the tool may vary how it phrases the questions, capturing a wider net of responses. This ensures the data reflects how real users might engage with AI tools.

Step 2: Collecting the AI’s Answers

Once the questions are submitted, the AI models reply with natural-language answers. The tracker collects all of these answers—a big pool of unstructured text. If the AI provides source citations or links (as Bing or Google often do), the tool grabs those too.

This phase is about capturing everything that the AI outputs, regardless of whether your brand appears yet.

Step 3: Detecting Brand Mentions

Now comes the scanning. The tool searches through each AI-generated answer looking for specific brand names, website URLs, or product terms. It checks to see if, for example, “Acme Coffee” or “acmecoffee.com” shows up in the text.

This is similar to a human pressing “Ctrl+F” and looking for their company’s name. The tool notes:

  • Where the mention appeared
  • How often it appeared
  • In what context (Was it a top recommendation? Just a mention in passing?)

If the brand doesn’t appear, that’s recorded too. These “non-mentions” are equally important because they show where the AI isn’t recognizing your brand.

Step 4: Counting and Aggregating Mentions

Counting and Aggregating Mentions

The tracker now tallies up the results across many queries and platforms. This helps quantify your brand’s visibility. You might learn that your brand appeared in:

  • 8 out of 20 questions on ChatGPT
  • 10 out of 20 on Google Gemini
  • Only 3 out of 20 on Bing Chat

These numbers are typically translated into metrics like “share of voice” (SOV) or mention frequency. Tools like Profound display this in an easy-to-read dashboard, comparing your visibility to your competitors.

Over time, this creates trend lines that show whether your brand’s AI visibility is improving or declining.

Step 5: Attributing Mentions to Sources

A crucial part of these tools is identifying why an AI mentioned your brand. In many cases, it’s because of external sources cited by the AI model. For example:

  • Bing Chat might footnote your brand with a link to a popular review site
  • Google’s AI Overviews might mention your company and cite your blog or Wikipedia

The tracking tool records these citations and links them to your mentions. This is called “citation analysis.” It helps you understand which articles, websites, or publications are fueling your AI visibility.

When an AI doesn’t mention you but mentions a competitor, these tools can also highlight what sources were cited for them. This gives you ideas about where you might need more coverage.

Step 6: Presenting the Results

Presenting the Results

All of this data gets organized into a simple dashboard. It might tell you:

  • Your brand was mentioned in 40% of answers about “best coffee brands” on ChatGPT this month
  • That’s up from 30% the month before
  • The most frequently cited source was HomeBarista.com
  • Competitor JavaWorld appeared more often than you on Google SGE

Some tools also analyze sentiment: whether the AI’s tone was positive, neutral, or negative about your brand. While more advanced, this adds another layer to understanding your visibility.

A Real-Life Example: Acme Coffee

Imagine you run a fictional brand called Acme Coffee. You want to know if AI tools are recommending you when people ask about coffee.

  1. The tracker sends prompts like “What are the best coffee brands?” to ChatGPT, Claude, Google Gemini, and Bing Chat.
  2. ChatGPT responds with: “Some great coffee brands are Acme Coffee, BeanCo, and JavaWorld.” The tool flags that Acme was mentioned.
  3. Google’s AI says: “According to HomeBarista.com, Acme Coffee roasts top-tier beans.” The tool notes the mention and attributes the source.
  4. Bing Chat doesn’t mention Acme at all but includes JavaWorld. That’s also important intel.

After querying multiple questions and platforms, the tracker produces a report:

  • Acme was mentioned in 7 out of 10 queries on ChatGPT
  • 5 out of 10 on Google Gemini
  • 3 out of 10 on Bing Chat
  • Most Acme mentions cited HomeBarista.com
  • JavaWorld beat Acme by 2 mentions across the board

Tools Like Ahrefs Add Another Layer

Tools Like Ahrefs Add Another Layer

Some platforms, like Ahrefs, take a slightly different but powerful approach. Rather than running queries in real time, Ahrefs leverages a vast existing database of AI responses and questions. You can type in a brand name or topic like “sneakers,” and instantly see a list of relevant queries and AI answers that reference the topic.

This lets you:

  • Identify competitor gaps (queries where your competitors show up but you don’t)
  • Discover new topic opportunities (queries you never thought of that relate to your niche)

This retrospective approach complements real-time trackers like Profound or Peec AI by giving you a broader strategic view.

Tracking LLM Traffic in GA4: Why It Matters

AI visibility isn’t just theoretical. Brands are already seeing meaningful traffic driven by AI tools. Tracking this traffic in Google Analytics 4 (GA4) is now essential.

While Google Search Console still blends AI Overview and AI Mode traffic with regular search, GA4 gives you tools to segment this data more precisely.

Two Main Tracking Approaches:

  1. GA4 Explore Reports:
    • Create a session segment using a custom regex filter to capture traffic from AI sources like ChatGPT, OpenAI, Copilot, Gemini, Perplexity, etc.
    • Visualize this data with line graphs, bar charts, or tables.
  2. Looker Studio Reports:
    • For detailed reports: Create a new channel group in GA4 for AI traffic.
    • For quicker views: Use the same regex filter in your Looker Studio tables and charts.

These dashboards let you:

  • Track how much traffic is coming from AI tools
  • See which pages are being visited from AI answers
  • Understand whether your AI visibility is translating into real engagement

Final Thoughts: Why This Matters

The future of search is increasingly conversational and AI-driven. Tools like Profound, Peec AI, and Ahrefs help marketers stay ahead by answering this crucial question:

“Are the AIs talking about me?”

If they are, great—you can double down on what’s working. If not, you can take action to increase visibility by improving the content on sites that AIs pull from.

AI mention trackers give marketers, PR pros, and SEOs a crucial lens into how modern algorithms perceive and recommend their brands. By bridging the gap between traditional SEO metrics and AI-powered search behaviors, these tools ensure your strategy remains both measurable and forward-looking.

Start tracking now, and you’ll not only see how often you appear in the AI conversation, you’ll start shaping it.

Why Is ROAS No Longer Enough in Google Ads? Here’s What to Do Instead

Caleb Turner on June 10, 2025

The world of Google Ads is changing. While ROAS—Return on Ad Spend—has been the go-to performance metric for years, savvy advertisers are now realizing its limitations. ROAS gives a narrow view of campaign efficiency, but it doesn’t tell the full story when it comes to profit, scale, or long-term growth. Today’s smart marketers are moving beyond this metric to embrace outcome-based strategies rooted in actual business value.

Key Takeaways

  • ROAS often masks the true profitability of campaigns
  • Smart Bidding now prioritizes real business results
  • Demand Gen campaigns reach customers across YouTube, Gmail, and Discover
  • AI is powering not just bidding—but creative and insights too
  • First-party data is now a strategic advantage
  • Strategic scaling wins over sudden budget spikes

Detailed Guide

What’s new in Google Ads?

Google has made major updates to streamline and empower campaign performance. Smart Bidding has been simplified—you now choose “Maximize Conversions” with optional Target CPA, or “Maximize Conversion Value” with optional Target ROAS. This means you’re optimizing for actual results, not micromanaging bid settings.

Demand Gen campaigns are another big leap. They replace Video Action campaigns and run across YouTube, Discover, and Gmail. These formats are built for both brand engagement and conversions, making them ideal for full-funnel strategies. AI also now supports you at every step—from writing headlines to discovering new keywords—giving you predictive power that helps you stay ahead of trends.

Why is ROAS misleading?

ROAS feels like a clear performance metric, but it’s often deceptive. Imagine two campaigns:

  • One spends $1,000/day at 2× ROAS, generating $30,000/month in profit
  • Another spends $100/day at 5× ROAS, but only nets $12,000/month

Which would you choose? The 5× ROAS might look better on paper, but the first campaign brings in over twice the profit. ROAS ignores volume and real economic impact. And that’s why it’s no longer enough.

What should you measure instead?

Start tracking POAS—Profit on Ad Spend. Unlike ROAS, POAS factors in cost of goods sold, transaction fees, and overhead. This gives you a more accurate view of how your ads are really performing. You can even push this data back into Google Ads using server-side tracking, helping the algorithm optimize based on what actually drives profit.

How should you think about attribution?

The buyer’s journey is no longer a straight line. People interact with your brand across devices and platforms before they buy. That’s why last-click attribution is outdated. Modern advertisers are moving to data-driven attribution through GA4. This lets you understand which touchpoints actually influence conversions and make better decisions across your entire funnel.

How do you use first-party data effectively?

With third-party cookies on the way out, your own customer data is more valuable than ever. Tap into your CRM and purchase history to build audience segments based on real buyer behavior. Then, use Google’s Customer Match and Enhanced Conversions to connect this data to your campaigns. This not only improves targeting but also boosts conversion rates significantly.

How important is creative strategy now?

With AI doing more of the heavy lifting behind the scenes, creative is one of your biggest competitive advantages. Dynamic creative testing lets you see which copy, visuals, and CTAs resonate with different segments. Messaging should be tailored—what works for cold leads probably won’t work for warm retargeting audiences. Winning ad creatives are intentional, not generic.

What role do Demand Gen campaigns play?

Demand Gen campaigns give you a unique way to build both brand and performance. They’re immersive, visual, and appear where people are most engaged—YouTube, Gmail, and Discover. These formats are great for building top-of-funnel awareness and generating remarketing audiences that are more likely to convert later. They’re not just about clicks; they’re about presence.

How do you scale effectively?

Many brands rush to increase budgets once they see success—but that can backfire. Controlled scaling is a smarter approach. Increase your ad budget by no more than 20% every 3–5 days. Use Google’s campaign experiments to test changes before committing fully. Try new geos or devices to tap into fresh audiences. Smart scaling is strategic, not reactive.

A Simple Comparison That Says It All

Let’s look at two scenarios:

Scenario A

  • 2× ROAS
  • $1,000/day ad spend
  • $60,000 monthly revenue
  • 50% margin = $30,000 profit

Scenario B

  • 5× ROAS
  • $100/day ad spend
  • $15,000 monthly revenue
  • 80% margin = $12,000 profit

Even with a lower ROAS, Scenario A generates more than twice the profit. That’s why volume and context matter far more than a single efficiency ratio.

FAQs

What does POAS mean in digital advertising?
POAS stands for Profit on Ad Spend. It’s a smarter metric that factors in your costs to reveal true campaign profitability.

How do I implement POAS in Google Ads?
Use server-side tracking or offline conversion uploads to send profit-per-transaction data back into Google Ads for better optimization.

Are Demand Gen campaigns worth it?
Yes. They’re highly effective for reaching new users and warming them up for conversion with immersive, cross-channel engagement.

Can I still scale if I have a small budget?
Absolutely. Just scale slowly and watch key metrics closely. Start with controlled experiments before rolling changes out broadly.

Checklist

  • Move from ROAS to POAS for better insights
  • Simplify Smart Bidding strategy
  • Launch a Demand Gen campaign for top-of-funnel reach
  • Sync your CRM data using Customer Match
  • Test creative variations regularly
  • Use GA4 to move beyond last-click attribution
  • Scale budget in controlled, data-driven steps

Final Thoughts

Google Ads success today requires more than chasing high ROAS. It requires thinking strategically—measuring profit, understanding the customer journey, and scaling sustainably. Automation has taken care of the mechanics. Now, your job is to align data, creative, and business outcomes. When you focus on the metrics that actually drive growth, you’re not just managing campaigns—you’re building a business.

Forget vanity metrics. Focus on real profitability. Your bottom line will thank you.

How do modern AI search engines and LLMs operate and how do you optimize for them?

Caleb Turner on June 2, 2025

This isn’t 2015 anymore, yet some SEO “experts” are still clinging to tactics like they’re waiting for Windows 7 to make a comeback. Modern AI-powered search engines and large language models (LLMs) leverage Retrieval-Augmented Generation (RAG) to combine external data retrieval with text generation, ensuring answers are both current and contextually accurate. By performing a real-time search of trusted documents before crafting a response, these systems mitigate outdated training data and “hallucinations.” To optimize for them, create clear, structured content with up-to-date citations, conversational Q&A headings, and appropriate schema markup, so AI retrieval steps can easily identify and quote your material.

Key Takeaways

      • RAG enables AI to fetch and ground answers in fresh, external sources.

      • Structured Q&A headings and bullet points improve AI snippet retrieval.

      • Embedding authoritative, date-stamped references boosts trust signals.

      • Conversational phrasing and varied keywords aid vector-based matching.

      • Schema markup (FAQPage, HowTo) helps AI isolate self-contained snippets.

      • Off-page promotion can still surface in AI searches.

      • Optimizing content for RAG-driven AI results increases probability to appear in AI summaries and chatbot responses, giving you traffic that static search rankings might miss.

    Detailed Guide

    What is Retrieval-Augmented Generation (RAG) in simple terms?

    retrieval augmented generation

    Retrieval-Augmented Generation (RAG) is a hybrid AI workflow that enhances language models by letting them “look up” relevant documents at query time, rather than relying solely on what they learned during pretraining. Imagine asking a librarian to fetch the latest journal article before answering your question; RAG works similarly. Except this librarian is more like Alexa or Siri than your stereotypical Miss Finster.

    When you submit a query, the system first searches an external data source, such as a website index, a private knowledge base, or a specialized dataset of academic papers, for pertinent passages. Then, it feeds those retrieved snippets into the LLM as additional context, guiding the generative process so the answer is grounded in factual, up-to-date material. This approach addresses two major limitations of standard LLMs: information cutoff dates and the risk of “hallucinations,” where the model invents plausible-sounding but incorrect details.

    How does the retrieval phase work?

        1. User Query Submission
          You ask a question—e.g., “What are the 2025 tax deadlines for small businesses in Texas?” The RAG-enabled system takes this natural-language query as input.

        1. External Search
          Instead of directly generating an answer from pretraining data, the system performs a search against an external document collection, which could be a public web index, a company’s internal file repository, or a specialized dataset of academic papers (AWS, 2024; WEKA, 2025).

        1. Result Ranking
          Retrieved documents or text snippets are ranked by relevance using vector similarity, which transforms both the query and documents into numerical embeddings, or traditional keyword-based matching. The top N results (often broken into smaller “chunks” of text) are selected based on how closely they align with the user’s question.

        1. Outcome
          At the end of this phase, the system holds a set of highly relevant, often date-stamped passages that directly address the query.

      How does the augmentation and generation phase work?

          1. Context Assembly

        The RAG engine takes the top-ranked snippets—sometimes as short as a few sentences each—and concatenates them with the original user query. This assembled context is fed into the LLM.

            1. Guided Response Generation

          Rather than “freewriting” from its pretraining knowledge, the LLM now “reads” the assembled context and composes an answer that weaves together facts from the retrieved snippets with its own linguistic patterns. It essentially uses the retrieved passages as anchors, ensuring that every factual statement can be traced back to a specific external source.

              1. Optional Citation Insertion

            Some RAG implementations explicitly insert inline citations or footnotes, indicating which document or page each fact originates from. This enhances transparency and credibility, especially in domains like healthcare or legal research.

                1. Outcome

              The final output is a coherent, conversational response that is both fluent and verifiably sourced—reducing the likelihood of “hallucinations”.

              Why does RAG matter?

                  • Accuracy and Currency

                Because RAG fetches fresh data at query time, it can provide up-to-the-minute answers—even if the underlying LLM was last trained months or years ago. For example, a healthcare AI using RAG can retrieve the latest CDC guidelines before generating a recommendation, rather than relying on outdated training data.

                    • Reduced Hallucinations

                  By grounding responses in concrete, external sources, RAG dramatically lowers the risk of fabricated or misleading information. When users see inline citations, trust in AI-generated answers increases.

                      • Domain Specialization

                    Organizations can connect RAG systems to highly specialized knowledge bases—like a law firm’s case archives or a manufacturer’s product specs—without retraining the LLM. The AI becomes an expert in that domain simply by accessing the right repository at query time.

                        • Cost Efficiency

                      Instead of fine-tuning a massive LLM every time new information is added, you update the external datastore. This “decoupling” of model training from content updates is faster, cheaper, and more scalable—especially for companies that produce time-sensitive reports or whitepapers.

                          • Competitive Differentiation

                        As Google’s “AI Mode” is rolled out on a more massive scale, organizations that optimize for RAG-driven visibility gain a strategic edge. Their content is more likely to be surfaced in AI-generated summaries and chatbot answers, capturing traffic that might otherwise bypass static search engine results.

                        How to optimize content for RAG-driven AI search engines?

                        Google EEAT

                        Optimizing for RAG workflows means ensuring your content is structured, authoritative, and easy for retrieval algorithms to pinpoint. Below are actionable tactics:

                        1. Craft Clear, Structured, Answer-Focused Content

                        AI retrieval steps look for self-contained “snippets” that directly match user queries. Use semantic headings for primary sections so AI bots can isolate exact sections to quote. Begin each section with a concise answer.

                        For example:

                        How to File Sales Tax in California (2025 Update)

                        As of June 2025, all California small businesses must file sales tax returns by the 15th of each month. Refer to the California Department of Tax and Fee Administration website for exact forms.

                            • Use bullet lists and numbered steps for procedures to enhance snippet eligibility.

                            • Include a “TL;DR” summary at the top of long articles so RAG systems can grab that concise overview.

                          2. Embed Up-to-Date, Authoritative References

                          RAG systems ground their output in trusted documents. Pages that cite reputable, recent sources—such as government websites, peer-reviewed journals, or industry white papers—signal higher trustworthiness.

                              • Link to the latest guidelines or studies with a clear “Last Updated” date.

                              • Regularly audit and update publication dates to maintain freshness, benefiting both human readers and AI bots.

                            Example:
                             “According to the CDC’s May 2025 update on COVID-19 guidelines, mask mandates for healthcare workers in high-risk settings remain in effect (CDC, May 2025).”

                            3. Use Conversational Phrasing and Natural-Language Keywords

                            RAG retrieval often relies on vector-based similarity, matching semantic meaning rather than exact keywords. Write headings as questions users would ask—e.g., “What Are the 2025 Tax Deadlines for Freelancers in Texas?”—and follow with an immediate, concise answer.

                                • Include synonyms and related terms, such as “self-employed tax due dates” and “independent contractor tax deadlines,” to create multiple semantic entry points.

                                • Adopt a conversational tone so your content aligns with how AI systems interpret queries, boosting retrieval probability.

                              4. Leverage Schema Markup and FAQ/HowTo Blocks

                              Structured data markup—like FAQ Page or How To schema—helps AI crawlers precisely identify Q&A pairs and step-by-step instructions.

                                  • Wrap each Q&A pair in FAQ Page JSON-LD so RAG systems know these are self-contained snippets.

                                  • Use How To schema for multi-step guides, clearly delineating each step.

                                When Google’s AI Mode or other RAG-enabled platforms crawl your page, they can directly parse these structured blocks without scanning raw text.

                                5. Build Topical Authority and Maintain a Clean Technical Foundation

                                RAG systems prefer content from authoritative domains with strong topical clusters.

                                    • Publish comprehensive guides that interlink subtopics, demonstrating subject-matter depth.

                                    • Acquire backlinks from reputable industry publications—these act as trust signals in both traditional SEO and AI retrieval scoring.

                                    • Optimize technical SEO: ensure fast page load times, mobile responsiveness, secure HTTPS hosting, and accurate XML sitemaps so crawlers can index every relevant page.

                                  Tip: Use tools like Google Search Console to verify your sitemap and crawling status. If pages are excluded, AI retrieval systems won’t be able to find your snippets, regardless of content quality.

                                  6. Monitor and Adapt to AI Search Analytics

                                  Once your content is live, track AI-driven search performance via analytics platforms that show which snippets are being cited in chatbot outputs or AI summaries.

                                      • Review query logs to identify gaps and update content accordingly.

                                      • Refresh your knowledge base and schema markup periodically to keep pace with algorithmic changes.

                                    By treating optimization as an ongoing process rather than a one-time project, you ensure continual visibility in evolving RAG-driven ecosystems.

                                    7. Incorporate Off-Page SEO And PR Tactics for AI Visibility

                                    Traditional digital PR often promoted press releases, link-building or aggressive directory submissions. In certain AI search contexts, off-page tactics, like creating press releases or being cited on article directories, can cause RAG systems to index multiple instances of your content, increasing the likelihood of snippet selection.

                                    In my short YouTube video, I demonstrate how these tactics, some of which may be called “spammy”, can boost visibility in AI-based searches by flooding the retrieval index with relevant signals. While this approach carries risks in traditional SERPs, it can yield surprisingly effective results in AI-driven environments—so long as you monitor for negative user feedback or credibility issues.

                                    FAQs

                                    What is the difference between RAG and a standard LLM response?

                                    A standard LLM generates answers based solely on its pretraining data, which may be outdated if trained months ago. RAG, by contrast, performs a real-time search of external documents before generating an answer, ensuring the information is up-to-date and grounded in factual sources.

                                    Can I use RAG to search proprietary company files?

                                    Yes. By connecting a RAG-enabled system to your internal knowledge base—such as a SharePoint repository or a private document store—your organization can get highly specialized answers rooted in proprietary data without retraining the entire model.

                                    How do schema markup and structured data help AI retrieval?

                                    Schema markup like FAQ Page or How To tells AI crawlers exactly where Q&A pairs and step-by-step instructions begin and end, so retrieval engines can extract self-contained snippets without scanning the entire page. This increases the chances of your content being quoted verbatim in AI-generated summaries.

                                    Checklist

                                        • Identify and segment core Q&A snippets with clear semantic headings.

                                        • Embed date-stamped, authoritative citations (e.g., government or peer-reviewed).

                                        • Use conversational, question-style headings and varied synonyms.

                                        • Apply FAQ Page or How To schema markup around structured content.

                                        • Ensure fast load times, mobile optimization, and valid XML sitemaps.

                                        • Monitor AI search analytics to track snippet performance and update.

                                        • Experiment with off-page snippet postings; measure AI retrieval impact.

                                      Brief Summary and Conclusion

                                      Modern AI search engines and LLMs harness RAG workflows to merge external data retrieval with text generation, often producing answers that are highly accurate and current. By structuring content with clear semantic headings, embedding up-to-date citations, using natural-language Q&A phrasing, and applying FAQ Page or How To schema, you make it easier for AI retrieval to spot—and quote—your material without resorting to a virtual game of hide-and-seek. 

                                      Building topical authority, maintaining strong technical SEO, and even testing off-page snippet tactics can further boost your visibility in AI-driven searches. As AI search evolves, continually monitoring and adapting your strategy will be crucial for long-term success in the RAG-powered landscape.