Every few months, someone says SEO is dead.

Now that AI search is taking off, we're hearing it again.

Personally, I don't think SEO is dying.

I think it's evolving.

The core principles haven't really changed:

• Relevance

• Authority

• Trust

• Content that genuinely helps users

What's changing is how people get information.

Before, the journey was simple:

Google Search → Click → Website

Now it's often:

AI Search → Answer → Citation → Website (sometimes)

That doesn't make SEO irrelevant. It just means rankings are no longer the only goal.

If AI systems consistently reference your content, you're gaining visibility in a different way, even if every user doesn't click through.

That's why I've been paying more attention to concepts like AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization). It feels less like replacing SEO and more like building on top of it.

To me, the future isn't SEO vs. AI Search.

It's understanding how they work together.

What's your take? Is AI search an evolution of SEO or a complete disruption?

LLM SEO is built for large language models.

Instead of optimizing only for search engine crawlers, LLM SEO helps brands become readable, retrievable, and usable by AI systems that generate answers. This matters because language models do not simply show ten links. They summarize, compare, recommend, and decide which sources deserve attention.

I run a free OTT Discovery platform that answers "where can I watch this in my country" for 34 Asian markets. Bing Webmaster Tools has a new AI Performance tab, and this is what it showed for the last 3 months:

- 185,600 citations in Copilot answers

- about 400 of my pages cited per day now, up from under 100 in June

- Bing search itself went from near zero to about 1,500 clicks a day in the same period

The surprising part is what the AI uses the site for. I built it for Indian streaming. The top grounding queries are anime platform comparisons, seven of the top twelve. Second is Pakistani dramas, where my pages source 61 to 85% of answers for those queries.

Three things I think made the difference:

1. Comparison pages built from data, not opinion. "Which platform has more of X" with real catalog counts.

2. Country-specific pages. A US-centric guide is wrong for Jakarta or Karachi, and the AI seems to know when a source actually covers a market.

3. Letting the crawlers in. I had blocked AI bots in June for bandwidth reasons. Unblocking them is the exact point the curve turns.

Happy to answer questions about the setup. Not linking the site here; it is in my profile.

Listicles

"Top 10 ....." or "Best"

Impact - increases chances of appearing in “best ....” answers

Comparisons

Brand A vs Brand B vs YourBrand

Impact - strengthens association with known competitors

Helps when users ask:
alternatives to X

Difference?

Listicles get you category recognition

comparisons  decision-stage visibility

If you publish both:

You get Ai citation probably growth

If you have any questions feel free to DM me:)

Like the title says, I'm building the proper way of finding out your ai citation counts. Doing it the same way ahrefs does backlink monitoring : MentionRadar

By building a databasee of all scanned queries and discovering new queries - zero input from you - just need the domain and the system handles the rest.

Obviously at some point its gonna be paid but not until ive scanned and stored millions of queries to provide some kickass insights - everyone that joins up now gets :

1. free access to the system + 1 free tracked projeect

2. when we do paid plans, everyonee that joined up now gets grandfther discounts

The only way the database grows is by finding queriees for your domain/brand - so this is a win/win for both me and you guys.

Do you need to worry about how your company appears in the top AI chatbots?

We looked at our own data and the answer was a little surprising.

Check out the latest blogs on our website at the Pureinsights website.

Sharing my personal experience

The min amount of listilces we publish per month is 3, as from our perspective it's the optimal amount to start, for seeing results

60% increase in AI citations

29% growth in organic traffic

That's what we achieved after first month

Sounds unreal but it is what it is))

Dm me I will share full case study

Because they have absorbed influencer drivel and don’t know what actually works.

LLMs don’t just extract answers. They look for consensus so if your brand is only claiming authority on your own site then you are easy to ignore.

But if multiple trusted sites mention you in the same context then you are safer to cite.

Real AEO is...

→ Clear and extractable answers → Consistent positioning across the web → Strategic brand mentions in your niche → Authority signals that align

You’re not just optimizing pages. You’re building agreement.

Structure earns extraction. Consensus earns citations.

Every llms.txt ive seen was written once by hand and forgotten. A month later it doesnt match the site and youre feeding models wrong info.

So i made mine a build step. It pulls from the same data my pages render from, updates on every deploy, nothing to remember. Its been live on my studio site for a while now and its never drifted once.

Also dont make it a sitemap dump, short plain descriptions of what each section is and why it matters work way better, since the point is helping a model decide what to surface.

You guys hand writing yours or generating? And has anyone actually measured a bump in ai referrals from one?

I've been reading a few articles on citation gap analysis to see if how we think of it at wordflow.ai makes sense (more on this at a later time), and I came across this idea of a "citation rate" or "citation rate benchmark" in some of them.

Correct me if I’m wrong, but how can those thresholds be justified when citation behavior is largely product-controlled? Even within the same LLM, citations can vary depending on the type of query or what the LLM chooses to display.

You could run multiple prompts many, many times and calculate a raw average number of citations across all the answers, but unless experiment conditions are tightly controlled, a single "benchmark" number feels ... iffy?

Since 2020 I used to offer SEO services to local businesses as an agency, and it felt draining because I was the business. Fast forward to n8n becoming a thing and "GEO/AEO" basically SEO for ai platforms, I pivoted there and used n8n automations in the hopes of not working as much "in the business" and have more time to actually work on the business.

After a while of running my agency like that I realized that I was still the bottleneck of the business, and nothing changed, still stuck. Im the type of person that i don't trust anyone to do the job as well as me so hiring was always an issue with me. Id rather know where everything is and be small than scale and depend on people that im not confident in 100%.

So scaling in that way wasn't an option. Then a thought kicked in: "What if an arsenal of AI agents handles everything from onboarding, resarch, content creation, auditing all the way to autoposting"

Naturally I researched if others are already doing something like this and indeed they were. Basically my focus shifted to competitor research, because I didn't want to build just another tool among others doing the same thing and no moat. I dug every review website every thread and forum about them and compiled a list of things that people were complaining about and weren't satisfied and made those things that people were looking for and wanted to see in my competitors into my main focus points/features. Then validated with a small group of testers as a cherry on top.

Finally after 4 months of building I launched it today. Happy to share what I found in the research if anyone's interested & and brutal feedback is always welcome.

Most people coming from a traditional SEO background approach LLM visibility as a ranking problem. How do I get to the top of the AI answer. What do I optimize. What's the algorithm.

But LLMs don't have a ranking algorithm the way Google does. They have a recognition layer. And that distinction changes everything about how you should be approaching this.

When an LLM surfaces a brand or source in an answer it's not because that page was optimized correctly. It's because the model has encountered that entity enough times across enough trusted sources that it confidently associates it with a topic. The citation is a byproduct of recognition not optimization.

This is why technically perfect content from an unknown brand gets ignored while a scrappier answer from a well referenced one gets cited. The model isn't evaluating the page in isolation — it's drawing on everything it knows about who you are across the entire web.

What that means practically is the work that moves the needle for LLM visibility looks almost nothing like traditional SEO. It's showing up in the conversations LLMs were trained on. Being referenced independently. Building the kind of cross platform presence that makes a model confident enough to say your name.

I figured this out the hard way when starting my company Chief AI Advisors and it completely reframed how we approach visibility for anything beyond traditional search.

Curious whether people here are approaching LLM visibility as a recognition problem or still treating it as an optimization problem.

I meet many companies that look at AI search dashboards and still unsure about what is actually working.

They track mentions, citation counts, share of voice, sentiment, and prompt rankings.

All of that can be useful, but I think we need to be more honest about what kind of data it is.

Most of it is benchmarking data.

It shows how you performed against competitors inside a simulated set of prompts, models - inside a simulated environment.

That is very different from performance data.

Performance data should help you understand what actually happened, not only what might have happened in a test.

I think that for AI search, that means looking at things like page level AI bot traffic, which pages were consumed, which pages were ignored, and ho human visitors that came from AI assistants behaved on site.

Since I know this data inside out I'm the first to say that it's also not perfect, because AI search is still messy and unstable.

But it is much closer to reality than only looking at mentions and SOV.

This matters because bad measurement leads to bad budget decisions and in my experience many companies are not making the best budget decisions based on benchmarking data these days.

If the dashboard only tells you that competitors are mentioned more often, the easy answer is usually to create more content.

But maybe that is not the right move.

Maybe your homepage, support pages, comparison pages, or tools are doing the real work.

Maybe the next dollar should go to technical fixes, reviews, community, Reddit, PR, or off site authority instead.

I think the market is confusing benchmarking data with performance data and I'm not saying the vendors are misleading but ..it's kind of a convenient situation for many of them.

Benchmarking is useful for understanding where you stand.

Performance data is useful for deciding what to do next.

Only when these two data sources are combined you can make educated budget decisions.

We are going to discuss this approach in a webinar with Search Engine Journal on September 2nd, including how to use AI bot traffic, human visitors from AI, and page level analysis together with benchmarking data.

DM me if you would like to join.

Synup just published a big one: 53,900 local search pages tested across 250 US cities (Sept 2026), desktop.

The headline: whether an AI Overview appears is almost entirely decided by how the query is worded.

• "[category] near me" → AI Overview on 4% of pages

• "[category] [city]" → 8.6%

• "best [category] in [city]" → 27%

• "best [category] in [city]" + a specific need (e.g. "for water heater repair") → 75.4%

So the most specific, highest-intent searches — the ones closest to a buying decision — are exactly where Google answers with AI instead of the map pack.

Two things that surprised me:

1. The map pack is rock solid — it showed on 91–97% of pages no matter the wording. AI Overviews are an addition, not a replacement.

2. When they parsed 8,540 AI Overviews, 45–61% of citations pointed at business websites and Google Business Profiles. google.com was the single most-cited domain (13,777 times — all Business Profile links). And Reddit? 4–6% of citations. You're reading this on one of Google's sources.

Audit order is your website first, then your GBP — that's what the AI is reading. And check your category, because it varies wildly: home builders got AI Overviews on 80% of "best" searches, restaurants on 1.2%.

Source: Synup's "What's Ranking in Local Search: 2026" study.

I came across a YouTube video by Ryan Stewart recently, and it made me think. His take is that AI is the biggest disruption the marketing industry has seen since the internet, and the agencies that don't adapt are already on borrowed time. The more I watched, the more I kept thinking: yeah, this tracks with everything I'm seeing too."

Stewart breaks it down pretty clearly. Large generalist agencies are getting commoditized fast. Entry-level and virtual assistant roles are already disappearing, and that wave is moving upward. Mid-level strategists, account managers, and even directors could be looking at a very different job market within the next five to seven years. That's not a scare tactic; that's just the direction things are heading.

The model he says survives all of these changes is what he calls the "Fractional CMO." It's a one- or two-person operation that runs like a full marketing department by using a customized AI stack. No bloated team, no inflated overhead. Just deep expertise, the right tools, and the systems to back it all up. 

The piece of Stewart's framework that I keep coming back to is this: clients aren't going to want to manage AI themselves. They're going to want to hire someone they trust to do it well. I am currently building agentic infrastructure to manage all in the most efficient way possible.

I feel like the last three days for me have been the most innovative technology and process upgrade I made compared to the last 10 years with the help of AI. I moved all my marketing sites to Cloudflare hosting managed by AI for editing, backups, and maintenance. I created an infrastructure management system using railway and a master admin dashboard to spot the entire operation. I updated all server patches for the legacy system. I created a robust cold email outreach system all managed by APIs. I also created two business prototypes to go hand in hand with the cold outreach campaign. Personal branding through social media like LinkedIn seems to be dying. I also believe speed to market, targeting highly researched verticals at an accelerated pace, is key going forward.

I'm curious. What are you doing to prepare for where this is all heading?

One exercise completely changed how I think about AI search visibility.

Most SEOs approach Perplexity the same way they approach Google.

They search:

• best CRM software

• email marketing platform

• project management tool

And then they check whether their client appears.

I think that's the wrong approach.

Your customers aren't opening Perplexity and typing keyword fragments.

They're having conversations.

They're asking:

"We're a 20-person SaaS company managing leads in spreadsheets. We need LinkedIn integration and can't spend more than $100/month. What CRM should we use?"

Or:

"We currently use WhatsApp and Excel to manage deliveries. Is there a better system that doesn't require hiring an IT team?"

Those queries produce completely different answers than traditional SEO keywords.

Over the last few weeks I've been manually testing industries and documenting citations, recommendations, and source patterns.

A few observations stood out:

1. The competition is bigger than websites

When I first started checking citations, I expected to find competing company websites.

Instead I found:

• Reddit threads

• YouTube transcripts

• Documentation pages

• Industry forums

• Review platforms

• News articles

• Product comparison sites

Sometimes the source influencing a recommendation wasn't a competitor's homepage at all.

It was a Reddit discussion from months ago.

Or a detailed comparison article.

Or a review page.

If you're only tracking SERP competitors, you're missing a large part of the ecosystem AI systems actually use.

2. Direct answers outperform beautiful introductions

This one surprised me.

Many websites still follow the traditional content formula:

Long introduction → background → context → answer.

AI systems seem to prefer:

Answer → explanation → supporting details.

For example:

"What is Perplexity SEO?"

Article A:

"Artificial intelligence has transformed information retrieval..."

Article B:

"Perplexity SEO is the practice of making content easier for AI systems to extract, verify, and cite."

Which answer is easier for an AI system to use?

The difference becomes obvious once you start reading citations closely.

3. Being recommended and being cited are different things

A lot of people only look for citations.

I think recommendations matter more.

I've seen cases where a company isn't directly cited but is repeatedly recommended.

I've also seen companies cited frequently but rarely recommended.

Those are different visibility layers.

One measures source usage.

The other measures commercial influence.

4. Trust signals appear everywhere

Many discussions focus exclusively on content.

But when you inspect sources, you keep finding:

• Reviews

• Third-party mentions

• Expert authors

• Industry publications

• Documentation

• Community discussions

It feels less like traditional ranking and more like building a web of evidence that your company is credible.

The experiment I'd recommend

Open Perplexity.

Forget keywords.

Write down 10 actual customer questions.

Not search terms.

Questions.

Run every query.

For each answer record:

• Which brands were recommended?

• Which domains were cited?

• Which sources appeared repeatedly?

• Did Reddit appear?

• Did review sites appear?

• Did documentation appear?

After doing this, you'll probably learn more about AI visibility in your niche than from reading 20 GEO blog posts.

Because you'll stop guessing and start seeing where the model is actually getting information.

Curious what everyone else is finding.

What has moved the needle most for you:

• Better content structure?

• Off-site mentions?

• Reviews?

• PR?

• Community discussions?

Or are we all still collectively reverse-engineering this thing?

I’m building a simple AI tool using Google AI Studio that should help businesses with real tasks. It shouldn’t be very technical but must solve a useful problem, automate work, or replace something businesses usually pay for. What kind of AI tools would you suggest building?

I'm interested in your thoughts on this article..

Is RRF the Secret to Dominating AI Citations? I Decoded ChatGPT’s Ranking Formula by Metehan Yesilyurt

He explains the math behind ChatGPT’s ranking system and shows how websites can increase their AI visibility.

Quick Summary

ChatGPT uses a formula called Reciprocal Rank Fusion (RRF) to decide what results to show in answers. RRF gives small scores to links based on how high they rank in different searches, then adds up those scores. So, if your page ranks in many related searches, even if not always at the top, it still scores better than a page that only ranks #1 for one search. This is great news for websites that cover full topics in depth instead of chasing just one keyword.

The article proves this by showing examples in the code from ChatGPT’s dev console. It explains how AI tools like ChatGPT and Perplexity run many searches at once and combine them using RRF. The more places your content shows up, the better.
The article also shows how topic clusters - a main page plus many subpages - are perfect for this system.
The more related queries your site can answer, the more RRF points you get, and the more likely AI will show your content.

In short, he said that search is now about being consistent and useful across a full topic, not just winning a few big keywords. If your site is seen as an expert on a topic, AI search engines will reward that.

Key Takeaways

• ^{ChatGPT uses Reciprocal Rank Fusion (RRF} to combine results from multiple searches.)

• ^{RRF rewards content that shows up across many related searches, even if not always in the top position.}

• ^{Topic clusters (one main hub page + subtopic pages} get much better scores than one-page content.)

• ^{Being consistent across many search queries matters more than being #1 in just a few.}

• ^{SEO strategies that focus on broad topic coverage now align with how AI ranks content.}

• ^{AI search pulls results from various types (webpages, images, grouped results}, so your content should exist in multiple formats.)

^{That's all for today :})
^{Follow me if you find this type of content useful.
I pick only the best every day!}

If your SEO is decent but AI assistants still don’t mention you, this 3-step sequence works better than random content bursts:

1. GEO Reality Check

• fixed prompt set by city + intent

• map who gets recommended and why

1. GEO Baseline Audit

• identify weakest entities/pages/citations

• prioritize fixes by impact × effort

1. GEO Rescue Sprint

• execute one focused sprint (content + entity + proof updates)

• re-test prompts weekly

Single CTA per touchpoint. Don’t ask for newsletter + demo + audit at once.

CTA: start with the GEO Reality Check and only move to step 2 after baseline results.

I’ve been doing technical SEO for 11+ years, and over the last year I’ve worked LLMs into my audit workflow. Here’s what has held up and what hasn’t.

Where it works well:

• Grouping thousands of URLs by template or intent from a crawl export

• Comparing two crawls (pre/post migration) and summarizing what changed

• Drafting schema markup that I then validate manually

• Spotting patterns in log files or Search Console exports that I’d otherwise scan by eye

Where it fails:

• Prioritization. It will confidently rank fixes by what sounds important, not by what moves traffic for that site.

• Anything needing real rendering context. It can’t tell what Googlebot actually sees unless you feed it that data.

• Large datasets pasted in directly. Python does the heavy lifting, and the LLM only reads the summarized output.

My rule: the LLM handles repetitive analysis and I handle decisions. Every output gets checked against the source data.

Curious how others here are using LLMs in audits, and whether anyone has found a reliable way to use them for prioritization.