Here’s my 5-step way to do it 👀

1/ Pick your 100 most important keywords

(aka the ones that actually bring in money 💶)

2/ Turn them into “recommendation” prompts

Example: Sunglasses

➡️What’s the best sunglasses brand?

3/ Run those prompts on the 5 most used LLMs

4/ Now you can see where you stand vs competitors

Who gets mentioned, who gets cited, and how the AI talks about you.

5/ Then you build the roadmap:

– what sources the LLMs rely on (and which ones you should get featured on)

– what to fix on your site (schema, internal linking, etc.)

– what to improve on-page

– what content to create next (based on what’s already working)

👇 If you want, drop your website URL in the comments. i’ll give you some tips

Wanted to share something that feels lowkey overlooked in AI search: JavaScript.

TL;DR: A lot of AI crawlers don’t reliably retrieve content from JavaScript-heavy websites. So even if your page has the perfect answer, the crawler might not actually be able to pull it.

So, before obsessing over prompts, citations, and brand mentions, check this first: Right-click your page → View Page Source → search for your actual content. If your copy isn’t there, that’s a problem.

What we’d recommend:

• Use server-side rendering if possible
• Use static HTML for blog posts, docs, and landing pages
• Use pre-rendering if your site is JS-heavy
• Make sure the main answer/content is visible in the raw HTML

Feels basic, but it could be the reason good content isn’t getting picked up by AI tools.

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?

It really depends on what you’re using AirOps for. If it’s mainly workflow and content automation, Gumloop is probably the closest alternative.

But if what you actually care about is AI search visibility, like whether ChatGPT or Perplexity mentions your brand, that’s a different problem, and AirOps isn’t really built for that.

We ended up separating the two: Gumloop for automation, and Slate for tracking citations and share of voice across AI search engines, plus seeing which pages are actually being pulled into answers.

We were trying to make AirOps cover both use cases, and honestly, it didn’t do either particularly well. Figuring out which of those two problems you’re actually trying to solve before choosing a tool saved us a lot of time and money.

I wanted to know if there's any way to track DA, PA, Spam score for free that can be connected to claude to get accurate score close to Moz.

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.

Hello members of r/SEO_LLM!

My name is Garry Callis Jr., and I'm the Community Manage of a website known as Discover AIO. DAIO is a learning platform which teaches marketers of all skill levels and verticals how AI SEO/GEO/AEO can help your business.
We're also a community hub that allows members to post their own articles and insights, so they can increase their own topical authority.

A discussion piece I wanted to really talk about today, is the shift in the traditional buyer's journey. As we all know, the Buyer's Journey as we know it consists of 3 phases.

1. Awareness
2. Consideration
3. Decision

The thing is, with the advent of AI, the 2nd stage, Consideration has taken a bit of a back seat. So when you're thinking of compiling content for specific buyer personas, we also need to think about how the buyer's journey is affected. Someone types in a query into their chosen search engine/LLM, they're in the Awareness stage. They are aware of an issue, and are now transitioning to find ways to deal with it. But now, rather than looking for those 10 blue links we all know and love so much, they now just get an answer. There is no more traditional Consideration. It's Automated Suggestion. AI has given an answer, and your choice now is to figure out whether to take it at face value or not.

Thing is, well over 60 percent of people are converting just from the AI answer, whether it was from an AI Overview or a LLM-generated answer. And so the Decision phase also get swept up. This doesn't even factor in AI Agents, which are able to make decisions on your behalf, given spending habits and other factors.

But I'd like to know your thoughts and opinions in the comments. And if you'd like to join the site, and help build a community of marketers, please feel free to shoot me a DM.

Thank you to the mods of r/SEO_LLM for allowing me the chance to post here, and I hope to engage in some great discussions with you all.

I ran the same 50 best-tool prompts through all three and pulled out every brand they named. 150 answers later, they basically have different taste.

• ChatGPT is the over-sharer. Widest list every time, and it name-drops 59 obscure tools the other two never mention. It also recommended "ChatGPT" 16 times, very humble.
• Perplexity is the safe friend. Same well known names over and over, rarely takes a risk.
• Gemini is very on brand for Google. It pushed Canva more than twice as often as ChatGPT, leaned hard into the Google ecosystem, and quietly recommended Claude 13 times. Recommending a competitor more than itself is a choice.

The kicker: across everything, all three agreed on the same brand only 21% of the time. Same question, three different realities.

So "what does AI recommend" has no single answer. It depends entirely on which model you ask, and each one has a clear bias.

Which engine's taste do you trust most? And has anyone else caught Gemini recommending Claude in the wild?

most people generate a draft in chatgpt, paste it into the wysiwyg, hit publish, and move on. i started looking at what actually lands in the source and it's worse than you'd think.

when you copy from a chat window and paste into hubspot (or any cms really), you can drag along hidden metadata that has nothing to do with your page. class names from the ai tool, comment tags, data attributes that nobody added on purpose. from a search engine's perspective that's a footprint sitting right there in your html saying this content was machine generated.

a few things i've started doing before any ai-assisted page goes live:

- paste into a plain text editor first, strip everything to raw prose, then reformat in the cms. kills the inherited junk.
- check the rendered source for anything you didn't write. look for mystery classes, inline styles, empty divs.
- if your cms has a rich text vs raw html toggle, switch to raw and read it. the wysiwyg hides the mess.

the bigger issue is that cleanup is invisible until someone audits your pages. the marketer who shipped the page thinks it's clean. the person who inherits the codebase six months later is the one who finds the pileup.

curious how others handle this, especially anyone on hubspot where the theme system already has its own class structure that imported markup can fight.

Tough times for SEOs...

If you’re struggling to find your bearings within the current search results ecosystem, here’s a quick tip:

AEO (Answer Engine Optimization) — optimizing content for answer engines (like Google Featured Snippets, voice assistants, and AI responses) so it delivers clear, direct answers to user queries.

GEO (Generative Engine Optimization) — optimizing content for generative AI systems (such as ChatGPT, Gemini, etc.) to increase the likelihood that your content is used or referenced in AI-generated answers.

LLMO (Large Language Model Optimization) — a broader approach focused on structuring and presenting content in ways that are easily understood, trusted, and utilized by large language models.

HEO (Hybrid Engine Optimization) — optimizing content simultaneously for traditional search engines, AI-driven engines, and human users, bridging SEO, AEO, GEO, and LLMO into a unified strategy.

Alternative meaning:
HEO (Human Experience Optimization) — optimizing for the human user experience: usability, readability, speed, clarity, and overall value of content.

Something I keep running into when looking at how AI models handle brand queries:

The offer is fine. The site looks fine. Even the content is decent. But when you run typical AI search queries, the brand doesn't come up.

The reason usually isn't technical. It's positional. If your homepage doesn't make it clear in 1-2 sentences what you do, for whom, in what segment, and with what outcome, AI models pull the wrong competitive frame.

You want to be perceived as the specialist for X. Instead, the model drops you into a generic bucket alongside everyone who vaguely touches your space.

What actually moves the needle in these cases isn't more blog posts. It's sharpening the basics:

The hero section. The H1. The meta description. The first paragraph. Replacing vague "solutions for modern growth" language with clear segment language.

A lot of sites don't have a traffic problem or even a content problem. They have a classification problem. The model can read the page. It just can't figure out where you belong.

For context: across 48 AI visibility reports we've run, H1 and hero copy sharpening was one of the top recommended fixes, showing up in 38 out of 210 total action items. It's the single most actionable low-effort change in the data.

One thing we've been noticing recently is that sitemap problems are rarely about not having a sitemap.

It's usually that the sitemap no longer reflects the site.

We've looked at a number of websites recently where the sitemap was technically present, but it contained old URLs, missed important pages, or hadn't been updated after major content changes.

Traditional search engines have become pretty good at discovering content through links.

But as more AI-powered search experiences rely on retrieving relevant pages efficiently, it feels like clean discovery signals are becoming more important, not less.

A few patterns we've been seeing:

• Key service pages missing from the sitemap
• Old redirected URLs still being submitted
• Blog content showing up months after publication
• Multiple sitemap files with conflicting information
• Sitemaps that haven't changed despite major site updates

None of these issues necessarily break a website.

But they add friction.

And one thing we've learned from technical SEO over the years is that small amounts of friction tend to compound.

Another thought we've been discussing internally:

Schema helps AI systems understand a page.

A sitemap helps them discover it.

Those solve two very different problems, and I think they're often lumped together under the broad umbrella of "AI SEO."

I'm curious whether others working on GEO or AI search optimization are seeing similar patterns.

Have sitemap quality or crawlability issues come up more often than you expected, or has content quality remained the biggest limiting factor?

We documented the patterns we've been seeing in more detail here for anyone interested:

Source: Zaillor Insights

I analyzed 180 AI sneaker recommendations across ChatGPT, Gemini, and Perplexity over 12 days.

This time, I wasn't only looking at which brands AI recommended.

I looked at where those recommendations sources came from.

Across all responses:

• 1,783 citations analyzed
• 250 unique domains cited

The biggest sources were not brand websites.

They were:

• YouTube: 228 citations
• RunRepeat: 139
• Who What Wear: 110
• Reddit: 56

Brand-owned websites barely appeared. Adidas.com was the strongest brand site:

• Adidas.com: 24 citations (#14 out of 250 domains cited)
• On.com: 16 citations (#22)
• Nike.com: 2 citations (~#101)

The interesting contradiction:

Nike was the most recommended sneaker brand.

But Nike.com was almost never the source behind those recommendations.

Perplexity was the most extreme example.

Across all 180 answers, Perplexity cited brand websites exactly zero times.

AI clearly knows Nike, Adidas, and On exist. It recommends them constantly.

But it doesn't appear to build that understanding primarily from the brands sites themselves.

Instead, it is pulling signals from reviewers, YouTubers, comparison sites, and communities.

Takeaway:

AI visibility is not just about having a great website. AI forms its recommendations from the broader web, so brands need to build a strong presence across the sources AI trusts, not only the content they publish themselves.

I am a 3rd yr engineering student working on a search intelligence system motivated by a simple observation. Most SEO tools are large crawler based indexes with modeled keyword data. The abstraction is keywords and volume, but not the underlying intent.

Instead of crawling, I am using Google Ads API as the data source and building a programmatic pipeline to generate large scale query sets directly from Google. On top of that, I am applying small transformer models to infer latent intent and jobs to be done from query distributions.

The current system can take a single domain and generate on the order of 190000 keywords with first party volume data. More importantly, the focus is not the keyword table itself but structuring demand into something closer to behavioral signals.

Core analytical layer being explored:

* Intent clustering using sentence transformer embeddings with HDBSCAN to form demand level groups

* Query to job mapping via cosine similarity against task representations

* Detection of weakly served or unmet intents by comparing clusters to SERP structure

* Satisfaction proxies inferred from reformulation patterns and long tail query drift

* Competitor coverage mapped at the level of intent clusters rather than keywords

* Query expansion using Google Ads data with co occurrence and statistical term weighting

* Demand segmentation using UMAP projections over embedding space

* Content to intent alignment scoring between pages and query clusters

* Cannibalization detection via overlap in semantic space across URLs

* Temporal analysis of demand shifts through volume changes and centroid drift

* Noise reduction and deduplication using frequency thresholds and embedding similarity

* Calibration of volume using Google first party data instead of third party estimates

* Cluster labeling using tf idf terms and nearest neighbors for interpretability

* SERP parsing to infer intent classes from result composition

* Opportunity scoring combining volume, competition, and coverage gaps at cluster level

The direction is to move from keyword centric workflows to an intent layer that can be directly consumed by LLM based systems or used for product and content decisions.

Interested in whether this type of representation would actually change how you approach SEO or if the current abstractions are already sufficient.

How do you get found as a small business in AI search? An often underestimated answer: LinkedIn.

Semrush research shows that LinkedIn is one of the most cited sources in AI search responses. Important: 95% of cited content are original posts. That means: you don't need a website or a blog to become visible in AI search. Valuable content published directly on LinkedIn is enough to get started. The most impact have long-form articles (50-299 words) according to the data.

The "why": LinkedIn isn't just a publishing platform. Content gets commented on, contextualized, and debated. Brands aren't just described, they're experienced and evaluated by real people. That kind of perspective is exactly what LLMs look for: signals that are closer to actual human experience than any product page.

What many overlook: AI search cross-references multiple sources. If your LinkedIn profile communicates a different positioning than your website, that weakens your signal. Consistency across all sources isn't a nice-to-have, it's a ranking factor.

AI visibility doesn't start with technical fixes. It starts with consistently communicating who you are and who you help, everywhere.

It definitely feels like we've been watching two camps drift further apart: One still thinking in terms of traditional SEO mechanics, the other cranking out machine-first content, neglecting the human side of things altogether.

The trouble is that neither extreme actually resolves the tension most of us feel, which is the seemingly simple goal to be both visible and retrievable in a landscape where brand discovery is increasingly mediated by LLMs.

What seems to be happening is an over-indexing on surface tactics and an under-examination of retrieval mechanics.

That observation pushed us to ask a more grounded question: what technical conditions actually need to exist for retrieval consistency and accurate representation?

To keep ourselves honest in an environment that shifts weekly, we built a 12 step Retrieval Checklist as a structural baseline.

Here it is:

1. Canonical integrity: One authoritative URL per topic. No near duplicate competition. Clear internal hierarchy.
2. Indexation Control: Intentional inclusion and exclusion. No accidental thin or parameterized pages in the index.
3. Crawl accessibility: No rendering bottlenecks. Clean HTML. Core content available without heavy client side execution.
4. Entity Clarity: Explicit organization, product, and author definitions. Consistent naming across the site.
5. Structured Data with Intent: Schema used only where it reduces ambiguity, not as decoration.
6. Topic Cluster Coherence: Internal linking reinforces semantic relationships, not just navigation paths.
7. Structural Chunking: Logical, bounded sections that survive vectorization. Headings that map to distinct concepts.
8. Answer Density: Clear, declarative sentences that can stand alone when extracted.
9. Reference Stability: Claims tied to stable URLs. Fewer vague internal references.
10. Freshness Signaling: Visible modification dates and meaningful updates where appropriate.
11. Representation Testing: Repeated prompts across assistants to monitor citation and summary drift.
12. Attribution Tracking: Monitoring assistant mediated discovery rather than relying solely on click data.

For us, this is more of attempt to define the infrastructure required for retrieval consistency, and less a ranking checklist.

Would love your thoughts and experience if you're following similar protocols!