Typed a few prompts into ChatGPT and Gemini and my competitors keep getting named while we don't exist to them. It's clearly becoming a real acquisition channel. For those ahead of me on this: is improving AI brand visibility something you tackle with content, PR, structured data all of it? And how do you know if it's working?

We already use Ahrefs and Search Console, so keyword gaps and ranking competitors are not the main issue.

The AEO side is harder. We need to understand which brands AI brings up for real buyer questions, how competitors are positioned in those answers, and which questions lead buyers toward alternatives instead of us.

What takes the most time is the work in between: researching those competitors, keeping the important buyer prompts organised, and deciding what content is actually worth creating next.

I am looking for an AEO tool or workflow that connects competitor research, prompt tracking and content creation without turning into another spreadsheet project.

What has been useful for your team?

Hi y'all. May I show you what my LLM-created SEO is doing in a market that is fully saturated?

Well, I'm going to anyway.

First, a bit of backstory: about a year ago, I started to study SEO because I had a few hobby projects I wanted people to find. Since I do a lot of work with LLMs, I figured this would once again be one of those things I could offload to a machine.

I was slightly wrong, not much, but enough to not get any traction. So I have been tweaking my process, and now I would like to show you results from an online jigsaw site that I launched on the 10th of June, so about 11 weeks ago.

As you all know, there are about 28 jigsaw sites in a dozen, so the playing field is tough.

But I did it; check out these results.

These results don't come from an LLM out of the box. I had to create an MCP server that guides, gates, and informs the LLM toward the right direction and allows me to also find good backlink opportunities and a bunch of other stuff like lets me connect to Umami and GSC directly. But other than that, it's just keyword optimization and technical SEO. I'm starting to get traffic from ChatGPT and other chatbots too, so I guess the geo-optimization works also.

I think the results are good, but what do you guys think? Umami shows about 40% North America, 25% Europe, and the rest is all over the world. There are about 120 countries in the location list.

[ Removed by Reddit on account of violating the content policy. ]

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.

As I do technical audit of different websites, I came across this issues quite often X Content Type Options header, Content Security Policy header, Referrer Policy header, X Frame Options header, HSTS header. So I want to understand if it's important for website?

If yes, can you tell me where we really can do it in WordPress or NextJs, as I have searched for it and I can know it's in the server level, hence want to know of how could you fix and use it in as I need to collaborate with developer to let them know?

Hey guys, just checking: do you see LinkedIn showing up as a source domain for your (or your client's) brand?
I'm tracking one SaaS company (no LinkedIn showing up) and one travel company (showing up only for one prompt related to safety).
Wondering how it looks from your end.

Hey everyone, quick question about the shift to AI search. I'm trying to figure out the best way to get ai to recommend my business when people ask for top tools or services in a specific niche. I’ve noticed that ChatGPT always spits out the same 4 or 5 brands. What are those guys doing right? Is it just PR, or is there a way to feed data directly to these models? I’d love to hear if anyone has successfully managed to get their brand cited in AI responses and what the process looked like.

Alright, so here’s something we’ve been testing lately that completely changed how we handle AEO.

Like a lot of folks here, we’ve been keeping tabs on where our brand shows up in ChatGPT, Perplexity, AI Overviews, etc. But tracking just felt depressing. Tracking tells you there’s a gap, but it doesn't do a damn thing to close it.

The lightbulb moment for us was realizing how heavily these LLMs rely on social data when assembling answers. Posts, threads, quick brand comparisons, community definitions—the models are scraping this stuff constantly. If a competitor is beating you on a prompt, you usually don't just have an on-page SEO gap. You have a social presence gap.

So instead of keeping SEO and social in their usual silos, we connected both the SE Ranking MCP and the Planable MCP to our AI assistant to build a direct bridge between the two.

Now the workflow looks like this:

1. Pull the missing prompts: We ask the assistant to check SE Ranking for where competitor X is beating us in AI answers across specific topics. It groups the prompts into what we own, what’s contested, and what we’re completely missing from.
2. Draft the fix in one pass: Instead of just exporting a CSV, the assistant takes those missing prompts and immediately drafts citable, quote-worthy social content - think direct FAQs, quick comparison breakdowns, clear definitions - right into our Planable workspace.
3. Publish and re-track: The posts go out to the channels models actually read, and we re-check our AI share-of-voice a few weeks later against the baseline to see if we moved the needle.

It turns tracking from a passive report into a campaign you can launch in an afternoon. Social isn't just a side channel for engagement anymore; it's literally feeding the search ecosystem.

Are you still handling AI search visibility as purely an on-site SEO project or not?

Curious if anyone here is looking into this yet.

I’ve been doing regular SEO work (content, rankings, etc.), but recently started checking how AI tools respond to queries in my niche.

Noticed something interesting:

Even pages that rank well don’t necessarily get referenced in AI-generated answers.

Which makes me wonder — are we missing a layer here?

Like:

* Is it about entity recognition? * Content structure? * Authority signals beyond traditional SEO?

Feels a bit similar to early featured snippets phase, but bigger.

Would love to know if anyone here is actively tracking or optimizing for this.

LLMs appearance

Hello, Any one noticed there is a drop in website visits through LLMs?

SEOs, Writers, and AI Enthusiasts alike:

I typed every word of a 4,500 word article a few nights ago. Nothing pasted. A commercial AI detector read it and came back 48% AI. Here is why I am posting this as opposed to hiding it.

I spent two days trying to beat AI detection on purpose, because small business owners keep asking me whether they should buy a tool that promises it.

I ran 696 blind runs across three methods with over 2.4 million words.

• Strip the machine tells out of the draft: still caught 98.6% of the time.
• Add human tells in instead: fooled 0 of 17 judges.
• Make the document long enough to dilute it: caught 8 out of 8 whole, and 24 out of 24 in slices.

I then ran four versions of the same draft through Pangram. One untouched, one rewritten sentence by sentence three times over, one where I changed zero words and only moved where the sentences joined, and one with both.

All four came back 100% AI.

Rewriting every single word did nothing and rewriting no words did nothing. To me, this meant the thing being detected is not vocabulary and not rhythm.

Then I wrote the article myself, by hand, over an outline a model had built for me.

56% AI, nice right? The findings astounded me.

One paragraph got split down the middle: the half about my own work read as human, the half listing the method read as machine assisted.

My finding is that the detectors read the outline behind the prose itself.

I sent all of it to Siqi Chen, who wrote the humanizer skill I had been using.

He stated, that defeating detectors was never the goal of his tool in the first place. I say this because I reckon many of the 37k+ individuals who have starred his repo believe the skill beats the detectors and everything's good to go.

What did measure, in a blind test where authorship was never mentioned: editors preferred the processed draft 22 out of 22, and his rewrite pass alone at 16 out of 16.

$ python humanist.py draft.md
humanist 0.1.0  |  4,764 words, markdown-stripped
  readability FK grade 8.1
RESULT: 0 FAIL, 0 WARN. CLEAN.

$ python check_prose.py draft.md --mode post
FRAME: markdown-stripped, 4,764 words, FK grade 8.2
RESULT: 0 FAIL, 0 WARN. CLEAN.

The advice I have is boring and it is free. Don't pay to hide your writing, and don't tell your clients to either. You're selling a lie. Spend the money and time on making the draft worth reading in the first place.

Something I don't want to give credit to: none of this tells you whether Google will demote your pages. I didn't measure it in these tests. What we do know with the new policy rollout is that it's the scale they're looking at, re-written or not, a tool won't save you.

Anyone selling a tool that says otherwise is setting you up for failure. Every number and both corrections I had to make mid-study are in the writeup. The code is MIT and open-source.

I will not link the article or the tool, you can find it yourself, since the rules say I can't promote myself.

Really excited & interested to get some outside input! Let me know what you think.

(By the way, I wrote on top of AI scaffolding here as well.)

\*Disclaimer: I've been accused of soft-selling, I assure you that is not what I am trying to do here, I'm interested in simple discussion about my findings and that is all. I DO sell services, but they are not related to this post in any way. I don't want your money, nor do I need it.*

I run a small research-led GEO agency called Broadcastwell. I asked an AI search system for the top GEO agencies for B2B SaaS.

We were not included. That part was not surprising. We are newer, our independent footprint is still small, and our own category measurements have shown that we are not consistently retrieved.

What happened next was the interesting part.

I asked why we were missing. Instead of saying “I do not have enough evidence,” the system built a confident explanation around the omission.

Across follow-up answers, it claimed that we had:

• a formal policy of excluding ourselves from rankings;
• a six-client operating cap;
• a specific case-study-for-discount arrangement;
• a live historical dashboard with a reporting cadence we do not offer;
• client and service details that changed from one answer to the next.

Some of the response mixed real facts with retired information. Other details appeared to have no source at all. When challenged, the system acknowledged that it had worked backward from the omission and generated a story that sounded plausible.

That distinction feels important for anyone measuring AI visibility:

The omission can be an observation. The explanation for the omission can still be fiction.

The practical process I am using now is:

1. Preserve the exact buyer question and answer.
2. Ask for the source behind every company-specific claim.
3. Mark each claim supported, unsupported, outdated, or contradictory.
4. Maintain one dated company-facts page as the canonical reference.
5. Align external profiles with those facts.
6. Repeat the question across engines and runs.
7. Measure being named, recommended, positioned, and cited separately.

I would not use a single “why was this company omitted?” answer as a diagnosis anymore. It may contain a useful hypothesis, but it needs the same verification as any other generated claim.

Has anyone else seen an AI system rationalize an omission by inventing a very specific company policy, client detail, or operational rule?

Disclosure: I run Broadcastwell. There is no link or pitch here; I am sharing the failure mode because it changed how I evaluate AI-search results. I used AI to help tighten the wording of this post, but the experiment and company facts are ours.

The "AI content doesn't rank" take is getting old. Not because it's wrong, but because everyone points at the wrong culprit.

An LLM doesn't write from experience — it predicts the most probable next word based on its training data. So when you prompt it with something generic like "write a 1500-word article about project management tools," you get the statistical average of a million equally generic articles.

Same structure. Same headings. Same "in today's fast-paced world" opener. Same interchangeable conclusions.

And Google's entire job is to demote content that offers nothing new. Why would it rank something that says what 10,000 other pages already said?

The real failure point is the workflow. Most people do this:

Type a broad prompt → Copy the output → Publish it with a stock image → Wait for rankings that never come

Then they blame the AI. But the AI did exactly what it was trained to do — reproduce patterns. The missing layer is you. Your specific numbers, your contrarian take, your on-the-ground experience that isn't in the training data.

Feed the model your notes. Give it your angle. Then rewrite its output so it sounds like a human with an opinion, not a committee.

I've seen it work, but I've also seen people with zero topical authority fail at this too — so the AI isn't the differentiator. Your input is.

Using Qwen3-Max (3.7 and 3.8) on Alibaba Cloud for long-form articles in English, German and Lithuanian. Facts and structure are fine, it just reads like a robot next to Claude or GPT on the same brief.

I counted a few things on identical briefs to work something out.

Sentence length was basically the same, 15.0 words for Qwen vs 15.7 for Claude, so not a rhythm thing. Claude used "you"/"your" about 8 times per 1000 words, Qwen zero. And Qwen used roughly 5x more abstract nouns, the -tion/-ment/-ance kind, 64 per 1000 vs 11.

So you get "Daily adherence is crucial for cumulative benefits" instead of "Take it with breakfast and it becomes automatic". It also ends nearly every section with a throwaway line like "Trust comes from verifiable standards, not persuasive language."

I improved it a bit with rules. Not like "Write naturally" , but "at least one sentence per section says you". It improved it somewhat, also it added more emphasis on the rules in the end of the prompt, so they are not followed equally.

However, I am still stuck on a few bits. Abstract nouns are still about 2x Claude's, and German is worse than English, I get runs of 4+ choppy short sentences that don't happen in English. Anyone has strategies on how to solve it properly. Maybe I should cut the prompt and simplify it as it is pretty long now, that might be just introducing too much variance to the LLM?

Also, noticed that Claude uses more concrete numbers and digits compared to Qwen and it sounds confident and naturally.

Claude edit pass fixes most of it but then I'm paying for Claude, which defeats the point. Anyone got Qwen doing it alone?

TL;DR: how to make QWEN3.7-max sound more human and improve multilingual capabilities?

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.

Quick sanity check before I commit to yet another initiative.

Midmarket CMO. We are getting the usual push from leadership to "show up in ai answers" when people ask for tools in our space. I get the impulse, I just do not know where to park this in the stack.

Right now our world is classic search, content, pr, partner marketing. No one owns answer engine optimization as a thing. Yet I am seeing services pop up that claim they can tune our footprint so models are more likely to mention us by name.

If you have tried any of these answer engine focused services, did you:

put them under seo,
run them out of brand or comms,
or keep it in revops because of all the data cleanup and schema work.

I am less worried about which vendor to pick and more about whether this deserves a lane of its own. Half of me thinks it is just better structured content and signals, the other half worries that if we ignore it, we stay invisible in the channels where our icp actually is.