r/SEO_LLM

Did you buy a separate AI visibility tool, or do you use it inside an all-in-one SEO platform?

What did you choose and why?

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.

Lo que más me funciona a mi, para generar contenido, son las skills de brief, article y editor, además de un humanizer. Para que el contenido quede 100% como me gusta, lo creo dentro del proyecto de cada cliente que ya tiene brand guide, voice tone y más.

Otros skills super potentes han sido los de serp analyzer, content optimization que busca los contenidos con caída de tráfico y ofrece mejoras.

no sé si a alguien más le pasa pero últimamente siento como si mi claude estuviera corrupto! me lanza error por todo, se queda sin créditos reparando flujos que solían funcionar sin problema, no consigue lo que le pido: mcp, skills, proyectos. Se ha tornado bastante frustrante y complicado.

Las tareas programadas sirven un día y al otro se rompen.

La verdad es que claude me ha ayudado mucho pero también pierdo horas/días arreglando o entendiendo problemas (si es que los créditos me alcanzan).

The new generative AI search guide directly calls out llms.txt, chunking, AI text files, and special schema as unnecessary. AI Overviews and AI Mode use the same retrieval and ranking systems as core Search.

We were approached by a company to help setup their content generating workflow with no human in loop. We pushed that they should have human in loop to fact check, verify claims and eventually help build the trust that LLMs love. They did not like our suggestion and moved on but I kept thinking if human in loop is important or is it possible to achieve the right kind of content via AI agents. Can it help with AI visibility

I’m seeing so many tools and "AI optimization" checklists flagging a missing llms.txt file as a critical error.

Google has been pretty clear - they don't need it to crawl or understand your site for LLMs. It feels like the new "Meta Keywords" tag - total snake oil.

• Are you guys actually seeing any traffic/ranking benefit from adding one?
• Or is this just another case of devs over-engineering for a problem that doesn't exist?

Been noticing more people using ChatGPT, Gemini, and Perplexity instead of traditional search for recommendations.

What’s interesting is that some brands consistently get mentioned while others are completely invisible, even when they’re well-known in their niche.

From what I’ve seen, things like:
• Online brand mentions
• Reviews & reputation
• Clear website content
• Topical authority
• Consistent positioning

seem to influence this a lot.

Curious if other founders/marketers here have noticed changes in traffic, discovery, or customer behavior because of AI tools yet.

OpenAI GPT 5.4 Extra high is currently the best model for anything SEO related and other technical work.

I use it daily for SEO work.

I'm genuinly optimistic about the kind of response I'd get here, so we know our users are asking AI models for product recommendations instead of using Google directly, but running manual prompts to check our visibility isn't scalable.

I don’t understand yet, if everyone is just guessing and hoping, or is there an actual data-driven system people are using to track these brand mentions automatically?

Update: GentrackAI is what I've found to be the most useful for this in the long run.

Lately there’s a lot of discussion around llms.txt, with some calling it the new robots.txt for AI crawlers.

Some say adding it helps LLMs better understand website content and improves visibility in tools like ChatGPT, Perplexity, or Gemini. Others think it’s mostly hype and not something that makes a measurable difference right now.

Has anyone actually tested this and seen real impact? Curious whether adding llms.txt is becoming important for AI visibility or if it’s still too early to matter.

I’m part of the team at an AEO platform called LightSite AI. We posted some analytics here before, but most of it was about technical bot behavior patterns across our client base.

This time, we asked our AI agent to analyze anonymized data across our clients and look specifically at what kinds of pages actually get human traffic and conversions from AI search.

There is a pattern.

When tested at scale, human visitors from AI search usually don’t land on homepages, pricing pages, or generic product pages.

They land on pages that directly answer something - this part is probably sounds trivial so here are some concrete examples.

Top 4 patterns that worked in temrs of landing human visitors from AI:

A. Listicle with audience + geography qualifier

Example: /blog/best-[category]-for-[audience]-in-[region]

This was one of the strongest informational patterns. The winning pages looked like:

“Best spend management software for small businesses in the US”

Pattern: Best [category] for [audience] in [region]

Why it works: LLMs love comparison answers, and the title matches how people actually ask prompts. Usually the prompt includes the category, the buyer type, and the geography.

B. Tool-named technical how-to

Example: /blog/automating-[workflow]-with-[named-tool]

These did surprisingly well with technical audiences.

Pattern: [verb] [outcome] with [named tool]

The best pages named a specific product, library, or workflow. Not a broad thinkpiece. More like:

“Automating GitHub issue creation with Claude Code”

Lesson: blog titles that name a specific tool often perform better than generic concept posts because LLMs treat them almost like documentation.

C. Template / utility pages

Example: /templates/[artifact]

This was the most underrated category.

Template pages worked both as informational answers and as useful tools. They also converted much better than regular editorial pages because the intent was already clear.

Examples:

• /templates/invoice
• /templates/estimate
• /templates/crm

If the audience would download a checklist, calculator, template, or worksheet, it should probably have its own indexable page.

D. Narrow-vertical how-to

Example: /how-[specific-audience]-can-[specific-action]

These are cheap to write and surprisingly durable.

Examples:

• how attorneys can use YouTube Shorts
• resources for deaf interpreters

The pattern is simple: pick a narrow audience that big publishers ignore and write the specific how-to they need.

What this means for content structure:

Slug patterns that worked:

• best-[category]-for-[audience]-in-[region]
• how-[audience]-can-[action]
• [verb]-[outcome]-with-[named-tool]
• /templates/[artifact]

Slug patterns that did not show up much:

• “The Future of X”
• “Why X Matters”
• generic thought-leadership noun phrases

The first sentence also matters. The best pages usually answer the title immediately instead of opening with context.

Another pattern: one named entity per post. A tool, a vertical, or a region. Posts without a named entity were much weaker.

Our main takeaway: AI visitors land on answers, not positioning.

how are you guys reporting AI traffic to management along with regular SEO reporting? one is referral traffic from GA4. are you guys utilizing tos like Peec AI? focusing on metrics like visibility rate?

Let me start with a disclaimer, because the hype around this topic is getting a bit out of hand. Unlike many others here, I don't think that generative engine optimization, GEO , AEO or whatever you call it- is some magical new discipline.

There is a huge overlap with traditional SEO. If your technical SEO is garbage and your content is thin, no AI hack is going to save you. But it is NOT 100% overlap.

That 10% to 20% difference between SEO and GEO is important and risky enough for a brand like ours to seriously look at the nuances of how to build trust and authority with AI.

My team has spent the last few months testing almost a dozen tools to figure out how to really improve AI visibility of a brand. What we realized is that 90% of the tools out there are just expensive dashboards. They scrape LLM outputs, put them in a pretty pie chart, and tell you that you are losing visibility, ok I get it, marketers wnat to know and always hungry for data (even when it becomes counterproductive). But what do I actually do about it - there is a huge difference between data and actionable insights.

I think that to actually move the needle, you need a holistic approach that covers both content generation and technical infrastructure. You have to control what the bot reads and how the bot behaves when it hits your server.

Here is a breakdown of the stack we tested, what we kept, what we threw out, and what actually worked for us.

1 - The Legacy Giants (Ahrefs / Semrush)
I have to include them because you can’t ignore them. Yes, they are all releasing AI Search features and no, they aren't there yet. It feels to me that they are doing it because the demand is there and they have to add some features anyway. I personally wouldn’t use them for this.
Pros:

• You still need them for backlink profiles and traditional search volume (Google and SEO is very far from dead, and traditional SEO still heavily informs LLM training data).
• Great site audit tools for basic technical hygiene (broken links, toxic domains).
• Cons:
• They are trying to retrofit an old paradigm (10 blue links, search volume) onto a new paradigm (RAG, conversational answers).
• They don't track how AI bots fetch your data in real time, let alone optimize for it.
• Bottom line: Keep your subscription, but don't expect their new features to solve your generative engine optimization problems anytime soon.

2 - Writesonic (and similar AI content factories)
We looked at Writesonic, Jasper, and a few others for the content side of the play. If you want AI visibility, you obviously need entities and topical authority. Writesonic is a beast for content velocity. It’s moved way past just being a basic GPT wrapper and has some genuinely good SEO features built into the workflow now.

Pros:

• Incredible for scaling up glossary pages, FAQs, and top of funnel content.
• The brand voice training actually works pretty well if you feed it good guidelines.
• Very intuitive UI; you can train a junior marketer on it in an hour.
• Good integrations with WordPress and other CMS platforms.
• Cons:
• It is purely a content play. It does absolutely nothing for your technical architecture.
• Just writing AI friendly content isn't enough if the LLM bots can't parse your site properly when they fetch it.
• You still have to figure out what to write on your own. It doesn't tell you where your visibility gaps are in Perplexity or Claude.
• Bottom line: If your only bottleneck is writing words on a page, it’s great. But it won't fix your underlying AI discoverability issues.

3 - LightSite AI (technical + content agent)
This one took us a minute to wrap our heads around because it’s not really a visibility tracker, and it’s not just a content writer. It operates as both a technical and content agent. It gives a pretty complete picture of how to actually build trust and authority with AI at the structural level, this is the closest thing we found to a complete solution. Instead of just giving you a dashboard of mentions, LightSite builds a machine readable technical layer on your site and gives you an agent to execute fixes (both on and off page).
Pros:

• Holistic: It bridges the gap between technical infrastructure and content execution.
• The dynamic technical layer: Shaping bot behavior via skills/endpoints is an advantage over other tools.
• Execution instead of simple observation: The agent identifies a gap (e.g., "ChatGPT thinks your competitor has a better pricing model"), suggests the content fix, and can actually execute the content updates or outreach campaigns.
• Tracks bot logs vs. human traffic, which is critical for real attribution (not vague mentions or SOV etc).
• Cons:
• Integrating it required a buy in from our techcnial team and we had to go through security testing since it plugs into the website.
• The learning curve is steeper because it does require a change in mindset - from keywords only to bot behavior / technical part.
• Bottom line: If you want a system that actually builds the technical infrastructure and acts as an agent to help you execute, this is the strongest platform we tested. But you have to be willing to do the integration work.

4 - Brand24 / Mention (The PR Trackers)
We tried using traditional social listening tools that have pivoted to AI Mention Tracking. They basically ping the LLMs with prompts and track if your brand is recommended.
Pros:

• Great for the CMO's weekly report. The charts look beautiful.
• Good for broad sentiment analysis (does the AI think we are expensive, cheap, reliable?).
• Very easy to set up. No dev resources needed.
• Cons:
• Zero actionable insights. Okay, ChatGPT recommends our competitor 60% of the time. Why? And how do I fix it?
• LLM hallucinations make this data incredibly noisy. You can prompt Claude three times and get three different brand recommendations.
• Completely disconnected from your actual website backend.
• Bottom line: Good for benchmarking your PR efforts, practically useless for a technical or content team trying to do actual GEO work.

5 - Surfer SEO / Frase
We still use these, but we had to re evaluate how we use them in an AI first world. These tools are built around NLP and entity optimization.
Pros:

• Still the best way to ensure your content is dense with the right entities.
• If you want an LLM to understand your page, scoring high on Surfer/Frase is a great baseline.
• Excellent workflow for human editors.
• Cons:
• They are still fundamentally built for Google’s traditional ranking algorithm (TF-IDF, keyword frequency, etc).
• They assume the end goal is a human reading a SERP. They do nothing to help headless AI agents interface with your backend data.
• No bot tracking or technical deployment features.
• Bottom line: Essential for your writers, but it’s only half the battle. They optimize the text, but not the delivery mechanism to the AI.

My Takeaway for 2026
If you are just buying a tool that shows you a dashboard of AI Share of Voice, you are wasting your money.

The brands that are actually building trust and authority in AI search right now are doing two things simultaneously:

1. Pumping out highly specific, authentic, helpful, entity rich content (using tools like Writesonic/Surfer).
2. Fixing their technical layer so LLMs can cleanly parse that content as data (using platforms like LightSite AI).

My advice - stop obsessing over rank tracking, stop looking for shortcuts and stop buying dashboards - understand that this is a holistic play, SEO is not dead but there are nuances that have to be handled and honestly no one knows where all this is going so keep creating value for your users on every step of the way and you will be fine.

Hi everyone,

I am a final-year Commercial Economics student (Netherlands) conducting research for my thesis on Generative Engine Optimization (GEO), the practice of making brands and businesses visible inside AI-driven search tools like ChatGPT, Perplexity, and Google AI Overviews.

What the research is about

Search behaviour is shifting fast. Instead of clicking through to websites, people increasingly get answers directly from AI systems. My research investigates how Dutch B2B companies experience this shift, what pain points they run into, and what they actually need from a service that improves their visibility inside these AI tools. The findings will be used to develop a validated go-to-market strategy for a GEO service.

Who I am looking for

I am looking to interview people who are:

• Working in a marketing, digital, or growth role at a B2B company
• Responsible for or involved in online visibility, SEO, or content strategy
• Based in or operating in the Netherlands (Dutch or English interview, your preference)
• Curious about what AI-driven search means for their brand

The interview is semi-structured, takes approximately 30 minutes, and can be held remotely via Google Meet or Teams.

What you get in return

Every participant receives a free GEO audit, a concrete analysis of how visible your brand currently is inside generative AI systems, including actionable recommendations.

Interested?

Drop a comment below or send me a DM. Happy to answer any questions about the research first.

Thanks in advance!

We looked at 10.4M clicks and 54M impressions across 419 Quebec-based SME websites over 16 months, then compared the current post-AI Overviews click distribution with pre-AIO CTR benchmarks.

The biggest takeaway wasn’t “SEO is dead”.

People still click organic results.

They just seem to click much less deeply into the page.

Positions 4-10 lost around 70% of their click share compared to pre-AIO benchmarks.

That means they went from capturing around 30-45% of page-one clicks to 10.8% (post-AIO).

Barely 1 out of 10 clicks.

The pattern was pretty blunt:

- The Top 3 captured 89.2% of all page-one organic clicks
- Position #1 alone captured 63.6%
- Position #7 averaged a 2.6% CTR
- Positions 4-10 captured 10.8% of page-one clicks, compared to around 30-45% before AI Overviews

So the question for me is not just “can we rank?”

It’s “is this ranking still useful if it doesn’t get clicked, cited, or searched for directly?”

With visibility spreading across Google results, AI answers, forums, reviews, social platforms and branded search, I’m curious how other SEOs are adapting.

When a keyword seems capped around positions 4-8, do you keep pushing for the Top 3, or move effort toward long-tail keywords, AI citations or entity/brand visibility?

And what signals do you use to decide when a ranking is still worth chasing?

There are a lot of AI visibility and AI Overview tracking tools popping up right now but most of them seem built for general SEO use. Wondering if anyone has found something that works well specifically for ecommerce, product pages, category pages, that kind of thing. Or are you just using general tools and adapting them? Curious what the ecommerce agency people here are actually running.

Most LLM readiness discussions focus on content quality. I wanted to see the structural layer, what makes a page actually get opened and cited by OpenAI web search.

I built a CLI tool called Prelude by Symphony (open source, MIT, runs via npx) that uses the OpenAI Responses API with web_search_preview to trace which URLs the model actually opens for a query, not just which it searched, but which it read.

I ran it on two sites. Results:

Site A — 99/100, Grade A:

• Schema types: Answer, FAQPage, ImageObject, Organization, SoftwareApplication, WebSite
• 29 valid headings, H1: 1 ✓
• Chunking quality: excellent (8 viable of 61 paragraphs)
• GPTBot: allowed / ClaudeBot: allowed
• Issues found: 1 (low — missing BreadcrumbList)

Site B — 50/100, Grade D:

• Schema types: none
• Headings: 1 total, H1: 0 — broken
• Chunking quality: poor (0 viable paragraphs)
• Robots.txt: not found
• Issues found: 9

Site B had real content. The problem wasn't what it said — it was structurally invisible to LLMs.

The 3 things that explain the gap:

1. Valid H1 hierarchy — LLMs use headings to understand page structure before reading content
2. Structured schema (JSON-LD) — without it, the model can't identify what type of entity the page is
3. Content chunking — paragraphs need to be independently meaningful to be citation-ready

If you want to check your own site, search for "symphony-prelude" on npm or GitHub — the audit command is free and doesn't require an API key. The trace command uses your own OpenAI key.

Happy to discuss methodology or run a comparison on anyone's site in the comments.

Been digging into how Google AI Mode actually pulls sources and the YouTube integration is way more prominent than I initially gave it credit for. The video chips thing is interesting, instead of a blue link you get a clickable timestamp dropping you straight into a specific moment in the video. That's a pretty different user behavior compared to traditional search. The Ask YouTube feature that rolled out for Premium users is basically conversational search layered on top of video content, follow-up questions and all. Combined with the AI-powered carousel doing topic summaries with direct video segments, it feels like Google is quietly, making YouTube one of its major citation sources for AI Mode answers rather than just a supplementary one. Worth noting it's not the single dominant source, Google's own properties collectively account for a meaningful chunk, of citations, but YouTube's share has grown noticeably and it's punching well above what most people expected. For anyone doing AEO work, this probably changes how you think about video structure. The 15-second answer block concept makes a lot more sense when you realize the system is literally extracting a clip to surface inside a response. Short, direct, front-loaded answers in the first 20 seconds or so seems to be where the citation advantage sits. Curious if anyone here has actually tested video content against text-only pages for AI, Mode citations and seen a measurable difference in how often you're getting pulled into answers. Would love to see some real data on this.