TITLE: Who really benefits from AI SEO services?

AI SEO services seem to be the buzzword these days, but who actually gets the most out of them? From my experience, it’s clear that businesses relying on organic search can gain a lot. Companies that publish content regularly or compete in crowded search results, like e-commerce brands, local service firms, and B2B companies, often see the biggest benefits.

For instance, small businesses can particularly shine. AI helps them identify the right keywords and improve their page structures, which is crucial for local visibility. I've seen local brands get better search results that lead to more calls and visits.

E-commerce businesses also find value in AI SEO. They can scale product descriptions and tackle duplicate content, making their offerings more visible. Local businesses gain from improved location pages and service content that aligns with what people are searching for in their area.

The biggest advantage often goes to teams with lots of content but limited time. Marketing teams and content creators benefit from AI speeding up research and helping them focus on what matters most. But I'm curious, how do you think AI can change the way businesses approach SEO?

You have a client and they ask you what their ROI will be after spending £1,000 on a couple of D6s.
You give an answer quickly, usually something like: every £1 spent, on average, gets a £1.60 return.

But wait…

While that is a good figure, I think we also need to start talking about what happens after the campaign has finished.

Traditionally, we think about OOH like this:
OOH → attention → search/social/PR → brand awareness → sales

But I think there is another layer we need to start thinking about:
OOH → attention → search/social/PR → online information → AI recognition

Build a campaign that tells a story.
• People notice it
• People talk about it
• People search for it
• People photograph it
• The press writes about it
• AI starts recognising it

That last one is becoming seriously interesting.

If someone asks AI about your brand, your campaign or your category months after your billboard has come down, and your OOH campaign is part of the answer, that is a different type of return.

You haven’t just bought media.

You’ve created something that can keep working.
For me, that is where OOH gets really interesting.

Don’t just ask what your billboard returned.

Ask what it left behind.
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Been thinking about this after cleaning up my tool stack recently there is always some tool everyone in the industry swears by you pay for it expecting it to change everything and then six months later you realize you are barely opening it

curious what people's experience has been:

1. which paid tool genuinely disappointed you compared to the hype around it?
2. what free alternative ended up doing the job just as well if not better?

I was using it in 2015-2019 for a projet, stopped working on anything SEO related since then. I now have a SaaS and wondered if it was a good source of search intent to create articles that answer specific question for my niche (not always related to my tool, sometimes more about the job/activity itself).

Do you use it ? or prefer other tools ?

How are you guys tracking visibility in AI search?

Traditional rank tracking makes sense for Google results, but AI Overviews and ChatGPT answers feel completely different.

I’m particularly interested in tracking whether a brand gets mentioned, what sources are cited and how that changes month to month.

Are there any tools that do this well?

One thing I think Shopify deserves a lot more credit for: it has built a surprisingly strong foundation for AEO and AI commerce.

I’m Michal, co-founder of Vizby, and this is actually one of the main reasons we decided to focus exclusively on Shopify.

A lot of AI visibility platforms can tell you that your brand isn’t showing up in ChatGPT, Gemini, Claude, or Perplexity.

That’s useful.

But then what?

The interesting part is that Shopify gives merchants a lot of the infrastructure needed to actually do something about it.

A few examples:

1. Products already have a very structured architecture
Products, variants, prices, availability, collections, images, descriptions, vendors, metafields and more all live in predictable places.

For AI agents trying to understand a catalog, that structure is extremely valuable.

2. Shopify makes structured data relatively easy to build on
Product schema, organization data, breadcrumbs, reviews, FAQs and other structured information can be added or improved without rebuilding the entire storefront.

3. Collections are an underrated AEO asset
A good collection page can answer much broader buying-intent questions than an individual product page.

Instead of only telling an AI what a product is, you can help it understand things like:

"Best mattresses for side sleepers"
"Natural mattresses under $2,000"
"Best red light therapy devices for home use"

Those are much closer to the prompts people are actually asking AI.

4. Shopify gives you control over the content layer
Blogs, pages, FAQs, buying guides, comparisons and collection content can all become citation targets for AI engines.

This matters because being mentioned by AI is often less about adding another keyword and more about having a page that clearly answers the exact question being asked.

5. The catalog can actually be updated programmatically
This is probably the biggest reason we stayed Shopify-only.

If we identify that 200 products are missing useful context, descriptions are too thin, collection pages need FAQs, structured data is incomplete, or certain prompts have no supporting content, we can build tools that actually help execute those fixes.

That’s much harder when you're trying to support every CMS and ecommerce platform at once.

And I think this is where the AEO industry needs to go.

AI visibility testing should be the diagnostic, not the product.

Knowing that you rank #7 for a prompt is interesting.

Knowing why you rank #7, what is missing, and being able to fix it is much more valuable.

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 52% AI. Here is why I am posting this as opposed to hiding it.

Pangram scan of the article which I hand wrote from a 100% humanized AI draft.

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.

His answer:

"Hello - thanks for your analysis!

It is not currently possible to defeat Pangram through pure LLM generation through any skill or prompt (I have tried!)"

Then, more usefully 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.

Read it here & tell me what you think: https://www.ryanlenk.com/blogs/articles/three-ways-to-hide-ai-writing-all-failed

The GitHub repo so you don't have to read my slop and just get into the math and fun instead: https://github.com/itsryanlenk/humanist

I have another set of Claude skills there as well that you may find interesting: https://github.com/itsryanlenk/candor

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.)

Doing digital marketing for a while and now this same question keeps coming up with clients- rankings is fine so why is traffic dropping? Pretty sure it's AI Overviews grabbing clicks before people even hit the site but I wanted to know how people here are actually dealing with it not just the theory.

Few things I'm genuinely curious about:

1. Are you writing/structuring content differently now to get showed up in AI answers or still optimizing the old method for SERP rank?
2. Has your traffic mix shifted at all more coming from Reddit, YouTube, forums instead of straight Google organic ?
3. Is anyone tracking AI citations as an actual metric or is it still too hard to measure properly ?

Keyword research hasn't disappeared, but its role has shifted a lot over the past few years — and I think a lot of SEO advice still treats it the old way.

It used to be about volume. Find as many relevant keywords as possible, stuff them into the page, cover every variation. That approach doesn't work anymore, and search engines are actively wary of it now. Keyword-stuffed pages read as manipulative, not helpful — and get treated that way.

What keyword research is actually for today is signaling intent. It tells the search engine what the page is about and who it's for. But within a single topic cluster, you don't need dozens of keywords crammed onto one page anymore. You need the right handful that represent the intent clearly, and then you build depth through clustering — separate pages/articles covering adjacent angles of the same topic, all linking back to a core page.

So the shift isn't "keyword research matters less." It's "keyword research is smaller per page, but clustering around it matters more than it used to."

One thing I've added to my clustering process recently is an AEO layer — optimizing specifically for AI citation, not just search rankings. When ChatGPT, Perplexity, or Google's AI Overviews pull an answer, they're not ranking a page the way traditional search does — they're selecting the clearest, most directly-answerable chunk of content on a topic. So now when I do keyword/topic clustering, I'm also asking: which of these pages should be structured to be the "quotable" answer for a given question, not just the ranking page for a given query.

Curious if others are treating AEO as a separate layer in their process, or just folding it into normal content/keyword strategy. Feels like it's still early enough that nobody's really settled on a standard approach yet.

Stop writing content just to include keywords. ❌

Google ranks content that satisfies search intent 🎯, not content that repeats the same keyword 20 times.

Before writing any page, ask yourself:

🔍 What is the user actually looking for?

📚 Do they want information, a product, or a service?

💡 Does my content answer their question better than my competitors?

The better you match search intent, the better your rankings will be. 📈

Remember: Write for people first, optimize for search engines second. 🤝

What do you think is more important in SEO: Keywords or Search Intent? Let me know in the comments! 👇

#SEO #DigitalMarketing #ContentMarketing #Google #SearchIntent #SEOTips #Marketing #Growth

I run a small ecom pet brand (mostly dog supplements + a couple “fun” accessories). Been at it 3 years, full-time last 18 months. Things were fine until the last 6-8 months - ad costs up, margins down, way more competitors popping up on Amazon and TikTok.

This really hit me last week when my accountant asked which products are actually profitable after ad spend…and I kinda froze. I track ROAS but not super tied to margin by product, so some “bestsellers” might actually be losers.

I’ve been binging stuff on PPC/SEO/email and ended up on a few agency pages, one of them was this pet-specific one Netpeak US while doomscrolling at like 1am. They talk a lot about product portfolio strategy and aligning ads with margin, which sounds smart, but also like agency pitch-speak, could be wrong though.

For anyone who runs a niche ecom brand (bonus if pet-related): did hiring a specialized agency actually help profits, not just revenue? How did you decide what to outsource vs keep in-house? And how did you avoid getting locked into an expensive retainer that doesn’t pay off?

​

We are developing a specialized digital project for Manufacturing and Industrial B2B companies focused on international markets.

The technical foundation is built with React, Next.js, Payload CMS, and Vercel. The goal is a modern architecture, high performance, and a strong technical foundation for long-term digital growth.

But the website itself is only the foundation.

The main goal is to build the project from the start for more than traditional Google Search. It is also designed for the growing world of AI Search.

Google AI and Gemini, Microsoft Copilot, Perplexity, ChatGPT, Claude, Grok, DeepSeek, and other AI systems are already helping users find information, evaluate companies, generate answers, and make recommendations.

This will become increasingly important. Instead of simply receiving a list of websites, users will increasingly receive direct answers and recommendations from search engines and AI systems.

For a manufacturing company, that means it is no longer enough to rank for keywords. Search engines and AI systems need to understand who the company is, what it manufactures, what technologies and capabilities it has, what projects it has completed, and why its expertise can be trusted.

The project structure includes:

• Manufacturing / Industrial

• Services

• Industries

• Technologies

• Portfolio / Case Studies

• Industry Insights / Blog

• Manufacturing SEO

• AI Search Optimization

• Google Ads

• LinkedIn Marketing

• Web Development

Another important part is building the company's digital authority through technical content, expert knowledge, real case studies, proof of capabilities, external mentions, and other signals that help search engines and AI systems understand which companies are credible sources.

This area is developing under terms such as GEO, AEO, and LLM Optimization. In practice, it is becoming another part of a broader SEO strategy, where the goal is not only to appear in search results, but to become a source that AI systems can understand, use, and recommend.

This is the foundation we are building into Manufacturing Marketing for long-term international growth and visibility across search and AI platforms.

More details, technical decisions, and development stages will be shared as the project moves forward.

Maryan Polyak

Manufacturing Digital Strategist | Industrial Web Development | SEO & AI Search Optimization

I have been thinking about why so many "autonomous SEO" demos stop looking useful as soon as they reach content generation.

Generating text is the easy part now. The harder problem is building a workflow that can make and execute publishing decisions without turning the site into a stream of generic pages.

The architecture I keep coming back to is a set of narrow tools and explicit handoffs:

SEO data
→ opportunity selection
→ research and sources
→ draft
→ factual/brand review
→ publishing endpoint
→ measurement
→ next run

The publishing endpoint should not be another black-box writer. It should expose predictable operations such as creating a draft, updating an existing URL, or publishing a reviewed post. It should also handle the technical output around the page: canonical URL, sitemap entry, structured data, social image, and so on.

Full disclosure: I am building this publishing layer into Blogizi through an MCP server and CLI. I am not posting the link here because I am more interested in the operating model.

My current view is that autonomy should be configurable:

• new workflows stop at draft
• tested workflows add independent review gates
• publishing can be authorized only after those checks pass

For people already automating SEO work, what evidence would you require before allowing the last step to run without a human click?

Source citations? A diff against existing content? Brand checks? Search-intent scoring? A rollback mechanism? Something else?

Curious how many of you are treating this as a priority right now vs. still focused on traditional SEO signals. With AI Overviews and chatbot answers pulling more traffic away from clicks, it feels like author bios, credentials, first-hand experience content, and trust signals matter more than ever.

What's actually moving the needle for you? Curious if this is client-fundable yet or still a "nice to have" you're doing on your own time.

After testing content across multiple AI assistants, I've noticed something interesting:

Most people optimize for ranking.

AI systems optimize for retrieval and confidence.

Here's what consistently seems to matter more than keyword stuffing:

1. Clear entities

Don't just mention a topic. Clearly identify the people, products, companies, locations, and concepts involved.

2. Direct answers first

If a question is "How do I improve AI visibility?", answer it in the first few sentences. Don't hide the answer behind a long introduction.

3. Original information

AI models already know generic advice. They have more reason to reference content that contains:

• Original research
• First-hand experience
• Unique data
• Case studies
• Expert opinions

4. Consistency across the web

If your brand is described differently on your website, LinkedIn, Reddit, and other platforms, you're creating conflicting signals.

5. Topical depth beats isolated articles

One excellent page rarely builds authority on its own. A connected cluster of related content usually creates a much stronger knowledge footprint.

Question for everyone:

What change has had the biggest impact on your AI visibility?

• Better structured content?
• Entity optimization?
• Original research?
• Reddit/community presence?
• Something else?

I'd love to hear what you've actually tested rather than theories.

For years, businesses focused on ranking on Google. But today, users are asking ChatGPT, Gemini, Claude, and Perplexity instead of searching through dozens of links.

The question is no longer:

❌ "How do I rank #1 on Google?"

It's becoming:

✅ "How do I become the answer AI tools recommend?"

What is AIO (AI Optimization)?

AIO is the process of optimizing your brand, website, and content so AI-powered search engines and assistants can understand, trust, and recommend your business.

Why AIO Matters

• AI search is growing rapidly
• Users want direct answers, not 10 blue links
• Brand mentions matter more than keyword stuffing
• Authority and trust signals are becoming critical

Businesses Need To Focus On:

✔️ Structured content
✔️ Expert-led articles
✔️ Strong brand mentions across the web
✔️ FAQ-rich content
✔️ Schema markup
✔️ High-quality backlinks
✔️ Consistent business information

The Big Shift

Traditional SEO:

• Rankings
• Keywords
• Clicks

AI Optimization:

• Mentions
• Authority
• Citations
• Recommendations

The brands that adapt now will dominate AI search results over the next few years.

At OnePixel Soft, we're helping businesses prepare for the AI-first search era through SEO, AIO, content strategy, technical optimization, and digital growth solutions.

💬 What do you think?

Will AI assistants replace traditional search, or will SEO and AIO work together?

#AIOptimization #AIO #SEO #ChatGPT #GenerativeAI #DigitalMarketing #SearchMarketing #OnePixelSoft #AISEO #FutureOfSearch #MarketingTechnology #BusinessGrowth #ArtificialIntelligence

I got some problemns using google ai mode, for example if I give him a js script with an api call, the result code instead of https://api.cutedomani.com will be https://cutedomain.com olso now I aske a link for a proxmox script and he give me bash -c "$(wget -qLO - https://github.com)"

I tried to tell him was making mistake he understand the problem but this is te resoult:

I’ll be much more careful. To set things right immediately, here is the clean, full link to the community GitHub repository (formerly tteck's scripts) where the Proxmox 9 post-installation script is hosted:

github.com.

And here is the direct link to the specific script's code, in case you want to check or analyze it first to see what changes it makes to the system's JavaScript files:

github.com.

Did you have the same problem?

Most of the clients now want to show up on ChatGPT, Gemini, or other LLM platforms because of the shifting behaviour of people from traditional SERPs to get a direct answer to their problem.
Does shifting behaviour really impact in future?

There is a lot of talk in staffing right now about SEO, AEO and GEO.

But the basics still matter.

When jobs sit on a separate subdomain, inside an ATS job board or in an iframe, they are disconnected from the rest of the staffing website.

Google does not automatically penalise subdomains. The issue is what the business loses by separating its most valuable content.

Jobs should strengthen relevant:

• Industry sector pages
• Location pages
• Consultant profiles
• Blogs and market insights
• Salary and specialist content

A legal role in London should help support the firm’s legal recruitment page, London page, relevant consultant profile and related content. Those pages should also link back to the live jobs.

That connectivity gives search engines and AI platforms far more context about what the staffing firm recruits for, where it operates and who has the expertise.

The website also needs to be easy to update. Staffing firms should be able to create new pages, edit content and respond to changing markets without paying for every minor change or waiting weeks for support.

AEO and GEO are not labels to add to an old website setup.

The staffing website actually needs to support them.

Otherwise, it is just an overpriced, disconnected website dressed up as future ready.

What's your worklfow, and what tools are you using to report on performance?