hey guys, I’ve just been starting SEO and I want to get the best tips of how you go from maybe keyword research to have finished article what do you take into account for an article to actually rank on Google and we’re getting some clicks impressions I just want the gold tips of what you guys are doing? That is getting results today with AIO GEO or whatever. I’ve just landed a client. I need to do as your for them and I really want to get them results and do the right thing so what do you guys suggest doing to get the best ranking articles?

hey guys, I’ve just been starting SEO and I want to get the best tips of how you go from maybe keyword research to have finished article what do you take into account for an article to actually rank on Google and we’re getting some clicks impressions I just want the gold tips of what you guys are doing? That is getting results today with AIO GEO or whatever. I’ve just landed a client. I need to do as your for them and I really want to get them results and do the right thing so what do you guys suggest doing to get the best ranking articles?

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

I keep seeing more tools that promise to automate a big part of SEO: keyword research, content briefs, article generation, internal linking, publishing, etc.

Curious to hear from people who have actually paid for and used them. Like babylovegrowth, sorank, autoseo, blogseo stuff like that

Which tools have you tried?

What did they genuinely do well?

What felt disappointing or overhyped?

Did the content actually rank, or did it mostly save time?

And was there anything you expected the tool to handle that it just didn't?

Especially interested in experiences with tools like BlogSEO and similar platforms, but open to anything.

Would love actual user experiences rather than affiliate/review-site recommendations.

I've been looking more closely at traffic coming from ChatGPT, Perplexity, Gemini, etc. lately and I'm still not sure how much attention it deserves compared to normal Google traffic.

There's obviously a lot of talk about GEO/AEO and getting cited by LLMs, but I’m curious about what people here are seeing with actual sites rather than screenshots from SEO tools.

For those of you managing sites with decent traffic:

• Are you getting measurable visits from AI assistants?
• Is any of that traffic converting?
• Have you changed anything specifically to get cited more often?
• Or are you mostly just treating it as something to monitor for now?

My impression so far is that the visibility is interesting, but the actual referral volume still seems pretty small for most sites.

Would be interested to hear numbers/experiences from people who are tracking it.

I’ve spent the last few months building SEOryon, an AI SEO platform, mostly by myself.

The product is already live and working. It handles things like SEO research, content generation, fact-checking, GEO/AI visibility, publishing, etc.

I’m at the point where I don’t need someone to build an MVP from scratch.

What I’m looking for is someone technical who looks at an existing product and thinks:

>

I handle the product vision, marketing, growth and a lot of the product work. I’d love to find someone who could progressively take real ownership of the technical side.

Ideally you’re:

• full-stack / backend leaning
• comfortable with SaaS, APIs, databases and cloud infrastructure
• interested in AI
• entrepreneurial and actually enjoy building products
• able to challenge decisions instead of just executing tickets
• French or English speaking

I’m not looking for an agency or someone to rewrite the whole thing for no reason.

I’m looking for a builder.

I’m open to discussing equity for the right person if there’s a real fit, but I’d rather work together first and see how we collaborate before making anything serious.

If you’re a dev and this sounds interesting, or you know someone who might be a good fit, DM me.

Happy to show the product, architecture and what I’m trying to build.

I’ve seen a lot of panic around AI text watermarking recently, especially with the EU AI Act and Anthropic moving toward invisible markers in Claude-generated text.

The reaction I keep seeing is basically:

“Google will know it’s AI → Google will penalize it → AI SEO is dead.”

That conclusion skips a few very important things.

First: AI text watermarking isn’t actually new

Google has already had this technology for years.

In 2024, Google DeepMind expanded SynthID to text generated in Gemini.

SynthID changes the probability of which tokens the model selects while generating text, creating a statistical pattern that can later help identify the output as AI-generated.

To a human, the text looks completely normal.

There’s no:

<AI-GENERATED>

hidden in the HTML.

It’s closer to a statistical fingerprint in the generated language itself.

Google has since even open-sourced SynthID Text so developers can use similar watermarking technology themselves.

So the idea that:

>

isn’t really accurate.

This has been coming for a while.

Does a watermark mean Google will penalize the page?

There is currently no evidence of that.

More importantly, Google’s own Search documentation does not say:

>

Google says generative AI can be useful for research and content creation.

What Google warns against is something different:

generating lots of pages without adding value to users.

That’s part of what Google calls scaled content abuse.

And there’s a massive difference between these two things:

Example 1

Keyword:

>

Then:

>

Publish.

Do it 5,000 times.

Example 2

Research the SERP.

Understand search intent.

Collect reliable sources.

Use proprietary information from the company.

Analyze competitors and missing topics.

Build an evidence set.

Generate a first draft.

Rewrite awkward AI language.

Check factual claims.

Add examples, internal links, sources and useful information.

Review the final output.

Publish.

Both might technically contain AI-generated text.

But they are not remotely the same product.

And this distinction is exactly where I think a lot of the AI SEO discussion goes wrong.

A watermark tells you provenance. It doesn’t tell you quality.

Imagine Claude helps edit a paragraph that a human wrote.

That paragraph might become watermarked.

Imagine Claude translates an original research report.

Potentially watermarked.

Imagine an expert provides all the data, arguments and conclusions, and Claude turns their notes into readable prose.

Potentially watermarked.

Now compare that with somebody generating 10,000 generic articles from keywords.

Also watermarked.

The watermark alone can't tell Google which one is useful.

It only gives information about how the text may have been produced.

That would make it a pretty weak standalone ranking signal.

Could Google use AI provenance as one signal among many?

Sure.

That seems much more plausible.

Imagine Google sees:

• AI provenance detected
• 800 articles published this month
• almost identical structures
• little original information
• heavy semantic overlap
• no first-party experience
• content mostly summarizing other websites

That could strengthen a spam signal.

But now imagine:

• AI provenance detected
• strong topical relevance
• original company data
• expert input
• useful examples
• reliable sources
• accurate factual claims
• genuinely better answer than existing results

Why would Google automatically want to remove that?

Their own current guidance doesn't suggest they would.

Google itself is probably the best evidence that “AI detected = penalty” is too simplistic

Google built SynthID.

Google integrated it into Gemini text generation.

Google has continued developing AI-generated Search experiences.

And Google Search's public guidance still focuses on:

usefulness, originality, reliability and whether you're creating content primarily for people rather than manipulating rankings.

Google even published new guidance in 2026 about succeeding in AI-powered Search experiences, emphasizing valuable, unique, non-commodity content.

Not:

>

That distinction matters.

What I think actually changes

The era of:

keyword → AI writer → publish

is becoming much riskier.

But that doesn’t mean:

AI-assisted content → dead.

It probably means the opposite.

Good systems will need to become much more sophisticated.

Something closer to:

Research → Evidence → Generation → Editorial improvement → Fact checking → Publication

rather than:

Keyword → Prompt → Article

And honestly, I think that's healthy.

If watermarking makes it easier for search engines to separate industrial low-effort AI spam from genuinely useful content systems, then the companies doing real research and editorial work may actually benefit.

What about rewriting/humanizing AI content?

One important nuance:

Watermarks aren't necessarily indestructible.

Heavy rewriting, translation or combining AI text with other content can weaken some watermarking systems.

But trying to build your SEO strategy around “how do I remove the AI watermark?” is probably solving the wrong problem.

Because even if you remove today's watermark, another provenance system comes tomorrow.

Google has SynthID.

Anthropic is introducing its own mechanisms.

Other model providers are moving in the same direction because of regulation and AI transparency requirements.

Trying to stay one step ahead of watermark detectors becomes an endless arms race.

A much better strategy is:

make the final content good enough that it doesn't matter that AI helped create it.

So should you panic?

I don't think so.

The people who should probably be worried are the ones publishing thousands of near-identical AI articles with very little original value.

If you're using AI as one component inside a real content process — research, sources, first-party knowledge, editing, verification and actual usefulness — this development is much less scary than some posts make it sound.

The important question is no longer:

>

It’s:

>

If the answer is yes, I think you're focusing on the right problem.

Hey guys, just wanted to ask: do you think it's gonna impact search results? Do you think that Google will actually block content that's being generated by AI? The thing is that when I ask, for example, for some blogs to be published on my website, I sometimes actually re-write them with AI to make sure that they are easy to understand. The thing is that the watermark is gonna appear on those texts.

What do you think about this? Do you think it will impact SEO in any type of way that is written by Claude or any LLMs? I think that any LLMs need to do it now. What's your position in this?

My short answer is no. GEO is not replacing SEO.

SEO makes your information discoverable, understandable, competitive, and useful in search. AEO is about making the answer clear enough for a person or answer system to use. GEO adds a new visibility and measurement layer for generated answers.

The foundations overlap much more than the acronyms suggest.

If a page is not crawlable, does not render properly, has the wrong canonical, repeats what everyone else says, or cannot support its claims, adding an llms.txt file or another block of schema will not solve the real problem.

The five-layer audit I would run

1. Can search systems reliably access the page?

Before thinking about citations, check:

• Does the URL return the intended status?
• Is the page allowed in robots?
• Is the primary content visible in the rendered HTML?
• Does the canonical point to the correct owner?
• Is the page internally linked?
• Is it indexed and eligible for a normal snippet?

Google's current documentation says pages considered for AI Overviews and AI Mode must still meet normal Search eligibility requirements. Google does not document a separate technical admission system for GEO.

This sounds basic, but it catches a surprising number of “AI visibility” problems.

I have seen teams debate AI writing formats while the main copy was loaded from a client-side API that failed for anonymous users. The page looked complete to the logged-in editor and nearly empty to a clean browser.

No GEO tactic can rescue content that is not reliably there.

2. Does the page complete a real task?

A keyword is not the task.

Someone searching:

best CRM for a 20-person plumbing company moving from spreadsheets

does not need a generic definition of CRM. They probably need:

• Criteria for a field-service team
• Mobile and scheduling requirements
• Realistic migration risks
• Spreadsheet import support
• Pricing conditions
• A way to create a shortlist

The page should answer that decision directly near the top, then provide the evidence, trade-offs, procedure, and next step.

This is where answer-first writing is useful. It is not about forcing every paragraph into 40 words. It is about respecting the reader's time.

My test is:

>

If not, the answer probably needs work.

3. Does the page contribute information another result does not?

This is where a lot of AI-written content fails.

Ten articles read the same five sources. Each article paraphrases the others. The page may be grammatically fine, but there is no reason for a person or an answer system to prefer it.

Information gain can come from:

• A transparent original dataset
• A worked calculation
• A reusable template
• A real comparison methodology
• First-hand testing with documented conditions
• Screenshots that prove a specific behavior
• Failure cases
• Expert caveats
• A useful synthesis of conflicting studies

Adding length is not information gain.

If the only contribution is “the same advice, rewritten,” I would not publish it.

4. Can the claims be trusted and extracted accurately?

Every important claim should answer:

• Who or what does this apply to?
• When was the evidence collected?
• Which market, device, engine, or model was involved?
• What was the sample and denominator?
• What does the source actually prove?
• What is the material limitation?

For example, an observational study about desktop CTR for a selected keyword database should not become:

>

The method belongs beside the number.

The same applies to AI citations. A citation does not prove that the generated answer interpreted the source correctly. Review the claim, the cited passage, and whether the source is current.

5. Are you measuring the right things separately?

I would not use one “AI visibility score” as the entire GEO dashboard.

Track at least:

• Mention rate: How often the brand is named in a fixed prompt panel
• Owned citation rate: How often an owned URL is cited
• Citation accuracy: Whether the source supports the generated claim
• Referral sessions: Detectable visits from assistants or generated results
• Google generative impressions: Where the new Search Console report is available
• Qualified conversions: Whether resulting visitors complete a valuable action
• Revenue or retained value: Under an approved business definition

Keep the denominators visible.

Eight citations from twenty prompt runs is a 40 percent citation rate for that exact panel. It does not mean AI generated 40 percent of your leads.

A prompt panel is a sample you designed, not a census of everything users ask.

What Google explicitly says you do not need

Google's July 2026 generative AI guidance is worth reading because it removes a lot of manufactured urgency.

For Google Search, it says there is:

• No required llms.txt or special AI text file
• No special AI schema requirement
• No required AI writing style
• No universal micro-chunking requirement
• No ideal page length
• No need to create a separate page for every possible fan-out query

That does not mean clear structure or structured data is useless.

Semantic HTML helps people, accessibility tools, crawlers, and browser agents. Truthful structured data can describe visible entities and support documented search features. An llms.txt experiment may matter to a particular non-Google system if that system documents support.

The problem begins when a useful optional practice is marketed as a universal admission ticket.

My 30-minute SEO and GEO triage

If I had thirty minutes with one page, I would do this:

1. Fetch the URL without a logged-in session.
2. Record the status, redirect chain, robots state, canonical, and index directive.
3. Compare response HTML with the rendered DOM.
4. Confirm that the direct answer and evidence are present on mobile.
5. Write the one user decision the page owns.
6. Mark every claim that lacks a primary source or clear limitation.
7. Compare the page with the current result set and identify one genuine information gap.
8. Check Search Console page-level impressions, clicks, CTR, country, and device.
9. Capture a small fixed set of relevant AI prompts with engine, locale, date, mention, citation, and accuracy.
10. Choose the first failed layer and fix that before adding another tactic.

The important part is the order.

If rendering fails, fix rendering.

If the page answers the wrong task, fix the page purpose.

If the content is interchangeable with twenty summaries, create something original.

If measurement is weak, stop making attribution claims.

The practical difference between SEO, AEO, and GEO

This is the simplest definition I have found useful:

• SEO: Can the right page be discovered, understood, trusted, and selected in search?
• AEO: Can the page provide a direct, accurate, usable answer?
• GEO: Is the brand or source represented accurately in generated answers, and can we measure that visibility without inventing attribution?

They are not three independent magic systems.

They are overlapping lenses on the same information, technical, and measurement problem.

I am Amaury, the founder of SEOryon, so I spend an unreasonable amount of time thinking about this. I am not linking the product here because I want this post to be useful on its own.

What are you currently measuring for AI search: mentions, owned citations, generative impressions, referrals, conversions, or something else?

And which part is still impossible to measure reliably?