u/LucasFerrazSEO

I own an SEO agency in Brazil, and I have a fairly literal problem: my name is shared by at least two other public figures who show up in the same search results (a federal government official and a national newspaper reporter).

For years this meant a chunk of my "who is this person" signal was diluted or outright wrong whenever a model or a search feature tried to summarize me.

Over the past few months I tried a few things beyond standard Person schema.

FIRST STEP

First, an explicit llms-author.txt file separate from the main llms.txt, stating job title, agency, location and area of practice in plain sentences rather than relying on schema alone to carry that weight.

SECOND STEP

Second, adding the new Content-Signal header (ai-train=no, search=yes, ai-input=yes) to robots.txt, mostly out of curiosity about whether declaring intent at the header level changes anything measurable.

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Honestly, I don't have clean before and after numbers yet, this is closer to a live experiment than a case study.

What I can say is that Perplexity and Gemini answers referencing me as a SEO specialist have gotten more accurate over the last couple of months, though I can't fully separate that from the normal effect of more backlinks and mentions accumulating over time.

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What I'm actually curious about here: has anyone run a controlled test on Content-Signal headers specifically, isolated from other changes?

And for people who share a name with someone more famous or more indexed, what actually moved the needle, schema, sameAs links, a dedicated disambiguation page, something else entirely?

I've been testing the same type of query across ChatGPT, Perplexity and Gemini for a few different niches, swapping only the topic.

The same pattern shows up every time. Out of hundreds of pages that exist on a subject, the model pulls from the same three to five sources, over and over, even when newer or more thorough content exists elsewhere.

The pages that keep winning share one thing. They commit to specific numbers, specific dates, and specific named entities instead of paraphrasing a general idea.

A page that says "SEO consulting typically costs between R$2,500 and R$6,000 a month depending on scope" gets pulled into an answer.

A page that says "SEO consulting can vary a lot in price" does not, even if it covers the exact same topic in more words.

Volume of content doesn't seem to matter much once a site clears a basic threshold.

A blog with 40 posts that each commit to a real claim beats a blog with 400 posts that hedge everything, because the model is scoring how confidently it can restate your claim without getting it wrong.

Vague writing is safe for the writer and useless for a system trying to extract a fact.

Consistency across your own pages matters more than most people account for. If your about page says one thing, your service page says something slightly different, and a directory listing says a third thing, that inconsistency reads as noise to a model trying to build a stable picture of who you are.

Pick the facts that matter (years of experience, location, exact services, pricing range) and repeat them identically everywhere they appear.

The last piece is getting those same specific facts to show up on sites you don't control. A model trusts a claim more when it sees it independently confirmed somewhere else, not just repeated on your own domain.

This is the part most people skip, because it's slower than writing another blog post, and it's also the part that actually moves the needle.

Curious what others here are seeing. Is anyone tracking which specific pages get pulled into answers versus which ones get ignored, even within their own site?

Google and ChatGPT don't read your article top to bottom before deciding whether to recommend you.

They break it into pieces, retrieve the pieces that answer a specific question, and often show or cite that piece without anyone ever visiting your page.

If a paragraph only makes sense next to the one before it, it gets skipped.

This is called query fan out.

Instead of matching your page to one search term, the system splits a single question into several related sub-questions, then searches for the best passage to answer each one.

Your article isn't competing as a whole. Each paragraph is competing on its own.

That's why a paragraph depending on context from three sentences earlier is a liability now, not just weak writing.

If a retrieval system pulls that block out and drops it into an answer, it has to make sense in isolation, with the subject, the claim, and the reasoning all inside the same block.

The practical version of this is simple.

Keep each paragraph between 40 and 80 words, and give it exactly one idea. Not one idea plus a supporting example plus a caveat.

One idea, stated clearly enough that someone reading only that block, with zero surrounding context, still understands what you meant.

This helps regular Google rankings too, not only AI answers.

Google has pulled isolated passages into featured snippets and "people also ask" boxes for years, using the exact same logic: find the smallest chunk of text that fully answers the question, and rank it independent of the rest of the page.

It also happens to be better UX. Readers scanning on a phone don't parse dense blocks of connected reasoning, they grab one idea, decide if it's useful, and move on.

Writing for extraction and writing for a tired person skimming on the bus turn out to be almost the same skill.

None of this means write worse or dumb things down. It means every block has to earn its place on its own, because increasingly nobody reads the whole page before deciding to trust it, they read whichever paragraph got pulled out and shown to them first.

Anyone else restructuring old posts around this instead of just adding more keywords?

Most guides on external linking say "link to authoritative sources" and stop there.

That advice is technically true and mostly useless, because it never tells you what to check before you hit publish.

Here is the checklist I actually run through at my SEO agency and for all our clients.

Pull real information from the source you cite, not just a link for the sake of having one.

If you write "studies show X" and link to a homepage instead of the specific study, that link does nothing for the reader and nothing for your credibility.

Cite the specific page, the specific number, the specific claim.

Prefer the primary source over a summary of it. If a blog post is paraphrasing a government report or an academic study, go find the report itself.

Linking to the original protects you when the secondary source gets something wrong, and it reads as more trustworthy to both readers and crawlers.

Write anchor text that describes what's on the other end. "According to the 2026 IBGE services survey" tells the reader something.

"Click here" or "source" tells them nothing. NOTHING!

Audit your outbound links every few months. Sites get redesigned, reports get taken down, pages 404. A page full of dead links reads as neglected, and neglected pages tend to lose rankings slowly without anyone noticing why.

Don't pad a page with links to look thorough. Three links that support specific claims beat ten links added because a checklist said so. Readers notice the difference, and so do the AI systems summarizing your page for someone else's answer.

If a competitor's post genuinely has the best explanation of something, link to it anyway. Hoarding all authority inside your own site is a worse strategy than most people think, because it makes your content read like it exists in a vacuum.

What's the worst external linking mistake you've seen on a client site?

Over the last 2 years, we've gradually changed the way we approach SEO at our agency.

Instead of thinking only about rankings, we've started asking a different question:

Can Google and AI systems actually understand what this website is about, who it serves, and why it's trustworthy?

That shift changed almost everything.

I’m going to break down some of the principles we’ve been applying to client projects. I hope this helps you achieve good results.

1. User-first content (Helpful Content)

Every page should solve a real problem before trying to rank.

If the content wouldn't help someone make a decision, we don't publish it.

2. E-E-A-T isn't a section! it's everywhere

Instead of adding generic "About Us" paragraphs, we spread experience throughout the content.

• Real explanations
• Real decision criteria
• Real examples
• Clear limitations (when appropriate)

3. Editorial transparency

Whenever we make technical claims, discuss regulations or reference statistics, we cite reliable sources.

Users and AI systems should be able to understand where information comes from.

4. Titles that match the content

We've stopped chasing clickbait.

A title should accurately represent what's on the page.

That consistency seems increasingly important for both users and search engines.

5. Better page experience

Fast pages matter. But so does reducing friction.

Visitors should immediately understand:

• what the company does
• who it's for
• where it operates
• what they should do next

6. Query fan-out optimization

We're seeing AI search expand a single query into multiple related questions.

Because of that, we don't optimize pages around one keyword anymore.

We optimize around an entire decision process.

7. Price transparency

Whenever possible, we explain what affects pricing instead of hiding everything behind "Request a Quote."

Users usually don't expect exact prices. They expect context.

8. AI search readiness

We've been restructuring pages so they can be understood in small chunks.

• Clear definitions
• Standalone sections
• Answer-first paragraphs
• Less dependence on surrounding context

9. Answer-first writing

Instead of long introductions, the first sentence answers the question, always!

Context comes after. This has improved both readability and snippet extraction.

10. Readability

Not just shorter sentences.

The goal is making technical topics understandable for business owners who aren't SEO professionals.

Simple language often performs better than complicated explanations.

11. Search intent before keywords

We've become less obsessed with keywords.

The focus is matching the reason behind the search.

Two pages targeting similar keywords can require completely different content if user intent differs.

12. Internal linking as a knowledge network

Internal links shouldn't exist just to pass authority, they should explain relationships.

Each page should reinforce another page naturally.

We're building connected knowledge rather than isolated articles.

13. Information gain (MAYBE MOST IMPORTANT)

One rule we follow: every page should contain at least one insight that's difficult to find elsewhere.

Not necessarily groundbreaking, just genuinely useful.

14. Entity architecture

Rather than thinking about isolated keywords, we connect:

• Business to services
• Services to problems
• Problems to solutions
• Solutions to locations
• Locations to target audience

The result is a website that's easier to understand as a whole.

15. Next-action flow

Many websites answer the user's question...

...and then leave them with nowhere to go.

Every page should naturally lead to the next logical step, whether that's another article, a service page or a contact page.

16. AI citation optimization

We're also paying much more attention to something that's hard to measure today:

Can an AI system confidently mention this brand when answering a user's question?

That seems to depend less on keywords and more on consistency, topical depth, entity relationships and trustworthy information.

None of these ideas is revolutionary by itself.

But combining them into a single framework has made our projects feel much more consistent and, in many cases, more resilient to algorithm changes.

My professional tip: If you're experimenting with AI search, don't start by adding AI-generated content. Start by making every page easier for both humans and machines to understand.

Note: I am Brazilian, so I needed Google Translate's help to translate some things. I apologize if any expression sounds a bit strange.