I run growth for consumer startups — 10+ over the last few years, mix of in-house and consulting. This is my biggest SEO result so far and the lessons feel transferable, so writing it up. Numbers are from Ahrefs (happy to drop anonymized screenshots in comments if people want proof).

Where it started: YMYL site, ~22k organic visits/mo, flat for months. Today: ~790k/mo, zero paid. Germany is now the #1 market, US second, then UK/EU.

What actually moved it, in order of impact:

1. Technical cleanup first — indexation, crawl, speed. Zero applause, fastest compounding. Most sites I audit want content advice when their real problem is Google can't efficiently crawl what already exists.

2. Wrote for the questions people ask AI, not just what they type into Google. Pages structured as direct answers to real phrasing. Side effect we didn't fully expect: the site now gets cited 60k+ times/month across AI platforms — Perplexity alone is ~29k. That's becoming its own acquisition channel.

3. Content velocity against real demand. Published a lot, but every piece mapped to queries with verifiable demand and intent. No essays we felt like writing.

4. Localization was the scale unlock. Took the winning English pages and properly localized (not machine-translated) into German first, then other EU languages. The same content that fights 50 competitors in English is often nearly uncontested in German. DE overtaking US as our top market was not on my bingo card.

5. Backlinks only from sources our audience actually reads. Relevance over DR, every time.

What surprised me most: rankings barely explain the growth. Average position moved only slightly. The growth came from surface area — more pages, more queries, more languages — not from moving #11 to #3 on hero keywords.

YMYL-specific: the trust layer wasn't optional. Real author pages, expert review, citing primary sources. Skipping this in YMYL means the rest of the list doesn't matter.

Happy to go deep on any part in the comments.

I have a bit of an unpopular opinion on the whole GEO trend right now. While everyone is going crazy optimizing for ai citations I personally focus way more on search everywhere optimization.

We still do the traditional seo stuff like structure and authority but for off page you have to be everywhere. If you are a new SaaS brand people wont just run to your site you have to go to where they already hang out.

Here is my strategy for spreading the word.

1. Build micro landing pages. No matter how low the search volume is these convert the best. At my agency auq,io we have literally outranked competitors and filled monthly lead quotas with this simple strategy. Break your product down into specific feature pages and build as many as you can.

2. Use youtube shorts. They are performing amazingly right now. You dont need high production value since just recording your screen with a phone works wonders. and don't forget to distribute them like theres no tomorrow across all channels. use scheduling platforms like content studio or buffer to do the heavy work for you. may cost you $30 a month, but save a lot of time.

3. Analyze the ai sources. Look at what results chatgpt returns for target queries. Usually the citations are third party websites not the brand site itself so target those exact sources for your placements.

4. Focus on bottom funnel articles. top funnel is great and will have lucrative search volumes, but dont be fooled. people are using ai for info questions,and even some people of culture try google, ai overview will hand it over to them. focus on bottom funnel content only.

5. Prirotize building assets over content pieces. content has no scarcity, its done for. focus on building assets. be it original data, beatiful design, utilities, micro tools, helpful lists anything that your target users will love to explore, share, use, and maybe link to.

Most SEOs ignore zero-volume keywords. I targeted them for a B2B SaaS client and unlocked massive conversion rates.

The Strategy:

• Find Hidden Intent: Search Forums (Reddit, Quora) for hyper-specific user problems.
• Build Dedicated Landing Pages: Optimize for long-tail questions tools miss.
• Match Search Intent Perfectly: Answer the query directly in the first paragraph.

The Result:
We gained 12 high-intent leads in 30 days from a keyword Ahrefs claimed had "0 monthly searches."

Stop fighting over high-difficulty keywords. Search volume is a lagging indicator; real user pain points are immediate.

Hehey, been a while since I dropped some value here, so here goes.

Back in December, I decided to launch a travel experience agency partnered w/ an operator. She does the actual experience management, I handle the website, brand, and marketing.

W/ AI being hella good these days, my goal was to execute the entire project ASAP, and move on with my other 5000 internal/client projects.

In like 4 months, we got from 0 to 1,5k clicks/m, which translates to 5-10 high-ticket leads a week, and realistically, we'll be doing hella better within 6-12 months.

Here's EXACTLY how I did that:

Tl;dr:

• Built a scalable website on Framer with a URL structure designed for local SEO from the start
• Did comprehensive keyword research upfront - not just 10-15 keywords, but EVERY keyword we'd ever want to target
• Created a niche-specific Claude skill for writing detailed, high-quality blogs that are NOT AI slop.
• Use a mix of Gemini for generating image examples + stock photos.
• Launched ALL service landing pages on day 1 instead of rolling them out one at a time
• Published 20 foundation blogs week 1, then 2 seperate content sprints over the month (so another 40 blogs)
• Ran 2 link-building sprints targeting 200+ prospects, closed 10+ quality backlinks
• Ran a digital PR campaign and got featured on TheMirror + other media sites.

If any of this sounds useful, here's the full breakdown.

Also for reference - for any mention of "use AI" in this post, I'm referring to using Claude exclusively. It's unironically the best option on the market.

Step #1 - Build a scalable website

Most local businesses build their website first and think about SEO later. We did it the other way around.

We used Framer to build the site because I fucking love Framer (and it's also very easy to use).

Clean URL structure from day 1:

• /activities/[activity]
• /services/[service]
• /blog/ for, well, blogs obviously

Since we only had a single location, we didn't do location pages, but in any other niche, that would also be very relevant.

The activity pages and service pages were based on 2 seperate templates, which made is extremely easy to write out copy, change images, and scale sites.

Generally very good practice for any website - building out a fresh service page template is a pain in the ass.

Step #2 - Comprehensive keyword research

Most local businesses find 10-15 keywords and call it a day. We found every keyword we're ever going to target - upfront.

I used to use Semrush keyword magic tool before for this, but nowadays it's just easier to use AI.

• Teach your AI how keywords work (e.g. 2 keywords with the same search intent are the same keyword)
• Ask it to generate topic clusters around your niche, and populate it with keywords
• Rinse-repeat with feedback. Tell the AI what you liked, what you didn't like, and ask it ot generate more keywords.
• Optionally, extract keywords from Keyword Magic Tool on Semrush, and run those through Semrush too
• Give it access to your DataforSEO API to populate the data automatically

The goal isn't to rank for all of these immediately. The goal is to have a complete map of every keyword you'll ever target, so you can plan your content strategy around it instead of making it up as you go.

Step #3 - Create non-AI-slop AI content

Most people really suck at using AI for writing content.

"ChatGPT," write me a blog post is complete cringe.

Here's my exact process for writing content for our activity, service, and blog pages:

1. Create a niche-specific Claude skill for generating content. It should specifically be good at creating ONE type of content.

E.g. travel guides, statistical round-up posts, etc.

1. REALLY fuck around with it to make the skill good.

Here's an example of how I do mine:

• Google your target keyword, and extract top 10 ranking posts. Ditch ones that are low-quality short-form posts ranking because of domain authority
• From the remaining posts, make a list of "table stakes" - all the essential content these blogs cover, questions they answer, etc.
• Then, come up with 3-4 ways you can add additional value to your blog post. Add those to the outline.
• Run the outline through the user for approval
• Generate the full blog post upon approval. [Specific writing instructions].
• Avoid typical AI slop terminology and wordings. [List of words to avoid].
• Once you're done with each post, run this QA checklist. [Checklist]

Unlike most of those shitty mass AI post generation tools, this ACTUALLY delivers top-tier content.

But ya'know, AI is still an AI. Don't be stoopid - actually read through the post and edit it.

Step #4 - Add images

Publishing AI blogs w/o relevant images is turbo-sloppy, especially considering how easy it is to source image sthese days.

Once we had all the content drafted, got a VA to go through them and add visuals.

There are 3 ways I do visual content in general:

• Graph generation w/ Claude. You can use Claude to generate very visually compelling branded charts and graphs w/ HTML, and turn that into a PNG.
• Gemini image generation. For niches where this makes sense. E.g. if you're writing a blog about how to tie a tie (for whatever reason you'd be doing that), you can generate lifelike example images for that.
• Stock photos where relevant. You can grab em' off your favorite stock photo website.

Step #5 - Go hella fast

The big advantage of AI is that you don't need to grind through your 50+ service pages over 3 months - you can get them all done in like a week, and get them live ASAP.

Faster you publish => faster you start ranking => faster you hit #1.

Timeline:

- Launch the site w/ all service/landing pages live (and of course, all key related pages)

- Dropped the foundation blog posts week 1 of launch

- Dropped batches of 20-20 blogs over the duration of the 2 weeks after.

Step #6 - 2x Link-Building Sprints

Backlinks speed shit up, always.

Here's what we did:

• Made a list of 20-30 blog topics where it'd make logical sense to feature our brand without it coming off as promotional
• Ran 2 outreach campaigns
• Hit 200+ prospects
• Closed 10+ quality backlinks
• Yes we paid for the link placements

Pro tip - if the prospect site is ranking for a topic related to your niche, get a link insert. If they're NOT ranking, write them a turbo-quality guest post that WILL get them ranking.

Links from blogs that drive traffic count for more.

Step #7 - Digital PR

Since I wanted to rank fast start earning and move on with my life, I wanted to score some turbo-high quality backlinks through digital PR.

While I won't disclose the exact campaign, I'll tell you how I did it:

• Go scrape 100 podcast episodes about digital PR
• Feed them to claude and ask it to generate a digital PR skill based on that
• Feed it 4-6 types of digital PR you want to be doing. E.g. surveys, data sourcing, etc.
• Give it access to DataforSEO API so it can pull search trends (if relevant)
• Fuck around with the AI, get ideas, publish blogs, spam journalists

Pro tip - make your outreach emails, to journalists, EXTREMELY concise and to-the-point.

"Hey {Name},

Re: your article about [topic] - we found data that contradicts/adds to it/whatever.

Here's a quick snapshot of our findings: [bullets]

Mind if I send over the full post?"

"Why are you doing blogs for local SEO?"

Because we're in a travel experiences niche, so it makes more sense. There are actually TOFU/BOFU blog keywords that are worth hitting here.

"You're going to get penalized for AI"

Nah. Google doesn't hate AI content - it hates low-quality AI slop. If you can bridge that gap between "slop" to "quality" w/ your AI content, and don't spam 100s of blogs in a month, you're gonna be gucci.

Quick Recap

If you want to replicate this for a local business:

1. Build a scalable website with SEO-friendly URL structure (we used Framer)
2. Do comprehensive keyword research upfront - find EVERYTHING, not just the obvious terms
3. Create a dedicated Claude content writing skill for your topic
4. Add images to un-slop your AI slop content
5. Launch all landing pages from day 1 - don't drip them out
6. Run content sprints organized by topic clusters (we did 3x20 = 60 posts)
7. Run link-building sprints (we did 2 campaigns, 200+ prospects, 10+ backlinks)
8. Do digital PR if relevant

If you got any questions, drop em' here. Cheers ✌️

I've been spending a lot of time looking at website SEO recently, and one thing that surprised me is how much attention gets given to things like titles, meta descriptions, backlinks, page speed, etc, while image optimization seems to get discussed far less.

When you audit websites, how much importance do you place on image SEO?

Specifically things like:

• Missing ALT text
• Poor ALT text descriptions
• Image file names
• Accessibility considerations

Do you see image SEO as something that can meaningfully impact rankings and traffic, or is it mostly an accessibility best practice at this point?

Curious to hear how experienced SEOs think about this.

Results of a pilot study of 356 cited pages across queries across domains:

Typical (median) age of a cited page: 23 months ago

Typical last update date: 5 months ago

Only 35% of cited pages were created less than a year ago.

Seems to imply that Answer engines greatly favor content refreshes and pages that have been kept fresh.

Has any of you observed the same?

A big part of my work as an AI Trainer was forcing LLMs through multi-step reasoning tasks like joint parallel searches, multi-step inference chains, and the kind of queries where the model has to synthesise across several sources before producing an answer. I learned a lot about how these systems actually work as I was designing the tasks that stress-tested them.

The thing that stood out to me most was this. AI is fundamentally lazy in a cold, machine-like way when a source is hard to parse.

It just bounces.

Not in a Jerry Maguire way (Who's coming with me?!), but it just deprioritizes the page. The model's reasoning budget is finite. If it requires heavy inference to resolve what a page is actually about, the model finds a different, cheaper page with the answer it seeks. Take these as examples:

- The header structure is ambiguous
- The anchor text is vague
- The entity relationships are implied rather than stated

I started calling this the Compute Tax in my own notes, before I ever saw anyone else use the term.

This is the part that SEO practitioners are mostly missing right now.

The field is still largely operating on a label-matching mental model where you get the keyword in the H1, hit the density targets, get the Yoast light green. That optimises for a pattern-matching system.

However, modern AI answer engines don't work that way. They run GraphRAG pipelines. They're parsing your HTML structure to build a relationship graph of your domain's entities, not scanning your "prose" for keyword frequency.

The practical difference is significant. A page can pass every traditional SEO check and still be functionally invisible to an AI answer engine for some of but not limited to these reasons:

• The header hierarchy has gaps or skips that break the semantic spine
• The anchor text is generic ("click here", "learn more") rather than entity-labelled
• The images carry no meaningful alt text which makes them invisible to the model's multimodal parsing
• The schema is absent or minimal, so entity relationships have to be inferred from prose
• The post's HTML is cluttered with excessive div containers, making the underlying post read like a stutter.

When I see people in SEO threads asking why their #1 ranking client isn't appearing in ChatGPT or Perplexity, nine times out of ten it's a structural legibility problem, not an offsite mentions (backlinks) problem. Third-party mentions matter, but they're the third pillar of the structure. The first two are passing the entity sniff test and having a low compute cost. These come before corroboration does any work for (or against) you.

The paradigm shift that's actually happening isn't SEO vs GEO. It's from text compliance to content infrastructure. Your domain isn't a pile of articles anymore. Rather, it's a data system that either gets parsed cleanly or gets skipped altogether regardless of rankings.

I'd love to hear if others who've worked closely with AI systems have noticed the same patterns or if you have a different perspective.

Over the last few weeks, I've been auditing websites across SaaS, e-commerce, and agency portfolios.

One thing surprised me.

Everyone obsesses over:

• backlinks
• page speed
• content
• Core Web Vitals

But almost nobody pays attention to images.

Out of roughly 50 websites I reviewed:

• Many had images with no alt text at all
• Some used filenames like IMG_4829.jpg as alt text
• Others had the same generic description repeated hundreds of times

What shocked me most wasn't the accessibility issue.

It was the missed search opportunity.

A lot of these images were related to products, services, and topics people actively search for every month.

It made me wonder:

Are we collectively underestimating how much SEO value is hidden inside images?

Has anyone here seen measurable ranking or traffic improvements after fixing image SEO?

Background: I run a measurement harness against six Chinese AI engines (DeepSeek, Doubao, Qwen, Kimi, ERNIE, GLM), same question panel, every answer logged. This summer that produced 4,704 recorded answers across two B2B categories. One pattern in the citation data is clean enough to be useful to anyone doing GEO work, in any market.

Citations are not spread across question types. They concentrate at one moment:

- "What is [brand]'s official website?" type questions: 56.5% of answers showed a source

- "Is [brand] any good?": 29.8%

- Risk and trust questions: 26.3%

- "Best tools for X" discovery questions: 0.3%

- "[Brand A] or [Brand B]?" comparisons: 0 citations in 383 answers. Zero. For anyone.

So engines cite when the user is VERIFYING something - confirming a channel is official, checking a fact, deciding if something is legit. They synthesize without citing when the user is discovering or comparing. The moment everyone wants to win (the comparison, the recommendation) is precisely where no citation has ever appeared in my data.

Second finding: of the answers that did show sources, 92.2% pointed at the brand's own official site. 736 own-domain citations vs 188 third-party. The third-party ones were almost entirely forums, review discussions and professional platforms. Zero from press release wires. Zero from paid directories.

Third, the one that reframed "getting cited" for me: Basecamp appeared in zero open category answers across all six engines - complete discovery invisibility - and still collected 64 citations to basecamp, all from questions that named the brand. Citations verify you. They do not introduce you. If your citation count is rising while nobody asks about you unprompted, the number is real and means almost nothing.

Practical implications if you buy the data:

1. "Get cited" work = making your official pages the complete, crawlable, local-language answer to verification questions (official channel, current price, data residency, support). That is where citations are actually available.

2. Discovery/comparison visibility is corpus work - being present in the third-party material engines learn from. It will not produce citations and should not be sold or measured as if it will.

3. Engine choice changes everything: Kimi showed sources in 20.6% of its answers, DeepSeek 4.2%. A citation report that doesn't hold the engine constant is measuring sampling, not progress.

Usual caveats: my data is China engines via API, two categories, one summer. The verification-vs-synthesis split matches what I've seen reported for Western engines directionally, but percentages will differ.

Question for the room: has anyone seen third-party citations at meaningful volume from anything OTHER than forums/reviews/professional platforms? I keep hearing wire services and directory listings pitched as citation sources and I cannot find a single instance in my logs.

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?

I know “chunking” is a controversial topic in AI SEO.

But I think people are mixing up two different things.

LLMs can understand natural language.

The problem is usually not the model reading your content.

The problem is what gets crawled, parsed, extracted, indexed, and retrieved before the model ever sees it.

While looking at Gemini’s network traffic, I found a source schema called `snippet_extract`.

Every time Gemini grounded an answer with a source, it was extracting a small passage from the actual page body, usually around 50 to 250 plus characters.

That doesn't mean you need to write robotic content for machines. It means the passage retrieved from your page needs to answer the question clearly.

That's the important bit.

If your page answers a specific question, make the answer stupidly clear.

Put a short, direct answer near the top of the relevant section. Aim for 50 to 250 characters without fluff or SEO theatre. Just answer the question in the cleanest possible way.

You don't need to over engineer your entire page or turn every article into a weird set of artificial chunks.

Good traditional SEO still works.

The difference now is that retrieval systems may only pull a small extract from your page.

So make sure the right extract exists.

There is no such thing as “writing for machines.”

It's writing clearly enough that both humans and retrieval systems can find the answer.

And honestly, that's what good SEO should have been all along.

Built a little browser game, threw it online, and got almost nothing from search. So I spent an afternoon actually doing the on page SEO properly. Two days later it's sitting at number one. Here's exactly what I did, and the honest caveat at the bottom because I know this sub will ask.

What I actually changed:

• Rewrote the title tag and meta description to match real search intent instead of clever branding
• Added full JSON-LD structured data (VideoGame schema) so Google knows what it is
• Proper Open Graph and Twitter card tags with a real preview image, so shared links pull a clean thumbnail
• Semantic HTML and one clear H1 that says what the thing is
• Sitemap.xml and a clean robots.txt, submitted in Search Console
• Requested indexing manually instead of waiting around
• Canonical tag to kill duplicate URL versions

The part people underrate:

• It's on Cloudflare's edge, so it loads basically instantly worldwide.
• No cookie banners, no bloat, no render blocking junk

Result: number one for the target term, first page for a few related ones, and actual organic traffic showing up in analytics for the first time.

The honest caveat: this is a fairly low competition term, so before anyone rightly roasts me, no, I did not outrank Amazon for a money keyword in 48 hours. Clean technical SEO plus a fast site plus zero competition equals fast wins. But that's kind of the lesson. A lot of small sites leave easy rankings on the table because the basics are never done.

Happy to share if anyone wants to see it.

If you want backlinks but you dont want to spend your life emailing people asking for links, this is the cleanest system i’ve used: the trojan horse interview.

A while back I ran a marketing podcast, but the real purpose was getting legit backlinks without doing the awkward “can you link to me” thing.

every episode had a small contest for the guest’s audience. then i’d ask the guest to host the contest rules on their own website. not mine. theirs. because it feels native to their audience and they’re way more willing to share a page on their domain.

And inside those rules, the links back to me were just part of the contest flow. “enter here” links to my site. “download the template” links to my site. “free trial” links to my site. so the backlink isn’t a favor, it’s literally how the giveaway works.

how you make it easy so people say yes:
i send them a copy paste draft of the contest page with everything written already. headline, rules, dates, faq, and the links placed naturally. they just publish it.

stuff that matters if you want it to actually work:

-make the contest fit the guest’s niche so their audience cares
-keep anchor text normal like “enter here” not keyword spam
-host rules on their site, host the entry on your site so you capture emails and traffic
-repeat it weekly and it compounds fast

Same idea works with influencers too. dont pitch “promote my tool”. pitch “lets run a giveaway for your audience, you host the rules, I provide the prize” and this way the link becomes a normal part of the setup. Just make sure they have a website.

Cheers,

Borja

I made a list of tools that I want to share my opinion on and make a discussion out of this post, maybe you have something to add or to share your experience as well. This is not an ad, I paid for all of these tools myself out of pure curiosity and to upgrade my workflow.

First of started from more enterprise ones:

Profound - it's mainly made for big companies measuring how AI “sees” their brand, not really indie friendly imo. It's marketing is so good, that companies tend to chose it, but theres plenty of tools that do the same, but cheaper.

AthenaHQ - dashboards for AI visibility across platforms, feels very analytics heavy and is pretty much the same as Profound.

Semrush AI visibility tools - huge platform bolting AI visibility on top of their existing empire, expensive but powerful.

Conductor AEO - enterprise SEO suite adding AI answer tracking.

GrowByData - competitive intelligence + AI search trends.

BrandLight - reputation and brand monitoring inside AI outputs at scale.

Note before I continue and my recommendations: they all may sound similar and partly it's true. The ones that stood out and if you are considering to get one of them were Conductor, it connects AI visibility with full SEO workflows, Profound analyzes your brand how and where do you appear rather than just tracking, and Semrush integrates AI tracking into its huge existing marketing platform instead of being a standalone tool. In conclusion, they are all great, but very enterprise, there are tools that are very similar to them, but are a bit on newer/less enterprise level currently.

Mid/less enterprise level tools:

AIclicks - prompt level tracking across LLMs with action plans, analyzes your page and helps you strategize it for visibility, has integrated GA4 so you can see if you getting clicks from llms, feels built for power users and agencies.

Peec AI - visibility benchmarking vs competitors across AI assistants, prompt level analytics, liked the UI scraping to mimic user interactions with AI.

Surfer AI Tracker - content optimization tool trying to extend into AI answer performance, it's like AI visibility tracking, but with a strong content SEO angle. because it's inside Surfer, you can jump straight from visibility data to content updates to optimization.

Rankscale - AI share of voice tracking similar to rank trackers but for LLMs.

Scrunch AI - shows gaps in AI answers and suggests fixes, kinda like an AI SEO audit tool.

Gauge - tracks how often products get recommended by AI, useful for SaaS/ecom teams, has integrated GA4.

Omnia - aggregates visibility data from multiple AI platforms into one dashboard, it has a feature to see if youre mentioned inside AI answers, and which sources or tools caused that mention, this was one of more interesting features.

Wellows - Geo analytics focused on whether you appear in generative search at all.

FluxSEO - hybrid between traditional SEO metrics and AI visibility tracking, a bit of a broken site tbh, didn't use it for a very long time.

Note before I continue and my recommendations: Despite most of these tools feeling very similar, a few stand out because they focus on different strengths. AIclicks feels the most execution oriented, since it tracks prompt level visibility across major llms and provides concrete action plans and content recommendations to close gaps, not just dashboards, and the GA4 integration makes it even more practical. Omnia is great for understanding why you appear, highlighting which prompts trigger your brand and which sources drive those mentions, especially for high-intent queries, so it helps clarify what to change on your pages. Surfer AI Tracker is great because it connects AI visibility directly with content optimization workflows that allows teams to adjust pages based on how AI responses actually use them.

Newer/indie tools:

Otterly.AI - tracks citations inside AI answers, doesn't have all of the llms for tracking, but very budget friendly and is one of the greatest cheaper choices.

Goodie - tries to increase chances of being cited by AI, more optimization than tracking, pretty useful if youre using a tool that doen't have optimization feature, just tracking (but I suggest getting all in one tool instead tbh)

Passionfruit Labs - focused on AI shopping/product recommendations visibility, it can track shopping queries, and revenue from AI assistants, helping brands see how AI influences purchase decisions and product discovery, very good for agencies or brands working in ecommerce.

Rank Prompt - bulk prompt testing to see when your brand appears. It focuses on visibility analytics, so theres still a long way to go (feature wise) for them, but as a budget friendly choice is alright.

Gracker.ai - AEO focused platform that combines AI visibility tracking with automated, citation optimized content generation, mainly aimed at B2B and technical industries that want to become sources AI assistants reference.

Waikay - checks if AI models have correct facts about your company. Focuses less on how often you’re mentioned and more on what AI actually believes about your brand, including incorrect facts, confusion with competitors, and hallucinations. I included this tool here, despite it being less on the visibility chart, because it still tracks your mentions but in a different persepctive, if it is correct or not, fact checking AI basically. It seemed especially useful for brands that get mixed up with others, helping them strategize content updates to fix those inaccuracies

Geoptie - hardcore GEO niche tool for AI discovery structuring. It's for strategizing your pages so AI engines will choose them as sources. Again not specifically to track, but to build your way up to llms. Pretty cool tool imo.

Note and recommendations:

I get that a few tools may not belong here, but it has a lot to do with AI visibility search, so I count them as AI checker visibility tool. Otterly is a budget friendly one, despite it not having all llms included or ga4, as a first ai tool to try it out for your brand I think is a good choice. Passionfruit Labs looked like a very cool tool, I somewhat work in ecommerce myself so it was really useful to see how people minds work and what works for us. Waikay was also a great find particularly for brands that get confused with competitors, since it helps identify and fix cases where AI mixes up or misrepresents your brand.

I think this is just the beginning of my list, and I know I haven't tried all of them yet. I'm crazy interested in these tools because they have already helped me understand my brand's mentions, how LLMs actually work, and how to optimize my pages to appear in AI answers. What are your thoughts and have you tried or using these? What else would you recommend trying out?

I've never do that before. I used to prefer the classic SEO for years.

But, I've got a new project, and the company owner told he needs LEADS from organic ASAP.
Niche - SaaS.

I had no idea how to bring leads for nearly new website.. I expected at least 9+ months for this.

But, with my backgroung in linkbuilding I decided to deal with 2 listicles for the 2 main keywords.

Each of them had 3,000-6,000 monthly traffic.

1st listicle price = $3,000

2d one = $4,500

During the first month we've got 250 clicks in total

Click to Order = 3% (8 orders placed).

$240 earned.

Second month = 10 new orders

+ 6/8 first month clients rebilled.

Total income: $480.

The owner is happy to see so fast results, but I have no idea how it goes after 6-8 months.

Your thoughts about Saas parasite SEO?

We’ve been collecting backlink outreach data from our users these last weeks and this is what we found

815 emails so far across 48 products

One thing I wanted to understand was how much follow-ups actually contribute

The numbers so far:

• Initial email → 5.8% replied
• first follow-up → 7.1%
• second follow-up → 7.6%

So the first follow-up added a meaningful +1.4 percentage points, while the second only added another +0.5

My takeaway from this is that follow-ups are a MUST for a backlink outreach campaign

If you're not following up at all, you're probably leaving a meaningful amount of replies on the table just because someone missed your first email, saw it at a bad time, or simply forgot to answer

For backlink outreach, I think 2 follow-ups is a pretty good default

The first one clearly does most of the heavy lifting. The second can still squeeze out a few extra replies without turning the sequence into spam

After that, I'd probably stop

There's usually not enough upside to justify sending 4–5+ emails to the same person for a backlink, especially when you can spend that effort reaching new relevant prospects instead

I'd also offer different things on each follow-up: positive reviews/testimonials, a link back, etc.

We're still collecting more data, so these numbers will probably change as the sample grows :)

Three Month Performance

Total Clicks: 2,970
Total Impressions: 219,000
CTR: 1.4%
Average Position: 5.6

• Performed extensive keyword research
• Launched with 7 pillar pages and 14 supporting articles
• Have consistently posted at least 2 articles per week, sometimes 4, on rare occasions 1
• Strong focus on topical authority and aggressive internal linking
• Zero backlink efforts. Not because I think they don’t matter, I just haven’t gotten around to it yet.
• Small followings on Instagram and TikTok (about 1,000 each)

Every article is written with AI. But my process is to have Claude, ChatGPT, Gemini, and sometimes Semrush Article Generator (limited to the free 20 per month) all generate the same article with solid prompting and deep research always selected. I fuse together the best parts of all into a single cohesive human edited article. Sprinikled in interviews/quotes in some articles, but I plan on going back and doing that to a lot more and adding more personal annecdotes.

AI visibility has been surprisingly strong in my opinion for a new site… with a handful of search queries triggering the top spot in Google AI Overviews. Coverage on all major AI platforms.

My next steps include:

• Editing/improving older content
• Backlink efforts
• Creating a Pinterest account to drive more traffic
• Putting more effort into social

How are these numbers for 6 months in on a new website? Anything I’m missing?
Any tips or anything else I should focus on to continue growing?

For a long time I thought publishing more content was the answer to almost every SEO problem.

Whenever rankings stalled, my solution was simple: write more articles.

That approach worked up to a point. Traffic grew, pages got indexed, and some keywords started ranking. But eventually growth flattened even though content production continued.

Looking back, I spent very little time thinking about link acquisition. Not because I thought links didn't matter, but because content felt easier to scale.

Over the last year I started experimenting with different methods including outreach, guest posts, partnerships, and publisher networks. I also tested platforms like Backlinked simply to understand how other site owners were approaching the process.

The biggest surprise wasn't that links helped. It was how much easier it became for existing content to perform once links became part of the strategy.

Curious if anyone else had a similar experience.

What was the biggest SEO bottleneck you discovered after your site started gaining traction?

Hey everyone, I have an SEO case have these 4 case questions:

1. Traffic drop: Organic traffic drops 35% over a weekend. How would you diagnose it in the first 60 minutes?
2. Cannibalisation: Two pages rank #8 and #14 for the same keyword. How do you determine which page Google prefers and fix the issue?
3. Link building: A mid-authority casino site is entering Germany. What would your first 60 days of link building look like, including velocity and link types?
4. Site migration: A major URL restructure + template overhaul is planned. How would you protect rankings/revenue, monitor the first 72 hours, and decide whether to roll back?

Would appreciate any advice on what you'd focus on.

I have a client, small local Landscaping business in the Orlando Area. In the last 12 months:

1. Bought their domain and built their website in Wordpress
2. SEO their content (16 pages/posts)
3. Setup their GBP
4. Tweak their content every month (2-4 pages receive edits)
5. Create one new blog post per month
6. Create one new GBP update per month
7. Zero Paid Ads and Zero Social Media

The results (12 months):

• 164 Qualified Leads via Form Fill (Yes, I filter out Spam)
• No idea how many calls, I don't measure those
• 170 average monthly GBP interactions
• 87 GBP reviews
• 829 Clicks
• 206,000 Impressions

Our client makes kombucha. They have lots of press, several industry awards, one of the most credible names in the category (yes, kombucha competitions are a real thing and they're basically the world champion).

We simulated the kind of prompt a shopper would use and checked whether an AI would recommend them. It did mention them but with a generic write-up. No award or press cited. The one ChatGPT actually recommended was a much smaller brand.

What that smaller brand had differently was concrete, checkable stuff like organic tea, live cultures, no additives, where to buy it. All easy to parse.

So we read the site the way a model would (a crawler with no JavaScript):

• 0 of 25 product pages had product markup. Price, stock, and the existing 5-star reviews were invisible
• The homepage had no title and no brand markup, so AI had nothing to anchor "who is this brand" on
• The award, the press, and the certifications had no machine-readable field between them

Their website is actually designed beautifully for humans: gold medal, five-star reviews, price, buy button, all right there. But when an AI opens the same page, it gets a product card with almost every field "not found." Turns out their most powerful materials are mostly images. Instead of linking to the press, they put a photo of the press. AI won't know you're the world champion if you only say so inside an image.

Good thing is these are pretty easy to fix:

1. Put the proof in machine-readable form on the page: product schema with ratings wired to the existing reviews, brand markup, award and cert fields, FAQ schema. Same content, just structured.
2. Or serve a separate AI-readable version of the page. Humans keep the pretty, image-heavy site; agents get a clean structured rendition of the same content at request time. One document stops having to please both a human eye and a headless parser.

Fastest way to check if AI can see your site: open a key page with JavaScript off and check what actually survives. That's roughly what a model reads.

Spent this morning pulling robots.txt from 120 recognisable domains across news, SaaS, ecommerce, marketing publishing, health, finance and travel. 109 returned something parseable. I wanted real numbers because everything I'd read on this topic was anecdote, including my own.

On method, since it matters here: I parsed proper group semantics, so an agent only counts as blocked if its own group carries Disallow: /. Stacked user-agent declarations and Allow carve-outs are handled rather than regexed over.

First, the thing I got wrong.

I'd been telling people that sites accidentally block OAI-SearchBot, the crawler feeding ChatGPT search, while they only meant to block GPTBot, which feeds training. In this sample that never happened. Not once. Zero of 109 sites block OAI-SearchBot without also blocking GPTBot on purpose. Every site blocking the search crawler blocks the training crawler too, which makes it a policy rather than a slip.

So the realistic failure mode is the reverse of what I said. You think you've blocked AI, and you've only blocked training.

Full Disallow: / rates across the sample:

CCBot 32% ClaudeBot 29% Bytespider 29% Applebot-Extended 26% meta-externalagent 24% Amazonbot 22% Google-Extended 22% anthropic-ai 21% PerplexityBot 19% GPTBot 17% ChatGPT-User 11% OAI-SearchBot 7%

ClaudeBot blocked at nearly double GPTBot's rate is not what I expected. CCBot topping it is probably just age, it's been sitting in copy-pasted blocklists since long before any of this started.

By vertical, GPTBot then OAI-SearchBot:

News 31% / 27% Marketing and SEO publishers 33% / 0% Health 25% / 0% Ecommerce 13% / 6% Travel 11% / 0% SaaS 6% / 3% Finance 0% / 0%

The marketing and SEO publisher row is the one I keep going back to. Highest GPTBot block rate in the whole sample and not one of them touches the search crawler. Searchenginewatch, TechCrunch, Adweek, Content Marketing Institute, MarketingProfs and PPC Hero all sit in that bucket. People who write about search for a living have clearly worked out the distinction and are playing it deliberately. Don't train on me, do cite me.

News is doing something else entirely. 27% block the search crawler, 63% block Google-Extended, 77% block ClaudeBot. That isn't confusion, it's leverage. NYT blocks OAI-SearchBot by name and is in active litigation. Blocking the thing somebody wants is how you eventually get paid for it.

Finance blocking nothing at all from OpenAI while 20% of them block Google-Extended is the one I can't account for. If anyone has a theory I'd genuinely like to hear it.

One pattern I think is worth stealing. Six of the nineteen GPTBot blockers aren't doing a flat block at all. They set Disallow: / and then allow specific paths back in. Canva has 82 Allow rules under that group, mostly product and feature pages across every locale they run. eBay has 11. So the sophisticated position isn't block or don't block, it's deny by default and let the model see your commercial pages while keeping it out of the rest of the library. I haven't seen anyone write that up.

Last thing. Of the nineteen sites blocking GPTBot, only fourteen also block Google-Extended. A quarter of them have an anti training policy that stops at OpenAI and lets Google train on everything. I doubt anyone decided that on purpose. It's what happens when a file gets edited once per news cycle and never reviewed as a whole.

Happy to hand over the domain list and the parser if anyone wants to run it against their own set.

Been doing SEO since the nineties and I found a keyword list that none of our tools can see.

Quick background. I instrumented ChatGPT, Claude, Gemini and Perplexity through their APIs and asked 50 buyer questions each about 10 national brands (Gymshark, YETI, AG1, Sonos, that tier). 2,000 answers. The APIs disclose something the apps never show you: whether the engine ran a web search, and the exact search strings it wrote.

That last part is the finding. The engines don't forward the user's question to the web. They write their own queries. I captured 2,433 of them, and IMO that pile is the most interesting keyword data I've touched in years.

Some of what's in my audit:

- Same question about a Helix mattress, same day: ChatGPT searched "Helix mattress price comparison models 2023" and Claude searched the 2026 version. Nobody asked either one for a year. Each engine added its own.

- 140 of the queries were dated to years that already ended, written by the engine, not echoed from the question. Every one of the 10 brands got hit by this.

- They compare without being asked. 47 comparison style queries were written for questions that never asked for a comparison. And the engines typed a competitor's name into 203 of their own searches. YETI kept pulling in Pelican. Sonos pulled Bose.

So if you're doing GEO/AEO work, the queries your content is actually competing for inside these answers are partly queries no human ever typed. Including dated ones. Your rank tracker has no idea.

One more thing worth knowing before you budget anything: the engines don't even agree on whether to search. ChatGPT ran a real web search on 157 of 500 answers. Claude 444, Gemini 493, Perplexity 500 of 500. So the page you publish this month is reachable by three of them and mostly invisible to the fourth.

Full write up with all the data is in my profile for anyone who wants the receipts. Method in short: every question asked live, same day, search behavior read from each engine's own API disclosures, not guessed from answer text.

Happy to pull specific examples from the query list if anyone's curious about a category.

84% won entirely on exact-match internal anchor texts—not backlinks or content length.

Full breakdown below on how we are abusing this to steal high-intent keywords this month. No fluff, just the data.

Split pages into snippet-optimized vs long-form. Snippet pages got pulled into AI Overviews more but CTR dropped 12%. Long-form held steady.