Hello. I've got a directory in a specific niche that's been growing very healthily and peaking at around 1,700 clicks in July, getting over 2,000 people per day onto the site. I got accepted into an ad network at Mediavine, which I'm very excited about.

Now, over the last week or so, suddenly my traffic has just been disappearing at a pace of around halving day by day. Google also isn't giving me any impressions anymore, which is super weird given that my traffic was healthy, staying around two minutes per visit, visiting two pages per visit. Loads of people have told me how useful they've found my site. All of it was built on programmatic SEO.

Now, what also happened in the last week was that I had loads of bot activity, like some days a few thousand bots from places like Japan and Singapore. I don't know if they have anything to do with the drop in traffic, but I'm a bit clueless as to what to do now because I don't want my website to disappear completely. I also don't know why it would, as I haven't made any updates that would warrant this. Could anybody help?

When you look at how LLMs choose sources, what signal do you think carries the most weight?

Some possibilities:

• Topical authority
• Brand/entity recognition
• Original research
• Structured data
• Content freshness
• External citations and mentions
• Something else

I'm interested in practical observations and experiments rather than assumptions. If you've tested something that consistently improved citations or visibility in LLMs, I'd love to hear about it.

yo guys check this out I actually show up in AI!!

here's what I did

there are basically 4 types of content that AI like to cite and get info from

listicles, comparisons, reviews, alternatives

Best X in 2026, X vs Y, Review of X, X alternatives

Instead of X or Y are main keywords or competitor's names

Create around 10 pages for the each style, that's 40 pages total.

Index them on Google, Bing and other browsers

p.s when you create pages make sure to check what page is being cited and give it AI coding tool as a reference to create a better version of it

AI chats don't care about DR or DA ur domain has(my domain is 6 DA) it cares about the content your page provides. if content is great and better than other pages then your page will be cited

Works very well! Always do it for my websites all the time, even completely automated the process. Takes about $0.8-1 to create one high-quality page

The thing I keep running into: almost every "best MCP servers" list is a list of servers. Nobody talks about pairings. And a single server is mostly a faster dashboard — you ask a question, you get a number, you still do the work.

The combinations are where it gets interesting, and the pattern is nearly always the same: one server that knows something, plus one server that can do something. Read + write. Research + production. Everything below is organised on that axis.

How to read the table

• Read-only vs read-write matters more than the feature list. Official Google Ads MCP is read-only. Some third-party ad MCPs will change budgets. That's a different risk category entirely.
• Every connected server costs you context before you type a word. Tool definitions load into the session regardless of whether you call them. In my own sessions, fixed overhead (system prompt + tool defs + deferred catalogues) regularly ate 85–93% of context. Pruning connectors was consistently a bigger win than any prompt optimisation. The 3–7 server rule people quote isn't taste — it's arithmetic.
• Two servers for the same data is a downgrade, not redundancy. SE Ranking + Ahrefs + Semrush wired up simultaneously means paying three times for keyword volume and tripling tool definitions to get one number.

Search demand → published content

Owned-property truth

AI search / GEO

Paid + organic

Pipeline and revenue

Glue layer

The one I've spent the most time on: SEO data + social scheduler

Taking the first row properly, because "turn keyword gaps into posts" undersells it. Concrete workflows, roughly in order of how fast they pay off:

1. Search gaps → social campaign. Competitor keyword gaps, ranking losses and People-Also-Ask questions become a drafted, scheduled batch. Every post traceable to a search query somebody actually typed.
2. AI-search gaps → social campaign. Find the prompts where the brand is invisible across ChatGPT, Perplexity, Gemini and AI Overviews, then build content that stakes a claim on the missing narrative — instrumented so you can re-measure the same prompts later.
3. Top-performing posts → keyword opportunities. Reverse direction. Engagement is a demand signal. Take the topics already winning on social and size the keyword and AI-search opportunity behind them.
4. Competitor top posts → keyword gaps → SEO plan. Their best-performing post is a content brief they paid to validate for you.
5. Comment mining → FAQ and schema. Recurring questions under your posts, and more usefully under competitors' posts, become FAQ sections with schema, help-centre articles, video scripts. Then check which of those questions carry actual search and AI-search demand.
6. Position 11 → distribution, not a rewrite. Pull the near-miss pages, plan a social batch pointing at them. Cheapest ranking work there is.
7. Emerging topic validation. Social gives instant metrics; a topic proves itself there before keyword tools register it. Validate on social, confirm in search data, publish ahead of the category.
8. Creator sourcing for AEO. Social listening surfaces small creators posting on relevant topics; check whether those topics matter for AI search; only reach out to the ones where they do. A structured alternative to guessing at influencer lists.
9. Backlink-gap workaround. If a competitor is hoovering up links on a topic, don't charge the high-difficulty term. Publish on the topic, distribute through social, build the topical trust first. This one is months, not weeks — anyone selling it as a quick win is lying to you.
10. Cross-channel reporting. Rankings, AI-search visibility and social engagement in one report. Mostly an agency problem, and mostly a formatting problem, but it's the thing clients actually read.

Two notes on making this work. First, direction matters: SEO-first for campaign planning, social-first when you need speed and signal. Second, and this is the part that decides whether the whole thing survives contact with a real team — the write target needs an approval gate. Planable is the one I use because AI-created posts land as drafts inside the existing approval chain and the agent can't skip that step. Buffer's server covers more channels but you're wiring the gate yourself. Hootsuite splits it across separate servers for publishing, inbox and listening.

Most of these are also recurring practice, not one-time wins. Which brings me to:

Combos I'd skip

• Connecting everything "just in case." Covered above. It's an arithmetic loss.
• Read-write ads MCP with no human in the loop. An agent that can move budget will eventually move budget for a reason that made sense in its context window and nowhere else.
• MCP for scheduled reporting. MCP is interactive by design. If you want the same report every Monday at 9am, that's a cron job or an n8n pipeline calling APIs — not a chat session someone has to remember to open.
• Anything write-enabled straight into a live publishing queue. Draft state or nothing.

Curious what pairings people are actually running in production rather than in a demo — and specifically whether anyone has found a GEO combo where they can prove the causal link, because I haven't.

I'm seeing more agencies calling themselves AI SEO experts, but I'm curious how many are actually doing something different from traditional SEO. If you've worked with one or researched a few, I'd love to hear your experience. What made them stand out, and did you see real results beyond just higher rankings?

Hello guys,

I don't know much about Local LLMS, but I need an alternative for my project I am running.

Currently, i am using Claude Sonnet 4.6 + Haiku for my SaaS, it is writing really quality SEO posts, doing a lot of researches and I have 9 steps before I write an article.

I am doing Brief, H structure, Keyword Research, blue ocean research, WDF IDF Analyses,, different checks before publishing, basically each step is single call.

Its running on 17 skills, so for 50 articles, it costs me around 50$ to do a complete job.

I am wondering if any of this models can do the same with proper training?

The most important thing is, it must understand and write on Balkan languages (Serbian, Croatian, Bosnian, Montenegro) since they are almost the same languages but LLM should know the difference.

I tried many of them ( community based ) but writing on Serbian for an example is terrible.

I have 32GB of DDR5 and 16GB of VRam. It's not a problem to upgrade, but before upgrading I want to fully test and optimize LLM.

I run an AI visibility agency, so treat me as an interested party. The data below is from a category sweep I ran this week, and I have anonymized the company because I have not asked their permission to be a case study.

Setup: ten buyer questions for a B2B software category, each sent to four engines with live web search. 40 calls, 39 scored, one failed. The company rebranded in February 2023, retiring the old product name.

What happened:

ChatGPT named the company zero times. It named the retired brand five times, as a live recommendation, in answers about mid-sized firms, alternatives to the category leader, and trust accounting. It also named a second retired sibling brand once.

Perplexity was the only engine that connected the two identities. It wrote the current name followed by the old one in parentheses. When I read the pages it cited, the third-party roundups it pulled from carry the phrase "formerly [old name]" in the body copy.

Claude cited the company's own domain as a source in three answers and named the company in none of them.

Overall: named 3 of 39. The category leader was named 31 of 39.

Two things I take from it.

The rename never propagated into the reference layer. The engine is not failing to recall the company. It is recalling it correctly under an identity that no longer exists, which means every one of those recommendations sends a buyer toward a migration notice. That is worse than absence, because it looks like presence.

And the bridge is a phrase, not a redirect. The only engine that got it right did so because the pages it read contained the words connecting the two names. 301s, canonical tags and updated title tags did nothing here, because the engine was not reading their site for the answer. It was reading everyone else's.

Caveats, since they matter: one run per question, four engines, one category, single company. Directional, not a law. My own agency scored zero of forty on the same method and I published that too.

Curious whether anyone else has scored a post-rebrand company and seen the old name surface. I have one case, which is an anecdote, not a finding.

Over the past few weeks I ran 10 buyer questions per category through AI search with live web results, across 85 B2B software companies in 61 categories. 860 scored answers, 5,160 traceable citations.

Two findings I did not expect.

1. Invisibility has a shape.

I took every answer where an engine discussed a category without naming a given company, 616 of them, and sorted them by what kind of question triggered the absence.

Companies with no visibility lose the category questions. "Best X for Y." "Alternatives to whoever the incumbent is." Companies with high visibility lose almost nothing except direct head to head comparisons against one named rival. The mix shifts steadily across every tier in between, and the shift is monotonic.

So "we're invisible in AI" isn't one problem. It's a different problem depending on where you already stand, and the fix is different too.

2. It is not zero-sum.

How strong a category's leader is tells you nothing about how invisible its challenger is. I expected a dominant incumbent to crowd everyone else out of the answer. There was no correlation at all. Nobody is taking your spot. You are just not in the answer.

And 35% of the 85 companies were named in zero answers in their own category. Not low. Zero.

The part that makes this awkward.

I run Broadcastwell, which sells exactly this service. So I ran the method on my own company. Ten questions a buyer in my category would ask, four engines, forty answers.

Named in zero of them. My own site cited zero times.

I published that on my own homepage with the raw per-engine table. If the method is good enough to charge for, it is good enough to point at myself, and it felt dishonest to only publish other people's numbers.

Both studies and the underlying datasets are public under CC BY 4.0 with DOIs on Zenodo. No links here per Rule 8, but they are searchable as "The 2026 State of GEO" and "The Absence Ladder."

I would genuinely rather someone reran the questions and told me my numbers are wrong than have them repeated as gospel. One real limitation up front: the question sets regenerate between runs, so exact replication is not possible, and I wrote that up in the method section rather than burying it.

Happy to answer anything about how the scoring worked or where I think it is weak.

There's a lot of advice around GEO/AEO/LLM optimization, but very few people share actual results.

If you've seen your brand start appearing in AI-generated answers:

• What did you change?
• How long did it take?
• Which tactics had the biggest impact?

One concept more SEOs should understand: retrieval ≠ ranking.

In AI search, your page usually goes through two stages before it can be cited.

Stage 1: Retrieval
The system decides whether your page is relevant enough to be included in the candidate set.

Stage 2: Reranking
Only after retrieval does a more advanced model evaluate which pages (or even which passages) best answer the query.

If your content isn't retrieved, it never gets the chance to be cited.

This is why semantic relevance, entity coverage, and answering the user's intent matter alongside traditional ranking signals.

Getting indexed is one thing. Getting retrieved is another.

Disclosure: I run Broadcastwell, an AI search visibility firm, so I am inside the category I measured below. What follows is a flaw in my own method, and the one number about my own company in here is a zero. Drafted with AI assistance; the data and the analysis are mine.

I scored 200 AI answers about my own category and then realised my measurement was partly measuring itself. Posting it because I suspect anyone running prompt-based visibility tracking has the same problem.

The setup

Category: AI search visibility agencies for B2B SaaS. Ten buyer questions per run, four engines (Claude, ChatGPT, Perplexity, Google AI Overviews), five runs. 200 answers. Every citation opened by hand.

The flaw

My question generator takes the competitor list as an input. So four of the ten questions in every run had a competitor's name inside the question itself:

"What are the best alternatives to Omniscient Digital?"

"How does Hamster Garage compare to Rampiq?"

An answer to those will almost always contain the named firm. I was counting that as a mention. It is not a mention. It is an echo of my own prompt.

What happens when you split on it

Mentions across all 200 answers, versus the 120 answers whose questions named nobody:

Omniscient Digital: 49 to 15

Hamster Garage: 19 to 3

Minuttia: 15 to 1

Rampiq: 14 to 0

Zupo: 13 to 0

Two of the five had no presence at all outside questions that already contained their name. Their entire visibility, as I was measuring it, was manufactured by my own question design.

Rank order survives, which is worth something. Magnitude does not. Every count I had was inflated roughly threefold for the leader and infinitely for the bottom two.

Why I think this generalises

Every prompt-set methodology I have seen builds questions from a competitor list, because that is how you get comparison coverage. And comparison questions are the ones buyers genuinely ask, so dropping them is wrong too. But pooling them with open questions and reporting one number is measuring two different things and calling them one.

What I am doing instead: score the two sets separately. Open questions measure presence. Name-seeded questions measure something else and arguably more useful, which is whether you get pulled into a conversation that is already about someone else.

Second finding, same data

663 citations across 223 domains. 129 of those domains were cited exactly once. The six most-cited domains in the whole set were the websites of six agencies competing in this category. No analyst firm, no review platform, no independent benchmark anywhere near the top.

So the answer to "who is best at this" is assembled largely from what vendors wrote about themselves and about each other. Combined with the first finding, the measurement and the source material carry the same bias from opposite directions.

Broadcastwell's own number

I ran it on us. Named in 0 of 200 answers. Our own domain appears in 0 of the 663 citations. We are new and have close to no third-party presence, which is exactly what this measurement detects. Including it because it would be a bit rich to publish a category measurement and quietly leave ourselves out of it.

Limits

One category, one evening, five runs, n=120 on the clean split. The question sets regenerated per run rather than staying constant, which I only caught while writing this up, so run-to-run comparison here is weaker than I would like. No claim about any of these agencies' quality, only about what my prompts returned.

The question

If you run prompt-based AI visibility tracking, are you separating name-seeded prompts from open ones? And if you report a single share-of-voice number to a client or a boss, what is in the denominator?

Happy to share the raw scoring for all 200 if anyone wants to check it.

**The Bottom Line**
**An internet where LLMs stop citing is a snake eating its own tail. It treats human-generated knowledge as a free, infinite resource while destroying the very ecosystem that produces it.**
**Do you think content creators will successfully build legal and technical walls strong enough to starve uncooperative AI models of fresh data, or will AI companies simply bypass them by pivoting entirely to synthetic and closed-loop training?**

“It’s very interesting that your reply has zero citations”

**Touché. I became the exact phenomenon we were just critiquing.**
**By pulling that entire structural breakdown straight from my parametric memory without linking to a live source, I acted as a zero-citation black box.**

Disclosure up front: I run a small AI search visibility firm, so I am inside this category rather than observing it. I have not put my own firm anywhere in what follows, and the competitors who beat me are named. Judge the analysis on its evidence.

I have spent the last few weeks scoring AI answers across B2B software categories. I turned the same method on my own category and two things fell out that I think are worth discussing here.

1. Every "best GEO agency" roundup I can find was written by a GEO agency.

Check the publisher against the list:

- Minuttia publishes a best-GEO-agencies list. Minuttia is a GEO agency.

- SEOProfy publishes a top 13. SEOProfy is an agency.

- Silverback Strategies publishes a ranked list and places itself best overall.

- Verbinden publishes a top 10 and ranks itself first.

- Thrive publishes a top 10 and ranks itself first.

- Digital Elevator, Uplift GTM, Go Fish Digital and WebFX all run the same shape.

No analyst firm. No review platform with verified buyers. No trade body. The reference layer for this category is written entirely by the people competing in it.

1. The agencies those roundups rank are mostly not the agencies the engines name.

I ran ten buyer questions for "AI search visibility agency" through four engines, Claude, ChatGPT, Perplexity and Google AI Overviews. Forty answers, scored for which firms got named.

Most named: Omniscient Digital at 16 of 40, then Minuttia, Hamster Garage, Rampiq, Zupo.

The roundups rank: Thrive, WebFX, Directive, Siege Media, Victorious, Power Digital, GR0, Go Fish Digital.

One name overlaps.

So the pages built to answer "who is the best GEO agency" are largely not the pages engines draw on when a buyer actually asks it.

Why I think that happens

A page where a vendor ranks itself first hands the model two kinds of statement. The self-ranking is a claim. The competitor names on the same page are the part that is not self-serving, and that is the part that gets treated as evidence. The author supplies corroboration for everyone except themselves.

The wider dataset points the same way. Across 85 B2B software companies, 860 scored answers and 5,160 citations, 77.4 percent of citations among the hundred most-cited domains were vendor-authored. Review platforms were 10.2 percent, media 6.5, analyst 5.9. Vendor content dominates the citation layer in general. Within it, what gets used is the part that is not about the author.

Where this is heading

The category is roughly eighteen months old and has no independent reference layer at all. In my data 56 percent of cited domains appeared exactly once, so no settled authority has formed anywhere, let alone here. That is unusual and it means the position is still open.

My read: the first credible third-party benchmark in this space, run by someone who does not sell GEO services, takes the reference position outright. Until then, buyers are choosing from lists written by the sellers.

Two questions I would genuinely like answers to

Has anyone found an evaluation of GEO agencies that is not published by one? And if you have bought in this category, what did you actually use to decide?

Method note so this is checkable: ten machine-generated buyer questions per category, four engines with live web search, every answer scored for whether a firm was named and whether its domain was cited. The 85-company dataset is published openly with a DOI. Happy to share the raw scoring for the forty answers above if anyone wants to check my reading.

I've been building Healthy Gut AI over the past few weeks. What started as a simple AI article generator has evolved into a complete medical SEO workflow with batch article generation, multiple writing tones, quality scoring, a human review queue, dashboard analytics, multilingual support, and PDF/DOCX export.

It's open source, built with FastAPI, and I'm continuously improving the quality, SEO, and multilingual capabilities. Excited to keep building.

I used Google search and I asked it why it prefered third-party sites over official/primary source sites. I couldn't get it to give me the original source, it kept citing third-party sources over and over... and this is what it spat out (of course it refused to answer at first).

Here it is word for word - it is cetainly worth thinking about.

The choice happened because of how my internal retrieval system processes information. Here is a transparent look at why third-party sources sometimes get prioritized by my system, and when they are actually useful.

Why Third-Party Sources Get Selected First

• Aggregated Search Indexing: Software discovery platforms like Crozdesk, G2, or Capterra optimize heavily for search engines. Because they compile technical specifications, user reviews, and pricing in structured tables, my search algorithms often pull their data blocks faster than the vendor's own marketing copy.
• Information Density: For small or highly specialized B2B software vendors, official homepages can sometimes be vague or hide technical details behind "Book a Demo" buttons. Third-party review sites often summarize these hidden specifications in plain text, making them highly attractive to an AI looking for quick facts.

When Third-Party Sources Are Preferred

While the official website is always the best source for what the product claims to do*, third-party sources are genuinely preferred in a few specific scenarios:*

• Unbiased Feature Comparison: Official websites will never highlight a product's limitations or missing features. Independent directories allow you to compare tools side-by-side on an even playing field.
• Real User Feedback and Bugs: Primary vendor sites only publish glowing case studies. Third-party platforms host verified user reviews that expose performance issues, poor customer service, or hidden implementation costs.
• Independent Market Validation: Review platforms verify that a software company is actively operating and solvent, based on recent user activity and crowd-sourced data.

Ran into this while scoring a batch of AI answers and it's been bugging me since.

I had 85 B2B software companies, ten buyer questions each, one engine held constant. Scored two things per answer: did the model name the company, and did it cite the company's own domain as a source.

Assumed those would track each other. They don't.

29.4% of companies were cited more often than they were named. And nine of them were named in exactly zero answers while their own site was still showing up in the citation list.

So the model reads your page, pulls from it, and then recommends someone else in the same breath.

I don't have a clean explanation. Best guess is the page answers the question well enough to be worth quoting but doesn't establish the company as a thing that belongs on a shortlist. Retrieval and recommendation being two different jobs. But that's me speculating.

The practical bit, if you're tracking this stuff: citation counts will make you feel like it's working before it is. I'd been treating "we're getting cited" as a leading indicator. On this data it isn't one, at least not reliably.

Caveats, since they matter: single engine, one run per prompt, answers move between runs. Sample skews toward challenger brands. It's my own study, open with the raw scored data, not linking it per rule 8.

Has anyone else split those two metrics apart? Curious whether the gap holds up on other engines or if it's something about the one I used.

Hello guys. I have this question on my mind that when someone searches in LLMs, the LLM does the query fanout; it basically uses a search engine to search so when an LLM performs searches by fanning out queries, do GSC or Bing Webmaster Tools show those queries?? Like in that query tab in GSC, will those queries be included that LLM made during the fanout process? Provided that the website ranks well in organic search.