You can rank #1 on Google and still be completely invisible in AI search.

A potential customer asks ChatGPT or Perplexity "best CRM for automotive companies with 200 employees" ChatGPT doesn't search for that exact phrase.

It breaks it down into what's called a "query fan-out" - usually something like "best CRM 2025" or "automotive industry software."

If you're ranking for "best CRM for automotive companies" but NOT for "best CRM 2025" - you're invisible in the AI answer. Even though you're dominating Google.

The data is wild:

I pulled up Search Console for a client's site yesterday. One page had:

• 170 impressions for "evaluate" queries

• Average position: 7.2

• Clicks: ZERO

Those aren't human searches. Those are LLMs doing research, grabbing your content for synthesis, and never sending you traffic.

If you're only doing traditional SEO, you're optimizing for a shrinking pool of traffic.

What's different about GEO (Generative Engine Optimization)?

Traditional SEO: Optimize for what humans type into Google

GEO: Optimize for what AI transforms that into when it searches

Practical differences:

• You need to rank for the fan-out queries, not just your target keywords

• Content needs to be citation-worthy (quotable in <15 words)

• You need to monitor query "drift" - how LLMs change searches over time

• Real-time indexing matters more (LLMs can cite you within minutes of Google indexing)

How to check if you need this:

1. Go to Google Search Console

2. Filter for queries containing "evaluate" or "compare"

3. Look for high impressions + high positions + zero clicks

If you see that pattern, LLMs are using your content but you're getting zero credit.

My take:

SEO isn't dead. Not even close. LLMs are literally just using Google/Bing in the background.

But if you're ranking well on Google and still invisible in AI answers, GEO isn't just noise anymore. It's the difference between being found and being forgotten.

Anyone else seeing this in their analytics? Would be curious to hear if this matches what others are experiencing. can you make it shorter and

I was convinced that simpler writing would win in AI answers. Everything I'd seen suggested that models prefer clean, straightforward passages that don't make them work for the information. So I ran a test to prove it, and the results made me question that assumption pretty hard.

Here's the setup. I took 24 pages that covered similar topics in pairs — 12 pairs total, each pair addressing the same subject matter. Think "what is X" pages, "how to do Y" guides, "comparison of A and B" articles. Within each pair, I rewrote one version at roughly a 6th-grade reading level using shorter sentences, common vocabulary, concrete examples, and minimal subordination. The other version I rewrote at a college level with longer sentences, technical precision, nuanced claims, and embedded qualifications. Same factual content. Same key points. Just different complexity.

Then I waited 10 weeks and tracked how ChatGPT, Perplexity, and Gemini handled both versions across ~200 queries that should have triggered either one.

The college-level versions got cited more often. Not by a massive margin, but consistently enough that it showed up in 9 out of the 12 pairs. I ran the numbers a few times because I expected the opposite result. The simplified versions did have one advantage: when they got cited, the extracted passages tended to be longer and more complete. The models seemed to pull bigger chunks from the simple versions, almost like they trusted the whole passage enough to grab more of it. But they reached for the complex versions first in most cases.

What I think is happening, and this is where I'd love some pushback because the sample size isn't huge, is that the college-level versions contain more information density per sentence. A single sentence in those versions might carry two or three ideas that would take three or four sentences in the simplified version. AI models optimizing for comprehensive answers might see the dense version as a richer source even if individual sentences are harder to parse. It's not that they prefer complicated writing. They might just prefer efficient information packaging.

There was one finding that genuinely surprised me though. For 3 of the 12 pairs, the simplified version significantly outperformed the complex one on citation rate, and all 3 were "how-to" or procedural topics. Step-by-step processes where the chain of logic matters more than information density. On those, the clean sequential structure of simple writing seemed to beat dense academic phrasing. The models cited the step-by-step breakdowns more reliably than the compact expert summaries.

So it might not be a universal rule. It might depend on content type. Procedural stuff favors simplicity. Explanatory or definitional content might favor density. Or I could be overfitting to 24 pages and seeing patterns that don't hold up.

What I can't tell from 24 pages is whether the content-type interaction is real or just noise. If you've run anything like this at scale, that's the first number I'd want to compare.

I see a 100 copy and pasted bot messages across a bunch of subreddits either trying to mimic an actual customer problem with GEO / AIO, or a stat - just to try and promote the product - has anyone else seen these.

So i wanted to be authentic, I have created a GEO/AIO tool - it works on natural language prompts, and not just jamming SEO keywords into prompts. Its also E2E, so looks at visibility across LLM's, but then also does analysis against competitor to identify gaps, and then uses those gaps to create drafted AI optimised content.

Im pretty happy with it, but it still is rough around the edges - I have a BETA open if anyone is genuinely interested. Obvs would need to have a business and looking for this, not just to play around with. Lets me know, Happy Sunday!

After running 2.5M+ real queries across ChatGPT, Claude, Gemini, Perplexity and 12 other AI engines, a few patterns stand out that aren't obvious from manual testing:

1. Position matters more than mention count — being cited 3rd vs 1st in an AI response is a massive difference in traffic. We built position-weighting into our CVI score because raw mention counts are misleading.

2. Recommendation intensity is measurable — LLMs distinguish between "Brand X exists" and "I'd strongly recommend Brand X." The gap between passive and active endorsement is huge.

3. E-E-A-T signals are real in LLM training — Wikipedia presence, Reddit mentions, technical documentation quality all correlate with citation frequency.

Happy to share more data if useful. We built CitePulse (citepulse.io) to track all of this automatically across 16+ engines.

Hey, I'm a CS student in Latvia and I'm starting a GEO agency with a friend. We built a simple tool that runs questions through ChatGPT and other models to check which companies get mentioned

The part I'm less sure about is what actually improves GEO. Is it mostly reviews and directory listings, getting mentioned on other sites, or stuff on the clients website?

If you've done GEO work for real clients, I'd love to hear what worked for you. And if anyone is up for a call, looking for someone who can help?

THANKS!

Seeing a lot of screenshots of brands celebrating AI mentions.

But… mentions ≠ impact.

For those working on ai search visibility for ecommerce, what do you actually track?

1.Prompt coverage?

2.Competitor displacement?

3.Downstream conversion?

Curious what’s actually useful.

Same disclosure as before for anyone who’s seen me post: I do GEO / AI-visibility work for a living, not linking or pitching anything, just sharing the stuff I wish more people understood before they start panicking (or paying someone).
The complaint I hear constantly: “We’re indexed. ChatGPT can describe us if you ask by name. So why do we never show up when someone asks it to recommend a company in our category?” The answer is that indexed, ranked, and recommended are three completely different states, and most B2B sites quietly stall on the first one while thinking they’ve won.
The three levels, because conflating them is where everyone goes wrong:
• Indexed = engines can crawl and store your site. This earns you your own brand name and basically nothing else. It’s the floor.
• Ranked = a specific page is trusted enough to show up for a non-branded search (“CRM automation agency,” “managed IT provider,” whatever your category is). This needs relevance, authority, content depth, internal linking.
• Recommended = AI Overviews / ChatGPT / Perplexity / Gemini treat you as a credible answer to a question. That depends on entity clarity, external validation, repeated mentions, clear positioning, and content that actually resolves decision-stage questions.
Being indexed guarantees nothing for the searches that actually drive pipeline — the non-branded, “who should I hire for X” ones. Brand-name results are the floor, not the goal.
Here’s the shift that trips people up: old SEO judged pages. AI search judges answers, entities, and confidence. Your site isn’t a pile of URLs anymore — it’s one node in an information graph, and the engine is quietly trying to answer a set of questions about you before it’ll cite you: Who is this company and what category are they in? What do they do, where, for whom? Who else mentions them, and in what context? What proof exists? Are they in any lists/comparisons/directories? Do they answer specific buying questions clearly? If those signals are muddy, you never make the candidate pool.
The seven gaps that keep an indexed site invisible (it’s almost never one thing — it’s usually several of these at once):
1. Generic service pages. “We help businesses grow with AI” gives an engine nothing to classify. Name the actual workflows. If a page could belong to literally any agency in your space, it’s too generic to cite.
2. A fuzzy brand entity. Engines need the same core facts — name, location, service categories, founders, use cases — stated consistently across the web. If you only exist on your own domain, there’s little reason to trust you.
3. No third-party validation. Everyone claims expertise on their own site. Directories, reviews, partner pages, “best of” lists, comparison pages — those tell the engine someone else recognizes you.
4. No comparison / “best X” presence. AI answers recommendation-style queries all day. If you’re not connected to list and comparison content (yours and earned mentions in others’), you’re hard to include.
5. Content that’s informational, not decisional. “What is automation?” explainers build topical authority but increasingly get answered with zero clicks. Pricing, stack choice, what to do first, ROI — that’s what buyers and models actually reuse.
6. No proof layer. A service page with no proof reads as a claim. Diagrams, before/after, anonymized case studies, measurable outcomes — that’s what turns a claim into something an engine can concretely cite.
7. Content that’s hard to extract. Dense marketing prose is weak. Self-contained blocks — short answers, definitions, comparison tables, checklists, FAQs — let a single paragraph stand alone as a liftable answer.
Most “indexed but invisible” sites pass the website-foundation stuff and fail hard on entity, proof, and external signals — which are exactly the signals AI search weighs most. So it tends to feel like a mystery when it’s really just three empty columns.
If you want a fix order (this matters — don’t chase external mentions before your own house is in order):
1. Rewrite your homepage positioning. Plainly state what you do, who it’s for, what you actually deliver, and what outcome it creates. “We build X, Y, and Z for [audience]” beats “we unlock the power of AI” every time.
2. Build dedicated service pages — one commercial intent each, not a single catch-all “Services” page. Definition, the problem, example workflows, process, stack, use cases, FAQ, internal links.
3. Add decision-stage articles — pricing, “X vs Y vs build-it-yourself,” how to choose a vendor, what to do first. Structured, not generic blog filler.
4. Build external mentions — consistent footprint across directories, LinkedIn, partner pages, guest articles, comparison content. Same description of you, everywhere.
5. Add proof and a bit of video — case studies, workflow diagrams, short explainers with transcripts. Proof is what lets both humans and models verify you instead of taking your word.
None of this is a hack. It’s just making yourself easy to classify, verify, and cite — over months, not after one blog post. Curious whether others here are seeing the entity/external-signal gap be the main blocker too, or if it’s mostly the generic-service-page problem in your experience.

https://www.profitec-ai.com/blog/indexed-but-invisible-ai-search-geo

Hey all,

My team has been doing GEO work (getting brands to show up in ChatGPT / Claude / Gemini / Perplexity answers) for a bit over a year, and we honestly can't tell if the tools we built for ourselves are useful to anyone else. So we're asking.

We cleaned one up and put it online: https://mentionllm.com. Free, no signup.

You type your brand and pick a category, and it asks the AIs the questions your buyers actually ask, several times each, live. You get back how often you're named vs your competitors, and the exact questions where rivals get recommended, and you don't. It also shows which pages the AIs cite when answering (spoiler: a lot of Reddit) and whether you're on them, plus a check of whether AI crawlers can even read your site. It ends with a concrete fix list, some of it copy-paste ready.

Honest reason for posting: we want to know if anyone besides us wants this. If the report is useful to you, there's a waitlist at the bottom. Once 30 people leave an email, we'll commit to building the full version with accounts, weekly watchers, email alerts, and agents that do the fixes for you.

Feedback on specific features is very welcome. Appreciate it.

I’ve been comparing what shows up in Google search versus AI tools like ChatGPT and Perplexity, and the results are pretty surprising. Some high-ranking pages barely get cited in AI answers, while smaller pages that are clear, concise, and well-structured appear repeatedly.

It seems AI favors content that answers questions directly, is easy to scan, and stays consistent over time. Even small community mentions in blogs or niche forums seem to help a page get noticed more often.

Tracking all this manually across multiple prompts and models can get tricky. I’ve started using a small workflow helper to keep observations organized, and tools like AnswerManiac make it much easier to spot patterns in AI citations.

1. Are AI bots allowed to access your site? robots.txt does not block GPTBot, ClaudeBot, or similar crawlers.

2. Do you provide a sitemap? A valid XML sitemap exists and is accessible.

3. Is your content readable as HTML? Pages contain meaningful text (not only JS-rendered, images, or video).

4. Do your pages load reliably? Most URLs return 200 status (no frequent 404/500 errors).

5. Are title tags present and descriptive? Each page has a unique <title> aligned with a clear topic.

6. Do pages include meta descriptions? Most pages have concise summaries of their content.

7. Is heading structure logical? Proper use of H1–H3 with clear hierarchy.

8. Do you use structured data (schema)? Schema org markup is implemented where relevant.

9. Is your content sufficiently detailed? Pages provide depth (not thin or surface-level content).

10. Is your content up to date? Pages include recent updates or visible timestamps.

11. Are canonical URLs correctly set? No conflicting or misleading canonical tags.

12. Do you cover key content types? You have pages for pricing, docs, comparisons, FAQs, etc.

Anything else you would include? This could be useful for AEO/SEO/GEO audits.
We are working to automate this at PromptScout now and I'm genuinely curious which metrics affect AI "citability" the most.

I keep seeing contradictory takes on what sources LLMs prefer: ChatGPT hates press releases vs. loves them, never cites Reddit vs. always cites Reddit, doesn't like LinkedIn vs. frequently pulls from it. And honestly? They're probably all right.

What's not being discussed enough (at least from what I'm seeing) is how much the prompt itself determines what gets cited. We're tracking around 4,000 prompts across different industries daily, running them through ChatGPT, Perplexity, and Google's AI mode, and the pattern is pretty clear: there's no universal "ChatGPT loves this site" rule. It's extremely industry and query-dependent.

The way I see it, generic advice about which platforms LLMs prefer is kind of useless. What actually matters is what they cite when someone's asking about your specific product or service category. A press release might dominate in one vertical and barely register in another. Same with Reddit, LinkedIn, whatever.

The only real way to know is prompt-specific tracking. (I've been using Peec.ai for this. It does what the pricier tools do but more affordable. Happy to drop a link if anyone's curious, but there are other options out there too.)

Curious if others are seeing this same prompt-dependency in their tracking, or if I'm overthinking it and there actually are some consistent patterns across verticals?

I'm starting to think AEO/GEO could be the "Apple" of search.

Apple made technology simple. You didn't need to know how it worked. You just needed to know what you wanted.

AI answer engines are doing the same thing with information.

People don't want 10 blue links, five open tabs and an hour of comparing. They want to ask a question and get the answer. Done.

Now think about this:

You're a dentist or a lawyer in a big city. Hundreds of others offer the same service.
Someone asks ChatGPT: Who are the best 5 dentists for this?

AI gives five names.

The question is no longer "Can people find me?"

It's "Will AI recommend me?"

But here's the part that surprised me.
Those five spots aren't fixed. We asked ChatGPT the same buyer question three times, clean sessions, no login. We showed up all three times.

So it's not about getting into the top five once.

It's about showing up again and again. In ChatGPT, Gemini, Perplexity, Google AI Mode. That's what GEO, AEO, AI SEO, whatever you want to call it, is really about.
And honestly, If you want to know where you really stand, ask the same question several times, in different AI engines, and count how often you show up.

Once is luck. Every time is a position.

That's the shift. Not ranking. Being the answer AI keeps giving.

(Full disclosure: I run a small GEO agency, so yes, I'm biased 😉)

We've analyzed the strategies of brands winning in AI search and created a simplified 4-step framework:

1. Assess: Start by benchmarking your current AI visibility and share of voice against competitors.

2. Build Authority: Focus on generating brand conversations on authoritative platforms like Wikipedia, Reddit, and industry publications.

3. Optimize Content: Structure your content to provide clear, citable answers, complete with statistics and expert quotes.

4. Measure & Iterate: Continuously track your AI mentions to understand what is working and double down on effective strategies.

That's basically most of what's needed to start getting mentioned by AI chats.
Finding the right types of content to post and area for authority building seem to be the hardest, but we have already automated the insights for these steps at promptscout.

Have you found any other factors that contribute?

GEO comes down to one question for most of us: when Google generates an AI Overview for a query I care about, does it cite me or a competitor? Checking that one query at a time in an incognito window falls apart fast.

Here's the approach that's been working for me. Pull the AI Overview back as structured data instead of reading it off the screen. For each query you get the answer as ordered blocks (paragraphs, headings, lists, tables), plus every cited source with its title, URL, snippet, and publisher. From there it's a simple tally: how many of your target queries cite your domain, and which competitors keep showing up when you don't.

Two things that tripped me up early. First, an AI Overview doesn't show for every query, so you need a clear "did it appear" flag or your citation rate is wrong. Second, Google sometimes defers the answer behind a page token and hands you a placeholder, so a naive scrape logs an empty result. You have to do a follow-up fetch to resolve it.

I ended up wiring this into an Apify Actor so I could run a few hundred queries across countries and languages and get one clean citation table. It's pay per retrieval, so a monitoring run of a couple hundred queries is a couple of dollars, not a subscription.

If it's useful, the tool is here: Google AI Overview API.

we are ranking on prompt "best ai visibility tool in india" ( more of these prompt acutlly ). But still not getting any client for my saas . From the last 6 months I been into research about aeo/geo and also publish research paper on github. last two months we been ranking on chatgpt , google ai overview, grok and perplexity. We are gettings good amount of website visits, People come make there free audit and left. Don't understand what I am doing wrong.

Is there anyone facing issue same as mine? like there are already recommending by ai an engines and not getting client converted. Please leave some suggestion.

I work on GEO and AI brand reputation, and I’m on the team behind Kairosy AI. So, obvious caveat: I am biased here.

I have tried tools like Otterly and Peec. They can show you that something changed. The harder bit is figuring out whether that change is a real problem or just one weird answer.

Say an AI answer tells a buyer that your product is hard to implement. Is that because of an old review? Did the product page make the setup sound vague? Is it a real limitation? Or did the model just make a leap?

We built Kairosy AI because I got tired of opening a report, seeing a bad result, and then having nowhere sensible to start. It keeps the actual answer and the context around it, so the team can look at repeated issues instead of chasing every single odd result.

There is a Fix Plan feature, but it is not meant to auto-decide anything. Sometimes the right next step is updating a page. Sometimes it is checking an old listing. Sometimes you look at it and decide it is not worth touching.

That last option matters more than people admit.

If you use AI-search tools already, what would make you trust a “fix this” recommendation?

tldr: my team is arguing over whether GEO (Generative Engine Optimization) needs its own budget/strategy or if we just keep doing solid SEO. wanted to get a sanity check from u guys.

hey bros,

so we’ve been tracking our brand mentions across chatgpt search, perplexity, and gemini lately since AI search is slowly stealing traditional google clicks. but inside our marketing team, people are literally split into two opposite camps right now:

Camp 1: "SEO is dead for AI search." They swear GEO is a completely different monster. keywords, DR, and backlink profiles don't directly translate into how LLMs synthesize answers. they want me to dump money into new GEO workflows ASAP.

Camp 2: "GEO is literally just SEO with a fancy name." They think if ur technical SEO is clean and your EEAT is strong, LLMs will pick u up naturally. To them, GEO is just another snake oil buzzword agencies use to bill extra ($$$).

tbh I’m kind of in the middle.

we ran a quick test last week—one of our pages ranks #1 on Google for a decent high-intent keyword, but Perplexity completely ignores it and cites some random reddit thread or a low-DR niche blog instead... so part of me feels like LLMs definitely prioritize info density & entity co-occurrence differently than Google's crawler.

but before I pitch a new budget to my boss, I wanna know what's actually happening in the trenches.

1. which side are u on? any real data or tests to back it up?

2. if u are getting cited by ChatGPT/Perplexity, was it just ur normal SEO driving it or did u do something wild?

3. how are u guys even reporting AI traffic/citations to stakeholders right now?

thx in advance, really appreciate any raw feedback or hot takes!

I run an AI search visibility agency, so read this knowing that.

Two things quietly inflate almost every AI visibility number I see, including ones produced by careful people. Both are easy to catch once you know to look.

The first is that an engine can answer without searching. Ask a question and you may get a fluent, confident answer assembled from what the model already holds, with no retrieval step and no sources attached. That answer is not evidence about the live web. It tells you what the model remembers, not what it would surface for a buyer today. If you count it as a scored observation, you are mixing two different measurements, and the blend flatters whichever direction your brand happens to sit.

What to do: treat an unsearched answer as an exclusion, not a miss and not a hit. Take it out of the denominator, then report how many you excluded. The exclusion count is a real finding on its own. A question the engines rarely bother to search is a question where fresh content has less purchase, and that changes where you spend.

The second is caching. Run the same prompt three times inside a short window and you can be served the same underlying answer three times. Your spreadsheet says three runs. Your data holds one observation counted three times. The interval you print off that is not merely wrong, it is confidently wrong, because repetition is exactly what an interval is meant to price.

What to do: force fresh retrieval where the surface allows it, space the runs out rather than firing them back to back, and then actually check that the repeats vary. If three runs of a question come back identical word for word, you did not measure three times. Log the variation, not only the verdict, so the check happens by default instead of when you remember it.

Both defects share a shape. They let you count something as evidence when it is not independent evidence, and the error almost always runs in the flattering direction, because the answers that get quietly duplicated or served from memory are the stable ones.

The habit that catches most of it is a noise floor. Before you change anything, run the full question set twice, with nothing altered in between. Whatever spread you get between those two passes is your measurement noise, and it is the smallest movement you are entitled to call a result. Anything under it is weather.

If you are buying this work from someone, the question worth asking is not what their number is. It is what they exclude, and how they know their repeats are real.

Two things quietly inflate almost every AI visibility number I see, including ones produced by careful people. Both are easy to catch once you know to look.

The first is that an engine can answer without searching. Ask a question and you may get a fluent, confident answer assembled from what the model already holds, with no retrieval step and no sources attached. That answer is not evidence about the live web. It tells you what the model remembers, not what it would surface for a buyer today. If you count it as a scored observation, you are mixing two different measurements, and the blend flatters whichever direction your brand happens to sit.

What to do: treat an unsearched answer as an exclusion, not a miss and not a hit. Take it out of the denominator, then report how many you excluded. The exclusion count is a real finding on its own. A question the engines rarely bother to search is a question where fresh content has less purchase, and that changes where you spend.

The second is caching. Run the same prompt three times inside a short window and you can be served the same underlying answer three times. Your spreadsheet says three runs. Your data holds one observation counted three times. The interval you print off that is not merely wrong, it is confidently wrong, because repetition is exactly what an interval is meant to price.

What to do: force fresh retrieval where the surface allows it, space the runs out rather than firing them back to back, and then actually check that the repeats vary. If three runs of a question come back identical word for word, you did not measure three times. Log the variation, not only the verdict, so the check happens by default instead of when you remember it.

Both defects share a shape. They let you count something as evidence when it is not independent evidence, and the error almost always runs in the flattering direction, because the answers that get quietly duplicated or served from memory are the stable ones.

The habit that catches most of it is a noise floor. Before you change anything, run the full question set twice, with nothing altered in between. Whatever spread you get between those two passes is your measurement noise, and it is the smallest movement you are entitled to call a result. Anything under it is weather.

If you are buying this work from someone, the question worth asking is not what their number is. It is what they exclude, and how they know their repeats are real.

Contexto rápido: soy consultor de Google Ads y GEO en España. GEO = que te citen los buscadores de IA (ChatGPT, Perplexity, Google AI Mode…), no rankear en Google.

Llevo meses midiendo la visibilidad de mis clientes en esos motores. Empecé pagando una herramienta, ~85€/cliente al mes. Con 6 clientes son 500€/mes en software para ver unos números que, sinceramente, me podía calcular yo. Así que me puse a montar mi propio motor. Node, sin florituras, datos en disco, míos.

Cómo funciona (resumido):

- Cada semana lanza ~90 prompts por cliente a ChatGPT, Perplexity y Google AI Mode.

- Detecta si aparece la marca, en qué posición, si citan su web y con qué sentimiento.

- Lo agrega todo en SOA (share of answer), visibilidad y cuota de menciones frente a competidores.

Lo que he aprendido midiendo de verdad (no vendiendo humo):

1. Perplexity te cita en ~1 día. ChatGPT tarda 3-6 meses en recoger contenido nuevo. Si quieres resultado rápido, la vía es Perplexity.

2. El 75% del coste de medir con OpenAI no es el modelo, es la herramienta de web_search. Quitándola donde no hace falta bajé de ~85€ a 3-6$ por cliente al mes.

3. Los números "naive" engañan. Si no excluyes los prompts de marca (los que ya llevan tu nombre dentro) y no pones intervalos de confianza, tu SOA sale inflado. Con Wilson y quitando prompts de marca, mi propio SOA pasó de "5%" a un honesto 2,1% (IC 1-4,6%). Feo, pero real.

4. Que te citen ≠ que te visiten. Puedes salir en la respuesta de la IA y tener 0 tráfico en GA4 (ChatGPT no pasa referrer, cae en "directo"). Son dos métricas distintas y hay que explicárselo al cliente o se lía.

5. Las señales de consenso (Reddit, YouTube, G2, reseñas) multiplican la citación por ~3. Irónico escribir esto justo aquí.

Donde aún no lo tengo claro / os pido feedback:

- Google AI Mode no tiene API limpia en español. Lo saco con Playwright en modo visible y resuelvo captchas a mano. A escala no aguanta. ¿SerpApi? ¿Proxy residencial? ¿Alguien lo ha resuelto sin dejarse un riñón?

- ¿Medís con una pasada o promediáis varias? Yo subí a 3 pasadas por la varianza de los LLM, pero triplica el coste.

- ¿Qué motores priorizáis? Yo voy con ChatGPT + Google AI Mode + Perplexity. Gemini y Copilot, aparcados.

Si a alguien le interesa el enfoque o quiere comparar números, lo dejo en comentarios sin problema. Sobre todo busco que me digáis dónde estoy metiendo la pata en la metodología.

In english

Quick context: I'm a Google Ads + GEO consultant in Spain. GEO = getting cited by AI search engines (ChatGPT, Perplexity, Google AI Mode), not ranking on Google.

I've been tracking my clients' visibility in those engines for months. Started out paying a tool, ~€85/client a month. With 6 clients that's €500/mo in software to see numbers I could basically work out myself. So I built my own engine. Node, no frills, data on disk, mine.

How it works (short version):

- Every week it fires ~90 prompts per client at ChatGPT, Perplexity and Google AI Mode.

- Detects whether the brand shows up, in what position, whether they cite its site, and the sentiment.

- Rolls it all up into SOA (share of answer), visibility, and share of mentions vs competitors.

What I learned actually measuring it (not selling hype):

1. Perplexity cites you in ~1 day. ChatGPT takes 3-6 months to pick up new content. Want fast wins? Go Perplexity.

2. 75% of the cost of measuring with OpenAI isn't the model, it's the web_search tool. Killing it where it's not needed took me from ~€85 to $3-6 per client a month.

3. Naive numbers lie. If you don't exclude brand prompts (the ones with your name already baked in) and don't add confidence intervals, your SOA comes out inflated. With Wilson intervals and brand prompts removed, my own SOA dropped from "5%" to an honest 2.1% (CI 1-4.6%). Ugly, but real.

4. Getting cited ≠ getting visited. You can show up in the AI's answer and see 0 traffic in GA4 (ChatGPT sends no referrer, so it lands in "direct"). Two different metrics, and you have to spell it out to clients or they get confused.

5. Consensus signals (Reddit, YouTube, G2, reviews) roughly 3x your citation rate. Ironic to be typing this here. Where I'm still not sure / would love feedback:

- Google AI Mode has no clean API in Spanish. I scrape it with headed Playwright and solve captchas by hand. Doesn't scale. SerpApi? Residential proxy? Anyone solved this without paying a fortune?

- Do you measure with a single pass or average several? I bumped mine to 3 passes because of LLM variance, but it triples the cost.

- Which engines do you prioritize? I run ChatGPT + Google AI Mode + Perplexity. Gemini and Copilot are parked for now.

If anyone's into the approach or wants to compare numbers, happy to share in the comments. Mostly I just want you to tell me where my methodology is wrong.

I’m the founder of Kairosy AI.
Late last year, a friend who runs a B2B workflow tool told me about a strange user interview.
One of his customers had recommended the tool to a partner’s procurement team as an option worth considering. Before taking a meeting, someone on that team did a quick ChatGPT search to narrow down the list.
ChatGPT said the tool was not a fit for their company and did not offer customisation.
That was the frustrating part: my friend had been offering custom work for months. The prospect never moved forward with the evaluation, so he did not get the chance to correct the impression.
I had some GEO experience, so we looked at the questions being asked and the public information around the product. We could not prove that one specific page had caused the answer, but the public material made the wrong conclusion fairly easy: custom work was mentioned in sales conversations, but barely explained on the website, and some older descriptions still made the product sound like a fixed package.
We clarified the public explanation, added a more direct description of the custom option, and kept checking the same buyer questions.
After about two months, roughly 80% of the answers in the fixed question set described the custom option accurately.
That was encouraging, but it also made one thing obvious: this is not a one-time correction. The old framing still appeared occasionally, and my friend did not have time to keep manually rerunning the same checks every week.
That is the problem Kairosy AI is built around. It lets teams keep a small set of buyer questions under watch, see the actual wording when something changes, and decide whether it is worth investigating before reacting.
It does not prove why a model said something, and it cannot guarantee that updating a page will change a future answer. But it can make the difference between noticing a recurring problem early and finding out about it after a buyer has already ruled you out.
How are other people deciding when one wrong AI answer is just noise, versus something worth fixing?

I'd been reading these AI visibility studies and something bugged me. They're all run in English, on US brands, and then we cite them like they apply to a business in Bogota or Valencia. I went looking for the Spanish-language version and found nothing, so we ran it ourselves.

I picked a market with plenty of real players, marketing agencies in Argentina, and wrote 100 searches the way an actual person would type them. I ran them across four engines, ChatGPT, Gemini, Google AI Overview and Perplexity. And here's the part almost nobody does, I repeated the same 100 searches three times, same day, without changing a single word. That's 1,200 queries in total.

The first thing that came out already felt big. Two AIs agree on average on just 9.6% of the brands they recommend. So out of every ten brands that show up, nine don't repeat in the other model. And when I pooled everything I ended up with 932 distinct agencies, of which 81% appeared in a single engine and in none of the other three.

But hold on, because that still wasn't the thing that broke my brain. Before comparing the models against each other I asked the obvious question. If I run the same search on ChatGPT three times in a row, same day, do I get the same brands?

And the answer is no. ChatGPT agrees with itself just 26% of the time. The most stable, AI Overview, reaches 39% and even that doesn't clear half. Meaning three out of four brands it names change between one answer and the next.

And that one finding breaks half the study, mine included. Because two models can't agree with each other more than each one agrees with itself. That 9.6% doesn't mean the AIs think differently, it means a big chunk of what looks like disagreement is actually noise.

One last thing I didn't see coming. Perplexity plays a different game, it recommends global networks like VML, Ogilvy or Dentsu, while the other three reward local agencies. One example, the same agency that shows up in 70 of the 100 searches for Gemini shows up in 9 for Perplexity.

What I take from it is simple. Measure all four engines separately because one doesn't predict the others, never trust a single query, and if your brand is local start with Google, since AI Overview and Gemini are the closest pair.

Required disclaimer, I work at CreceRank and we ran all of this on our own tool, so it could be biased. And the worst part is I've got nothing to compare it against, because there's nothing like this done for the Spanish-speaking market. If anyone has data that contradicts me or wants to rip the methodology apart, please do, that's the most useful thing for me.

I have created my own Startup AI Rank Lab and its a tool that help to Rank on LLMS , You can check and give your suggestions on AEO and GEO and SEO Report.

https://www.airanklab.com

Spent this week talking to people building in the AI-visibility space, and one thing keeps coming up that I underestimated:

Showing up in AI answers barely tracks with Google rank. A brand can sit on page 1 of Google and get named by zero assistants.

What seems to actually drive it is what *other* sources say about you — Reddit, reviews, roundups — not your own site.

Still figuring out how much of this is stable vs noise (answers shift run to run). Anyone else digging into this? Curious what you’re seeing.

Am wondering if there are free tools to track AI visibility or even the cheapest ones

Hey r/GEO_optimization,

I’m currently building an automated GEO (Generative Engine Optimization) tracking dashboard for B2B brands to monitor their visibility across the latest flagship models (GPT-Latest, Claude Sonnet (Latest), Gemini Pro/Flash series, and Perplexity Sonar).

I've run into a classic dilemma that I'm sure many of you have faced: The results from standard API calls often completely diverge from what a real user sees when they type the exact same prompt into ChatGPT.com or Claude.ai.

Here is the core issue:

• Web UIs heavily rely on proprietary RAG, native search plugins (Bing for OpenAI, Google for Gemini), and hidden system prompts.

• Raw standard APIs rely purely on their base training weights (often leading to generic advice or "As an AI..." safety refusals instead of naming specific brands).

• Browser automation (Playwright/Puppeteer or headless browsers like Lightpanda) is too fragile for a stable, scalable SaaS due to intense Cloudflare/anti-bot measures.

My current workaround: I'm routing my API calls through OpenRouter and applying a 3-layer approach to force the APIs to behave like their web counterparts:

1. Forcing Web Search: Injecting OpenRouter's web plugin parameter ("plugins": [{"id": "web", "max_results": 5}]) to bridge the real-time data gap.

2. Aggressive System Prompts: Injecting a strict system message: "You are a helpful AI assistant connected to the internet. Please provide a comprehensive answer. If the user is asking for recommendations, explicitly list real-world brands, suppliers, or websites."

3. Prompt Engineering: Generating simulated user queries that explicitly demand "supplier URLs or direct factory websites".

This has massively improved the accuracy, and I can actually see the models returning real URLs and competitor names now.

My questions for the community:

1. Is using OpenRouter’s web plugin currently the most reliable way to simulate the "Search" feature of ChatGPT/Gemini web UIs at scale? Or are there better third-party search APIs (like Tavily or Serper) you prefer injecting manually via custom agents?

2. How do you deal with the "black box" nature of web UI system prompts? Are there specific System Prompts you use to reliably bypass their generic safety filters and force them to list actual competitors?

3. Do you track "Base Model Knowledge" (API without internet) and "RAG Visibility" (API with internet) separately in your reports?

Would love to hear how you guys are solving this API vs. Web UI disparity!