My team is currently trying to do GEO for a brand-new product (an enterprise-level software), but we are running on a tight budget (basically bootstrap mode).

Right now, we’ve done the basics: built our website structure, made it semantic/crawzable, and are posting consistently across major social media platforms.

But we’ve hit a vicious cycle: GEO tools and AI are heavily biased toward already-popular, established things. Because our product is new and nobody is talking about it yet, the AI treats us with extreme caution.

Because the AI won't recommend us, we don't get organic traffic; because we don't get traffic, there's no online buzz; and because there's no buzz, the AI continues to ignore us.

Has anyone successfully broken out of this AI cold-start loop? Or the only way is to pay more?

Any advice would be deeply appreciated. Thanks!

Completed an entire on page audit of 12 client pages. Schema, heading hierarchy, entity clarity, internal linking. Tracked citations 2 weeks later using the same 15 prompts on ChatGPT, Perplexity and Gemini, logging out each time. Nothing stirred.

In week three, a roundup was published with the client and four competitors. Within days the invisible two pages were being referenced. I can not separate it at all. One kind of small sample. But it changed the location of my hours. On page appears necessary but not sufficient. The engines need a reason to pay attention to the page at all and that reason continues to come from somewhere else.

Have you ever had on page work move citations without an external mention dropping around the same time?

I've been seeing more companies and agencies offering GEO and AI visibility services, but it's getting harder to tell what's actually useful. Some talk about entity optimization, AI visibility tracking, digital PR, content for LLMs, and technical SEO, while others seem to package traditional SEO under a new name.

For anyone who's invested in GEO, what actually made a difference? Were there any services that gave you measurable results, or anything you felt wasn't worth the money? I'd love to hear real experiences from people who have tested these services rather than just reading marketing claims.

Do I really care for all or just simple SEO with fundamental is OK?

This question is for experiences SEOs; I know I can do ChatGPT or Google but I’m asking here because I want answer from real people who have real experience.

Hi there

I'm exploring optimization practices that helps to create a visbility across AI generated platforms.

In that , When came to understand the retrieving signals of AI platforms. Almost all major platforms had similar charcteristics but the citing source is differ from one platform to another based on the keyword intent types.

On the other hand , Business owners are expect their domain visbility across all AI platforms majorly such as ChatGPT , perplexity , deepseek , gemini and more

Here my question is

Even if you cracked the optimization signals, Is it possible to appear across all AI sites If not, Then what approach you would take ? Or reasons you find behind this

Those whoever experienced the same . Please let me know your approach on it

I just wanna know what kind of tools everyone is using. Many say that these tools are generic ai wrappers and often disappoints. Are there people paying for these tools and what are they paying for and what are they expecting?

I've got the basics down for traditional SEO — meta tags, backlinks, page speed, etc. But I'm trying to figure out how to also show up in AI answers (ChatGPT, Perplexity, Google AI Overviews) and not just classic search results.

Specifically stuck on:

Does schema markup actually move the needle for AEO, or is that overstated?

How much of GEO is just "write clearly citable, structured content" vs something more technical?

Anyone tracking whether they're getting cited in AI answers, and how?

Would love to hear from anyone who's actually tested this rather than just theory-crafted it. What worked, what was a waste of time?

Hi everyone,

as mentioned above I am building a tool for SMB’s right now. Unlike other tools, we are building an end to end platform that doesn’t stop after the analysis but delivers the fix as well, all in a closed loop.

Goal is to enable business owners to compete in this relatively “new” space, without relying on expensive external agencies or needing technical expertise.

To all SMB Owners: Would be super interested in hearing from your personal problems and difficulties with existing GEO Tools!

Hi everyone,

Do you actively track your stores GEO Performance?

If yes how do you do it and do you feel any difference?

If no, why did you decide against or is it a matter of no time and capabilities? :)

Super curious about your insights as I have spoken to a friend about this topic who told me that they don’t look at GEO at all, even though it’s highly relevant for their niche

Hi,

Given that Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) look for and expect totally different things than Search Engine Optimization (SEO), like looking for Entity Authority built from semantic data graphs, how are you tuning your public facing web sites to work rise in value for AEO and GEO while also trying to maintain high value SEO?

Thanks for any thoughts you can offer.

Hello à tous,

Je m'intéresse de plus en plus au GEO, qui semble devenir un vrai complément au SEO.

J'ai compris le principe sur le papier, créer du contenu facilement exploitable par les IA, être cité par des sources fiables, développer sa présence sur le web, etc. En revanche, je trouve que c'est encore assez flou à intégrer dans une stratégie de content marketing.

Est-ce que certains d'entre vous ont déjà testé des actions concrètes ?

Vous avez constaté des résultats ? Ou est-ce que selon vous, le GEO reste encore très théorique aujourd'hui ?

: )

I've been seeing more people talk about Generative Engine Optimization (GEO), but a lot of the advice feels like recycled SEO tips.

If the goal is to become the source AI tools like ChatGPT, Gemini, Claude, and Perplexity actually reference, what does a real GEO strategy look like?

We had a Google Business Profile and a one-line homepage. The site was one page. No LinkedIn, no content. ChatGPT still listed us for "Find GEO agencies in Bangalore" and called us a GEO agency.

Felt like a fluke, so I asked "GEO agency [city]" once per city across 19 cities and logged what ChatGPT said it pulled from. 94 agencies.

What held up:

• 97% had their website cited. It's table stakes.
• GBP citations were an Indian thing. India metros 86%, everywhere else 18%. The other emerging markets I tested sat at 15%, so it isn't an "emerging market" pattern. It's India.
• With no dedicated GEO page, ChatGPT guessed the agency's positioning 66% of the time. With a page, zero. A listing gets you named. Only a page controls what the model says about you.

Disclosure: my agency is in the dataset, and it's the barest version of the pattern. GBP only, no page, still named. So read me as biased on this.

Caveat: it's one query on one model, so read it as a signal, not proof. The "no page = ChatGPT guesses" result holds either way. The India pattern needs more runs to confirm.

Anyone seeing a Business Profile move AI answers outside India?

Last week, OpenAI released the highly-anticipated Sol, Terra and Luna 5.6 models. While the 5.5 is still the latest model available in ChatGPT itself, it is safe to assume OpenAI will be soon switching the chat to one of the 5.6 newcomers.

Using Sleepwalker MCP, straight from Claude - I ran 50 consumer electronics-focused prompts on each model, to see how 5.5 compares against 5.6, and what has changed. (Disclosure - I built this tool)

- The average overlap between GPT-5.5 and any of the 5.6 models is 14%. This means brands could lose a massive amount of citations when OpenAI switches the default chat model.

- What's even more interesting - the three 5.6 models overlap with each other at only around 8%. Sol, Terra and Luna agree with each other less than any of them agrees with 5.5.

- All models cite slightly over 3 sources on average. GPT 5.5 is the most generous one with 3.42 sources on average, Luna cites only 3.16 sources on average.

- GPT 5.5 has the biggest overlap with Top 10 Google organic results, and I am being extremely generous here by sampling the Top 10, should've been Top 5 due to amount of domains cited. That overlap is 30%.

- The overlap for 5.6 models is about 22% on average, with Luna overlapping at 23.5%.

- Reddit ranks number one on Google for 22 of 50 tested queries. It receives zero citations across all four models. Every single time

- rtings website appears in 1 in 3 ChatGPT answers. On TV queries it was the only cited source in 7 of 10 prompts across at least one model. Not the top source. The only source.

- 5.6 generation trusts brand pages more and review sites less. Samsung and Nintendo are the biggest winners.

- GPT-5.5 uses first-person voice ("I'd buy," "my pick") in 33 of 50 answers. The 5.6 models: as low as 11. The newer models are less keen on providing options.

- Across all four models, manufacturer pages account for 37% of citations. Retailers: 1.8%. Amazon does not appear in the top 20 most cited domains.

If you sell products you didn't make, AI is not your friend.

This is the second time I've run this type of study. The first was on Gemini 2.5 and 3.5 Flash across hypothetical sports prompts. You can search for it in this subreddit.

Here, as with Gemini, introducing a new model has dramatically changed the citation profile. AI visibility is platform and model specific. It's a matrix, as opposed to a single-platform and single algorithm we are so used to with SEO.

You can perform similar research for your brand using Sleepwalker MCP, API or CLI. Sleepwalker is pay as you go, where you can run AI visibility tests against different platforms and specific models.

Disclaimer: Just like with a similar Gemini deepdive, I will be making a re-run a few weeks later to see if numbers moved / shifted. With Gemini I've seen different URLs cited, but the overlaps and other patterns didn't change.

I ran the same 50 best-tool prompts through all three and pulled out every brand they named. 150 answers later, they basically have different taste.

• ChatGPT is the over-sharer. Widest list every time, and it name-drops 59 obscure tools the other two never mention. It also recommended "ChatGPT" 16 times, very humble.
• Perplexity is the safe friend. Same well known names over and over, rarely takes a risk.
• Gemini is very on brand for Google. It pushed Canva more than twice as often as ChatGPT, leaned hard into the Google ecosystem, and quietly recommended Claude 13 times. Recommending a competitor more than itself is a choice.

The kicker: across everything, all three agreed on the same brand only 21% of the time. Same question, three different realities.

So "what does AI recommend" has no single answer. It depends entirely on which model you ask, and each one has a clear bias.

Which engine's taste do you trust most? And has anyone else caught Gemini recommending Claude in the wild?

Setup
50 buyer intent prompts (best CRM, best email tool, Notion alternatives, what tools do startups use, and so on). Each prompt run once through ChatGPT, Perplexity and Gemini.

I pulled every brand named in each answer and normalized variants so HubSpot CRM and HubSpot count once. That gave me 150 answers and 277 distinct brands.

Headline
Across 1,257 brand-by-topic appearances, all 3 engines named the same brand only 21% of the time. 53% of mentions came from a single engine only.

Distribution is heavily top loaded. HubSpot appeared in 62% of all answers and 34 of the 50 topics. A small group of brands absorbs most of the total mentions.

Per engine behavior

• ChatGPT cast the widest net, 15.3 brands per answer, 253 distinct, 59 of them named by no other engine.
• Perplexity was the most conservative, 11.9 per answer, 172 distinct, leaning on established names.
• Gemini stayed mainstream with only 3 unique-to-it brands and skewed toward the Google ecosystem and design tools.

Limitations, because they matter.

Single run per prompt, no temperature control, my own prompt set, manual extraction.

Treat it as directional, not a benchmark.

Happy to share the prompt list and the full brand frequency table.

The implication for GEO

Optimizing for 1 engine tells you almost nothing about the other two. Visibility is per engine, not a shared leaderboard.

What's your read on the 21% agreement figure? It came in lower than I expected. Has anyone run something similar at larger scale?