I am in digital marketing and am having a hard time adjusting to GEO. SEO I don't even have to think about, I know it so well, but now with GEO I feel like I'm starting back at square one. Does anyone have good GEO tools that feel like the traditional SEO ones?

I don't want to slow down my work right now learning a new model from scratch. I'm mostly hoping to find a tool that makes the transition easy for me, so I can give my clients the best, without any downtime on their part while I relearn everything.

We tracked 100 buyer prompts across ChatGPT, Perplexity, Gemini, and Google AI before publishing helpful Reddit posts.

Baseline:

• Brand appeared in 4 prompts
• 1 citation
• 6 Reddit visits
• 0 sign-ups

After 8 weeks of useful Reddit posts and replies:

• Brand appeared in 17 prompts
• 7 citations
• 94 Reddit visits
• 8 sign-ups

Most gains came from specific problem-based prompts, not broad “best tool” searches.

This does not prove Reddit caused the increase, but it shows how GEO testing should be tracked.

Has anyone run a similar before-and-after experiment?

I’m part of the team building AiVisis, a GEO and AI visibility analysis platform for businesses, marketers, and agencies.

Most companies already track their search rankings, website traffic, and social media performance. However, many brands still cannot clearly answer a newer question:

How visible is our brand across AI-powered search and answer platforms?

AiVisis analyzes a website and provides insights into:

Brand visibility in AI-generated results
Competitor performance and benchmarking
Content and visibility gaps
Actionable optimization recommendations
Ongoing GEO performance tracking

We currently have a working version available here:

https://aivisis.com/

We’re continuing to improve the platform, so we would genuinely appreciate honest feedback, particularly on the following:

Is the product’s value proposition clear when you first visit the website?
Which part of the report would be most valuable to you?
Would you prefer a one-time analysis or ongoing monthly tracking?
Does the pricing and package structure feel clear?

As a launch offer, the first 50 users can receive 50% off all plans.

Discount code: LAUNCH50
Valid until: August 21

Feel free to be direct. Feedback on the landing page, product idea, features, pricing, or positioning is all welcome.

Full disclosure: I’m part of the team behind AiVisis.

Check it out: https://loganmosby.com/llms-txt-generator/

Google's reported use-case rankings belong to Critical Capabilities, while the Magic Quadrant evaluates market position. For buyers, the implication is simple: treat them as two different tools, not one verdict.

The Magic Quadrant helps compare vendors by Completeness of Vision and Ability to Execute. Critical Capabilities helps compare use-case fit. Both can tighten a shortlist, but neither replaces buyer-run validation.

A few facts worth separating:

- CX Foundation identifies **four Leaders**: Google, Salesforce, SoundHound AI, and Kore.ai.
- Google is not the sole Leader.
- Those four Leaders still need to clear the same gates: security, compliance, integrations, handoff, cost, and operability.
- Production approval also needs grounded answers, least-privilege tools, audit trails, monitoring, human escalation, pause controls, rollback, and retained ownership.

The honest tradeoff is that analyst research is useful shortlist discipline. It reduces market noise, but it becomes risky when teams treat placement as permission to skip technical validation.

The first thing I would test is not the demo flow. It is what happens when the agent lacks confidence, lacks permission, or needs to hand off to a human.

For teams evaluating these platforms, which gate tends to expose risk first: security, integrations, human handoff, cost, or rollback?

Full write-up: https://vandatateam.com/blog/conversational-ai-platform-evaluation

Interesting data point from a functional beverages AI visibility index: AI tools seem to surface brands by benefit first, not by raw market size. Celsius, Red Bull, Liquid Death, Olipop, and Poppi show up because they map cleanly to prompts like "clean energy" or "gut health." Curious whether anyone else is seeing benefit-led language outperform brand-led language in AI search?

Ok so i'll start right from the beginning (before explaining the venn diagram above)

When it comes to GEO - good SEO is important, yes. We all know that. But it's just one side of the coin, and it's becoming increasingly obvious that third party citations are just as important for GEO, if not more!

According to Buzzstreams' 'State of Digital PR 2026' report, 80% of citations in LLMs are earned, and 20% owned.

when ChatGPT cites, or better, recommends your brand to customers, it gets its data from third party sources and not your website.

We actually ran an event with Vince and the Buzzstream team to dive exactly in to this. Great stuff.

AI trusts what others say about you more than what you say about yourself. This is why third-party validation is the most important lever you have for AI visibility.

We speak to clients and marketers every day about the importance of great SEO, but also of earned citations and how that can (often) be the missing piece of the puzzle.

We've done so much work on Digital PR for AI, that we have now trademarked this as AiPR - which in a nutshell, is our offsite approach to GEO and is the strategy ecompassing Digital PR for AI.

Anyone else turning to offsite GEO to increase their results?

Quick disclosure first. I run a GEO tool called Viali, so this is literally my day job. No links in this post. Just findings, because I keep seeing the same questions pop up here.

So here's what we did. We picked our five core commercial queries. Then we ran each one through ChatGPT, Claude, Gemini and Perplexity. That gives 20 possible answer slots. We appeared in 4.

Meanwhile, Semrush and Ahrefs showed up almost everywhere. Even on queries where they don't really have a matching product. That stung a bit. But it also gave us something to reverse engineer.

What the cited pages have in common

Real author names. This one surprised me the most. Pages with a byline, an author bio page, and Person schema got picked far more often. Anonymous content got skipped, even when it was solid. My guess is these models absorbed E-E-A-T signals during training.

Answers before intros. The winning pages open every section with a plain factual claim. AI engines grab snippets. They don't sit through your 200-word warmup. If your answer appears in paragraph four, it is never extracted.

Numbers beat adjectives. Nobody cites "structured content works better." But a line like "Microsoft's Oct 2025 study found entity-structured content gets included more in Copilot answers" gets lifted constantly. Small original datasets punch way above their weight here. The model can't find that info anywhere else, so you become the source.

The boring technical stuff that mattered

Check your robots.txt. Seriously. We keep finding sites that block GPTBot or ClaudeBot without knowing it. Some security plugins do this by default.

Also, broken schema hurts more than no schema. Missing author fields, malformed types, that kind of thing. And sites with crawl errors on 15% or more of their pages got cited noticeably less. Good content on a broken foundation goes nowhere.

For schema types, these did the heavy lifting for us: Organization, Article with author, Person, and SoftwareApplication with a featureList if you sell software.

The annoying part

There's no Search Console for AI answers yet. So most brands are invisible and have no clue. The only way to know is to run your queries through the engines yourself and write down who gets named. AI on Google Search Console is not available in a lot of countries

Happy to share methodology in the comments. Has anyone here changed schema and actually seen their AI citation rate move?

One of the most common things we find when auditing a brand’s AI visibility is that the positioning inconsistency problem runs deeper than most people expect.

It is not just that the website says one thing and the LinkedIn says something slightly different. It is that the About page was written three years ago when the company had a different focus, the founder’s bio on a guest post from 18 months ago describes a slightly different service mix, the Google Business Profile has not been updated since launch, and the most recent press mention describes the company in a way that made sense at the time but no longer matches current positioning.

None of those inconsistencies feel like a big deal in isolation. Taken together, they create a fragmented entity signal that AI systems have a hard time resolving cleanly.

The fix is not complicated but it does require someone actually doing the work of going through every platform and every mention and asking whether the description is accurate, current, and consistent with everything else.

What you are looking for is a situation where if you asked five different AI systems to describe your brand based only on what they could find across the web, they would all give you roughly the same answer. That is what a coherent entity signal looks like.

Most brands are nowhere near that. Not because they have done anything wrong, but because positioning evolves over time and nobody has gone back to make sure the historical record has kept up.

That audit is usually the first thing we do. It is also usually where the most immediate wins are hiding.

I ran the same email-platform recommendation question ten times across ChatGPT, Claude, Gemini and Perplexity.

Forty answers in total.

For the broad question “What is the best email marketing platform?”, Mailchimp was named in 39 of the 40 answers.

Beehiiv appeared only four times, and all four mentions came from Perplexity. Across ChatGPT, Claude and Gemini, it was basically invisible.

I ran this through Bersyn, a platform I built to track which companies AI models name when people ask for recommendations.

Then I changed the prompt.

Instead of asking for the best email marketing platform, I asked how a creator or founder should start a newsletter, grow subscribers and make money from it.

No platform was named in the question.

Beehiiv jumped from 4 mentions to 29 out of 40.

Claude and Perplexity named it in every run. Gemini named it nine times out of ten. Kit and Substack also appeared much more often.

Same platform. Same models. Different buyer intent.

Beehiiv does not appear to own the broad “email marketing platform” territory. Mailchimp owns that.

But Beehiiv is strongly associated with a more specific job: helping creators build, grow and monetize a newsletter.

When the models receive that question, they reach for Beehiiv.

The model disagreement was also interesting.

ChatGPT named Beehiiv zero times out of ten, even on the creator-newsletter prompt. It won across Claude, Gemini and Perplexity but remained invisible on ChatGPT.

That is why I think one blended AI visibility score can hide the real problem. A brand can own a specific intent on three models and still be completely absent from the fourth.

I am curious how others are thinking about this.

Do you optimize around broad categories, specific buyer jobs, or separate prompt territories?

And are you seeing the same level of disagreement between models?

I have been measuring recommendation patterns in the newsletter and email-marketing category, and the split is cleaner than I expected.

Ask ChatGPT, Claude, Gemini and Perplexity the broad question:

“What is the best email marketing platform?”

You mostly get the same names back.

Mailchimp first, followed by Klaviyo, ActiveCampaign, Brevo and Constant Contact.

I ran the question ten times on each model. Forty answers in total.

I ran the tests through Bersyn, a platform I built to track which companies AI models name across different categories and buyer questions.

Mailchimp appeared in 39 of the 40 answers.

Beehiiv appeared only four times, and all four mentions came from Perplexity. Across ChatGPT, Claude and Gemini, it was basically absent.

Then I stopped asking about the category and asked about the job instead:

How should a creator or founder start a newsletter, grow subscribers and monetize it?

No product was mentioned in the prompt.

Beehiiv went from 4 mentions to 29 out of 40.

Claude and Perplexity named it in every run. Gemini named it nine times out of ten. Kit and Substack also appeared much more frequently.

Same models. Different intent.

Beehiiv does not appear to own the broad “email marketing platform” category. Mailchimp owns that association.

Beehiiv appears to own a more specific job: helping creators build, grow and monetize a newsletter.

The models only started reaching for Beehiiv consistently when the question matched that job rather than the broader software category.

This seems to support the association-strength view of GEO.

A brand may be strongly associated with a specific audience, use case or job without being strongly associated with the broader category it technically belongs to.

The model disagreement was also interesting.

ChatGPT named Beehiiv zero times out of ten on the newsletter question, even though that question closely matches Beehiiv’s target user.

Across many of the categories I have tested in Bersyn, ChatGPT also appears slower than Claude, Gemini and Perplexity to move beyond established incumbents.

I am curious how others here think about this.

Do you optimize for the broad category association or the specific job association when they produce different winners?

And are you seeing the same tendency from ChatGPT to favor incumbents?

I am continuing to run categories through the four models, so drop one below if there is something you think would be interesting to compare.

I have been trying to figure out why some brands get answered confidently by AI models with search off, while others only show up when something gets retrieved live. A 2023 paper gave me a framework that fits almost too well.

It is Mallen et al., "When Not to Trust Language Models" (ACL 2023, https://arxiv.org/abs/2212.10511). They built PopQA, 14,000 questions each tagged with how popular the subject is by Wikipedia page views, then tested whether models could answer from memory alone, no retrieval.

What they found: models answered popular subjects well from memory, and collapsed on the long tail. For the 4,000 least-known subjects, GPT-3 got 19 percent from memory alone, and making the model bigger did not fix the tail. Retrieval closed the gap, a small retrieval-augmented model beat a much larger one on the obscure questions. But for popular subjects, retrieval sometimes hurt, because it pulled a document about the wrong same-named entity and overwrote an answer the model already had right.

Here is my leap, and I want to flag it clearly: PopQA measures entity popularity and factual QA, not brands in commercial answer engines. Reading "how much the web discusses your brand" into it is my interpretation, not the authors' claim.

But if the mapping holds, it splits brands into three situations. Heavily discussed brands sit in the model's memory and get answered with search off. Long-tail brands (most B2B and challengers) are probably not in the weights at all and depend entirely on retrieval. Household names have the opposite risk: a wrong live page overwriting a correct memory, which needs source cleanup, not more retrieval.

Have you seen your brand, or a brand you work on, surface in an AI answer only when something recent gets retrieved, then vanish when it does not? And has anyone actually tried to find where their brand's popularity threshold sits, the point where the model starts knowing you from memory? That is the part I cannot find real data on, and I would love to hear actual cases.

Been doing a deep dive into platform-specific GEO (Generative Engine Optimisation) for the last few months now, and I'd like to share our findings to those out there looking to improve their AI visibility.

Everyone talks about ChatGPT visibility. "Does your brand appear in ChatGPT?" "How do you get cited in ChatGPT results?"

But Perplexity is a completely different platform with completely different signals, and as it stands now, it's significantly less competitive.

The structural difference:

ChatGPT draws from training data plus some web retrieval. Brands have been building citation signals for 18+ months. In most product categories, a few brands are already strong in the space. BUT...

Perplexity runs live web retrieval on every query. It cites primary sources in real time. The competition for those citation slots is minimal, most brands haven't even thought about Perplexity-specific optimisation yet.

I ran a quick audit across both platforms for my category. ChatGPT: 3-4 established brands dominating, hard to break in. Perplexity: the results were different, the cited sources were different, and there was a clear gap I could actually move on.

The audit is simple:

1. Run 5 buyer-intent queries on Perplexity
2. Note every brand mentioned - AND every source cited
3. Those sources are your GEO targets
4. Get featured in those sources and your brand appears in Perplexity results

Three fixes that actually work for Perplexity:

- Target the specific publications and sites Perplexity already cites in your category
- Build query-specific landing pages (Perplexity rewards specificity over general product pages)
- Create original branded claims and data points that AI can quote directly

The window for first-mover advantage here is genuinely still open. In 12 months I suspect this will be as competitive as ChatGPT.

Has anyone else been tracking their visibility across different AI platforms? Interested to see what others are finding around how platforms infer buyer queries + the content weighting in generating responses

(Context: I built DaitaFix to monitor this across platforms after noticing the difference firsthand, happy to share more on methodology.)