I have done a few experiment, ChatGPT and Google AI mode and publicity and find those LLM models citing differently. If only looking at chatGPT, whats a good GEO optimization strategy that works - not theory rather proven methods
Quick one from auditing brand visibility across LLMs and search the last few months. Case in your brand name matters more than people plan for. Checked this 5-7 times across different brands. Pattern is consistent.
What actually happens once you're in the wild:
ChatGPT and Claude normalize. Write "BrandName" or "brandname" or "Brand Name" - the model picks the version it saw most in training, not the one you registered. Launched in the last 12 months? You don't have a "most common" version yet. Model picks one. Often wrong.
Perplexity and Google AI Overviews quote sources. Your site says "BrandName", a blog says "Brandname", Reddit says "brand name" - all three land in one citation block.
Mixed-case (iPhone-style) brands lose worst now. eBay, iPhone, GitHub survived because they had 20 years to train the world. New brand doing "myCompany" in 2026? LLMs lowercase you, search title-cases you, Reddit does whatever.
Handles flatten everything. X is case-insensitive in lookup. Instagram and TikTok URLs are lowercase. Your "BrandName" becomes "brandname" the moment anyone shares.
Schema. org "name" is canonical for Google. If JSON-LD says "BrandName" and your H1 says "Brandname", the knowledge graph picks one. You don't vote.
What I'd do today:
Pick ONE case form. Same in every meta tag, schema field, footer, bio, signature, press boilerplate. Boring, only lever you fully control.
Mixed case? Accept 30-40% of mentions in the wild will be wrong. Plan for it.
Type "what is [yourbrand]" into ChatGPT, Claude, Perplexity, Gemini. Three different cases back means a source problem - your site, Wikipedia, the first 10 high-authority mentions.
Wikipedia, when you qualify, locks the case form for Google. Worth the work.
Miser pay twice on this one. Fix it before the model decides for you.
Anyone seeing the same in their data?
P.S. My own store struggles from this mismatch. Step-by-step I fixing it
I've just finished reading Google's new guidance on optimising for AI Overviews and AI Mode and one thing became very clear:
Google doesn't see GEO or AEO as separate disciplines from SEO.
Google says, AI generated search experiences still rely heavily on the same core search systems that have powered rankings for years. AI responses use RAG, meaning Google first retrieves relevant pages from its search index and then generates answers from that information.
Some interesting takeaways:
My biggest takeaway:
The moat isn't content volume anymore. It's original knowledge.
If an AI can generate your article from information already available online, it's probably not creating much value. But if you're sharing real experiences, proprietary insights, experiments, customer stories, data, or expertise, that's the kind of content Google seems to be rewarding in both traditional search and AI search.
Curious what everyone else thinks.
Curious how people here are handling reruns / sampling methodology for GEO / AI visibility tracking.
When you measure brand visibility in ChatGPT, Perplexity, Gemini, Claude etc.:
- how many prompt variants are you using per intent cluster?
- how many reruns per prompt?
- how often do you refresh measurements (daily, weekly, realtime)?
Right now I’m testing:
- ~15 prompt variants
- 3 reruns
- daily/weekly tracking
- limited reruns
But I’m noticing pretty high variance between sessions and models, especially in ChatGPT and Perplexity.
Trying to figure out what people consider statistically “good enough” before drawing conclusions for clients.
Feels like a lot of GEO tooling is still using methodologies that are way too small to be reliable.
If AI systems rely heavily on third-party mentions and trusted platforms, PR/community visibility might become a huge ranking factor.
At I/O yesterday Google showed off information agents inside Search. You give the agent a topic (say "crypto license Lithuania 2026"), and it scans blogs, news, social, and Reddit 24/7, pinging you back when something matches.
So I keep thinking, this is basically a user-facing version of what we do through monitoring tools like Pulse, Mention and similar ones. Except the agent sits right inside the search bar where the user already lives.
Curious what people think:
If a user is hooked on an information agent, will they ever actually land on our site?
Do we now need to optimize specifically for the signals these agents pick up on (freshness, structured data, social mentions)?
Has anyone figured out by what signals the agent decides "okay this is worth notifying the user about"?
Been watching my analytics for the last few months and the "direct" bucket keeps creeping up in a way that doesn't match anything I'm doing in social or email. Suspect a chunk is clicks from AI answers but I can't prove it.
For people who have actually looked into this: have you found a clean way to attribute traffic from ChatGPT, Perplexity, or Gemini? UTMs only help if you control the source. Server logs catch some referer but most of these tools strip it or send the user through a redirect.
A few things I've tried:
All three are messy. None of them give a clean attribution number.
What's actually working for you? Or is everyone in the same boat and we're all just guessing?
The more I look into AI visibility/GEO, the more it feels like reviews and reputation signals are doing a lot of the heavy lifting.
The brands I keep seeing usually have:
It feels less like 'best SEO wins' and more like 'which brands look most consistently credible across the web?'
I'm curious how people are actually managing this side of GEO in practice.
Are you actively investing more into reviews/reputation now because of GEO?
And are there any tools/platforms that are helping with that side specifically?
Quick background: I run a startup. We were doing the standard SEO playbook — keywords, backlinks, on-page stuff. It worked okay. About two weeks ago I shifted our focus to what people are calling GEO, and the traffic pattern changed in a way I didn't expect. We picked up around 2K new users, and when I dug into the referral data, roughly 40% came through ChatGPT-connected searches. I can share analytics if people want proof — not trying to flex, genuinely was surprised.
So what is the difference between SEO & GEO?
SEO is keyword-driven. You match what people type into Google.
GEO is a different game. It's about understanding how people query AI tools — which is way more conversational and intent-driven than a Google search. Nobody types "best project management tool 2026" into ChatGPT. They say something like "I'm managing a 5-person remote team and need something lightweight, what should I use?"
The mental model that clicked for me: every product is basically competing in a speech contest inside the AI model. The one the model trusts most gets recommended. Keywords don't win that contest — authority and clarity do.
The thing most people miss
About 90% of ChatGPT and Gemini users are on the free tier. Free-tier users rely heavily on web search — meaning the AI pulls from live web results to generate answers.
Here's the key: you can't really control what the model "knows" from training data. But you can influence what gets pulled in during web-connected search. And from what I've seen over a few months of testing, these sources tend to carry the most weight:
That's basically the pecking order. If your content lives in those places with real authority signals, it gets fed into the AI's answer.
What I actually changed
I stopped thinking in keywords and started mapping intent chains — what does someone actually ask an AI when they have the problem my product solves? Then I worked backwards:
Nothing fancy. No secret tool. Mostly just shifting the mental model from "rank on Google" to "be the answer ChatGPT gives."
Honest caveat
This seems to work well for startups with focused products and clear use cases. I've talked to people at bigger companies trying to do GEO and it's a totally different beast — tons of different intents, legacy content everywhere, brand complexity. Not a simple plug-and-play.
But if you're running something small and focused? The window is wide open right now. Almost nobody is optimizing for this yet.
Happy to answer questions or share more specifics. Still figuring this out myself.
I have a simple question. What are the basics of getting started with GEO. Like website technical elements, robot txt, LLM txt file, schema.. these are the things I superficially know of. What is the core hygiene Factor to make sure website is crawlable and citable in LLMs. I hope I am making sense.
On May 15, 2026, Google quietly updated its Search Central documentation with a new guide: Optimizing your website for generative AI features on Google Search. If you work in SEO, AEO, or GEO, this is the closest thing to an official rulebook we’ve gotten for the AI search era.
I read it carefully. Here’s my take.
SEO Fundamentals Still Run the Show
Google’s central message is simple, generative AI features in Search (AI Overviews and AI Mode) are built on the same ranking and quality systems that have always powered organic Search. There’s no separate AI index. No parallel algorithm. The same systems that decide what ranks in blue links also decide what gets cited in AI responses.
Google explains two mechanics behind this:
Retrieval-augmented generation (RAG), also called grounding. The AI doesn’t make up answers from training data. It pulls relevant, fresh pages from the Search index and generates responses based on what it retrieves. The clickable links you see in AI Overviews? Those come from RAG.
Query fan-out- when you ask “how to fix a lawn that’s full of weeds,” the model generates related queries in parallel (“best herbicides for lawns,” “remove weeds without chemicals,” “how to prevent weeds in lawn”) and pulls additional search results to build a more complete answer.
What this means in practice: if your page ranks well, is indexed, and serves real user intent, it’s eligible to surface in AI features. The visibility game hasn’t changed mechanics. It’s changed in what kind of content gets pulled.
What Actually Moves the Needle Now
Google’s guide reframes existing SEO best practices for the AI search context. The shift in emphasis is real, even if the fundamentals aren’t new.
First-Hand Experience Is the New Differentiator
This is the single most important thing in the guide. Google contrasts two types of content:
• Commodity content: “7 Tips for First-Time Homebuyers”: common knowledge anyone could write
• Non-commodity content: “Why We Waived the Inspection & Saved Money: A Look Inside the Sewer Line”, unique, experienced perspective
The example is deliberate. Generative AI is exceptionally good at producing the first kind. It cannot produce the second without a human who actually lived it.
If your content can be replicated by an LLM in ten seconds, it has no business advantage in an AI search world. The pages that get cited are the ones with point of view, lived experience, original research, and depth that goes beyond what’s already on the open web.
For anyone in travel, finance, healthcare, or any high-stakes vertical: this is the entire game now. First-hand reviews, expert breakdowns, original data, real photographs, on-the-ground reporting. Not summaries of summaries.
Technical Foundations Still Matter
Nothing exotic here, but Google was explicit:
• Pages must be indexed and eligible to show with a snippet to appear in AI features
• Content must be crawlable
• JavaScript SEO still applies, Google can render JS, but you have to follow the rules
• Page experience, Core Web Vitals, reduced duplicate content, all still relevant
• Semantic HTML helps (but doesn’t need to be perfect)
If your site has technical debt blocking indexation, no amount of “GEO strategy” will fix it.
Images, Video, and Multi-Format Content
AI features pull in images and video alongside text. Following existing image SEO and video SEO best practices is enough, there’s no special AI-format you need to produce.
Local and Commerce Signals
For local businesses and ecommerce, Google reaffirmed that Merchant Center feeds and Google Business Profiles feed directly into AI experiences. They also mentioned Business Agent — a conversational layer where customers can chat with your brand on Search.
The Mythbusters: What You Can Stop Doing
This is the section that will save the industry millions in wasted effort. Google explicitly called out the following as not required for visibility in Google’s generative AI features:
LLMS.txt files and other “special” markup. You don’t need to create new AI-specific files, markdown versions of your site, or any new machine-readable formats. Google may crawl these files, but they get no special treatment.
“Chunking” content. There’s no requirement to break content into tiny pieces. Google’s systems understand multi-topic pages. No ideal page length exists.
Rewriting content just for AI. You don’t need to rewrite in a “GEO-friendly” style. AI systems understand synonyms and intent. You don’t need every long-tail variation captured.
Seeking inauthentic mentions. Pursuing brand mentions across the web purely for AI visibility is not effective. Google’s spam systems and quality systems both filter against this.
Overfocusing on structured data. No special schema is required for AI features. Keep structured data for rich results, but it’s not a GEO lever.
Agentic Experiences: The Next Layer
The guide ends with a short but important section on AI agents, autonomous systems that book reservations, compare products, and complete tasks on a user’s behalf. These agents read your site differently: through screenshots, DOM structure, and accessibility trees.
Google pointed to two resources: the agent-friendly website best practices guide on web.dev, and the emerging Universal Commerce Protocol (UCP), which will allow Search agents to do more.
If your business relies on conversion, agentic readiness will matter in the next 18 months. Clean DOMs, accessible markup, and clear product data will be the baseline.
I've been running a content SEO/AEO operation through Claude Code for about a year and finally cleaned up the slash commands into something forkable. Sharing because the Claude Code crowd is the right audience for this pattern.
The pipeline is 7 slash commands chained together. Each command is a markdown file in .claude/commands/ with a strict role + output contract — Claude reads pipeline.yaml for state, runs one step, pauses at a human gate, and updates the state file. Stateless re-entry, so I can stop mid-post and pick up next day with /seo-daily.
The flow: /seo-research (Perplexity Deep Research API, ~$0.45/post) → /seo-brief → /seo-write → /seo-optimize (10-check scorecard) → /seo-publish (Sanity HTTP API → IndexNow ping). 4 human gates so I keep judgment over angle, brief, copy, and publish decision.
One brand I run this for: 131 → 964 avg impressions/day in 12 months (7.3×). Monthly impressions 2,142 → 39,240 (18×). Blog content from this pipeline drove 51.8% of all GSC impressions across 119 posts. Honest caveat — clicks didn't grow proportionally because titles/meta weren't tuned for CTR yet; that's the next iteration (/seo-refresh command in roadmap).
Technical things I'd flag for anyone considering similar:
- Sanity MCP's create_documents_from_json overwrites your custom _id with a UUID, breaking deterministic frontends. The publisher uses Sanity's direct HTTP mutation endpoint instead. Documented in the repo.
- Brand voice lives in one YAML (config/seo-settings.yaml). The commands read it; no hardcoded brand anywhere. Fork → swap one file → you're running your brand.
- Pluggable CMS — Sanity is the reference impl but swapping to WordPress/Contentful/Webflow is one file edit.
Repo: https://github.com/viren040/content-seo-orchestrator (MIT)
Genuinely curious what other patterns Claude Code users are running for content/marketing ops. The slash-command-as-pipeline pattern feels under-explored.
I’ve noticed a new claim floating around lately: some people are starting to say that using screenshots instead of generic stock images will increase "AI trust" in your website.
We already know that Google encourages original images as part of your content. But what about AI? AI doesn't actually "see" or recognize an image the way a human does; it basically slices the image into tokens to process and read it.
Honestly, I’m highly skeptical of this whole "AI trust" narrative. From my perspective, I don't think AI models even possess a functional mechanism that equates to "trust" in the way these people are claiming.
When it comes to visuals, I strongly believe their primary role is to help humans better understand the context—which aligns perfectly with Google’s emphasis on "people-first content."
I really don't think Google (or any Generative Engine) is going to give your site a magical ranking boost just because you used an original screenshot instead of a stock photo.
What are your thoughts on this? Is "AI trust" an actual thing we should be optimizing for, or is this just another GEO superstition? Would love to hear your takes!
Been noticing more people using ChatGPT, Gemini, and Perplexity instead of traditional search for recommendations.
What’s interesting is that some brands consistently get mentioned while others are completely invisible, even when they’re well-known in their niche.
From what I’ve seen, things like:
• Online brand mentions
• Reviews & reputation
• Clear website content
• Topical authority
• Consistent positioning
seem to influence this a lot.
Curious if other founders/marketers here have noticed changes in traffic, discovery, or customer behavior because of AI tools yet.
From u/lilray on X (via GlennGabe) - thanks for sharing
As of May 7, 2026, FAQ rich results are no longer appearing in Google Search. We will be dropping the FAQ search appearance, rich result report, and support in the Rich results test in June 2026. To allow time for adjusting your API calls, support for the FAQ rich result in the Search Console API will be removed in August 2026.
As this sub and many of our related experts that we share, like u/jakehundley - Mod of r/agency - a great sister sub to r/SEO and r/SEO_Digital_Marketing - this isn't surprising.
As we said - Google doesnt actually read FAQ Schema anyway - because less than 0.001% of site qualify
[ Removed by Reddit on account of violating the content policy. ]
Everybody is talking about how AI results are taking away traffic, and I imagine that might be true for some blogs probably - if your blog is about "what is chachacha", then AI can answer that quickly.
But if your business and website is some service, I don't understand how AI could take away your traffic/clientelle? If user needs his tire changed, how would AI help?
OK - AI can suggest the best service providers in area, so somebody out there is going "oh, we gained so much clients because of AI?"
But all the discussions, suggestions, tips, articles etc & etc are as if SEO/GEO is some one-size-fits-all/singular entity with no differentiation. Even from Google's documentation and communication: web = many blogs about topic X.
What about shops, service businesses, marketplaces, directories, SaaS products, local providers, booking platforms, comparison portals, B2B suppliers, clinics, agencies, job boards - nah, create a blog about them or at least make page as bloggy as possible.
/rant over 😄
Here's what none of them told you.
The update covers Google Search. AI Overviews. AI Mode. One ecosystem, one policy, one surface.
ChatGPT. Perplexity. Copilot. Gemini standalone. Claude. No equivalent policy exists on any of them. No enforcement mechanism. No guidance. No rules.
Which means the brands celebrating yesterday's update have solved roughly 20% of the problem and declared victory.
But the policy gap is not even the real issue. The real issue is what we see in Conversational Survival Rate data across platforms.
Remediation is platform-specific.
The evidence architecture that lifts your brand to a T4 purchase recommendation on ChatGPT doesn't transfer to Perplexity.
What moves Gemini standalone doesn't move Copilot.
Each platform has different retrieval logic, different training provenance, different evidence hierarchies.
A brand that fixes its Google AI performance can simultaneously be losing the final purchase recommendation on every other platform - and have no way of knowing it.
We have tested this across categories. The CSR differentials across platforms for the same brand, with the same content, are not marginal. They're large.
The platform that recommends your brand most often is frequently not the platform your customers are actually using to make the decision.
Google's guidance document published alongside the policy update says foundational SEO solves the AI problem. It doesn't.
That advice is true for Google Search. It is incomplete everywhere else.
And "everywhere else" is where a growing share of purchase decisions are being made.
Brands that treat yesterday's update as closure are making a measurement error. They're assuming the room Google cleaned is the room that matters.
AIVO Meridian measures all five rooms. CSR tells you exactly where your brand is surviving - and where it isn't.
Are you an SEO, an AEO or a GEO? Which one (or combination) really works in AI search, across all platform?
Many traditional SEO tools and Search Consoles I’ve seen just aren't cutting it anymore now that AIs like ChatGPT and Perplexity are giving direct answers. I have no idea if my SaaS is getting mentioned or recommended at all.
I’m trying to know if anyone has figured out a reliable way to track brand visibility and mentions in GEO?
I may want to know exactly the workflows, or tools that are actually working for you in real scenarios… Thanks for any input
i have seen many people on reddit saying GEO is just SEO, how many of you agree with it and why ?
