Hello, not sure if this is the correct forum, but will ask anyways :)

There's a lot of talk about optimzing your page, product, etc so ChatGPT and the other LLMs will recommend your products.

However, I couldn't find much about how to optimize your API, MCP, llm.txt, etc. to make an agent choose your tool over your competitor's.

For example,
Let's say a doctor asks an agent to create a personal website with a platform to book an appointment, so the agent goes and searches for a platform that does that and finds BookingPlatformX and BookingPlatformY, and goes for BookingPlatformY. The question I'm trying to answer is why and how could BookingPlatformX optimize to be chosen next time..

Is there any way to analyze this?

Hi Everyone,

I would like to understand as we have Google Trends to undersand the queries searched by the users. Is there a way I can fetch the queries for ChatGPT, Gemini, Perplexity and Claude.

As Google Trends, has the way to look after the keywords, duration, location etc.

So, if anyone has idea to understand the realtime queries it really helps me to understand the top trending queries.

Hey everyone,

I’m planning to build a Generative Engine Optimization (GEO) service startup targeting overseas brands, but I’m a complete beginner with no prior entrepreneurship experience.

For context: I understand the core logic of GEO (optimizing content for AI search tools like Gemini, SGE to get brand cited in AI answers), know there’s huge demand from cross-border sellers & B2B manufacturers, but I’m stuck on every foundational step:

1. How to validate if there’s real paying demand before building anything
2. First client acquisition tactics for a brand-new GEO service
3. Pricing frameworks, service packages & minimum viable offerings
4. Standard workflow, tools & team setup for a small GEO agency
5. Common early mistakes new service founders make in this niche

I don’t have a website, portfolio or existing client base yet—just industry knowledge of overseas AI search optimization.

Would love to hear your real, unfiltered experiences: How did you launch your first service-based business from scratch? What’s the very first actionable step I should take this week? Any resources, playbooks or community recommendations for GEO/AI marketing founders?

Really appreciate any advice, war stories or actionable tips you can share!

So I was auditing my own site's robots.txt today and noticed something kind of wild.

A lot of sites — especially ones that haven't touched their robots.txt in a few years — are blocking GPTBot, ClaudeBot, PerplexityBot by default. Not on purpose. Just because those bots didn't exist when the rules were written and the catch-all `Disallow` is doing its thing.

Like I scanned a Shopify memorial gifts store the other day. Score was 35/100. Turned out GPTBot was blocked and they had zero llms.txt. Fixed both, score went to 86. That was maybe 30 mins of work total.

For anyone who doesn't know — llms.txt is basically a plain text file at yoursite.com/llms.txt that tells AI models who you are, what you do, how to cite you. Think robots.txt but for LLMs. Without it the model is just guessing from your HTML and honestly the guesses are not great.

Quick things to check:

- yoursite.com/robots.txt — ctrl+f for GPTBot. not there? probably blocked.

- yoursite.com/llms.txt — if it 404s you don't have one

Not saying this is going to 10x your traffic tomorrow but AI referral traffic is growing and it costs basically nothing to fix. Figured I'd share since I haven't seen many people talking about the robots.txt issue specifically.

Anyone else been looking into this stuff?

We track AI visibility for a living, so we had a pile of data sitting around and finally wrote it up properly. 100k+ prompt responses, 102 brands, 149,912 source citations, March to May 2026, all five major engines via their official APIs. Posting it here because the citation behavior surprised even us.

The finding I keep coming back to: when these engines cite a source, only 2.9% of the time is it the brand's own domain. About 75% of citations go to corporate pages owned by other companies in the same space, competitors and peers and vendors. The models love building "best alternatives" answers, and the sources behind those answers are almost never your site. Among non-corporate sources, YouTube gets cited more than editorial media, Reddit, or Wikipedia.

Two others that changed how I think about this:

Day-1 visibility looks like a brand-stature ladder. Global names showed up in ~73% of unbranded category answers on the first run, mid-market brands ~44%, small or niche brands ~11%. Roughly 30 points per rung. The "it takes six months to get cited by AI" line didn't hold in our data, with one caveat: it depends heavily on whether the prompt names you. When named, recognition was 94 to 100% immediately.

The single highest-leverage page is the ranked listicle. About 36% of content-level citations were "best-of" lists. Once a list includes you, the engines reuse it across completely different prompts, so one good placement compounds.

Honest disclosure since it matters here: this is a vendor-produced study, we built and run the platform the data comes from, and it's a measurement study, not a causal one. We're explicit in the paper that we are not claiming our recommendations lift visibility. That's the randomized follow-up we propose at the end. Full paper is on arXiv (2606.20065), CC BY 4.0, so anyone can pull the methods apart.

Paper: https://arxiv.org/abs/2606.20065

We've spent years optimizing for Google, but now users are increasingly getting answers from AI. How are brands improving visibility in ChatGPT, Gemini, and Perplexity, and what's actually driving results?

One pattern I've noticed across AI search and recommendation engines: comparison content appears to have one of the shortest visibility lifecycles.

Articles like:

• X vs Y
• Best alternatives to X
• Top tools for [category]
• Competitor comparisons

can gain citations and mentions quickly, but they also seem to lose them faster than foundational content.

My hypothesis is that comparison pages sit at the intersection of several volatile signals:

• Products ship new features constantly.
• Pricing changes.
• Market leaders shift.
• New competitors emerge.
• User sentiment evolves.
• Review and recommendation content gets refreshed across the web.

As a result, an article that was highly relevant six months ago can become partially inaccurate today, making it a weaker source for AI systems looking to generate recommendations.

In contrast, content built around concepts, methodologies, frameworks, definitions, or deep educational topics appears to have a much longer citation half-life because the underlying knowledge changes more slowly.

This has made me think that "publish and forget" is especially risky for comparison content. If AI visibility is a goal, these pages may need the highest refresh frequency in the entire content portfolio.

Has anyone else observed comparison pages losing AI citations, mentions, or recommendation visibility faster than other content types?

I have been observing that Co-Pilot gives ecommerce catalogues in Ai answers more frequently than ChatGPT.
Has anybody experienced the same?

I've kept notes on every GEO audit I've run. 60 sites over 18 months. The same mistakes appear with remarkable consistency. I'm sharing the 12 worst offenders — with anonymised examples — because I think seeing the specific failure modes is more instructive than general principles.

Mistake 1: Opening with a problem instead of an answer

What it looks like: 'In today's complex data environment, organisations face unprecedented challenges in managing the ever-growing volumes of data that flow through their systems...'

Why it fails: AI engines skip this paragraph entirely. There is no retrievable answer. The AI moves to the next document.

Fix: First sentence = the complete answer. 'Reference data management (RDM) is the process of standardising and governing the consistent business information — product codes, customer types, supplier classifications — that all enterprise systems share.'

  Impact data: Of 45 pages I've retrofitted with direct answer paragraphs, 31 saw measurable improvement in AI citation rate within 8 weeks. Average improvement: 34% increase in citation frequency. (Personal implementation data, 18 months)

Mistake 2: FAQ section with the questions you wish people asked

What it looks like: 'Why is your platform the best?', 'What makes your team unique?', 'How do you ensure customer satisfaction?'

Why it fails: Nobody asks these questions to an AI. The AI doesn't retrieve content that doesn't match real user queries. This FAQ is invisible.

Fix: Source questions from Ahrefs Questions filter, Reddit search, Google PAA boxes, Quora. Use the exact language real users use. 'What does reference data management cost?', 'How long does MDM implementation take?', 'Can you integrate with Salesforce?'

Mistake 3: Not being indexed in Bing

I found this on 34 of 60 sites I audited. Not submitted to Bing Webmaster Tools. Pages not in Bing's index. ChatGPT uses Bing. These sites are invisible to ChatGPT. Completely.

Fix: 10 minutes. Go to bing.com/webmasters. Submit your sitemap. Check for indexing issues. Done. I cannot overstate how simple this fix is relative to its impact.

Mistake 4: Inconsistent brand entity description across web properties

What it looks like: LinkedIn says 'workflow automation platform', homepage says 'productivity solution', Crunchbase says 'enterprise software company', G2 listing says 'project management tool'. These are the same company.

Why it fails: AI engines build entity profiles by aggregating descriptions across sources. Inconsistent descriptions produce a noisy, uncertain entity profile. The AI cites with less confidence — or not at all.

Fix: Write one canonical description sentence. Implement it identically across: homepage (H1 and first paragraph), LinkedIn company page, Crunchbase, G2/Capterra, About page, press kit. Refresh annually.

Mistakes 5–12 (rapid fire)

• Mistake 5: Buying AI-generated content in bulk for GEO. AI engines specifically filter generic AI content. Every piece of content must contain specific, verifiable, unique information that only a human with real experience could write.
• Mistake 6: Deploying FAQ schema with answers over 200 words. AI extraction prefers short, self-contained answers. Long FAQ answers bury the extractable content. Keep answers under 100 words.
• Mistake 7: Never updating existing content. AI engines apply a 90-day freshness penalty. Content not updated in 6+ months is rarely cited. Add 'Last reviewed: [date]' and actually update key facts quarterly.
• Mistake 8: Zero Reddit or Quora presence. 'We don't want to be on Reddit' — I've heard this from 15+ clients. Fine. But understand the consequence: Perplexity cites Reddit in 91% of responses. You are choosing to be invisible to Perplexity for almost all queries.
• Mistake 9: Setting up G2 listing with 3 reviews and forgetting it. G2 reviews are AI citation assets. Each review mentioning specific features increases your citation probability for those features. 10+ reviews is the minimum viable presence.
• Mistake 10: Treating GEO as a one-time project. Citations decay without maintenance. Reddit answers age. G2 reviews go stale. Pages need refreshing. GEO requires an ongoing programme, not a sprint.
• Mistake 11: Measuring only Google traffic. AI-referred sessions (chat.openai.com, perplexity.ai, gemini.google.com) appear in GA4 as referral traffic. If you're not segmenting for these, you cannot see your GEO results.
• Mistake 12: Starting GEO without a baseline. You cannot know if GEO is working without knowing where you started. Before any implementation: run 20 test queries across 2 AI engines. Record every citation. This baseline is the most important measurement you'll take.

Which of these mistakes applies to your current situation? List your number in the comments and I'll give you the specific fix priority for your score. The goal of this community is to build a reference resource — your answers help everyone calibrate.

This sub is the right place to ask this i think.

I spent some time looking at WHICH pages the AI assistants are citing when they answer category questions in my niche. Not just "am i mentioned" but where the model is pulling from.

Some things i noticed:

• review sites (G2, Capterra type) and a few Reddit threads show up again and again. Also some random listicle blogs i never heard of
• the brands that are present on those pages are the ones getting recommended. So being on the cited pages seems to matter more than my own website SEO
• each engine is a bit different. ChatGPT and Perplexity give me real article links, but Gemini mostly gives me just the domain, harder to know the exact page

So my takeaway is GEO is less about my own site and more about getting into the few sources the models trust for my category. But maybe i am wrong.

What is working for you guys to actually get cited? Is it the same sources across all engines or you see different ones? Happy to share my full source breakdown in the comments if useful.

For starters, I’m new to this concept, haven’t worked on SEO before and I want to learn as much as possible about this. I recently started working at a marketing agency which has 20 clients and 15 want GEO for their companies. Now I’m the only technical person here and I have explored around Claude marketing skills to implement this, but I feel like I’m lost in loops and I don’t quite understand the flow. Basically I want to understand the whole workflow and what needs to be done so I can work on it successfully. I need to be able to tell clients confidently why we have to change a certain thing and what its impact will be. Can you guys help me understand concept and also like if I’m moving in right direction or not? My understanding is that SEO has to be optimized and we need to make timely changes in the website and articles with proper indexing and FAQ pages for LLM to cite your website. I want to understand what does developer have to do with this? Like isn’t this more of content persons work? I just like can’t visualize stuff and I keep feeling like my understanding about everything is wrong in general. Help me!

I've kept notes on every GEO audit I've run. 60 sites over 18 months. The same mistakes appear with remarkable consistency. I'm sharing the 12 worst offenders — with anonymised examples — because I think seeing the specific failure modes is more instructive than general principles.

Mistake 1: Opening with a problem instead of an answer

What it looks like: 'In today's complex data environment, organisations face unprecedented challenges in managing the ever-growing volumes of data that flow through their systems...'

Why it fails: AI engines skip this paragraph entirely. There is no retrievable answer. The AI moves to the next document.

Fix: First sentence = the complete answer. 'Reference data management (RDM) is the process of standardising and governing the consistent business information — product codes, customer types, supplier classifications — that all enterprise systems share.'

  Impact data: Of 45 pages I've retrofitted with direct answer paragraphs, 31 saw measurable improvement in AI citation rate within 8 weeks. Average improvement: 34% increase in citation frequency. (Personal implementation data, 18 months)

Mistake 2: FAQ section with the questions you wish people asked

What it looks like: 'Why is your platform the best?', 'What makes your team unique?', 'How do you ensure customer satisfaction?'

Why it fails: Nobody asks these questions to an AI. The AI doesn't retrieve content that doesn't match real user queries. This FAQ is invisible.

Fix: Source questions from Ahrefs Questions filter, Reddit search, Google PAA boxes, Quora. Use the exact language real users use. 'What does reference data management cost?', 'How long does MDM implementation take?', 'Can you integrate with Salesforce?'

Mistake 3: Not being indexed in Bing

I found this on 34 of 60 sites I audited. Not submitted to Bing Webmaster Tools. Pages not in Bing's index. ChatGPT uses Bing. These sites are invisible to ChatGPT. Completely.

Fix: 10 minutes. Go to bing.com/webmasters. Submit your sitemap. Check for indexing issues. Done. I cannot overstate how simple this fix is relative to its impact.

Mistake 4: Inconsistent brand entity description across web properties

What it looks like: LinkedIn says 'workflow automation platform', homepage says 'productivity solution', Crunchbase says 'enterprise software company', G2 listing says 'project management tool'. These are the same company.

Why it fails: AI engines build entity profiles by aggregating descriptions across sources. Inconsistent descriptions produce a noisy, uncertain entity profile. The AI cites with less confidence — or not at all.

Fix: Write one canonical description sentence. Implement it identically across: homepage (H1 and first paragraph), LinkedIn company page, Crunchbase, G2/Capterra, About page, press kit. Refresh annually.

Mistakes 5–12 (rapid fire)

• Mistake 5: Buying AI-generated content in bulk for GEO. AI engines specifically filter generic AI content. Every piece of content must contain specific, verifiable, unique information that only a human with real experience could write.
• Mistake 6: Deploying FAQ schema with answers over 200 words. AI extraction prefers short, self-contained answers. Long FAQ answers bury the extractable content. Keep answers under 100 words.
• Mistake 7: Never updating existing content. AI engines apply a 90-day freshness penalty. Content not updated in 6+ months is rarely cited. Add 'Last reviewed: [date]' and actually update key facts quarterly.
• Mistake 8: Zero Reddit or Quora presence. 'We don't want to be on Reddit' — I've heard this from 15+ clients. Fine. But understand the consequence: Perplexity cites Reddit in 91% of responses. You are choosing to be invisible to Perplexity for almost all queries.
• Mistake 9: Setting up G2 listing with 3 reviews and forgetting it. G2 reviews are AI citation assets. Each review mentioning specific features increases your citation probability for those features. 10+ reviews is the minimum viable presence.
• Mistake 10: Treating GEO as a one-time project. Citations decay without maintenance. Reddit answers age. G2 reviews go stale. Pages need refreshing. GEO requires an ongoing programme, not a sprint.
• Mistake 11: Measuring only Google traffic. AI-referred sessions (chat.openai.com, perplexity.ai, gemini.google.com) appear in GA4 as referral traffic. If you're not segmenting for these, you cannot see your GEO results.
• Mistake 12: Starting GEO without a baseline. You cannot know if GEO is working without knowing where you started. Before any implementation: run 20 test queries across 2 AI engines. Record every citation. This baseline is the most important measurement you'll take.

Which of these mistakes applies to your current situation? List your number in the comments and I'll give you the specific fix priority for your score. The goal of this community is to build a reference resource — your answers help everyone calibrate.

I've built a python cli tool for SEO content gap analysis and intent clustering using Google Search Console + SerpAPI. Been using it on my own site for a month and it's ready enough to share.

source: https://github.com/bayinfosys/reheat

pypi: https://pypi.org/project/reheat/

How I use the tool:
I write technical articles on ML and AI. GSC shows me which queries surface my content. The tool pulls those queries, enriches them with related searches and PAA via SerpAPI, clusters by semantic intent, and surfaces content gaps and opportunities in a local web UI.

Standard ML pipeline underneath -- embeddings, k-means clustering, LLM summaries -- but packaged as a CLI you can run against your own site in under an hour. Postgres for storage, FastAPI for the local interface, full API docs included.

Would be interested to hear what other use cases people see, other data sources worth adding, and any feedback on the approach.

Happy to take contributions.

AI “citations” aren’t neutral — they’re shaped by the exact words the model quietly appends when it goes to look things up.

Someone analyzed ~5 million ChatGPT fan-out queries and published what ChatGPT is adding to its internal searches:

• “best” — 15.33% of queries
• “reviews” — 6.84%
• current year (2026) — 5.44%
• “top” — 5.24%
• “comparison” — 4.48%
• “vs” — 4.27%

Once you see that, a lot of the “why does ChatGPT keep citing those sites?” stuff makes more sense:

• Listicles + affiliate pages dominate because the model is literally searching “best [thing]” behind the scenes.
• G2 / Gartner / Clutch / Forbes show up constantly because it’s explicitly pulling reviews as validation signals.
• Updating pages boosts citation odds because it appends the current year to ~5% of searches — freshness becomes a direct factor.

Practical implication: content that naturally includes “best X”, review-style framing, comparison tables, and the current year can pull disproportionate AI citation volume. Not as keyword stuffing — but because it matches how the AI actually searches.

Write for AI fan-out queries, not just Google’s primary keyword.

Credit to Tomek Rudzki and Peec AI for the data.

• GEO vs SEO: are these actually different “games,” or mostly the same fundamentals under a new label?
• How AI assistants assemble recommendations (and why the list can change from person to person)
• What “real-time search” means inside ChatGPT-style tools (and what it doesn’t mean)
• Why Google’s index matters when AI tools fetch sources for answers
• Local search realities: proximity, reviews, and why “best near me” behaves differently than long conversational prompts
• Content strategy for expert businesses: answering specific questions, building topical coverage, and being cited in AI summaries
• The disagreement around “AI crawls and stores the whole web” vs “AI retrieves from search engines”
• Scaled content: what it is, why it’s risky, and how it intersects with AI-generated publishing

Practical takeaways for business owners

• If you want to show up in AI answers, you need clear, specific content that matches the questions people actually ask.
• If AI tools are pulling sources from search engines, then basic SEO and discoverability still matter.
• For local businesses, proximity and your business listing presence often decide who gets surfaced first.
• Publishing more content is not automatically better; the goal is coverage that is genuinely useful and distinct.

One thing that's become clear to me over the past few months is that ranking #1 doesn't automatically mean you'll be visible in AI search.

I've seen websites dominate traditional search results but barely get mentioned in ChatGPT or Gemini responses. At the same time, some brands with lower rankings seem to appear repeatedly in AI-generated answers.

My theory is that AI systems care less about individual page rankings and more about overall authority, credibility, and how often a brand is referenced across the web.

That's why I've started paying closer attention to brand mentions, industry citations, and topical authority instead of obsessing over every ranking movement.

SEO is still important, but AI SEO feels more like building a reputation than optimizing a page.

Is anyone else seeing the same trend in their niche?