r/GEO_optimization

I'm a software engineer and have built plenty of websites and CRMs for clients

Now I'm building my own site and want LLMs to be able to read it and quote it from day one. So I collected the rules to archive it and wanna somebody to give me advice/review it

1. Let the right bots in

• Allow OAI-SearchBot in robots.txt
• Allow Claude-SearchBot and Claude-User
• Don't count on Google-Extended for AI Overviews. It only controls Gemini training. AI Overviews and AI Mode use the normal Google index, so normal crawling and indexing is what matters
• sitemap.xml linked from robots.txt, with lastmod

2. Don't hide pages by accident

• No cookie wall or popup covering the content on first load. Bots read what's there on the first fetch

3. Make it obvious who you are

• Organization schema on the home page (LocalBusiness if local) with name, url, logo. It ties the site to one entity instead of AI guessing
• sameAs links to 2+ profiles you really run. Other places confirming it's the same business
• Same brand spelling everywhere: title, footer, schema, socials. Small differences can split you into two "different" businesses
• About page with real people, names, photos, roles. Answers "who is behind this", which both Google and AI care about
• For local: address and phone in LocalBusiness schema, matching the Google profile. "Near me" answers read it from there

4. Write pages that can be quoted

• One H1 that says plainly what the page is about. Helps engines pick the right passage
• Tables and lists for prices, specs, comparisons. Easy to lift into an answer as is
• Real questions and answers on the page. FAQ schema is optional, Google stopped showing FAQ rich results for most sites in 2023, but short visible Q&A still gets quoted. If you add FAQ schema, every question in it must be visible on the page
• Visible "last updated" date plus dateModified in schema. Freshness counts for a lot of questions
• 300+ words of unique text on key pages, no thin duplicates. Very short pages rarely get picked
• Author name on articles, with Person schema. Someone accountable for the content
• No template leftovers like lorem ipsum or [COMPANY NAME]. They read as "not a real business"

5. Cover the questions buyers ask

• Public prices. "How much does X cost" is one of the most common questions. If the site says "contact us for pricing", the AI quotes someone else
• A page per main use case or industry. Long, specific questions land there
• A short "what is [term]" for the terms in your field. Definition questions are easy wins
• Reviews or case studies with real numbers (once you have them). AI likes first-party proof

6. Optional

• llms.txt with your key pages and a line about each. Cheap to add, but it's a proposed standard, not a ranking factor, so I'm not expecting much from it

While researching I scanned about 100 sites of small businesses which AI actually recommends

So what would you add, remove or do differently? Especially curious if anyone saw llms.txt change anything, and whether you block training bots like GPTBot or let everything in

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EDIT, what I’m changing after the comments, thanks all:

1. Test access per bot (curl with each user agent), and check you get the real page, not just a 200 (WAF/challenge pages)
2. Check the raw HTML has the H1, prices and Q&A without JS
3. sameAs only half counts. Make the profiles link back with the same name
4. 300 words and public prices are now guidelines, not hard rules
5. llms.txt is optional, no expected effect
6. Off-site mentions matter more than any of the above. Measure with GSC and Bing’s AI performance report

There’s a lot of advice around getting brands featured in ChatGPT, Gemini, Claude, Perplexity, etc., but I’m curious how much of it is actually translating into visibility.

For businesses: have you changed anything on your site, content, PR, or SEO and started seeing more AI mentions?

For agencies: what are you actually doing for clients that seems to make a difference?

I was reading a piece called “AEO Best Practices: How to Get Your Business Featured in AI-Generated Answers” and it covered things like entity signals, citations, content structure, authority, and answer-focused content.

Makes sense in theory, but I’d rather hear what people are seeing in the real world.

What has worked for you so far? And what turned out to be mostly noise?

A brand showing up in ChatGPT or another AI search tool sounds like a useful metric. But I'm not sure a simple visibility score tells us much on its own.

AI-generated answers can change depending on the prompt, context, and the sources retrieved. So if a tool says a brand is ranking third in ChatGPT, what exactly does that number represent?

I'd separate the measurement into three things:

• Presence: Does the brand appear in answers to relevant questions?
• Evidence: Which pages or sources are being cited when it appears?
• Business impact: Does that visibility lead to qualified visits, enquiries, or brand searches?

The first two can help explain what's happening. The third helps determine whether it matters commercially.

I don't think AI visibility tracking is useless. I just think we need to be careful about treating a fluctuating position as if it were a traditional search ranking.

How are you measuring AI search visibility right now? Are you tracking mentions and citations, testing prompts manually, or connecting it to actual business outcomes?

Was putting together a teardown of Profound alternatives (standard checklist: pricing, models tracked, API access).

Instead of skimming what other blogs wrote, I went through the vendors' actual checkout and pricing pages. It was messy:

One tool had a free tier listed across three different "Best of 2026" guides. That tier literally doesn't exist on their site anymore.

Quota misrepresentation is everywhere. A comparison table will claim Tool A offers "2,000 API calls," without mentioning that API access is locked behind a $189/mo tier, while the entry plan at $29 has zero developer access.

I almost published an embarrassing mistake myself on billing units before catching it on a second read.

I build an open-source project in the GEO/AI-visibility space, so I'm biased toward transparent docs, but this felt egregious.

Do buyers actually rely on these roundups for anything beyond a list of brand names, or is the consensus that third-party comparison numbers are basically placeholders until you verify them yourself?

Been trying to wrap my head around how AI engines decide what to cite for local searches. My husband runs a small HVAC company and I handle the website plus whatever this GEO thing is supposed to be. The advice online all seems aimed at software companies with 50-person content teams.

I spend evenings reading about how LLMs pull from paragraph chunks and how Reddit dominates citations. Fine. But when someone asks their phone for emergency AC repair near them, what actually surfaces? It's not blog posts about SEER ratings. It's Yelp, Angi, maybe a Facebook group rant.

I ran a tiny experiment. Asked four AI engines for best HVAC in our county. Two cited the same decade-old Angi listicle. One hallucinated a company that closed in 2019. The fourth gave a decent answer but pulled from a neighborhood forum post from 2021.

Feels like local GEO is just reputation management plus praying your business name shows up in enough forum threads. Or maybe I'm missing something tactical. Anyone here actually cracked this for blue-collar local services? Not theoretical stuff. Actual results.

I’m working on a search baseline for a consumer ecommerce client and I’m trying to understand whether image-led discovery surfaces a different set of pages than normal text search.

My plan is:

• compare Text-based vs Multimodal over the same date range
• look at which product/category pages over-index in each
• compare device + country
• then check downstream behaviour in GA4

What I’m less sure about is what’s actually worth diagnosing after that.

If a page performs much better in multimodal than text, would you first look at:

• image coverage / context
• Merchant Center data
• schema
• page structure
• device mix
• something else?

Also curious how much data people would want before calling the pattern meaningful.

I’m looking for actual GSC observations/tests rather than generic image SEO advice.

At the end of the day, what we all care about is the ROI. So for GEO, I created a pipeline that achieves this.

Data Layer (which is the measurement and stuff like that) -> Optimization Layer (Acting on the data and optimize website content etc) -> Revenue Impact layer (Where you measure what GEO has caused to the total ROI).  Then loop the first 2 layers until the quarterly report and depending on the revenue numbers change the strategy or keep it the same. Then repeat the loop.

I'd like to have your thoughts more on the optimization layer & the revenue impact layer (What is already being done and what is not done).

What do you think of this model?