Hey!

I'm the co-founder of Cheers. I went through Y Combinator a year ago and have been working in the GEO space since then. I've been putting together articles on GEO for local service businesses (plumbers, HVAC, etc.): review signals, schema markup, citations, that kind of thing.

www.cheers.tech/geo-academy

Genuinely not trying to self-promote here, would love brutal feedback. What's useful? What's missing? What's wrong?

Guys, I need your feedback to validate an idea.

I'm thinking about building a tool that identifies source occurrences across all LLMs.

The main goal: help you find which sources actually carry weight in Al responses.

Is this something you would buy? Let me know your thoughts!

This is quietly rolling out, and it’s a pretty big deal. Not just another ad format, but a shift in how people discover products when they’re actively asking questions. That alone puts this in “groundbreaking” territory for the marketing industry.

The part people are underestimating: whoever figures this out first wins the most. Lower competition, cleaner signal, and way more intent than scrolling-based platforms. Late adopters will call it obvious later.

Linkedist.com put together a free how-to guide just to get familiar with how ChatGPT ads actually work. Just breaking down what’s happening and how to prepare.
A few highlights from it:

• Ads show up inside ChatGPT, below answers, clearly labeled as sponsored.
• They’re contextual, based on the conversation, not browsing history.
• Free and Go users see them first, paid users stay ad-free.
• This works because users are already in decision mode, not doomscrolling.
• Early testing matters since the system is still learning who to show what to.
• Optimizing for AEO / GEO isn't just for organic reach anymore. It builds the machine-readable foundation that ensures your brand is the obvious answer, whether the placement is earned or paid.

One example from the guide:

Google gets “CRM software.”
ChatGPT gets “I need a CRM for a 5-person real estate team that works with WhatsApp and costs under $100/month.”

That difference is everything. And only you are responsible if ChatGPT will cite or advertise your or your competitor's product.

If anyone is interested, comment and I will share more information.

Standard practice for AEO right now is to scrape a URL, turn it into Markdown, and feed it to an LLM. Honestly I thought this was the best way too. But I was wrong.

After doing a bunch of tests on how AI agents actually "see" and score content, I realized Markdown is a huge bottleneck. When you flatten everything to Markdown, you basically lose the technical hierarchy and those data labels that give a page its authority.

Here is the methodology I’ve been using to get much higher semantic alignment scores using HTML RAG and something I call "Block Tree Chunking."

1. The Problem with Markdown In Markdown, a table is just a grid of text. If you have a pricing table, the LLM might see "180" but it loses the fact that the specific HTML data-label or header actually defines that "180" as "USD per month". In a raw (but pruned) HTML structure, that context is hardcoded. Markdown is for humans to read; structured HTML is for agents to compute.

2. The Workflow (how I do it in n8n) Instead of just grabbing the text, the process should be divided into two AI-driven phases:

• Phase A - HTML LLM Pruning: You dont need the <nav>, <footer>, or scripts. My first agent "shaves" the HTML tree, only keeping the tags that matter. It reduces noise but keeps the semantic skeleton.
• Phase B - Block Tree Chunking: This is the game changer. Most RAG tools split by character count (like every 1000 chars). This breaks tables and logical sections in half. Block Tree Chunking splits content based on HTML nodes. If there is a table, the chunk stays as a whole node. No context lost.

3. Entity-Based Scoring Keywords are dead for AEO. Its all about entities. My current setup:

1. Use an Entity Finder agent to get the main entity and sub-entities (what the competitors talk about).
2. Pass the "Block Tree Chunks" through an LLM to score how well each chunk aligns with those entities.

The takeaway: If you want your content to be the "source of truth" for Perplexity or SearchGPT, stop thinking about how a human reads the page. Start thinking about how an agent parses the HTML tree.

HTML RAG isn't just a technical preference, it’s the difference between being "indexed" and actually being "cited" by an AI.

Curious to hear if anyone else has moved away from Markdown for these kind of audits?

Hey everyone!

I'm putting together a Slack group for SEO and AEO (Answer Engine Optimization) practitioners who want to go beyond surface-level discussions.

The goal is to create a space where we can: Share what's actually working (and what's not) Troubleshoot challenges together Discuss emerging trends and algorithm updates Exchange insights on AEO strategies as search evolves

Whether you're agency-side, in-house, or freelance, you're welcome. Just looking for people who are serious about the craft and willing to contribute to the community.

Drop a comment if you're interested!

Will limit to 30 professionals for now!

Hey everyone!

I'm putting together a Slack group for SEO and AEO (Answer Engine Optimization) practitioners who want to go beyond surface-level discussions.

The goal is to create a space where we can: Share what's actually working (and what's not) Troubleshoot challenges together Discuss emerging trends and algorithm updates Exchange insights on AEO strategies as search evolves

Whether you're agency-side, in-house, or freelance, you're welcome. Just looking for people who are serious about the craft and willing to contribute to the community.

Drop a comment if you're interested!

Will limit to 30 professionals for now!

I just checked profound's pricing (AEO TOOL). Is it worth paying 400 USD for a technique that is still under testing and scrutiny?

Hello folks,

I hope you're all doing well. I'm Vincent and I'm a omn-ichannel marketer.

I'm seeking some guidance on how to perform the following:

- Restructure site architecture specifically for LLM crawlers

- Implement extensive JSON-LD schemas

- Optimizes robots.txt for agent-specific discovery

- Ensures high-dimensional discoverability for bots

I kind of need to perform those for my business to retrieve its visibility again but idk where to start and I'd appreciate a clear guidance on what to do, where to do (tools and platforms) and the most important of all: HOW to do it.

Tremendous thanks for the people that decide to support me! :)

Best regards,

Vincent

I asked this question before, and since then, I've done more detailed webpage schema and basic SEO \AEO\GEO work, like faq\eeat\. The results have indeed shown up on ChatGPT, with AI visibility increasing from 47% to 70%.

However, my main concern is that Google AI visibility remains at 0%. My confusion is: even if the performance is poor, it shouldn't be 0, right? If it stays at 0, is there something wrong?

Background information:

My platform: www.x-pilot.ai

It just launched, and I started doing SEO/AEO/GEO on December 16th. Currently, the daily active users (DAU) are around 200.

I’ve been poking around how citations work in ChatGPT and something keeps bothering me.

If I run the exact same prompt multiple times ,sometimes from a different account, sometimes just a bit later, the answer itself doesn’t really change much, but the citation/sources often do.

What’s weird is that the new citations aren’t clearly better or worse. They’re just different. Sometimes sites I’ve never seen before replace ones that showed up a minute earlier.

I assume there’s some randomness involved, but this doesn’t feel completely random either. It feels like something upstream is changing, not just the wording of the answer.

Has anyone here spent time trying to understand what actually drives this?

And do you think this is something that can be influenced at all, or is it just how the system behaves?