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4 contributions to AEO - Get Recommended by AI
Skool Update Plan #1
Hello everyone! Little bit of a late night message but I didn't want to let it go, specially since -last call didn't record-, we had a bit of a brainstorming session running ideas on our most engaged members Here's the core Ideas: 1- The BRAIN Framework has grown a LOT, Research, Audience and Network are new core pillars that will help strategy. 2- New focus Now the Community should be oriented for implementation we will skip the concepts and jump into action, sharing horizontally, so I won't be the only one talking I'm giving the frame and opening the floor for sharing what works and what doesn't, and unleash the power of the AEO community. 3- A Knowledge layer: An intake survey will get you in your knowledge level, mindset and fundamentals is where people will get context to be able to jump into the AEO Blueprint, and to those that are ready to implement we might open a closed knit space that is divided on "tracks" so specific groups get more targetted resources, advanced AI guides and skills so the highly motivated members are able to generate new knowledge and build authority by experimenting with AEO in a focused group of individuals with shared goals and needs. That's the idea, let me know what you think in the comments
Skool Update Plan #1
0 likes • 12d
Julian, the implementation shift I think is right, and the tracks idea is smart, most communities plateau because everyone's stuck consuming the same intro content regardless of where they actually are. Since you're asking for horizontal sharing, here's one real share: the biggest gap I keep running into is that brands are looking for a repeatable way to prove they are actually getting recommended more than it was last month. We landed on freezing the query set, running across five engines on a schedule, scoring on presence, position, and cross-engine consistency, specifically so it's not just "we think it's working." If there's a track forming for people who want to go deep and experiment I am in! Happy to share more if anyone has q's
Everyone’s Talking About Schema. Almost No One’s Talking About Measurement.
We’re all deep in debates about schema types, markup frameworks, and JSON perfection — what to tag, where to tag it, and how Google parses it. But in the rush to structure everything for AI, we’re overlooking the single most valuable piece of the puzzle: measurement. Traditional analytics was built for clicks. A traffic drop meant something broke. A traffic spike meant a win. But what happens when AI-driven search — Google’s AI Overviews, ChatGPT, Perplexity, voice assistants — answers the question before the user ever clicks? That’s not future tense anymore. It’s the norm. Schema, structured data, and rich snippets are essential — but they’re not the finish line. They’re the instrumentation layer that allows analytics to survive in a zero-click world. Without measurement built into this new technical SEO stack, we’ll know our pages are marked up beautifully but have no idea if they’re being understood or surfaced. And we’ve seen this story before. We once pushed meta tags, H1s, and keyword counts so hard that clients eventually asked: “What’s our return on all this?” That question forced SEO to grow up. It gave birth to analytics — the proof that all that optimization actually worked. Let’s not repeat that cycle. If we don’t evolve measurement alongside schema, we’ll end up right back there: optimized, organized, and unaccountable. So here’s the real challenge: While everyone’s chasing perfect schema syntax, who’s building the dashboards that prove it worked? Clicks used to prove visibility. Now comprehension has to. The brands that measure that shift will own the next era of SEO.
2 likes • 16d
Dan, I think your read on that tool is right, and it's the whole problem with this category right now. Everyone's measuring visibility, almost nobody's tying it to whether the brand actually gets recommended. Julian's Reach/Recognition/Reputation breakdown is the right frame IMO. What's been working for us is making Recognition and Reputation measurable the same way: freeze a set of commercial buyer queries with no brand names in them, run them across ChatGPT, Perplexity, Gemini, Claude and Copilot on a schedule, and score three things over time, how often the brand shows up, what position it's in, and how consistent that is engine to engine. Branded search lift is a good downstream signal too, Julian's onto something there, but it lags. The frozen query set gives you the leading indicator before the branded searches show up. That's the piece that turns "we think it's working" into a number a client will actually trust. Happy to share how we structure the runs if useful.
How do you prove your AEO work - is working? Brainstorm request
I'm asking a buyer's question. How do I prove my AEO additions are working? I'm asking at several levels. I realize that specialized tools like "Search Atlas" can do things (50 ai crawlers), are there alternatives? Are there other ways? If I put a speakable schema on a site, can I ask "Alexa or Siri" to go there and read it back to me? [ I realize I'm all over the place with this question, but KPI's run on proof... ] What do we have now? What can we access soon? /// Trying to anticipate the #1 question people ask? Is it worth the effort... and when will I know? PLEASE lets brainstorm... Thank you Kurt
0 likes • 16d
This is the right question, and the one most tools dodge. David and Julian are right that impressions and crawler activity are useful, but they're indirect, and a client will discount them. John's closer to it: the real proof is whether the engines actually recommend you. The move is to make that repeatable. Pick 5 to 10 commercial queries a real buyer would ask, no brand names in them, baseline where the brand shows up across ChatGPT, Perplexity, Gemini, Claude and Copilot, then re-measure the same queries every few weeks. Track three numbers over time: presence rate, average position, and how many engines agree. When those move, that's your proof, and it's a chart a client believes because it's the same instrument every run. Freezing the query set is the trick, so you're not measuring a moving target. Impressions and logs become the supporting signal, not the headline. Happy to share how we structure the runs if useful.
Anyone else confused when AI gives different answers?
Something I’ve been noticing, I’ll ask the same queries across ChatGPT, Perplexity, and Gemini and they all give different answers. Sometimes slightly different, sometimes completely different. And it makes me wonder If even the AI tools can’t agree, how do we know which one is actually “right”? Is it pulling from different sources? Different recency windows? Different confidence levels? I’m genuinely curious if anyone here has figured out how to explain these variations, because I’m still trying to wrap my head around it.
0 likes • 16d
Kurt and Julian nailed the why here. The part I'd add: the disagreement is measurable, and it's actually useful. You don't need to track every variable, just the one that matters, whether your brand gets recommended, measured across all the engines at once. We run a fixed set of buyer questions across ChatGPT, Perplexity, Gemini, Claude and Copilot on a schedule and score three things: how often each brand shows up, in what position, and how consistent it is engine to engine. The gaps are the signal. In one index we ran, the brand that led on one engine barely showed up on another, which tells you exactly where the work is. Treat the variance as data, instead of noise. Happy to share how we structure the runs if that's useful.
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Mike Stratta
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@mike-stratta-5516
Dad, family, health, culture, growth. Founder/CEO Arcalea. I measure how AI recommends brands.

Active 5d ago
Joined Feb 9, 2026
Chicago
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