A few weeks ago, a friend of mine who runs a small skincare label told me something that stopped me mid-coffee. She said her best customer that month found her “through ChatGPT, not Google.” She hadn’t run a single ad on either platform. Someone had simply asked an AI assistant for a gentle, fragrance-free moisturizer for sensitive skin, and her product got named in the answer.
That’s the world we’re operating in now. Search hasn’t disappeared, but it has quietly split into two lanes. There’s still the classic blue-links game, and then there’s this newer, stranger arena where AI tools read the web, digest it, and hand people a finished answer instead of a list of links to click through. If your content isn’t built for that second lane, you’re invisible to a growing share of the people looking for exactly what you sell.
This is where GEO, AEO, and LLMO come in. They sound like alphabet soup, but each one solves a slightly different piece of the same puzzle.
GEO, AEO, and LLMO: Three Names, One Shift
GEO (Generative Engine Optimization) is about making sure your content gets pulled into the answers generated by tools like Google’s AI Overviews, ChatGPT, and Perplexity. AEO (Answer Engine Optimization) is a bit older and narrower, it’s about winning the direct-answer spots (think featured snippets and voice search results). LLMO (Large Language Model Optimization) zooms out further still, focusing on how your brand gets represented, understood, and eventually cited whenever any large language model is asked something in your space, whether or not there’s a live “search” happening at all.
In practice, most businesses don’t need to obsess over the boundaries between these three. What matters is the underlying behavior shift: you’re no longer optimizing purely to rank. You’re optimizing to be trusted enough, and clearly written enough, that an AI system chooses to reference you when it builds an answer for someone else.
That’s a genuinely different game. Traditional SEO rewards you for winning a click. AI search rewards you for being quotable, chunkable, and credible enough that a model decides your sentence deserves to sit inside its response.
Why This Actually Matters Right Now
Here’s the uncomfortable part. Multiple industry studies published through 2025 have shown that AI Overviews and similar features are already cutting into the number of people who click through to websites, even when those sites appear as sources. Ahrefs’ large-scale analysis of AI Overviews found this pattern repeating across huge sample sizes of search queries. Google itself has published official guidance encouraging site owners to think about how their content performs inside these AI-generated experiences, not just in classic rankings.
None of this means organic traffic is dying. It means the definition of “visibility” is expanding. Being cited by name inside an AI answer, even without a click, still builds awareness, trust, and eventually direct searches for your brand. Ignoring that shift is a bit like refusing to optimize for mobile back in 2015. You could do it, technically. You just wouldn’t like where it leaves you.
Example 1: Restructuring Content So AI Can “Chunk” It Cleanly
Let’s go back to that skincare brand. Her original ingredients page was one long, flowing paragraph, nicely written, but structurally a mess for a machine trying to extract a specific fact. We broke it into short, self-contained sections: one for “what causes sensitive skin reactions,” one for “ingredients to avoid,” one for “how to patch test a new product.” Each section could stand alone and answer one question completely, without needing the rest of the page for context.
That’s the core idea behind chunk-level optimization. AI systems don’t read your page top to bottom like a human does. They pull small, semantically tight passages and stitch them into an answer. If your best insight is buried in the middle of a 400-word paragraph tangled up with three other ideas, it’s much harder for a model to lift it out cleanly. Short, declarative sentences that each answer one implied question travel far better.
Example 2: Earning Citations Through First-Hand Experience
A software client of ours kept publishing competent, well-researched blog posts that never once got mentioned when we checked AI answers for their category. The problem wasn’t quality. It was that the content read like it could have been written by anyone, or honestly, by an AI itself.
We changed the approach. Instead of “cloud storage tools generally offer these five features,” the rewritten piece said “we tested six cloud storage tools over 30 days and tracked upload speed by hand.” That small shift toward first-hand, specific, dated experience gave the content something a generic AI-written summary simply cannot fabricate: a real, verifiable point of view. Within a couple of months, that page started showing up as a cited source when people asked comparison questions in ChatGPT.
Example 3: Getting the Technical Foundation Right
A fitout and construction company came to us with great services that were, frankly, invisible to AI crawlers. No structured data, inconsistent headings, and a robots configuration that accidentally blocked a few AI bots entirely. We added schema markup identifying each services with price, location, and amenities as distinct, machine-readable service pages, cleaned up the crawl paths, and made sure OpenAI’s and Perplexity’s crawlers weren’t quietly locked out.
This is the unglamorous half of the work, but it matters just as much as the writing. If a page technically can’t be read or indexed correctly, no amount of clever phrasing saves it. This is exactly the kind of foundational build our team at SKYO handles for clients, structuring a website from the ground up so it’s readable by both people and machines, alongside the SEO strategy that gives that structure something worth finding.
Example 4: Letting Reviews Do the Trust-Building
A local dental clinic we work with had strong reviews scattered across Google, a few directory sites, and their own testimonials page, but none of it was tied together in a way an AI system could easily verify. We added Review and AggregateRating schema across the site and encouraged patients to leave detail-rich reviews mentioning specific services rather than generic five-star praise.
Reviews turn out to be a surprisingly powerful trust signal for AI search, especially for “best X near me” style questions. They’re third-party, they’re specific, and they’re hard to fake at scale. A model looking to answer “best family dentist for nervous kids” leans on exactly that kind of grounded, external validation.
Example 5: Measuring What’s Actually Working
An e-commerce client wanted proof this was worth the investment before committing to a long-term. Fair question. We started tracking brand mentions across ChatGPT, Perplexity, and Google’s AI Mode using a handful of the newer AI-visibility tools now available, alongside good old Google Search Console data to catch changes in impressions and click patterns tied to AI Overviews appearing on their queries.
Three months in, we had something concrete to show: the brand went from appearing in roughly one in ten relevant AI answers to nearly half of them, and direct, branded search traffic climbed alongside it. Measurement here looks less like classic rank tracking and more like brand monitoring with an SEO lens. It’s newer territory, and honestly, the tooling is still catching up, but ignoring it means flying blind.
Bringing It All Together
If you strip away the acronyms, AI search optimization really comes down to a few honest habits: write clearly and specifically, structure content so a single idea stands on its own, back your claims with real experience and credible sources, keep your technical foundation clean enough for machines to actually read it, and then measure whether any of it is moving the needle.
None of that is a trick. It’s closer to good, disciplined content and web practice that happens to matter more now because the audience reading your site includes machines making decisions on behalf of real people. Businesses that treat this as a bolt-on tactic tend to publish thin, disconnected content chasing AI queries. The ones that do it well, like the brands we’ve worked with at SKYO, treat it as an extension of solid web development and content strategy that was already worth doing.
We’ve written more on how this connects to traditional visibility in our piece on AI Overviews vs. Featured Snippets, and for a deeper technical roadmap, LearningSEO.io’s guide to optimizing for AI search is one of the more thorough free resources currently available.
If your website’s foundation, content, or technical setup needs a proper audit before you invest further in this space, our team at SKYO is happy to take a look and tell you honestly where the gaps are.
