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14 min readSearch is changing. Google still gives you a list of blue links, but more and more buyers now start with ChatGPT, Perplexity, or Gemini instead. Rather than clicking through multiple websites, they get a direct answer with a few supporting sources.
If your website doesn’t make the cut, you’re simply not in the discussion. Your content might be relevant, but if the AI doesn’t use it while generating its answer, potential customers may never come across it.
That’s the problem Answer Engine Optimization (AEO) is trying to solve. Unlike SEO, where you’re trying to rank higher in search results, AEO is about making your content trustworthy and easy for AI to use. For B2B marketing teams, this can’t be ignored. Plenty of B2B buyers are now starting their buying journey by asking an AI assistant a question instead of typing it into a search engine.
We ran into this with ZipTier. Our site was showing up on the first page of Google for several searches we cared about, so we hadn’t given AI search much thought. Then we started testing those same searches in ChatGPT and Perplexity and our name wasn’t coming up at all. That got us digging. We pulled a proper audit and went through the findings line by line. Some of our pages were still client-side rendered, which meant AI crawlers were landing on a blank loading screen. We had almost no structured data in place. Our off-site brand presence was thinner than we’d realized. None of it was unfixable, but it explained exactly why we were invisible to AI search despite ranking well on Google. Working through that list is largely what this article is based on.
There’s already a lot written about the content side of AEO. This article focuses on the technical side instead: what AI crawlers actually see, how answer engines choose sources, and what really makes a difference.
Traditional search follows a predictable loop: crawl, index, rank. Once a page ranks well, its position is usually fairly stable.
Answer engines work differently, and that changes how you should think about being visible. When someone asks ChatGPT, Perplexity, or Gemini a question, the system retrieves candidate sources in real time, pulls them into a context window, and generates a fresh response on the spot. There’s no fixed spot for terms like “lead capture tool” or “best B2B CRM.” Every answer gets built from signals like relevance, freshness, and how credible the source is.
This leads to two things worth knowing.
One: the same prompt doesn’t always pull the same sources. A 2026 study by AirOps found that only about 30% of brands stayed visible when the same question was asked repeatedly, and less than 20% stayed visible after five tries.
Two: getting cited doesn’t mean people will click through. The model can use your content to answer the question without sending anyone to your site. The goal isn’t to rank for certain keywords anymore. It’s to be part of the answer itself.
Key Insight
“Less than 20% of brands stayed consistently visible across five identical prompts. AI search results aren’t stable rankings; they’re live selections.”
AirOps, 2026 AI Visibility Study
Once you understand how AI crawlers pull content, the implementation part is a lot less mysterious. Most of the work comes down to making your website easier for AI crawlers to read, understand, and trust.
The big AI crawlers don’t run JavaScript.
Vercel and MERJ looked at over 500 million GPTBot fetches and found no evidence of JavaScript execution. ClaudeBot downloads script files about 24% of the time for training, but it reads them as text, not code. The only exception is Gemini, which uses Google’s rendering system.
This becomes a problem for websites that rely heavily on client-side rendering. When a user visits your site, their browser downloads the JavaScript and builds the page. An AI crawler, though, may only see the initial HTML. If that HTML is little more than a <div id="root"> and a few script tags, there’s basically nothing for the crawler to work with.
A lot of the pages you actually want AI to reference (pricing, documentation, FAQs, comparison pages, product details) are often built this way. If the important content only renders after JavaScript runs, there’s a good chance most AI crawlers never see it. This was one of the first things our audit flagged for ZipTier. Several pages we’d built dynamically had no fallback HTML, and the crawlers were picking up nothing but a loading state.
The fix is making sure your content exists in the initial HTML response. Whether that’s SSR, static generation, or pre-rendering depends on the page. The key is just making sure the content is present in that first HTML response.
A quick check:
curl -A "GPTBot" https://yoursite.com/pricing | grep "your key term"If nothing matches, GPTBot probably isn’t seeing your content.
One more thing worth auditing: all the major AI crawlers respect robots.txt. OpenAI, Anthropic, and Perplexity document their user agents and honor Disallow directives. If you’re accidentally blocking them, none of the rest of this matters.
Once your content is in the HTML, the next step is helping AI understand what it’s actually looking at. That’s where structured data comes in. Instead of making crawlers guess whether a page is a product, a blog post, an FAQ, or an organization page, schema tells them directly.
Add your Schema.org markup as a server-rendered <script type="application/ld+json"> block. Don’t inject it client-side. Most AI crawlers won’t execute the JavaScript needed to render it.
The schema types that seem to move the needle most on AI citations:
Research backs this up:
For us, structured data was one of the easier gaps to close. Adding Organization and Article schema to the right pages took a few hours. Quick win compared to some of the other items on the list.
Key Data Point
“Sites with Organization, Brand, and AboutPage schema were cited roughly three times more often in AI-generated answers than sites without it.”
Onely Research
llms.txt is a file you place at the root of your website that lists your most important pages in a format that’s easy for AI tools to parse.
Right now, its actual impact on AI citations is still unclear. No major AI provider has publicly said it uses llms.txt for answer generation, and recent research ranks it well below factors like crawlability, structured data, and content quality.
That said, it’s not useless. Developer tools like Cursor and Claude Code use it to navigate documentation more effectively, so it’s worth adding if developers are part of your audience. For everyone else, it’s a small investment that’s easy to set up and might pay off more as AI tooling evolves.
Here’s something that tends to surprise teams: off-site signals matter for AI citation visibility too. Ahrefs found that brand mentions across the web were about three times more correlated with AI citation visibility than backlinks. AI models are essentially piecing together a picture of whether a brand is reputable, trustworthy, and actively talked about. It is not just about whether something links to it. Our audit reflected this exactly: decent backlinks, but brand mentions outside our own site were thin. Slower to fix than a schema gap, but now we know where it sits on the priority list.
Community platforms also play a bigger role than most marketers expect. AirOps estimates that nearly half of all AI citations originate from user-generated content. Ahrefs found Reddit to be one of the most frequently cited sources in Google AI Overviews, and SE Ranking noted that sites with a strong presence on Reddit and Quora were much more likely to get cited in AI-powered responses.
That doesn’t mean flooding subreddits with spam links. Reddit users spot obvious promotion instantly. A much better strategy is getting knowledgeable people on your team to genuinely participate in discussions that are already happening.
Review platforms carry their own weight too, and it’s easy to overlook. Seer Interactive found that brands without a Trustpilot profile averaged around a 1% AI citation rate, while even a modest number of reviews improved visibility significantly. Keeping up profiles on G2, Trustpilot, and relevant industry directories now feeds AI discoverability the same way it’s always fed trust.
Which platforms matter most also depends on who you’re selling to. For B2B, buyers lean more on ChatGPT and Perplexity, so visibility on G2, Reddit, and industry publications carries more weight. For B2C, Google AI Overviews still dominate, and sources like Reddit, YouTube, and Wikipedia show up more often there.
Key Data Point
“Brand mentions across the web were about three times more correlated with AI citation visibility than backlinks.”
Ahrefs Research
There isn’t a single dashboard that shows your AI visibility across every platform. Google has added some AI search metrics to Search Console, so it’s still the best free option, but it doesn’t cover what a lot of B2B users now search on, like ChatGPT or Perplexity.
Paid platforms like Profound, Peec AI, Otterly, Semrush, and Ahrefs can track your share of AI search across a set of prompts. Otherwise, you can do it manually: gather the 30 to 50 most common buyer questions in your field and run them regularly through Gemini, Perplexity, and ChatGPT. Perplexity’s visible source data makes it especially useful for this.
AI answers aren’t consistent, so any single result won’t tell you much. Trends over weeks are what matter. We run a set of buyer questions through Perplexity every few weeks for ZipTier. The source panel makes it easy to see at a glance whether we’re getting picked up or not.
Another thing that’s easy to neglect is keeping your content updated. Most AI-based results favor information that’s recent and factual. If a site hasn’t been updated in years, a lot of AI search tools will skip it in favor of newer sources, especially on technical topics. That doesn’t mean rewriting every sentence every month, but it does mean refreshing technical documentation, examples, screenshots, descriptions, and similar content regularly.
Traditional search isn’t disappearing anytime soon, and neither is SEO. But more and more of the searches people run are getting answered by AI before they even open a search results page. Your content needs to be accessible to AI systems and trustworthy enough to be used as a source.
The good news for content and SEO folks is that most of the technical work behind AEO is the same work we’ve been recommending for years anyway. If you’ve already invested in good technical SEO, you’ve probably already covered the basics of AEO too. In a lot of ways, AEO is just good technical SEO adapted for how people search today.
AEO isn’t a one-time project. It’s ongoing.
Full-stack engineer exploring software engineering, AI and modern web development.
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