/videos/ai-visibility-intro-1.mp4
/videos/ai-visibility-intro-1.mp4
Is Your Enterprise Website Citable in AI Search? DevHandler’s Take on Adobe’s New LLM Optimizer Chrome Extension
Introduction:
Search is undergoing a fundamental change with the rise of AI-generated answers. Instead of only showing a list of blue links, modern search experiences (from ChatGPT and Bing Chat to Google’s generative search) deliver direct answers composed by AI. These AI systems pull information from web content – but they don’t experience your website the way human visitors do. In fact, AI engines often see only the bare basics of a page (title, navigation, a snippet of text), missing out on most of the rich content that users see. The result? Your site could be invisible to AI, even if it performs well in traditional SEO. And if your content isn’t accessible to AI, it won’t be cited in those AI-generated answers – meaning your brand might not appear in the very results that more and more customers are consuming.
This new reality makes visibility in AI search results a pressing concern for enterprise websites. Early data underscores the stakes: Adobe observed a 1,100% year-over-year increase in AI- driven traffic to U.S. retail sites, with AI-referred visitors being more engaged and even slightly more likely to convert than others. In other words, a growing and valuable segment of your audience is coming through AI answers. Ensuring that your content is “citable” by large language models (LLMs) – meaning the AI can read and reference your site when formulating answers – is quickly becoming as important as traditional SEO. This emerging practice has a name: Generative Engine Optimization (GEO), the art and science of optimizing content for AI-driven search. As Adobe’s Loni Stark put it, “generative engine optimization has quickly become a C-suite concern, with early movers building authority across AI surfaces and securing a competitive advantage.
At DevHandler, we’ve been tracking this shift closely. We’re a specialist Adobe partner with over a decade of experience delivering solutions on Adobe Experience Manager (AEM) and modern Adobe stacks like Edge Delivery Services (EDS). In fact, we built our own site on Adobe EDS using document-based authoring as a showcase of modern, AI-friendly architecture.
In this article, we’ll share our perspective on Adobe’s new LLM Optimizer Chrome extension – a diagnostic tool that reveals how AI sees your webpages – and what it means for enterprise marketing, SEO, and platform leaders.
We’ll start by explaining why AI search visibility matters and define what it means to be citable by an LLM. Then we’ll look at Adobe’s LLM Optimizer Chrome extension, exploring how it works and how our team has used it on enterprise sites. We’ll discuss real-world patterns we’ve observed (from rendering issues to content gaps that hurt AI visibility) and how to go from these diagnostics to a full GEO strategy. Finally, we’ll provide a practical checklist to get started and share how DevHandler can help make your enterprise website truly citable in AI search. Let’s dive in.
Why AI search visibility now matters for enterprise websites
When we talk about AI-generated answers in search, we mean the results provided by generative AI engines (think ChatGPT’s answers with citations, or Google’s Search Generative Experience) that synthesize information from multiple sources. Instead of a user clicking through to ten blue links, the AI itself delivers a consolidated answer – often with footnotes or links to the websites it drew from. Being one of those cited sources is becoming a new battleground for visibility online. If your content is referenced by the AI, you gain traffic and authority; if it’s absent, your competitors’ information might fill the gap.
This is why generative engine optimization (GEO) has emerged as the counterpart to traditional SEO. GEO is about optimizing your content and site infrastructure so that LLM-based search agents can discover, interpret, and confidently cite your pages. It’s a response to changing user behavior – consumers are starting to use AI chatbots and assistants as discovery tools for products and information, not just conventional search engines. Industry analysts are even predicting steep drops in traditional search traffic as AI search grows. For example, Gartner estimates that over 50% of organic search traffic could decline by 2028 as consumers embrace AI-powered search interfaces. Whether or not that exact number comes true, the trend is clear: a significant share of customer discovery is shifting to AI-driven channels.
Crucially, the users coming through these AI answers tend to be highly qualified. Adobe’s early data found that visitors arriving via generative AI sources spent more time on sites and viewed more pages (12% higher engagement) and converted slightly more (5% higher conversion rate) than visitors from traditional channels. These AI-referred visitors arrive “pre-briefed” by the chatbot – often they’ve essentially asked the AI for recommendations or answers, and the AI directed them to your site as a trusted source. Some research even suggests AI-driven traffic can convert at dramatically higher rates (one analysis found AI search traffic converting 23× higher than normal search traffic, albeit currently in small volumes). The takeaway for enterprise leaders is that AI search isn’t just a futuristic experiment – it’s an active and growing source of highly engaged customers. Ignoring it could mean losing out on both visibility and sales to those who optimize early.
Being “citable” by LLMs means your site’s content can be seen and understood by these AI systems in the first place. If an AI agent can’t effectively read your page, it certainly won’t cite it as an answer source. Unfortunately, many enterprise websites – especially those built with rich, dynamic front-ends or heavy client-side rendering – are not very LLM-friendly out of the box. A page might look fine to a human in a browser, but to an AI crawling it, the content could be practically non-existent. Ensuring your site is citable in AI search requires bridging that gap. In the next section, we’ll look at Adobe’s new tool designed to shine a light on this problem.
What Adobe’s LLM Optimizer Chrome extension actually does
Adobe’s LLM Optimizer Chrome extension (available free as “Is Your Webpage Citable?” on the Chrome Web Store) is a lightweight diagnostic tool that lets you view your site through an AI lens. In essence, it shows you a side-by-side comparison of what a page looks like to a human user versus what it looks like to an AI bot. With one click, the extension scans any webpage and measures its machine-readability – it then produces a citation readability score indicating what portion of your content is visible to AI crawlers. A higher score means more of your content is readable by AI, and thus a higher chance that your site could be sourced and cited by an AI system’s answer.
The extension works by attempting to fetch your page the way an AI agent would. In fact, it tries to retrieve the content as ChatGPT’s web crawler might do. If the page can be fetched in this “agent mode,” the extension shows that agent view – typically this is the raw HTML content that an LLM sees (often without running any of your site’s JavaScript). If the chatbot crawler can’t fetch it fully, the extension falls back to showing the initial HTML of the page (before scripts and dynamic elements execute) as a proxy for what most AI agents can access. It then also captures the human view – the fully loaded page with all visuals and interactive elements – for comparison. Any content that is present in the human view but missing from the agent view gets highlighted, because those are your blind spots where AI can’t see your content.
When you run the extension on a page, you immediately get a readability score and a visual diff of human vs. AI content. For example, we scanned an enterprise webpage and saw a stark difference: the agent view was nearly blank, capturing only 17% of the content, while the human view was rich with product details and images that the AI completely missed. The extension flagged that 531 words were invisible to the AI on that page, content that would never make it into an AI-generated answer. In the example above, the Adobe LLM Optimizer Chrome extension reveals how much content a typical AI agent sees versus what a human sees. On the left, the “agent view” of a sample page is almost empty – only about 17% of the page’s text is readable to an AI – whereas the fully rendered page on the right contains product listings and details. The extension reports over 500 words missing from the AI’s perspective, highlighting a major visibility gap
This kind of insight is eye-opening. It turns an abstract problem (“Is our content visible to AI?”) into a concrete score and visual of missing content. Even non-technical team members can grasp the issue when they see, side by side, what the AI is not seeing on a page. Adobe designed the extension as a no-setup, no-cost tool for exactly this reason – so that marketers, SEO managers, content authors, and developers alike can quickly diagnose AI visibility issues without needing a full crawl or enterprise software setup. It’s a first step in what Adobe calls the journey toward making a site “AI-ready.”
So, what does the extension actually uncover? In our use, it often confirms our suspicions: many visually impressive, JavaScript-heavy pages have shockingly low citation scores. The tool might say only 20% or 30% of the content is visible to AI, meaning the majority of the page (the product descriptions, pricing info, article text, etc.) never makes it to the AI’s index. In the enterprise context, these are frequently mission-critical pages – like product pages, support knowledge base articles, or thought leadership content – that aren’t being surfaced in AI answers at all. Next, let’s talk about what patterns we see repeatedly when we run the extension on enterprise sites, and why those issues occur.
What we at DevHandler see when we run the extension on enterprise sites
Our team has used the LLM Optimizer extension on a variety of enterprise websites (including AEM Sites implementations and modern headless front-ends), and we’ve noticed some common problem patterns. It’s often an “aha!” moment for clients when we show them the agent view versus their beautiful fully-rendered page – the contrast can be dramatic. Here are some real-world issues we frequently encounter that hurt LLM visibility:
- Client-side rendering hiding content: Many enterprise sites rely on heavy client-side JavaScript frameworks (SPA-style architectures) that load content only after the initial HTML is delivered. The extension often shows that in these cases the AI agent only saw the header, footer, or a spinner – and none of the main content. For example, a product listing or article text might be injected via XHR after page load, which never appears to an AI crawler that doesn’t execute scripts. The result is exactly what Adobe’s tool reports: maybe 80% of the content is missing in the agent view. As Adobe notes, if your site loads most content via JavaScript, agents may only get small snippets, and such pages score very low on citation readability.
- Dynamic content and interactive elements not accessible to crawlers: We see pages where important info is behind tabs, accordions, or login walls. An AI won’t click a tab or log in, so any content not in the default open state or publicly available HTML is effectively invisible. Similarly, content that requires user interaction (like a chatbot popup or a personalized greeting) won’t be seen by a generic AI agent. In one case, a client’s FAQ answers were only revealed when a user clicked each question – the agent view showed only the list of questions with no answers, meaning those answers couldn’t possibly be used in an AI-generated Q&A.
- Missing or poorly structured metadata: Sometimes the issue isn’t that the AI can’t retrieve the content, but that it can’t make sense of it. We’ve encountered pages lacking basic metadata (like descriptive <title> or meta description, or proper <h1> headings), which can lead to AI misinterpreting what the page is about. Adobe’s early analysis of LLM Optimizer clients found 80% had critical content visibility gaps, often including missing or invalid metadata that prevented AI systems from accessing or understanding key info. For instance, a product page without product name in the HTML (perhaps it’s only in an image or a script) is a page an AI might not categorize correctly. Likewise, if your page’s structured data or schema markup is broken, an AI might not attribute certain facts to your page.
- Non-text content or inaccessible formats: Enterprise sites heavy on images or PDFs can pose a problem too. If your key content is locked in an image (say, an infographic) without alt text, an LLM can’t read it. We’ve seen pages where promotional info was present in a graphical banner but not as HTML text – the extension duly shows that the AI “sees” none of the offer details. Similarly, content delivered via iframes from another domain or heavy use of canvas elements will likely not be indexed by AI crawlers.
From these patterns, a clear theme emerges: rendering and structure matter for AI visibility. Traditional SEO taught us to have crawlable HTML and good metadata for Google’s sake – now GEO is teaching us to do the same for AI’s sake, perhaps even more rigorously. It’s not that the AI algorithms need something esoteric; in fact, they need the fundamentals: clean, static, semantic content they can parse easily. Our own website is a good example of taking this principle to heart – we built DevHandler.com using Adobe’s Edge Delivery Services (document-based authoring) which produces static, server-rendered HTML with a clean structure, yielding excellent SEO and, by extension, strong AI-readability. (Our site scores a 100 on Lighthouse SEO and has virtually all content in the HTML by the time it reaches the user, meaning an AI agent can see everything important.) We practice what we preach: leveraging modern Adobe tech to ensure performance and accessibility to both humans and machines.
It’s worth noting that some enterprises are already experiencing the downside of not addressing these issues. If an AI like Bing Chat or Google’s Bard can’t find your product’s specs or your thought leadership quote because of one of the issues above, it might source that information from somewhere else (maybe a competitor or a third-party site). Over time, that erodes your share of voice in AI-mediated channels. The good news is that once you identify the gaps, you can start fixing them. The Chrome extension is great for spot-checking pages, but for a comprehensive approach, Adobe offers a fuller solution – which is where we turn next.
From diagnostic to GEO strategy: Adobe LLM Optimizer and DevHandler’s role
While the free Chrome extension gives you a quick page-by-page snapshot, Adobe’s full LLM
Optimizer product takes things to the next level. Think of the extension as a flashlight and the full LLM Optimizer as the floodlights and control center. Adobe LLM Optimizer is an enterprise application (available standalone or integrated with AEM Sites) that can audit your entire site continuously and help you optimize for AI visibility at scale. It monitors which of your pages are being cited by AI systems and even benchmarks your AI search visibility against competitors on key queries. In other words, it not only tells you “Page X is 20% visible to AI,” but also “Competitor Y’s page is getting cited for this query more than yours.” This kind of insight is incredibly valuable for forming a GEO strategy rather than just fixing pages in isolation.
Most importantly, LLM Optimizer provides actionable recommendations and even one-click implementations for many issues. Adobe has built a recommendation engine that analyzes your site for the kinds of gaps we discussed – content that’s hidden from LLMs, missing metadata, broken links, lack of alt text, etc. Early users discovered that a vast majority had serious issues (again, that 80% figure) preventing AI from accessing key content like product info or reviews. The tool surfaces these and suggests fixes, which could range from “Enable server-side rendering on these sections” or “Provide an HTML snapshot for this page” to “Add a meta description including keyword X” or “Include schema.org FAQ markup for these Q&A pairs.” Even external content opportunities are flagged – for instance, if an AI frequently cites Wikipedia for a topic related to your brand, LLM Optimizer might suggest beefing up that Wikipedia article or ensuring your site’s content covers that topic better.
One of the powerful aspects of Adobe LLM Optimizer is its ability to push changes quickly. It integrates with AEM, so if you’re using AEM as your CMS, some optimizations can be deployed with a single click once you approve them. For example, imagine the tool identifies that your blog article is missing an <h1> title (which hurts how an AI perceives its topic). With LLM Optimizer integrated, you might be able to add that title element or fix the template through an automated suggestion and publish it immediately – no long development cycle needed. It’s about closing the loop from insight to action rapidly. This is critical because GEO is a new field – it will involve a lot of iterative improvements as we learn what works. Having an optimization workflow built in (analytics → recommendation → implementation → verification) allows enterprise teams to adapt quickly in the fast-changing AI search landscape.
At DevHandler, we see our role as a strategic and technical partner in this journey. Adobe LLM Optimizer provides the toolkit and analytics, but making the most of it often requires interpretation and integration. Our team helps enterprise clients answer questions like: Which pages or content should we prioritize for AI visibility? What structural changes (like adopting server-side rendering or using EDS for certain sections) make sense for our platform? How do we implement these recommendations in a maintainable way? Because of our deep background in AEM and modern Adobe stacks, we can not only configure LLM Optimizer for your site, but also execute the needed changes – whether that’s updating AEM components for better HTML output, setting up prerendering for dynamic content, or even re-platforming parts of a site onto a more AI- friendly framework (like the document-based authoring approach in EDS that we champion). Our goal is to translate the insights from the tool into a concrete GEO action plan tailored to your site.
Let’s revisit the example from earlier: the page that was only 17% visible to AI. Using the extension alone, we identified that issue – mostly due to client-side rendered content. Now, with a full GEO strategy, we implement a fix: enabling server-side rendering for that content module, so that all those 531 missing words are delivered in the initial HTML. After the fix, we run the scanner again. Sure enough, the agent’s view is now nearly identical to the user’s view, and the citation readability score jumps to 100%. The images above illustrate a before-and-after scenario. On the left, the AI (agent) view previously missed most of the content (yielding only a 17% score). On the right, after implementing server-side rendering, the AI can see the entire page – achieving a 100% citation readability score (0 words missing). By making the page fully accessible to AI crawlers, we dramatically improved its chances of being cited in generative answers. This kind of result isn’t theoretical – it’s exactly what Adobe’s own teams have seen. Adobe’s marketing group used LLM Optimizer to discover content gaps on Adobe.com, applied the suggested fixes, and saw citations for Adobe Firefly increase fivefold in one week. For Adobe Acrobat pages, they achieved a 200% increase in AI visibility (versus competitors) and a 41% jump in AI-driven traffic after optimizing content and metadata. Those are real, measurable wins attributable to GEO efforts.
The bottom line is that improving AI visibility is a holistic effort – part technical SEO, part content strategy, part platform optimization. Adobe’s tools (extension + Optimizer platform) provide a data- driven foundation. DevHandler provides the expertise to act on that data within the context of Adobe Experience Cloud technologies. Together, we help ensure that an enterprise site isn’t just theoretically optimized for AI, but truly implemented in a way that an LLM can crawl, understand, and cite.
How enterprise teams can get started (a practical checklist)
Improving your website’s citability in AI search might sound daunting, but there are clear steps you can take today. Here’s a practical checklist to kickstart your Generative Engine Optimization efforts:
- Assess your current AI visibility: Start by installing Adobe’s “Is Your Webpage Citable?” Chrome extension and scanning a few high-value pages (product pages, landing pages, blog articles). Note the citation readability scores and what content is missing in the agent view. This is your baseline for where you stand.
- Identify critical content gaps: For any page with a low AI readability score, list what important content isn’t being seen. Is it the product description? Pricing info? An entire article body? These gaps highlight what key information you’re effectively hiding from AI (and thus from potential customers). Prioritize pages that are business-critical and have low scores for improvement.
- Fix technical blockers first: Address the structural issues preventing AI from reading your content. For pages loading content via client-side scripts, consider enabling server-side rendering or prerendering to deliver static HTML to agents. Ensure your robots.txt isn’t blocking important pages and that you’re not requiring logins or interactions for basic content. Also, add fallback text for images or interactive elements so there’s something for AI to read.
- Enhance your metadata and structure: Make sure every page has a descriptive <title> and meta description that summarize the content (AI uses these as clues). Use proper headings (<h1>, <h2>, etc.) to give structure. Check for missing alt text on images. If your page is about a product or FAQ, use structured data (schema.org) where possible – it provides explicit context that some AI models may leverage. Fix any broken or invalid metadata that Adobe’s tools flag.
- Leverage Adobe LLM Optimizer for site-wide insights: Consider onboarding the full Adobe LLM Optimizer platform for an in-depth audit. It can automatically scan your whole site for hidden content and metadata issues, and show you where you stand against competitors. Use its dashboard to monitor AI-driven traffic (e.g. are you getting visits from Bing’s chatbot or ChatGPT plugins?) and see which queries or topics you’re being cited for. This will help you quantify the opportunity and track progress.
- Implement recommended optimizations: Based on the extension’s findings or the Optimizer’s recommendations, systematically fix the issues. Tackle it like a new kind of SEO project – for each issue (e.g. “reviews section not visible to AI”), decide on a solution (e.g. “enable SSR for reviews component” or “add static text summary of reviews”). Where possible, use Adobe’s one-click optimizations if you have the tool integrated. For more complex changes, plan them into your development sprints. It might involve your development team or an experienced partner like DevHandler to implement things like prerendering or content restructuring.
- Monitor and iterate: After making changes, rescan the pages with the Chrome extension to verify improvements in AI readability score. Also watch your analytics for any uptick in AI- sourced traffic or chatbot references. Adobe LLM Optimizer’s reporting can connect increases in AI visibility to engagement and conversion metrics – use that to celebrate quick wins and make the case for further investment. GEO is not a one-and-done project; treat it as an ongoing part of your SEO/SEM strategy. Regularly audit new content and releases for AI-friendliness.
By following this checklist, your team will address both low-hanging fruit and deeper architectural fixes. The process involves collaboration between marketing/SEO, content authors, and developers – which is why having an internal champion or an external partner can accelerate the outcome. Even implementing a few of these steps (say, prerendering one particularly JS-heavy section, or fixing metadata on a set of pages) can significantly boost those pages’ citation-readiness. We’ve seen pages go from virtually invisible to fully readable by simply delivering server-rendered HTML to the crawler. The key is to start somewhere and build momentum.
Ready to Accelerate? – Schedule Your Discovery Session
The age of generative AI in search is no longer theoretical – it’s here, and it’s reshaping how customers find information and make decisions. Just as enterprises had to master SEO when Google rose to dominance, now we have to master AI visibility. The cost of doing nothing is that your website remains a blind spot to the likes of ChatGPT, Bard, and other AI agents, no matter how great your content is. The upside of taking action, on the other hand, is significant: you position your brand as an authoritative source that intelligent assistants rely on and recommend. As AI- driven search reshapes how brands connect with audiences, those who adapt today will lead tomorrow’s digital landscape. By making your content truly visible to both AI agents and human users, you’re not just keeping pace — you’re setting the standard for discoverability in the era of intelligent search.
At DevHandler, we’re excited about this new frontier. We’ve modernized our own site to be GEO- friendly and have helped clients start doing the same. The combination of Adobe’s LLM Optimizer tools and our experience with AEM/EDS provides a roadmap to ensure your brand is seen, cited, and selected in AI searches. Our message to enterprise marketing, SEO, and platform teams is simple: don’t wait. The earlier you diagnose and fix your AI visibility gaps, the faster you’ll gain a competitive edge in a search landscape that’s quickly changing. Use the tools at your disposal – run the extension, crunch the data, rally your team – and consider partnering with experts who know the Adobe ecosystem and the ins-and-outs of GEO.
In the end, making your website citable in AI search comes down to a marriage of great content and solid technical implementation. You already invest in great content for your customers – now ensure the machines can access and trust that content too. The companies that do will be the ones whom AI platforms quote and recommend, driving qualified traffic and leads in the process. It’s an exciting time to reinvent your search strategy. If you’re ready to take the next step, our team at DevHandler is here to help you navigate this journey and turn your enterprise site into an AI-visible, citation- worthy digital asset. Let’s make sure that when the next AI assistant is answering questions in your industry, your website is the one it cites.
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