AEO in Webflow

AI search has changed how people discover brands. The hard part isn’t just knowing whether you show up in AI answers. It’s knowing what changes will actually move the needle and then execute at scale.

Webflow AEO brings that loop into one place: measure visibility, get prioritized recommendations, and act directly in Webflow with the help of AI agents.

We’ll cover this in six parts: how AI search is changing discovery, how Webflow approaches AEO, what we measure in Analyze, how recommendations work and how to act on them, we’ll look into some settings, and finally, we’ll put it all together with a couple of examples.

Let’s start with how AI search is changing discovery. AEO is about making sure that when someone asks an AI tool a question that your business could answer, your site is the one it points to. Not a competitor. Not a generic summary. You. The goal isn’t just ranking for keywords anymore. It’s showing up in AI-generated answers in a clear, accurate, and favorable way.

So the question becomes: how do you actually improve that visibility? Which leads us to our next step.

Webflow approaches AEO as a continuous loop, not a one-time fix. The system is built around three actions: Measure, Recommend, and Act.

Let’s start with Measure: understanding how AI systems find and represent your site. Recommend is where Webflow agents surface opportunities to improve visibility. And Act is where you review, apply, and track those changes directly in Webflow.

Okay, agents. Let’s talk about agents.

Webflow uses two types of AI agents behind the scenes. These are technical AEO agents that focus on crawlability, schema, and site structure to ensure your site is optimally readable and understandable to AI. And then there are content optimization agents that evaluate whether your content is clear, authoritative, and likely to be cited in AI-generated answers. They also identify content gaps or opportunities based on tracked prompts and competitor coverage.

Together, they turn those findings into prioritized recommendations you can review before publishing. And you might be wondering: what are those recommendations actually based on?

That’s where data and data sources come in. AEO Analytics in Webflow draws from three distinct data sources. And it’s worth understanding what each one is before we look at it in Webflow.

First, AI bot insights: are AI bots visiting, for what purposes, and which pages are they crawling?

Then we have prompt insights: are you showing up for the questions you want to be showing up for?

And third, AI-referred human visitors: are AI answers sending real people to your site, and are they converting?

Those are the three signals we’ll be following throughout this walkthrough. Most AEO tools are complex, disconnected, and slow to implement. They don’t tell you whether AI systems are recommending you in the first place. Webflow gives you the full picture, from AI discovery all the way through on-site behavior, built right into the tools you already use.

That’s how Webflow is approaching it. Let’s start with Measure, which lives in Webflow Analyze.

Insights, here in the site overview under visitor behavior. What is it exactly that we’re looking at? We have the traditional user behavior tracking we just mentioned: sessions, top pages, events, and audience data. But let’s pay attention to Generative AI, which is already one of the top traffic sources for our site.

That tells us what AI-referred visitors are doing once they reach the site. But to understand how the brand is showing up inside AI answers, we’ll go one level deeper into AI Discovery.

Once we are there, you can see your visibility score. This shows the percentage of tracked prompts where your brand appears in AI-generated responses. You can also see citations showing which domains AI systems are referencing when answering questions in your space and, critically, how much of that share belongs to you versus other sources.

For this overview, there are two signals I want to focus on: human visitors from AI tools and LLM bot activity. Let’s start with humans.

Human visitors by source breaks down your actual visits by where they came from: AI-referred, organic, direct, or everything else. What makes this powerful is the AI-referred number. You can see it’s up this much percent, meaning more real people are clicking through to your site from AI tools like ChatGPT or Claude. That gives teams visibility into a fast-growing discovery channel they couldn’t easily measure before. And now you can track it just like organic or direct.

One thing worth knowing: if AI mentions your brand but doesn’t directly link to you, people will often just Google you or type in your URL. So some of that AI-driven interest will show up in your organic and direct numbers too. It’s not perfectly measurable, but it’s real.

And here’s where it gets really useful. You can look at these signals together: whether visibility is improving for the prompts you care about, whether AI-referred traffic is growing, and whether those visitors are converting. Together, those signals help you move from “Are we showing up?” to “Is that visibility turning into meaningful site traffic?”

And then we’ve got the bots. The LLM bots show you how often AI bots are visiting your site, which pages they’re visiting, and how those patterns change over time.

Pay attention to agentic bot visits in particular. Those happen when a real person asked an AI a question and the AI scraped your site to help answer it. That’s a strong signal that your content is already getting surfaced.

In this example, you can see how LLM bot visits are distributed across pages on the site. That tells you which content AI systems are accessing most and where you may have room to improve.

So now we know what AI systems are finding on your site.

Prompts let you define the questions you want your brand to show up for. Similar to keywords for SEO, you’re now targeting the questions your audience is asking an LLM. A good prompt is specific, written like a question in natural language, and tied to a decision your audience is actually trying to make.

There are three types worth knowing. Let’s break it down. Prompts that help you show up for solutions people are searching for, prompts tied to your brand that track how you’re being represented, and prompts around your product categories or content topics. A useful way to organize them is by intent, like awareness, evaluation, and decision, or by brand versus non-brand. That makes it easier to spot gaps and prioritize.

Right now, the best place to start is with the prompts you want your brand mentioned in. This includes questions that aren’t directly about you, but where you should be part of the answer. So start with what matters most to your business, then measure and refine. Webflow helps by suggesting prompts and showing where AI-referred visitors are interacting with your site.

Once prompts are set up, be sure to watch visibility score and citation rate over time. If you already rank well organically for a topic, start there. And if competitors are cited more often than you, use that as a signal for content gaps.

At this point, we understand where we stand: which prompts we care about, where we’re visible, where competitors are being cited, and how AI-referred visitors are behaving. Now the next question is: what do we fix? That’s where Recommendations come in.

The AEO site audit highlights issues and opportunities across your site so your content stays discoverable by both search engines and answer engines. And Webflow groups these into prioritized, actionable recommendations.

Like this one. It’s flagging a missing schema type on some of our pages. We can review exactly what it’s suggesting to add, and if it looks right, accept it.

Same thing here for content. The recommendation is to create content for this blog article. We can review the details, generate a brief, and create the draft, which goes directly into the CMS.

Once those changes are ready, we publish the site.

That’s the core flow: understand the issue, fix it, and ship the improvement.

Before getting into our examples, let’s look at some of the settings first. Is that the most exciting thing in the world? No, but it’s important.

Settings is where you configure the inputs AEO uses. You can define domains used for citation tracking, manage audience and targeting context where available, choose which issues and recommendations appear in your site audit, and keep organization details like brand names, aliases, and competitors accurate.

That context helps Webflow understand what counts as your brand, track citations more accurately, and generate more relevant recommendations.

Now that we’ve seen the core areas, let’s put the whole loop together with two examples.

A good way to see how this all fits together is to walk through it using the core framework we talked about in the beginning: Measure, Recommend, Act, or as we like to think about it in practice: understand, fix, ship.

For example one, we have some missing structured data.

First, we’ll understand. The AI Discovery panel shows a low visibility score for a key topic. The site audit flags a relevant topic tied to that page that is missing schema markup.

How do we fix it? Our Webflow technical agent highlights a specific recommendation to add schema markup. You apply it directly in Webflow with a few clicks.

And then we ship it. The change goes live, and after the next monitoring cycles, you can go back to your tracked prompts and see whether that page is now showing up more often in AI-generated answers.

Example two: our thin content is hurting citation quality.

Same idea. We’ll start by understanding. There’s a workflow that pulls the Analyze data, cross-references it against the existing site, and surfaces a ranked list of content gaps. For example, “How to manage project handoffs between teams.” It has high prompt frequency, three competitors getting cited, and nothing from us.

How do we fix it? A second workflow generates a full content brief, grounded in our brand voice already stored in Webflow, which then drafts a full article from that brief.

Which leads to that last step in the framework: shipping it. Now the content manager, or whoever is in charge of the content, can review, refine, and publish it directly to the CMS.

Both examples follow the same pattern: understand the signal, fix the issue, ship the improvement, and keep measuring. That’s the core AEO workflow in Webflow.

It’s important to know that AEO best practices will continue to evolve. Teams shouldn’t have to track every change themselves. Webflow keeps agents current with the latest research so the recommendations they give stay relevant.

The main takeaway is that AEO isn’t a one-time project. It’s an ongoing cycle: understand where you stand, fix what matters, ship improvements, and keep measuring what changes. Start with one prompt, one recommendation, and one change, then build from there.

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