The Three Layers to Winning Brand Visibility in ChatGPT

Every business wants to have better visibility on answer engines like ChatGPT, but most don’t know how to make that happen. While many marketers know the basics, the best AEO providers have a much deeper understanding of the process. This is important to understand when it comes to maximizing visibility for any brand and performing AEO/GEO at a high level.

Parametric visibility, First-party index visibility & External-search visibility

There are three distinct layers of visibility that you can potentially influence. Each represents a different part of the process in which an answer engine can draw information from when deciding what brands, companies, products, or sources to include in a response. Understanding which layer is contributing to an answer matters because the strategies for improving your visibility in each one are different. These are the three visibility layers:

Parametric visibility
First-party index visibility
External-search visibility

Understanding Answer Variability

Before we can talk about each of these three layers, we first need to make sure we understand why answers vary so greatly. Ask ChatGPT the same question 100 times and you’re probably going to see almost every one return a different answer. Why? Because it’s a probabilistic system. Instead of retrieving a fixed answer out of a database, it calculates a list of possible next words, uses probabilistic sampling to select one of the options, and repeats the process until it provides an answer. Now add in a bunch of different input variables and different models that are constantly evolving, and you can end up with wildly different responses from the answer engines. For example, a paid ChatGPT user will sometimes get a very different response that uses more thought and reasoning than a free user would get. Free users do not have access to the same models that a paid user has, or the range of thinking capabilities (low, medium, high, etc.), so this impacts the URLs retrieved when ChatGPT is creating the response it gives back to the user. And even for free users, being signed-in or signed-out can result in very different responses.

Now add in personalization factors: your current conversation, context, previous conversations or custom instructions you may have given your agent. What results is highly personalized answers. That’s why sometimes a person will think they have good visibility on ChatGPT, but for everyone else that brand is never mentioned. The variability and personalization here is also why spot checking ChatGPT here and there isn’t something you can rely on. You need a significantly larger set of data points in order to draw important conclusions from, which is why Hoverboard AI usually runs thousands of prompt checks every single month for each project.

Of course, many of these variables are outside a brand marketer’s control. So let’s focus on the things that you have the ability to impact when it comes to improving brand visibility.

1. Parametric visibility

Based on its training data, what does the model already know?

Ask a model:

“What are the best project management tools?”

If it responds with Asana, Monday.com, ClickUp and Trello without utilizing any external search, like searching Google, visiting certain web pages or even its own internal “Labrador” index (more on Labrador below), those brands are being recommended from knowledge already “baked in” to the model. The technical term often used for this is parametric knowledge. When it’s using that knowledge to reply to a prompt, the model is using its parametric memory. This is different from contextual or episodic memory (information the model has access to from a user’s past conversations, the current conversation, or other stored/user-specific context). Brands that are in a model’s parametric memory have an enormous advantage when it comes to being mentioned or recommended by answer engines.

How does a brand Get Into LLM Training Data and Model Memory

So how can you get into a model’s parametric memory? If you’re a large brand with great brand awareness, you have a huge advantage over competitors. Google has long favored large brands when it comes to SEO and at this time, we are starting to see the same thing with ChatGPT (and other answer engines).

Whether you are querying Google search or using ChatGPT, if you are researching the best electric vehicles in 2026 and you don’t get “Tesla” back as one of the brands to consider, that would be a poor user experience. Tesla is a PR machine. It has been covered in millions of news articles, reviews, videos, forum discussions, social posts and comparison pages. Elon is talked about non-stop. That creates an enormous amount of training data repeatedly connecting Tesla with concepts like electric vehicles, EVs, autonomous driving and charging networks. A smaller EV company may build an equally good product, but if nobody is talking or writing about that brand online, they will never be in ChatGPT’s “consideration pool” of candidate brands.

Now, obviously it’s not realistic for you to get the kind of online attention that Tesla does. But your goal (even if you are a small business or start-up) is to create the same effect on a smaller scale: generate enough independent, consistent mentions that the brand becomes strongly associated with its category.

For example, this means:

PR (earning coverage in news outlets, trade publications, blogs and other third-party media).
Publishing original research/studies
Generating industry coverage
Partnerships
Podcasts
Reviews
Reddit discussions
YouTube appearances
Brand advertising

All this contributes to your brand’s footprint. The more often credible sources connect your brand with a particular topic or category, the stronger your chance of earning that parametric visibility. By the way, these are all things you should be working hard to do even if answer engine optimization wasn’t a factor!

How ChatGPT discovers new pages

OpenAI operates different web crawlers, and each has its own purpose. GPTBot crawls public web content that may later be used to train or improve OpenAI’s models. But being crawled by GPTBot does not mean that information is immediately added to ChatGPT’s parametric memory. The content would first need to be selected for training and then incorporated through a future training or model-update process that changes the model’s weights.

That update does not necessarily have to arrive as an entirely new model called “GPT-6” or “GPT-7.” Models can be further trained or updated over time. The important point is that parametric knowledge only changes when the model’s weights change. Model weights are essentially the billions of numerical settings inside the model that are adjusted during training and determine the patterns and relationships it has learned. A crawler discovering your website by itself does not cause the current model to suddenly know your brand.

OpenAI also operates OAI-SearchBot, which serves a different purpose. It crawls public websites so their content can potentially be discovered and surfaced by ChatGPT’s search systems. This gives a new or lesser-known brand another path to visibility. ChatGPT may be able to find information about a company through search and retrieval even when that company has little or no presence in the model’s parametric knowledge.

Will an Answer Engine sometimes just use parametric memory to provide a response?

Yes, sometimes an answer engine only relies on parametric memory. Sometimes ‘search’ is intentionally turned off by a user. Sometimes the model decides if it needs more info in order to give you a good answer. In these instances, if you are not included in the parametric memory, that’s it. You pretty much have no chance of being mentioned or cited.

But what happens when an answer engine needs to collect more information before giving you an answer to your question? Then it retrieves information outside of its training data to help shape the answer it gives you. It will either use external sources (like Google, Bing, etc.) or if the model has its own index, it may use that. This is where first-party index visibility comes into play.

2. First-party index visibility

What does ChatGPT retrieve from its own systems?

Many current, local, niche or fact-sensitive questions that are asked of an answer engine require additional information in order for the LLM to provide a high-quality response. It’s in this first-party index that you have your next pathway to being mentioned, cited or recommended in the answers.

If you were to ask ChatGPT, “What are the top luxury SUVs for 2027?“, this would most likely result in it retrieving a set of URLs from its internal index, or from a third-party index (ex. Google), or a combination of both. Answer engines often rely on sources outside of its core training data to help improve freshness and ground the response in external sources. In some Hoverboard tests, we see ChatGPT retrieving more than 100 candidate URLs. Keep in mind though that retrieval does not mean every page was opened, read, cited, or materially used in the final answer. In this image below are two side by side snapshots from the Hoverboard AI Prompt Response Analyzer extension, comparing the URLs retrieved for a paid account versus a free user account. Here we can see that the paid version retrieved significantly more than the free one: 135 URLs in the paid version compared to 33 pages in the free version. It also ran 28 fan-outs (subqueries) compared to just 3 for the free, logged-out user. More fan-outs means more URLs that are retrieved.

In this example, OpenAI is using a combination approach of using third-party Google search search plus its own first-party index, though we clearly see in this specific example that most of the URLs were pulled from the internal index. For the paid user, ChatGPT retrieved 122 different URLs from its internal index (Labrador) out of 135 total. While OpenAI has not publicly documented Labrador as an official product or index name, we know this is the case through some excellent research by the team at Resoneo.

So even if your brand has poor parametric visibility, your brand still has a chance to be included (and possibly even recommended) in the answer that it gives the user via the retrieval process of the internal index.

How to Get into ChatGPT’s internal index

As mentioned earlier, ChatGPT discovers new information for its internal index primarily through OAI-SearchBot, which crawls the open web for possible inclusion into ChatGPT Search. However, crawling is only the first step. Before a page can influence an answer, OpenAI must be able to access it, discover and index it, and consider it relevant enough to be a retrieval candidate.

So the process really looks like this:

Can OpenAI access the page? → Can OpenAI discover the URL? → Does it index the page? → Does it consider the page relevant enough to retrieve for a particular query?

Technical

First thing is checking the technical aspects of AEO, and making sure OpenAI can actually access and discover your pages. A site can look perfectly normal to a user while still blocking (or confusing) an AI crawler somewhere in the stack.

Here’s what you need to ensure:

Allow OAI-SearchBot in robots.txt
Make sure your CDN, firewall or bot protection isn’t blocking OpenAI’s crawler IPs
Confirm important pages return a normal 200 status code
Avoid putting content you want included in the index to be behind login walls, redirect loops, CAPTCHAS and other barriers
Check for accidental noindex directives
Make sure important pages are internally linked and aren’t orphaned
Include important URLs in your normal XML sitemap
Make sure the pages can also be discovered through traditional search engines like Google and Bing

On-Page

The content on your own site is very important to the AEO process and getting into that internal index that ChatGPT uses. It’s vital to have content that directly corresponds to the types of questions people ask the answer engines. If someone asks: “What are the best med spas in San Diego?”, and you operate a med spa in San Diego, obviously you want your home page content to address that question and make the case for why that should be you. But, you also want to make sure you are addressing other things in your content like, “Which med spas offer Botox for forehead wrinkles?“, “Where can I get laser resurfacing in San Diego?” and “What med spas offer non-surgical facial rejuvenation?“. Each represents a possible fan-out query. The more useful, relevant pages you have that directly address those topics, the more opportunities your domain potentially has to enter the retrieval set.

For helping to increase the chances of your content being included in both internal and external indexes, these are the elements that you should implement on your content:

clear page structure
descriptive titles/headings
structured data (schema)
clean, high-quality and original content.
strong entity/category signals, etc.

These steps can improve the chances that OpenAI discovers, understands and retrieves your content from its own index. But ChatGPT does not always rely exclusively on that index. For some questions, it also sends queries to third-party search providers (we observe this being primarily Google in our testing this past month), creating another path for your brand and content to enter the answer.

3. External-search visibility

What can the answer engine find on the web?

External search is the third visibility layer, and it’s where ChatGPT reaches out to third-party search providers like Google and Bing in real time, mid-conversation, and pulls back whatever those engines surface at that moment.

The good news is that this is the layer you already know how to influence, and may be already influencing through SEO. With external-search visibility, we don’t need to wait for a parametric memory update or an internal-index update. If your pages rank well in traditional search engines, you’re already positioned to show up when ChatGPT goes looking for additional information in the retrieval process. If information about your brand is on the URLs they most commonly retrieve (and the sentiment is not negative), that should contribute to your overall answer engine visibility.

We talked about “on-page” in the first-party index section above, but let’s talk about off-page, because it’s highly important.

Off-Page

Off-page signals can also help increase the chances that your brand and content are discovered, understood and trusted by answer engines.

Things to focus on include:

Earn links from relevant third-party websites (including links to internal pages on your site when it is relevant)
Get mentioned in industry publications, news sites and trade publications
Generate reviews on reputable third-party platforms (at this time, Yelp is heavily used by ChatGPT)
Participate in forums and industry communities where your industry, brand or product may naturally be discussed (but don’t spam, as that can come back to bite you)
Get included in comparison pages, “best of” lists and resource pages
Appear on podcasts, YouTube channels and other industry media
Build consistent brand/category associations across the web

One important thing to keep in mind, the focus here isn’t building backlinks or mass quantity of off-page signals. In my 20+ years of studying the Google algorithm, I’ve seen way too many instances of websites getting “nuked” by Google because of spammy SEO tactics. Especially around links. I think you should be taking the same approach with AEO. Higher quality sites are more impactful and are “safer”. Retrieval systems are already becoming better at discounting manipulative or low-quality signals. Spammy tactics can already damage Google visibility and brand reputation, making them a poor long-term AEO strategy (especially if ChatGPT continues to rely on the Google search index). I mention this because there is a ton of terrible advice being given when it comes to generative search engine optimization. You don’t want to end up with penalties from Google and ChatGPT if you care about your brand visibility online.

We are already seeing ChatGPT make changes to try and ensure they surface more authoritative websites in their retrieval process, and reduce the chance of lower quality domains impacting their answers. In our recent testing, ChatGPT frequently used “site:” queries to limit retrieval to particular domains. This appears to favor sources the system has already selected as relevant or trustworthy, although OpenAI has not confirmed the reason.

In this example below, we see 15 fan-out queries that were used. Every single one used “site:” to look for information specifically from domains the model appears to already trust.

The Freshness Factor

For certain queries, content freshness is a big factor in retrieval. We don’t know how often ChatGPT updates its internal index, but search engines like Google are obviously better at updating their index. And there are often times when ChatGPT needs very fresh content to provide an accurate response. For example if someone asks what the Dallas Cowboys’ game score was on Sunday, the current stock price of NVIDIA, or what the top luxury vehicles for 2027 are, it’s going to need fresh information. When that happens, the answer engine does exactly what you would do: it searches the web.

Now take this prompt that was asked on a free, non-logged in account on ChatGPT – “Did any new trendy restaurants open up this week in NYC?“:

And now on my ChatGPT pro user account:

You’ll note that on a few of these fan outs you see “7d” or “10d”. That appears to be a freshness preference passed to the retrieval layer to help it find pages with more recent information. We’ve seen this freshness window be as small as 1 day and as large as 10 years (“3650d”), mostly from high trust domains.

So, freshness of content can also be an important part of your visibility on ChatGPT, increasing citations and getting more overall brand mentions.

One brand can have three very different visibility levels

Imagine there’s a smaller CRM that is competing with the likes of Salesforce, Zoho, Pipedrive, etc. When ChatGPT answers from its existing knowledge, the company appears in just 2% of relevant recommendations.

Parametric visibility: Extremely Low

When internal retrieval is involved, it appears 18% of the time.

First-party index visibility: Moderate

When broader web search is available, it appears in 61% of answers.

External-search visibility: High

A single visibility score is very helpful, but it doesn’t give you the whole picture about what is actually happening. In this example, the web has a much stronger association between the company and its category than the model itself does. Having strong external search visibility creates the kind of repeated brand-category association that could result in improved parametric visibility.

Understanding the visibility in each of these areas can give the marketing team a much more useful diagnosis. Publishing another hundred landing pages may not be the highest priority. The company may need more independent mentions, product comparisons, reviews, research citations, press coverage and repeated associations between its brand and the category it wants to own.

This is where Hoverboard AI is moving to, as we aim to become the best tool for any company doing answer engine optimization work.