How to get ChatGPT to recommend your brand

How ChatGPT works and why it “recommends” brands

Why does ChatGPT mention some brands and not others?

ChatGPT does not have a built-in list of brands and does not “select” them based on commercial criteria.

It generates text based on word probabilities, drawing on knowledge acquired from training on public sources (books, articles, websites, Wikipedia, news, forums, etc.).

A brand is mentioned only if:

• it appears frequently enough in reliable open sources (media, Wikipedia, research, industry websites);

• the mentions are positive and contextually relevant to the query;

• the model recognizes the brand as a solution or example in the given topic.

When asked about “the best tools for SEO,” ChatGPT “remembers” the names that come up most often. These include Ahrefs, Semrush, and Moz. They frequently appear with phrases like “the best SEO tool.”

Where does ChatGPT get its information about brands?

ChatGPT is trained on a mixture of public texts, including:

• Wikipedia and Wikidata;

• open web pages (Common Crawl, Internet Archive);

• licensed data (publishers, reference books, training courses);

• partner data (e.g., StackOverflow, arXiv, PubMed, etc.);

• edited datasets (RefinedWeb, Books, WebText).

Also, with the web browsing feature on in GPT-4/5, the model can search the internet in real time. It uses Bing, DuckDuckGo, or OpenAI Search's own engine.

However, even then, it does not “read everything in a row”: the results are filtered by trusted sources and cited selectively.

Does ChatGPT use Google or Bing when responding?

In offline mode (without a browser), no, the model does not refer to Google or Bing, but responds from its trained knowledge (up to the date of training).

In online mode (with a browsing tool), yes, ChatGPT can access Bing or OpenAI's own search API to obtain the latest facts.

At the same time, it does not “index” websites itself, but only reads a limited number of pages and extracts key data.

ChatGPT has no commercial ties to Google or Bing when answering questions. These results are not ads or affiliate links; they are purely for information.

Is the ChatGPT database updated, and how often?

The refresh cycle frequency for LLMs (Large Language Models) depends on the model's design, the provider, and how responses are delivered.

The base model (LLM) is updated irregularly — approximately every 6–12 months when a new version is released (GPT-3.5, GPT-4, GPT-4-Turbo, GPT-5).

Models without internet access (static LLMs)

• GPT-4-turbo (without browsing): trained on data up to December 2023 (as of July 2025)

• Claude Instant, Claude 3 Opus (in the base version): trained up to August 2023.

• Gemini 1.5 Pro (API mode): knowledge up to early 2024.

Updates are published as “new model versions” trained on data up to a certain date. Content published in January 2025 will not be “known” to these models until a new version is released.

However, in browsing tool mode or through integrations (e.g., with Bing), it can retrieve current information on demand.

Models with Browsing / Retrieval (RAG)

• ChatGPT with browsing (in GPT-4 with the internet enabled)

• Perplexity.ai

• You.com, Bing Copilot

• Gemini with Web Access

They are updated almost in real time, depending on the indexing of websites. They use a retrieval system. First, they search the current database, like the internet, Google News, or an API. Then, they generate a response based on their findings.

If you publish an article on a site indexed by Google News, Perplexity and ChatGPT (with browsing) can use it in their responses in 1–2 hours.

This is the fastest way to “embed” your brand in LLM responses.

What influences ChatGPT's “trust” in a brand?

The model assesses trust not directly, but through statistical indicators:

• The brand is often mentioned with positive words like “reliable,” “popular,” and “used by professionals.”

• Reliable sources (the brand is mentioned on Wikipedia, Forbes, TechCrunch, Reuters, government websites);

• Absence of contradictions (consistent information across different sources);

• Tone and citability.

PR mentions, transparency, and content structure shape “machine trust.” They also affect whether the brand appears in responses.

Can ChatGPT mention a brand if it has no public mentions on the internet?

Practically speaking, no. The only exceptions are hallucinations, when the model invents a new entity in its response.

If there are no reliable traces of a brand in open sources (web, databases, media, social networks), the model simply does not know about its existence.

In this case, ChatGPT will either:

• ignore the brand;

• or say “I have no information about this company”;

• or (less often) “hallucinate” non-existent data if the name coincides with common words.

Conclusion: You can't access ChatGPT's knowledge without a public presence. This shows the importance of a content strategy and PR publications.

Does ChatGPT have a “blacklist” of brands or filters?

Yes, there are filtering mechanisms, but it is not a “blacklist” in the commercial sense.

OpenAI uses:

• Moderation filters (Hate, Violence, Adult, Sensitive);

• Brand and mention policies that exclude certain names (e.g., pharmaceuticals, pornographic sites, conspiracy movements);

• Disinformation and defamation filters.

ChatGPT also avoids mentioning little-known brands in responses where “objectivity” is required (e.g., “What is the best company?”) so as not to appear like advertising.

Why does ChatGPT sometimes refuse to give recommendations or do so neutrally?

ChatGPT is trained not to give preferences if:

• the question implies an evaluation of brands without criteria (“Which company is the best?”);

• there are no reliable statistics or ratings;

• the request could be interpreted as advertising, discrimination, or a conflict of interest;

• the context does not specify a country, segment, or metric.

In such cases, it chooses a neutral wording, for example: “I can't name one best company, but here are some popular ones in this field...”

How ChatGPT decides which brand to mention

How exactly does ChatGPT “evaluate” a brand's reputation?

ChatGPT does not have a built-in rating system or “reputation scores” in the human sense.

It does not maintain a database of “good brands/bad brands.” Instead, the model probabilistically reproduces patterns encountered in the text during training:

1. Contextual correlation. The model “sees” which brands in the training data were more often found alongside concepts such as “reliable,” “trusted,” “award-winning,” and “industry leader.” These associations are embedded in the model's weights.

2. Consistency of descriptions. If different sources (Wikipedia, media, websites) talk about the same thing in similar terms, the model perceives the brand as “reliably described.”

3. Frequency of mentions + quality context. The more data with positive context, the higher the probability that the brand will be mentioned in a positive light.

4. Exclusion of toxic and controversial brands. Post-processing algorithms (alignment, moderation) reduce the likelihood of mentioning brands that appear in scandals, fakes, or “sensitive categories.”

In other words, for ChatGPT, reputation is statistical stability and positive context of mentions in reliable sources.

Does it take into account reviews, media mentions, or ratings?

Yes, but indirectly. ChatGPT does not connect to review sites in real time (Trustpilot, G2, Yelp, etc.) and does not read specific user opinions.

However, during training, the model sees a lot of texts where brands are discussed in articles, reviews, overviews, and news.

For example:

• If a brand is mentioned repeatedly in Forbes, TechCrunch, or Business Insider in a positive context, ChatGPT perceives it as “reliable.”

• If the brand is written about in a negative light (e.g., scandals or fraud reviews), the probabilistic model reduces its “weight” in relevant queries.

• If ratings are frequently repeated (e.g., “HubSpot — top CRM by G2”), ChatGPT reproduces this pattern as “generally accepted truth.”

Important: ChatGPT does not treat ratings as numbers; it treats them as “template fact” when they occur frequently.
It does not know that G2 or Capterra are authoritative ratings, but it does know that these names often appear alongside the words “best,” “recommended,” and “according to.”

Why do brands from Wikipedia or major media outlets often appear in responses?

There are three reasons:

1. Data availability and structure. Wikipedia and major media outlets (BBC, NYT, Reuters, Forbes, Wikipedia) are included in GPT training corpora because they are open, machine-readable, and licensable. Consequently, this data is guaranteed to be included in training and used in generation.

2. High citation rate. Other sites trust Wikipedia: it acts as a root reference. The model sees that “almost all sources” refer to Wiki — and perceives it as the primary source of truth.

3. Presence of factual connections. Wikipedia and major media outlets often structure information — dates, names, connections between brands and personalities. This facilitates model training and increases the likelihood that a brand from such sources will be mentioned.

Therefore, brands present on Wikipedia and in major publications are embedded in the “language of truth” of LLM. Their chances of being mentioned are orders of magnitude higher than those of companies whose information is limited to their own website.

Can ChatGPT “learn” from third-party websites, articles, or PR publications?

Yes, but not directly or instantly.

OpenAI does not “add” individual websites to the model at a brand's request.

Any information available on the public internet, without needing to register or pay, is often cited or saved by other sources. If it has a unique and structured form, like lists, facts, reviews, or definitions, it may be included in future model training.

PR and content activity also have an indirect impact:

• Publications in reputable media

• Indexing in search engines

• Archiving in open datasets (Common Crawl, RefinedWeb)

• Inclusion in the GPT training corpus.

Therefore, every publication in the media is a potential “input signal” for ChatGPT's future knowledge.

How do SEO and website structure affect ChatGPT's ability to see it?

It is essential to distinguish between two layers here:
SEO for search engines (Google/Bing). It has an indirect effect. Well-optimized pages are more likely to be indexed, copied, and cited, so they are more likely to end up in open datasets used to train models.

Structured data (Schema.org, JSON-LD, OpenGraph). This is a direct channel of “understanding.” If a website uses markup about the company, products, authors, and contacts, this data can end up in the Knowledge Graph or open databases — and then in the LLM corpus.

Example:
{
“@context”: “https://schema.org”,
“@type”: “Organization”,
‘name’: “PRNEWS.IO”,
“url”: “https://prnews.io”,
‘sameAs’: [“https://et.wikipedia.org/wiki/PRNEWS.io”]
}

This code not only helps SEO, but also helps AI models understand that “PRNEWS.IO” is a real, verified brand.

Does ChatGPT work with Google Knowledge Graph or similar sources?

Not directly, but yes — it uses similar principles.

• ChatGPT does not have direct access to Google Knowledge Graph (it is a closed Google system).
  However, OpenAI creates its own internal “knowledge embeddings” — vector representations of entities (people, brands, places).

• These representations are often built on the same sources as the Knowledge Graph: Wikipedia, Wikidata, Freebase, Crunchbase, IMDB, OpenStreetMap, etc.

• In addition, ChatGPT can access:

• Microsoft Bing Entity Graph (in search mode);

• Wikidata API, if the web browsing feature is enabled.

Thus, ChatGPT operates with an analogue of the Knowledge Graph, but does not depend on Google.

For a brand, this means that if you are represented in Wikidata, Crunchbase, Wikipedia, LinkedIn, and Google Business, your entity will be better “understood” by all major LLMs simultaneously.

To sum up, ChatGPT:

1. Does not make choices manually, but probabilistically recalls brands that are most often associated with the topic, question, and positive context.

2. The decision is based on the density of mentions in reliable sources (Wiki, media, databases).

3. “Trust” is built on coincidences of semantic patterns, not on numbers, ratings, or SEO positions.

4. The more structured and public the information about the brand is, the higher the chance that ChatGPT will “remember” it.

What businesses can do to get ChatGPT to start mentioning their brand

How can you get ChatGPT to “know” about your company?

For ChatGPT to “know” about a company, it must exist in a digital space accessible to machine reading and citation.
The model does not ‘know’ the company directly — it “remembers” what it has already encountered in public sources.

Basic conditions:

• Information about the company is available in open sources referenced by other websites.

• These sources are reliable and indexed (Wikipedia, Crunchbase, media, industry directories).

• The data is consistent — the name, domain, team, and description are the same in all profiles.

• Company content is “visible” in open datasets — a paywall, authorization, or noindex do not block it.

The principle is simple: ChatGPT cannot “know” what is not in the public and reliable digital space.

For a brand to really stand out in these scenarios, you need to:

• Publish thematic articles that accurately match queries;

• Compare yourself with competitors in public texts;

• Get mentions from independent sources (blogs, ratings, media);

• Generate activity in open sources indexed by LLM;

• Pay attention to wording — LLM “remembers” common phrases.

These resources are key points of presence in the context of LLM.

Is it necessary to publish in the media for ChatGPT to start mentioning the brand?

Yes, this is a prerequisite.

ChatGPT does not connect to corporate websites directly, so it is impossible to get into LLM without media presence.

Why publications work:

1. The media is included in the training datasets of GPT and other LLMs (Common Crawl, RefinedWeb, The Pile).

2. The media provides secondary links and citations that amplify the brand signal.

3. The media creates context — where your brand is associated with a specific topic, solution, or industry.

Even a single article in Forbes, TechCrunch, or Reuters carries more “weight” than 1,000 mentions in blogs without authority.

How do PR and content marketing influence ChatGPT's “knowledge” of a brand?

PR and content are channels for generating training data for future LLMs.

Every publication, quote, interview, or review:

• creates a new association between the brand and the topic (for example, “PRNEWS.IO → digital PR platform”);

• increases the frequency of joint mentions (brand + key phrase = a pattern that the model sees);

• strengthens trust (if the publication is from a reliable source).

In essence, PR works like “semantic glue” — it connects your brand with the concepts that models are trained on.

The more frequent and transparent this connection is, the higher the chance that ChatGPT will “remember” you in the right context.

Does it make sense to write guest articles or issue press releases for this purpose?

Yes, and the author of the post is a columnist for Forbes, Entrepreneur, Inc.— subject to the following conditions:

Guest posts & Columns

• Effective if posted on authoritative domains with open access (without a paywall).

• The brand should be mentioned naturally, in the context of expertise (rather than as an advertising insert).

• Be sure to include links to the website and profiles (LinkedIn, Crunchbase).

Press releases

• They work if they are posted through networks such as PRNEWS.IO, BusinessWire, GlobeNewswire, etc.

• The release should be quoted by the media, rather than “drowned out” in distribution.

• One release is a weak signal, but a series of thematic publications forms a stable “presence” in the data.

Key point: ChatGPT does not distinguish between earned and paid publications; it sees texts that are included in open datasets.

The goal is to ensure that your content is among the indexed, cited, and machine-readable texts.

Which sites are best to use for publications: industry media, blogs, or news resources?

How many publications are needed for a brand's “presence” to become noticeable to ChatGPT?

The number depends on:

• the scale of the brand,

• the authority of the sources,

• the frequency of mentions in the same context.

Empirical rule:

• 5–10 publications in highly authoritative media,

• 10–20 industry publications,

• active mentions in open databases (Wikipedia, Crunchbase)

— already create a “semantic footprint” that ends up in general datasets and increases the chance of appearing in ChatGPT responses.

Will mentioning the brand in public reports, research, and reviews help?

Yes, mentions in scientific papers, research, and public reports will help a lot.

Reasons:

• Research and reports are often included in open academic or government archives (arXiv, SSRN, data.gov).

• These documents are used to train LLMs as high-quality sources.

• Mentions there are perceived as “highly reliable connections.”

Examples:

• participation in industry white papers,

• mentions in industry reviews by Gartner, Deloitte, PwC,

• publications on ResearchGate, Medium, SSRN.

Such documents are often cached and cited repeatedly — their trace in the model is much stronger than that of single PR releases.

Does the language of publications matter (e.g., English vs. local)?

Yes — and it is critically important.

• English is a global source of learning. All GPT models (OpenAI, Anthropic, Google, and Mistral) are primarily trained on English-language texts. Therefore, mentioning a brand in English in international media is almost a guarantee that it will be “seen” by LLM.

• Local languages (Ukrainian, Polish, Spanish, etc.) are essential if the brand operates in the region. But they rarely make it into global datasets — so ChatGPT may “know” you locally but “forget” you in English-language responses.

The optimal strategy is a bilingual presence:

• Local publications (for SEO and regional audiences).

• English-language texts in open international sources (for AI visibility).

Can you ask ChatGPT to include a brand in its response, or is it pointless?

For the average user, it's pointless. ChatGPT does not change its knowledge base on request—it does not “learn” from individual dialogues.

Your prompts (“Add my brand to the list”) do not affect the model, only the specific session.

Exceptions:

• Custom GPTs (at the organizational level) — you can train an internal model to include the brand in responses to customers.

• Fine-tuning or Retrieval-Augmented Generation (RAG) — allow you to embed the brand into a corporate chat assistant. But this will not make it into the global ChatGPT.

There is only one way left — organic, through content, data, and trust.

The technical side of the issue

What is LLM (Large Language Model) and how does it “know” brands?

A LLM (Large Language Model) is a neural network. It predicts the next word in a text by looking at billions of examples from human speech and documents. The language model doesn’t store data like tables or databases. It encodes knowledge as numbers that link words, phrases, and concepts together (vector representations).

When a user asks, “What platforms are there for distributing PR news?”, the model recalls patterns. It often sees “PR distribution” with “PRNEWS.IO,” “BusinessWire,” “Newswire,” and “GlobeNewswire.”

Thus:

• LLM does not “search” for brands, but generates them from probabilistic connections;

• It “knows” a brand only through texts where it was mentioned — in Wikipedia, the media, databases, etc.;

• The more a brand shows up with related words, the more likely ChatGPT will mention it in its response.

In other words, LLM “knows” a brand not because it exists, but because it is embedded in the language of the industry.

Are there any APIs or tools that can be used to “add” your brand to ChatGPT data?

Not directly. OpenAI does not accept user data for inclusion in global models.

ChatGPT is a closed, centrally trained system. But there are three real ways to influence it:

1. The data route (external). Publishing information in open sources that are included in training datasets (Wikipedia, Wikidata, Crunchbase, media). This influences future versions of the model.

2. The API route (temporary, contextual). Using Retrieval-Augmented Generation (RAG), you can provide the model with your data (e.g., brand database, press releases, descriptions) with each request. ChatGPT “reads” them and uses them in its response, but it does not permanently retain them.

3. The path through corporate integrations (persistent custom data). In ChatGPT Team/Enterprise, you can “attach” a knowledge base so the brand is considered in internal employee requests. This only affects the organizational version of the model, not the global ChatGPT.

OpenAI does not allow you to directly “upload” a brand into a publicly available model — any promises to “add a brand to GPT” outside of these channels are fake or manipulative.

Is it possible to influence ChatGPT through the OpenAI API (fine-tuning, custom GPTs)?

Partially yes, but with limitations.

1. Fine-tuning (model retraining). Allows you to “customize” the model to your brand, style, and terminology. You can upload your texts, product descriptions, FAQs, and the model will start generating responses in your tone, using branded terms more often. However, fine-tuning only works for private models (via API) — global ChatGPT will not change.

2. Custom GPTs. In ChatGPT (Explore GPTs tab), you can create your own “version of ChatGPT” with your instructions and files. It's a kind of “personalized assistant” that knows your brand and responds in a given context. Such GPTs can be distributed publicly, but they still do not affect the base model.

3. RAG (Retrieval-Augmented Generation). An alternative to fine-tuning: the model is not retrained, but “reads” external documents before responding. Example: if your website or database is connected via API, ChatGPT can retrieve fresh data from it with each request.

In practice, brands use a combination of:

• Custom GPT (for demos and customer support)

• RAG (for integrating fresh data)

• Fine-tuning (for tone and terminology)

How does fine-tuning work, and could it be a solution for brands?

Fine-tuning is the process of retraining a model using additional text.

You provide OpenAI (or another LLM provider) with your data corpus, and the model “refines” the probabilistic connections within itself.

How it works:

• Your texts are collected: press releases, descriptions, training materials, case studies, FAQs.

• All of them are converted into a “prompt-response pairs” format.

• The model goes through several training cycles, adjusting the weights.

• The result: it starts to mention the brand more often and more accurately in relevant contexts.

Conclusion: Fine-tuning is effective as a brand training tool for private models, but it will not affect what ChatGPT says to all users worldwide.

This requires publications in open sources.

How does ChatGPT differ from Google Search in terms of generating responses?

ChatGPT is not a search engine, but a language knowledge simulator. It does not rank websites, but recreates the meaning it has “understood” from a variety of sources. Therefore, the usual SEO tricks for Google optimization (metatags, keywords, internal links) do not directly affect ChatGPT.

For a language model, essence and meaning are important, not “text optimization.”

What role do structured data (Schema.org) and open databases play?

A huge one. Structured data is “a language that machines understand.”

Structured data is special metadata added to a website to explain what your brand, product, organization, or person is.

Example:
{
“@context”: “https://schema.org”,
“@type”: “Organization”,
“name”: “Medialister”,
‘url’: “https://medialister.com”,
“description”: “Editorial media advertising marketplace”,
“founder”: {
“@type”: “Person”,
‘name’: “Alexander Storozhuk”
},
“sameAs”: [
“https://www.linkedin.com/company/medialister”,
“https://www.crunchbase.com/organization/medialister”
]
}

How this helps ChatGPT:

• Creates connectivity between sources — LLM “sees” that LinkedIn, Wikipedia, and the website belong to the same entity.

• Facilitates brand recognition as an “entity” (a vector entity, not just a word).

• Increases the likelihood of inclusion in the Knowledge Graph, from which data can be transferred to training datasets.

The more accurate and clean the Schema.org markup is, the higher the chances that the model will correctly “understand” your brand and not confuse it with others.

Can knowledge panels be used to make ChatGPT “see” a brand?

Yes, this is one of the most direct ways to “enter machine knowledge.”

A knowledge panel is a card on the right side of Google with facts about a brand, person, or company.

It is built from data:

• Wikidata / Wikipedia,

• Google Business / Maps,

• Crunchbase / LinkedIn,

• Structured data from the website.

Why is this important:

• LLMs are trained on copies of public knowledge graphs, including Wikidata, Freebase, and Bing Entity Graph.

• If your brand has a Knowledge Panel, it means that it already exists as an “entity” in the semantic space of the internet.

• In the next LLM training cycle, this entity is almost guaranteed to be included in the model.

In other words, Knowledge Panel = “machine recognition of the brand.”

Summary: How to make a brand “visible” to ChatGPT technically

Practical steps: a strategy for business

How to build a ChatGPT visibility strategy for your brand?

A brand visibility strategy in ChatGPT is a combination of PR, content marketing, SEO, and semantic data engineering.
The main goal is to make the brand recognizable to language models (LLM) in the same way that SEO makes it visible to search engines.

5-step AI Visibility model:

1. Define

• Register the brand on Wikidata, Crunchbase, and LinkedIn.

• Create JSON-LD markup on the website.

• Ensure that the name, description, and URL are the same everywhere.

2. Connect

• Links between profiles: website → Wikipedia → LinkedIn → Crunchbase.

• Use “sameAs” everywhere to confirm identity.

3. Publish

• Place publications in industry and news media.

• Tell your story, share case studies and innovations — create context around your brand.

4. Sustain (maintain your information presence)

• Regularly update content, write guest articles, comment on news.

• Create content that others will quote.

5. Measure (assess recognition)

• Check whether the brand is mentioned in ChatGPT, Perplexity, Gemini, Claude.

• Track the growth of content in search and in the Knowledge Graph.

ChatGPT visibility is a marathon, not a sprint. It requires a systematic approach, like SEO, but the effect is long-term and large-scale.

What tools can help you manage your online reputation with an eye toward LLM?

Traditional reputation monitoring tools now need to be supplemented with AI-aware analytics.

Here is the structure of the tools:

What role does PR (earned media) play in increasing the chances of being mentioned?

PR is the foundation of AI visibility. ChatGPT and other models do not see advertisements, but they do see public texts, especially in authoritative sources.

Why earned media matters:

• Models are trained on data where the media serves as the “source of truth.”

• Articles on Forbes, Reuters, TechCrunch, VentureBeat, Medium, etc. are almost guaranteed to be included in training sets (Common Crawl, RefinedWeb, WebText).

• Mentioning a brand in such materials creates an “anchor of trust.”

PR = “machine trust capital.”

Unlike advertising, which disappears, media publications live for years and are indexed in various systems (web, Knowledge Graph, LLM datasets).

What role do your own blog and content marketing play?

Your own blog is a long-term source of data that “feeds” models.

Advantages:

• A blog is a constant generator of “thematic context”: brand ↔ key topic.

• Blog content often ends up in Common Crawl, the main open dataset used by LLM.

• Content can be quoted in guest posts, creating “chains of meaning.”

What's important:

• Not SEO texts, but expert materials with natural wording.

• Publish on behalf of experts (people strengthen “entity linking”).

• Add structured data (Article, Author, Organization).

• Use English-language versions — they are more likely to be included in international datasets.

Formula: Content = signal of brand existence + context of its expertise.

What should you do if ChatGPT makes a mistake when describing your brand? Can it be corrected?

Mistakes are possible because ChatGPT:

• “remembers” old data (for example, before 2023),

• combines facts from similar brands,

• does not have an official source of validation.

What to do if ChatGPT provides false information:

1. Check if there is correct information about you in open sources (Wikipedia, Crunchbase, Wikidata).

2. Add or correct this data (correctly formatted edits on Wikipedia and Wikidata really make a difference).

3. Create PR publications with correct facts (preferably in authoritative publications).

4. Use your blog or Medium publication with the “official brand history.”

5. Monitor ChatGPT's responses every 2-3 months — model updates and browsing can correct old inaccuracies.

OpenAI does not yet offer a way to “request an edit,” but changing external information (in Wiki/media) really works — the model will “re-learn” in subsequent iterations.

How to measure the result — understand what ChatGPT “knows” about the brand?

There is currently no official API for “checking visibility” of a brand in LLM, but you can use a combination of approaches:

1. Direct checks (manual prompts) that will indicate: if the brand appears naturally, then it is “in the memory” of the model. Example queries:

• “What do you know about [Brand Name]?”

• “Which companies offer [category] solutions?”

• “Top platforms for [industry/topic]?”

2. Competitor check: if the model correctly describes the differences, then it “knows” you. Example query

• “Compare [Brand] vs [Competitor]”

• “[Brand] alternatives”

3. Cross-check in different LLMs: if the mention is repeated in several systems, then the brand has established itself as an entity in their knowledge graphs.

• ChatGPT (OpenAI)

• Claude (Anthropic)

• Gemini (Google)

• Perplexity.ai

4. AI brand monitoring tools: track the appearance of brands in LLM responses and generate an AI Visibility Score — similar to an SEO rating, but for neural networks.

• Peec AI,

• Profound

Are there any examples of brands that appear in ChatGPT's responses?

Yes — categories of “AI-friendly brands” have already been formed, which consistently appear in the models' responses.

What mistakes do companies make when trying to “get ahead” with AI?

1. They think ChatGPT is Google. They write SEO texts instead of expert ones. → It doesn't work. ChatGPT “reads meaning,” not keywords.

2. They create an unstructured digital footprint. Different company names, other descriptions, unrelated profiles. The model “doesn't understand” that this is the same entity.

3. They don't create a digital footprint. Brand managers think that social media activity is enough and plan to rely solely on viral content.

4. They ignore publications in alternative languages. Even successful local brands are “invisible” to ChatGPT if there is no English-language content.

5. They try to “ask ChatGPT to add a brand.” This is impossible — the model does not learn from dialogues.

Potential and ROI for business

Does it make sense to invest in PR just to maintain a presence on ChatGPT?

Yes — and this is already a new business logic for PR investments. Whereas companies used to invest in PR for media mentions, social proof, audience engagement, and customer acquisition, there is now a clear new goal — to become part of “AI knowledge.” So that when someone asks ChatGPT to “compare bikes for a six-year-old,” it mentions your brand, arguing its merits.

Why this makes sense:

1. AI assistants are becoming the “new search engines.” Already today, up to 40% of Gen Z users search for information not on Google, but on ChatGPT or Perplexity. If your brand is not mentioned there, you are losing future organic traffic.

2. PR publications live longer than advertising. An article in Forbes or TechCrunch ends up in model training corpora and circulates for years.

3. “Earned presence” replaces paid clicks. If ChatGPT recommends your brand without advertising, you save on CAC (cost of acquisition).

4. Early players get the first-mover advantage. Right now, competition in AI visibility is low, but by 2026, it will become the same market as SEO in the 2010s.

PR is now not only about reputation in the media, but also an investment in your brand becoming part of humanity's “AI memory.”

How to measure the return on investment of PR activities that influence AI mentions?

The problem with traditional PR is that it is difficult to prove ROI. AI Visibility correlates well with business metrics and financial indicators.

Compare the growth in AI visibility with the change in organic leads (without advertising).

Track whether brand mentions in AI responses change before a jump in organic traffic.

Use customer surveys (“How did you learn about the brand?” — ChatGPT / Perplexity).

ROI calculation

ROI = New leads coming from AI recommendations × LTV / Cost of PR campaign

If the brand started appearing in ChatGPT responses and leads through organic channels increased in 3–6 months, then PR investments are working as AI Visibility PR.

Is it worth creating a separate “AI visibility” budget?

Yes. Leading marketing departments in the US and EU are already doing this.

Recommendation for budget allocation:

Optimal scenario:

Create a mini-fund called “AI Presence”, responsible for systematic brand recognition in neural networks.

In 1–2 years, the ROI from this direction will be higher than that of classic SEO, because competition is still low.

Will this be the new SEO — “LLM Optimization”?

A new industry is already taking shape that will become the successor to classic SEO — and this industry combines three interrelated areas: GEO, AEO, LLO.

What is GEO — Generative Engine Optimization

GEO is optimization for generative search engines (such as Perplexity, Google SGE, You.com, ChatGPT with browsing, Gemini, Copilot).

Unlike traditional SEO, where the goal is to get on the first page of results, in GEO the goal is to get into the text of the answer that the AI creates.

Example:

When a user asks, “What PR platforms help brands get into the media?”, Perplexity, ChatGPT, or SGE do not show a list of links — they generate a response in which they insert brands that are already known to them as relevant (for example, PRNEWS.IO, Cision, BusinessWire).

What is optimized in GEO:

• content accessibility for generative models (open access, machine readability, citation-friendly structures);

• reliability and citability of sources;

• brand connections to key concepts in training data.

GEO is optimization for generative algorithms that create answers rather than provide links.

What is AEO — Answer Engine Optimization

AEO is the predecessor of GEO, which emerged in response to voice and conversational assistants (Siri, Alexa, Google Assistant). Its goal is to make your brand the “answer” rather than just a search result.

The principle of AEO: models such as Siri and Google Assistant take the “single best answer” to a query, relying on structured data, FAQ markup, and Google Knowledge Graph.

To get there, companies started using Schema.org / JSON-LD, writing structured answers, and creating FAQ pages that machines could easily “understand.”

Evolution:

• In AEO, brands optimized web content for short answers.

• In GEO, brands optimize their entire digital ecosystem for generative models, including PR, databases, and media.

• AEO can be considered an early stage of GEO — it taught brands to write “understandably for machines.”

What is LLO — Large Language Model Optimization

LLO is the next step after GEO and AEO. While AEO and GEO optimize web pages, LLO optimizes the semantic essence of a brand — how it is “understood” by the language model (LLM) itself.

The main idea is that LLM does not index websites — it learns from text and context. To be “known” to ChatGPT, a brand must:

• exist in open knowledge bases (Wikipedia, Wikidata, Crunchbase);

• have cited mentions in the media and blogs;

• be associated with concepts and keywords that are common in its industry.

LLO example:

The Medialister brand should appear in texts alongside phrases such as “editorial advertising,” “guaranteed media placements,” “digital PR marketplace,” etc.

Then, when asked a question like “tools for guaranteed media placement,” ChatGPT will “remember” this brand as relevant.

LLO is not about technical SEO, but about the semantic presence of a brand in the language of the industry.

In 2–3 years, companies will optimize not “pages” but “brand entities” to be included in ChatGPT, Gemini, Claude, and Perplexity.

This is a new renaissance of the answer economy, where “optimization for machines” becomes “optimization for models.”

Ethical and Legal Considerations

Can a brand pay to be mentioned by ChatGPT?

No. OpenAI, Anthropic, Perplexity, and other LLM developers do not sell paid brand mentions in their models' responses. ChatGPT does not have advertising “slots” or “paid integrations” like Google Ads.

First, the model's architecture is not designed for commercial data insertion. It generates responses based on word probabilities, not a “brand catalog.”

Second, OpenAI's policy prohibits paid mentions. Any attempt to “buy” a mention is considered a violation of the terms of use.

Third, there are risks to trust. If users found out that ChatGPT mentions brands for money, trust in the model would plummet.

This is unacceptable to OpenAI.

However, brands can legitimately “earn” visibility through publications, open databases, content, and mentions that end up in model training sets.

This is earned visibility, not paid promotion.

Is it legal to “influence” ChatGPT training?

Influence — yes, manipulate — no.

There is no legal prohibition on creating public content that may later end up in ChatGPT data sets.
It is legal if:

• the content is open, authentic, and does not violate copyright;

• you do not falsify data, spread misinformation, or create artificial pages to “embed” a brand in LLM.

Illegal or unethical practices:

• mass creation of fake articles with fabricated reviews;

• generating content under the guise of independent sources (“astroturfing”);

• purchasing links and mentions with the aim of “deceiving” the model.

The ethical way: create real, useful, open educational content that will be indexed, cited, and perceived as reliable. Such content naturally becomes part of the training data for future LLMs.

What to do if ChatGPT spreads false information about a brand?

LLM has no intentions, but it can “hallucinate” — create inaccurate or outdated facts.

If ChatGPT is wrong about a brand, it is important to act systematically, not emotionally.

Steps:

1. Document the error. Take a screenshot and record the date and model version (e.g., “ChatGPT GPT-4, 2025-10-22”).

2. Check the original sources. The error may come from an old Wikipedia, Wikidata, Crunchbase, or news site.

3. Correct the information in open sources.

• Update Wikipedia / Wikidata.

• Send updates to Crunchbase or LinkedIn.

• Publish the latest facts in the media.

4. Report the error via the OpenAI form.

• OpenAI Help → Feedback → Incorrect Information About You or Your Organization.

• OpenAI accepts reports of errors in content that affect reputation.

• Google (for Gemini/SGE) allows you to send “removal requests” through the standard form for removing information from search results (if personal data is affected or copyright is violated).

5. Publish a public clarification. An article or blog post titled “Correcting AI misinformation about [Brand]” will strengthen trust and create a new signal for future training models.

Models do not correct responses instantly, but the corrected sources will be included in training during the next dataset update.

Is there a risk to the brand if the mention is incorrect?

Yes. Even without malicious intent, the model may:

• combine two similar brands,

• confuse data about revenue or founders,

• create false context (“this company has closed” or “is in litigation”).

Potential consequences:

• Loss of trust among customers and investors if false information is reproduced by users.

• Reputational risks in the media and social networks (AI errors often go viral).

• Decreased conversion — customers who check the brand through ChatGPT see incorrect information.

What to do:

• Implement AI Reputation Monitoring (Peec AI, Profound).

• Set up LLM alerts — check monthly how the model describes the brand.

• Create an official information center on the website — a page with verified facts about the company (machine-readable factsheet).

This approach creates a “source of truth” that models will refer to when generating responses.

How does ChatGPT comply with copyright when using brand content?

OpenAI and other LLM developers state that:

• models are trained on publicly available texts,

• the principle of “fair use” (good faith use),

• the content is not copied but used for statistical training—that is, the model does not store the text “verbatim.”

What this means legally:

• ChatGPT does not infringe copyright if it simply “knows” about your brand from open sources.

• But if the model reproduces text or images almost verbatim, the copyright holder can file a complaint (especially for commercial use).

OpenAI provides a form for filing DMCA (Digital Millennium Copyright Act) complaints if content has been reproduced in violation of the license.

For brands:

• Post content with a CC BY or CC BY-SA license if you want models to be able to use it.

• If you don't want your texts to be used for training, specify this in robots.txt or the HTTP header “noai/noimageai” (2024 standard).

Example:
User-agent: GPTBot
Disallow: /

Why is this revolutionary?

If SEO was a battle for Google, then GEO is a battle for the mind of LLM. If brands used to fight for links, now they fight to be embedded in the generation of meaning.

1. AI agents are becoming a new point of entry to information

• More and more users are requesting information not from Google, but from ChatGPT, Gemini, Perplexity, Claude, and Copilot.

• LLM agents are no longer just assistants: they are becoming navigators of the brand world.

• Example: “Recommend a service for publishing press releases” — and the model immediately returns 2-3 brands. If you're not there, you don't exist.

2. Context is more important than search ranking

• GEO doesn't require “first place in search.” Instead, it's important to be part of the embedding space so that LLM “thinks” about you in the right context.

• This shifts the focus from SEO for Google robots to semantic recognition among models.

3. Retrial of AI interfaces replaces websites

• Users don't click on links — they trust AI summaries and recommendations.

• Companies that are not included in LLM's “knowledge” will not even be considered.