What Does an LLM Actually Know About Your Brand and Does It Match the Truth?

Right now, buyers in your category are asking ChatGPT, Perplexity, and Google AI Overviews about you. They are asking what you do, what you charge, who you work with, and how you compare to alternatives. The AI is answering. You have almost certainly not checked what it is saying.

85% of B2B buyers now form their vendor shortlist through generative AI research before contacting sales. 73% of consumers have made a purchase based on an AI recommendation. The AI answer to a category query has become, for a large and growing proportion of buyers, the equivalent of a first-page result except it is not a list of links. It is a confident, synthesised statement about which brands exist, what they do, and which ones are worth considering. Harvard Business SchoolFullstory

If that statement is inaccurate, the buyer has no mechanism to correct it. There is no competing result alongside it. There is no reason to doubt it.

But before thinking about what to do, it is worth understanding that not all AI systems work the same way. The strategy that matters for Google AI Overviews is different from the one that matters for ChatGPT or Claude and conflating them produces effort aimed at the wrong problem.

Two Different Systems. Two Different Problems.

Google AI Overviews use retrieval-augmented generation. This is a technique used to improve the quality, accuracy, and freshness of AI responses by relying on Google’s core Search ranking systems to retrieve relevant, up-to-date web pages from the Search index. The systems then review the specific information from those retrieved pages to generate a more reliable and helpful response. The Re-Wired Group

For Google AI features, the live search index is the source of truth. Visibility in AI Overviews is connected to the same signals that drive traditional ranking: crawlability, indexed content, quality, and E-E-A-T. The best practices for SEO continue to be relevant because Google’s generative AI features are rooted in the core Search ranking and quality systems. If you are indexed, ranking, and producing genuinely useful content, you are in the pool for AI Overviews. The Re-Wired Group

Third-party LLMs like ChatGPT, Claude, and Perplexity in their base forms work differently. Training data forms a model’s foundational knowledge: what it knows, what associations it has formed, and which brands it considers credible authorities in any given category. When ChatGPT recommends a brand without supplementing its knowledge with web search, that recommendation is coming from the model’s foundational knowledge. Summit Partners

For these systems, your current Google rankings are not the sole input. What the model knows about your brand was shaped by what was written about you across the open web: editorial coverage, third-party analysis, industry directories, comparison posts, review platforms absorbed and weighted before a training cutoff that you had no input on and may not know. If you launched a new product line in 2024 but the model’s core training set cuts off in 2023, the AI may still describe your brand incorrectly, without the update you made. WARC

These are two distinct problems requiring two different approaches. Treating them as one leads to wasted effort.

What Gets Wrong in Third-Party LLMs and Why

For general knowledge questions which includes brand facts, pricing, and product details, the average hallucination rate across models is 9.2%. That is roughly one in eleven AI responses about your company containing fabricated or incorrect information. Fullstory

The failure modes follow predictable patterns. Incorrect founding dates and leadership are common, with smaller brands frequently finding that LLMs assign wrong founding years, invent co-founders, or attribute leadership changes that never happened. False product capabilities lead prospects to believe you support a feature you do not ship. Wrong competitor set or category placement causes you to disappear from AI recommendations even if you rank well in traditional search. Acquisition or shutdown claims are stated with confidence and are simply untrue. Black Camel Agency

A common pattern is competitor bleed. Attributes from better-known companies in your space get assigned to your brand because the model associates those features with the category, not the specific company. If a larger competitor offers a capability you do not, there is a meaningful probability that model attributes that capability to you. The buyer acts on it. The reality disappoints. Black Camel Agency

The highest-risk hallucinations are the ones that change buying decisions or trigger support, legal, or partner fallout. A misquoted price point loses a lead before they reach your website. A wrong capability claim creates expectation debt your sales team has to resolve. Black Camel Agency

The Audit Every Marketing Leader Needs to Run

Most brands have no systematic picture of how AI models currently represent them. The starting point is straightforward, takes less than an hour, and will almost certainly surface something worth attention.

Query your brand across at least four platforms: ChatGPT, Claude, Perplexity, and Gemini. Do not only ask “what is [brand]?” That question will often return a broadly accurate summary. Ask the questions buyers actually ask. What does [brand] charge? What are [brand]’s core capabilities? How does [brand] compare to [competitor]? Who are [brand]’s typical clients? Document every response in full.

A brand that appears accurately in Perplexity may be wildly misrepresented in Gemini or ChatGPT. Each model sources, processes, and generates information differently and produces entirely different errors about the same company. Fullstory

You are looking for one of four conditions: absent entirely, present but outdated, mischaracterised, or present and accurate. Most brands, running this audit for the first time, encounter a mix of the first three.

What Accurate Representation Is Actually Built From

For Google AI Overviews, the guidance is clear and consistent with what has always driven organic performance. Creating content that people find unique, compelling, and useful will likely influence a website’s presence in generative AI search in the long run more than any other approach. A unique point of view, non-commodity content that is helpful and people-first, and content organised for human readers. These are the foundation. The Re-Wired Group

For third-party LLMs, the signal set is different. What these models know about your brand was shaped by the quality and consistency of what others wrote about you across authoritative sources. Editorial coverage in industry publications, named expert profiles with consistent attribution, original research cited by others, and genuine third-party analysis. These carry weight in training data because they represent the kind of sources models were trained to treat as reliable.

Just like the rest of Google Search, generative AI features can show what is being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic mentions across the web is not effective. Generative AI features depend on high-quality content and spam-blocking systems alike. The Re-Wired Group

The emphasis here is on authenticity and genuine depth, not volume. Brands that have invested in real thought leadership, original research, and editorial presence are the ones represented most accurately across AI systems. Not because they engineered it, but because quality coverage at scale is what both Google and third-party models treat as a reliable signal.

This connects to a broader truth that the last decade of performance marketing made easy to overlook. The editorial discipline (original research, named authorship, consistent third-party presence) was always the foundation of brand authority. AI search has simply made the cost of underinvesting in it visible in a new and more immediate way.

The audit tells you where you stand. What you do about it depends entirely on which system has the problem.

Sources: Google Search Central · AI Optimisation Guide (May 2026) · MIT NANDA Initiative 2025 · Bain and Company B2B Buyer Research 2025 · AllAboutAI Hallucination Report 2025 · ReSO LLM Brand Analysis 2026 · Yotpo AI Visibility Research 2026