AI is scanning your website (and it’s probably making things up about your brand & business)

AI knows more about your business than you might expect. It scans your website, analyzes mentions, gathers case studies and reviews, and creates a narrative about your brand—one ready to be shared with millions of users.

Nearly 58% of Google searches are now accompanied by AI summaries, and users are far less likely to click on links when these summaries show up. If your online presence isn’t clear and organized, AI may craft its own version of your brand. And it might be a very bizarre version of your brand, something you wouldn’t expect (and want) to see online.

Let’s explore how various AI tools shape brand perception and what you can do to make sure they’re telling your story accurately.

How AI sees your brand

Artificial intelligence doesn’t just read the text on your website. It interprets it. It considers customer reviews, media coverage, and even comments from forums. According to the Stanford Institute for Human-Centered Artificial Intelligence (HAI) and its AI Index 2025 Report, modern generative models heavily rely on open web data to learn and generate responses.

Every mention of your business becomes a piece of a semantic network, allowing the AI to construct an image of your brand. It identifies patterns, assesses their frequency and credibility, and synthesizes all this into what could be called an “average truth.”

These models don’t prioritize tone or strategic context. They simply focus on what’s most often repeated and seems the most consistent. If different sources describe your company in conflicting ways, the model assumes all are valid and fills in the gaps itself. For example, if your website highlights helping e-commerce stores grow revenue, a news article from a few years ago labels you as a “digital-first company,” and press releases refer to you as a “marketing agency,” the AI tool won’t simply choose one narrative. Instead, it will merge them into a hybrid identity where you’re a digital agency, a marketing studio, and, curiously, a “neural network provider for e-commerce.” Wait! Why would it even do something like that?

Why AI often gets it wrong

There’s never just one reason why AI makes mistakes, mixes things up, or invents details out of thin air. Here are the key culprits.

1. Outdated data

Most models are trained on vast amounts of web content, including websites, blogs, e-books, forums, catalogs, and articles, but this data can quickly become outdated. Updates about your company’s new direction, additional services, or even relocation may not be reflected in the model’s training set. As a result, AI might describe your business as it was years ago, rather than how it operates today.

2. Mixing sources

AI systems rarely distinguish between official websites, old business directory entries, Reddit comments, or outdated media articles. They merge these fragments into one narrative, often overlooking the varying levels of credibility and relevance among sources.

3. The context problem

AI struggles with nuances. Metaphors, irony, and marketing language can be taken literally. For instance, the phrase “We work like a Swiss watch” might be misinterpreted as a geographical reference or linked to watchmaking. This leads to false associations, incorrect geotags, and distorted descriptions. Researchers like Neil Sahota note that “most generative models rely on statistical correlations rather than true semantic understanding.”

4. Algorithmic averaging

Models like ChatGPT, Gemini, or Claude don’t conduct interviews, call your office, or verify information manually. They rely on statistical probability. If a thousand sources make similar claims, the model assumes they are true. This also means they can be easily manipulated by bad actors. Creating a thousand websites with false claims is easier than ever. In fact, AI website builders can help you do it in no time.

5. Error and hallucination rate

Even when trained on accurate data, large language models can still hallucinate, generating plausible yet fabricated facts. The OpenAI Transparency Report (2024) highlights the “hallucination rate,” which shows how often a model produces content not backed by real sources. Research from the PersonQA benchmark found hallucination rates between 33–48% for responses about companies and individuals, varying by model. Meanwhile, this analytical review by Drainpipe.io from 2025 reports average error rates of 2–5%, climbing to 10% or higher in specialized fields like finance and healthcare.

What does this mean for your brand?

OK, we get it, LLMs make a lot of mistakes. Is it really that bad, though? It’s just an app on people’s phones where they can ask questions and sometimes get gibberish instead of a fact-checked answer, right? 

Not quite. As AI becomes increasingly ubiquitous, these mistakes can (and will) cause real damage to your brand and business. Here’s how.

Reputational risks

If AI misinterprets your brand, you risk losing trust before users even visit your website. For example, a potential client might ask, “Who creates the best turnkey corporate websites in Budapest?” (spoiler: we do, contact us for rates) and receive an answer that either omits your company or inaccurately describes it. This creates an artificial reputation shaped by algorithms rather than your own communications.

A real-world example highlights this risk. In 2024, Air Canada faced reputational and legal issues when its corporate chatbot gave outdated information about bereavement fares. This misinformation led to a lawsuit, with a tribunal ruling the airline liable for negligent misrepresentation. What started as a technical error spiraled into a loss of public trust and financial damage.

Generative AI can perpetuate incorrect or outdated facts about your company, misrepresenting its size, geography, or services. When this happens, customers may struggle to decide which version of your brand to trust: the one on your official website or the AI’s depiction. According to this PwC AI Business Survey 2024, over 54% of executives report that AI-generated responses have influenced perceptions of their brand’s accuracy.

Losing control of communication

AI is rapidly becoming the first point of contact between brands and audiences. Numerous studies affirm this shift: a growing share of users in North America and Europe now turn to AI tools for information. For instance, according to AP News, more than 60% of U.S. adults have used AI to look up facts or recommendations.

This means your official website and traditional search rankings are no longer the only channels shaping public perception. Your brand now “lives” in AI-generated summaries. And if that representation is inaccurate or outdated, it can skew how users perceive your credibility, expertise, and relevance. What you actually say matters less than how AI interprets and retells it.

Loss of your unique brand voice

Generative systems tend to homogenize tone and vocabulary, turning once-distinctive brand communications into generic, corporate-sounding output. If your messaging isn’t clear and consistent across platforms, AI will default to the “industry average” instead of your authentic voice.

As a result, nuance disappears. The tone, emotion, and personality that make your brand memorable are replaced by safe, predictable phrasing. This Gartner study from 2025 found that 70% of marketers believe generative AI creates a “brand depersonalization effect,” erasing differentiation and weakening audience connection in digital spaces.

Basically, it can simply kill your marketing, PR and brand positioning, no matter what you do to stop that. So you need to always know what AI chatbots and search engines are telling their users about you and your business.

How to check what AI says about you

Verifying the accuracy of AI-generated information about your company is becoming essential for strategic reputation management. This process ensures that your brand data is consistent, current, and aligned with your official communications.

Audit response systems

Use leading generative and answer-based platforms, like ChatGPT, Gemini, Claude, and Perplexity, to see how they describe your brand. Use direct queries such as:

• “What do you know about [Company Name]?”
• “What services does [Company Name] provide?”
• “Where is [Company Name] located?”

Record the responses and compare them across platforms to identify any inconsistencies in wording or factual details.

Analyze data sources

Platforms like Perplexity and Google AI Overviews often provide links to the sources used in their responses. Identify which sources mention your brand most frequently, whether it’s your official website, media coverage, partner directories, press releases, or third-party references. Prioritize keeping information accurate and up-to-date across the sources your company controls directly.

Compare with official information

Create an internal report or spreadsheet documenting each AI-generated response, including:

• source or platform
• a summary of the information provided
• level of accuracy
• notes or recommended corrections

Check the consistency of communications

Evaluate how consistently your company is represented across major platforms, your official website, corporate profiles, partner directories, and press materials. Discrepancies in mission statements, founding dates, service descriptions, or contact information create noise instead of clarity for AI systems, increasing the likelihood of misinterpretation.

Assess the perception of the value proposition

Beyond factual accuracy, review how AI systems interpret your company’s positioning and competitive advantages. If the responses sound generic or overly broad, it indicates that your brand messaging lacks clarity or differentiation in publicly available sources.

Use AEO principles to correct perceptions

Verification is just the first step. If AI systems contain outdated or distorted information about your company, identifying the problem isn’t enough. You need to retrain the algorithms with accurate, consistent signals.

This is where Answer Engine Optimization (AEO) becomes essential. AEO is a strategic approach that helps generative and answer-based systems retrieve and present accurate, consistent, and verifiable information about your brand. By applying AEO principles, you ensure that machines understand your positioning as clearly as your audience does. 

So, let’s find out what AEO actually is and how to stay on top of it.

What is Answer Engine Optimization (AEO)?

Answer Engine Optimization (AEO) is a new discipline at the intersection of search engine optimization and artificial intelligence. Basically, it’s SEO but for LLMs.

In classic SEO, the goal is to maximize your brand’s visibility in search results, attracting clicks, increasing click-through rates (CTR), and climbing up the rankings. The primary focus is on the user entering a query and clicking a link. Success signals include keywords, backlinks, and organic traffic volume.

AEO, however, addresses a different challenge: it’s not just about being noticed but about being understood. The focus shifts from the user to the model generating the answer. AEO cares less about click-throughs and more about the quality of the data, how clear, consistent, and authoritative it is, and whether it comes from trusted sources. 

While SEO is measured by ranking position and clicks, AEO is evaluated based on how accurately and how often AI cites your brand in its responses.

According to this Gartner prediction, by 2026 traditional search engine volume will decline by around 25%, as users increasingly turn to AI-powered answer engines. It’s a monumental marketing shift that can’t be overlooked. This signals that SEO and search engine result pages are no longer the primary discovery tools for a brand’s online presence.

How AEO Works

Modern AI models don’t just search the web like search engines did. They analyze, compare, and synthesize information from multiple sources. 

When a user asks, “Which agencies specialize in migrating corporate websites to Webflow?”, the AI doesn’t look for a single website. Instead, it constructs a synthesized answer through several key stages of data processing. Here are these stages:

1. Source scanning

The model reviews hundreds of pages, from company websites and case studies to media publications and directories. According to BrightEdge (2024), 68% of online search queries are already phrased as questions, forming the foundation of the answer-driven search landscape.

2. Pattern detection

The AI searches for recurring patterns across descriptions, company names, and case studies. Information that appears consistently across multiple sources receives higher weighting.

3. Authority assessment

The model evaluates whether data aligns across credible sources, such as official websites, trusted industry media, and verified directories. Consistency across authoritative platforms enhances a brand’s semantic credibility.

4. Response synthesis

After analyzing all signals, the model generates a statistically probable, aggregated response. If your brand’s descriptions are clear, specific, and consistent, AI is likely to cite you as an example. If not, you might remain invisible among competitors.

How to manage what AI knows about you

The good news is that LLMs don’t create information from scratch. But they do look for the most consistent and repeatable signals. This means you can influence what it sees and cites. The key is to make your content clear, structured, and up to date.

Update content

Carefully review your website. Website copy, the “About Us” section, service descriptions, dates, and facts. Remove outdated pages or mark them as archived. Add precise statements i.e. “Founded in 2019 in Vienna. We develop corporate websites and analytics systems for medium and large enterprises.” Even small clarifications significantly reduce the likelihood of misinformation.

Create an “About Us” page with a clear identity

The About section is one of the first places search engines and AI models look to determine who you are and what you do. For algorithms, it’s not just a company overview; it’s a primary source of truth, containing definitions, mission statements, and industry descriptions.

To ensure accurate interpretation of your brand, your About Us page should clearly and concisely answer these four questions:

• Who you are: company name, structure, founding date, and location.
• What you do: core business and main services.
• Why it matters: your value proposition, mission, and why your approach is unique.
• What sets you apart: factual differentiators like case studies, partners, or quantifiable results.

Avoid vague phrases like “we create innovative solutions”. Word spaghetti dilutes your brand identity. AI models interpret clarity, consistency, and precision as signals of credibility. The more cohesive and concrete your messaging, the higher the chance it will be accurately represented in AI-generated responses.

Consolidate your brand voice

AI interprets your brand through recurring linguistic patterns. If you refer to yourself as a web agency in one source, a digital studio in another, and simply an IT company elsewhere, the algorithm may treat them as three separate entities.

To ensure AI systems associate all mentions with a single, unified brand, align your tone of voice and core terminology across every channel, your website, social media, directories, press coverage, and partner listings.

According to the Gartner Digital Experience Report 2024, brands that maintain a consistent voice across all digital platforms achieve 33% higher brand recognition and are significantly less likely to be misrepresented in generative search results.

Create trust points

AI doesn’t evaluate brands based on marketing flair. It “trusts” those whose claims are backed by credible, external validation. When algorithms like ChatGPT, Gemini, or Perplexity crawl the web, they seek patterns of authority: matching facts, verified citations, and repetition across trustworthy sources.

Your goal is not just to describe your company, but to build trust signals. Tangible proof points that reinforce your semantic authority. Here’s what helps:

• publications on authoritative platforms like Forbes, Medium, or TechCrunch, where your brand is mentioned in a professional context;
• executive interviews and thought-leadership articles that showcase your company’s expertise and industry perspective;
• case studies with measurable outcomes, providing real performance data such as conversion growth, cost optimization, or successful project delivery;
• client and partner mentions (where NDAs allow), which strengthen credibility by linking your brand to verified and reputable sources.

Add data structure

This may come as a shock to you, but… AI doesn’t view websites the way humans do (we were mind-blown ourselves). Without context, AI can’t always distinguish between a company name, a list of services, or general text. To help algorithms correctly interpret your brand, use Schema.org, the standard for structuring website data so it’s understandable to both search engines and generative AI systems

Schema.org markup is added directly to a page’s code and serves as a “hint” to inform AI what kind of information it’s reading:

• Organization: Name, contact details, address, and founding date.
• Service: Descriptions and features of specific services.
• Person: Information about founders, speakers, and company experts.

According to Search Engine Journal, websites with properly implemented schema markup are 1.5–1.8 times more likely to be cited accurately in AI-generated results. Because even robots need help.

Create an LLM.txt file

This file is placed at the root of your domain (e.g., example.com/llm.txt) and acts as an official source of truth about your company. It can include:

• a brief description of your brand and core activities;
• links to relevant pages (About Us, Services, Contact);
• exclusions – sections that should not be accessed or processed by AI systems.

Additionally, LLM.txt helps link your site to trusted external sources, such as verified profiles on LinkedIn, Crunchbase, Clutch, or media publications. This increases the likelihood that AI models will reference your official content instead of outdated third-party data.

Publish original research and case studies

Generative models prioritize proprietary content: data that exists only within your organization and isn’t duplicated across the open web. This can include analytics based on internal metrics, in-house research, client statistics, industry insights, or unique case studies.

For AI systems, such content isn’t simply material for citation. It’s a signal of credibility. If information appears exclusively on your website, the algorithm recognizes it as your original work and associates it with your brand’s expertise. This builds semantic authority – a strong contextual link between your company’s name and specific expert domains.

When AI systems generate answers, they aim to surface the most unique and verifiable sources to minimize the risk of hallucinations. Consequently, publications based on proprietary data are prioritized over generic articles and secondary commentary.

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AI is already telling your brand’s story, whether you participate or not. It doesn’t understand your intent (and we’re not sure it even wants to). It just sees what’s repeated most often. If you don’t take control of your digital footprint, algorithms will shape it for you, from outdated data, fragmented mentions, and someone else’s words.

In the age of answer-driven search, clarity and consistency are no longer optional, they’re strategic imperatives. The more precisely you define your brand, the more accurately AI will represent it.