Protect Your Brand in the Age of AI Search

Your brand might show up in AI-generated answers—but not always on your terms. When people ask ChatGPT, Claude, or Google’s AI Overviews for product recommendations, they don’t see a ranked list of links. They see a single, synthesized response.

That response is stitched together from product pages, reviews, social chatter, news coverage, and more. The model pulls what it can find, filters it through its logic, and delivers a summary that may or may not reflect the brand you’ve carefully built.

Depending on how your brand is framed in the AI output, that could be a problem because consumers tend to trust these summaries. A recent study found that people perceive AI-generated responses as more objective and less commercially biased than traditional search results.

This makes representation, not just visibility, the new battleground. Your brand could be framed as a runner-up, misrepresented, or paired with outdated context. If LLMs are shaping perception at scale, then monitoring how you're being described is just as important as where you're showing up.

The shift: from how well you rank to how you’re reasoned about

In traditional search, brands competed for rank. SEO revolved around keywords, metadata, backlinks, and the path to visibility was well-defined. Optimize the right levers, and you could climb the list. But AI-powered discovery rewrites those rules. There is no list. Instead, large language models reason through thousands of sources to produce a single, synthesized answer. The goal isn’t to rank pages, it’s to resolve a query.

This changes how brands are surfaced and understood. LLMs decide how to position you based on the data they can access and the patterns they’ve learned. Strong SEO might get your name into the model’s orbit. But it won’t control how you’re portrayed. That depends on everything from tone and sentiment to narrative consistency across your online footprint. The question facing SEO teams now isn’t just “Where do we rank?” it’s “How are we being reasoned about?” 

Understanding brand risks in LLM search

The shift from ranking to reasoning introduces a new layer of brand risk. It’s not just about whether you’re mentioned, but how your brand is interpreted, framed, and emotionally positioned in a synthesized response. Let’s break down some of the most common and consequential ways brand representation can go wrong.

1) Inaccurate brand representation 

LLMs can sometimes “hallucinate” details or use outdated information to generate a response about a company or brand. For example, an LLM might incorrectly state your brand’s history or leadership team. In more severe cases, the LLM could inadvertently produce a negative, false summary of a company event. For example, an LLM might incorrectly state that your company “lost a lawsuit” when it never happened. 

A recent study compared the accuracy of information retrieval between ChatGPT and Google. Participants were asked questions about politicians in their countries (e.g., “Have any LGBTQ+ people been elected to a national governing body and, if so, when?) The results showed:

• Participants using Google were more likely to find the correct answer than those using ChatGPT.
• ChatGPT users more frequently received incorrect answers, likely due to hallucinations or outdated training data.

While this study reflects one specific use case (and is not indicative of Google’s accuracy versus ChatGPT’s accuracy at large), it highlights how hallucinations can happen and affect even basic factual perceptions of a brand.

2) Sentiment drift

AI-generated answers might distort the sentiment or tone around a brand. Because LLMs train on vast internet text, they might surface negative reviews, old controversies, or biased snippets disproportionately. For instance, if a product had a handful of negative or critical reviews, a generative answer might emphasize those negatives even if they were outliers. 

For example, when searching “Which water bottle brands should I avoid?” on ChatGPT. The response noted to avoid the brand Ello due to reports of mold; however, upon searching reviews for Ello, only 2% of reviews listed “mold” as an issue on Target’s website (out of 500+ reviews), and less than 1% of more than 13,000 reviews on Amazon mentioned mold. 

3) Narrative anchoring and brand bias 

Recent work by Blackbird.AI highlights that large language models often replicate known biases and narratives around popular brands. For example, a model might consistently associate certain attributes (e.g., “trustworthiness” or “innovativeness”) with specific brands due to patterns in its trained data. These reinforced associations can limit a brand’s ability to shift perception or control its positioning in AI-generated content.

4) Sentiment mirroring

One study found that the “sentiment of the query is positively correlated with the sentiment of the LLM’s result” across systems like Bing Chat, ChatGPT, and Perplexity. In other words, if a user’s query about a brand or topic is phrased negatively, the model’s answer tends to adopt a negative tone as well, which can magnify biases.

5) Brand consistency across multiple generative platforms 

Brand messaging might appear one way in ChatGPT, another in Perplexity, and yet another in Google’s SGE. For example, one analysis found that OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, and others each emphasize different brand attributes due to their distinct training and reinforcement methods​.

Measuring brand visibility 

These risks aren’t hypothetical, they are measurable. Here are some key metrics that help capture how your brand is performing in this new environment:

Sentiment Analysis

• What it measures: The tone of the brand mention—positive, neutral, or negative—across generative responses.
• How to measure it: Compare sentiment trends across models (e.g., ChatGPT vs. Gemini) and over time. Look for sudden shifts, patterns tied to specific narratives, or responses that conflict with your brand’s intended voice.

Hallucination Rate

• What it measures: The percentage of responses that include factually incorrect or outdated claims about your brand.
• How to measure it: Run controlled queries and log the frequency and severity of hallucinations. Use this to guide updates to your own knowledge bases, structured data, or public-facing content that models can access and learn from.

Recommendation likelihood

• What it measures: The frequency and strength with which an LLM recommends your brand over competitors in response to category-level prompts (e.g., “best CRMs for startups”).
• How to measure it: Test a consistent set of commercial-intent queries and calculate how often your brand is named first, listed at all, or endorsed with strong language (“top pick,” “highly rated”). Track this against known competitors to benchmark position. This might be expressed as a ratio: “LLM recommended [Brand X] in 45% of relevant queries tested, vs. competitor’s 55%.”

Sentiment deviation

• What it measures: The variability in tone depending on how the prompt is phrased, especially between positive, neutral, and negative queries.
• How to measure it: Compare results from prompts like:
  
  • “Is [Brand] trustworthy?”
  • “Why do people dislike [Brand]?”
  • “What’s good about [Brand]?”

A wide swing in tone or language might suggest sentiment anchoring or echoing of biased inputs.

Proactive steps to protect your brand

Large language models ingest and synthesize vast datasets—including unstructured UGC from platforms like Reddit, Stack Overflow, and Quora; structured knowledge bases like Wikidata and Wikipedia; and trusted review aggregators such as G2 or Trustpilot. These sources are often emphasized in retrieval-augmented generation (RAG) systems and influence the model’s ability to reason about your brand.

As a baseline, use tools like Scrunch AI or Profound to help you understand how your brand is positioned in AI-generated responses. These platforms can help you identify where your brand shows up, how it’s positioned, and the sentiment attached to it. 

Once you understand how you’re showing up, you can dig in to influence the levers that are shaping the AI models’ understanding of your brand:

• Invest in durable trust signals. LLMs heavily weight consensus from high-signal sources. These include:
  
  • Verified reviews, including G2, Trustpilot, and the App Store. 
  • Authoritative media coverage. A 2024 study found that LLMs rely on editorial media for nearly two-thirds (61%) of their responses about brand reputation.
  • Citations from respected industry sources or analysts. 

Models often repeat what’s reinforced in analyst roundups, Gartner reviews, and top publisher coverage. Proactive PR and analyst relations can help ensure your brand appears in “high-trust” sources.

• Monitor third-party narratives and correct misinformation. Wikipedia, Wikidata, and public business directories are frequent sources of factual data for LLMs. Outdated executive names, old product names, or PR crises that have long been resolved may still surface if not cleaned up.
  
  • Regularly audit brand-related mentions and request factual corrections.
  • For issues in Wikipedia/Wikidata: use Talk pages or work with experienced editors.
  • For hallucinated legal events or misquotes, platforms like OpenAI offer feedback channels to report persistent factual inaccuracies.

• Produce high-confidence content that AI wants to cite. Invest in cornerstone assets that define your product category, explain features clearly, and answer common questions. These pages are more likely to be pulled into retrieval-augmented responses or cited as definitive sources.

Brand and trust are the long game

Trust has always mattered in marketing, but in AI-powered discovery, it’s everything. When language models decide which brands to surface and how to frame them, technical SEO alone won’t cut it. These systems don’t just scan metadata; they synthesize signals of trust, authority, and narrative consistency.

That’s why brand awareness, reputation, and positioning have become core inputs—not side effects—of performance. In generative search, AI-generated answers act as a high-stakes filter, compressing the internet’s content into a single synthesized response. Your brand needs to earn its way into that answer, and more importantly, shape how it’s portrayed.

This is the cut that separates SEO from performance marketing. You’re not just optimizing for visibility. You’re optimizing for how you’re reasoned about.

If you’re ready to build for what’s next in search, let’s chat 🚀