AI Brand Performance: Analyzing How AI Interprets and References Your Brand

Today, when users ask about the best family series or choose a TV, they increasingly turn to ChatGPT, Gemini, Claude, or Perplexity, rather than a traditional

This is a game-changer for businesses. The battle is no longer simply for a top ranking but for consistent representation in AI systems’ outputs and retrieval mechanisms.

Now, everything that thousands of independent authors write publicly about your company or brand matters. Content on websites, social media, and product pages can be analyzed by AI systems when it is publicly available or retrieved during user interactions. Your AI Brand Performance is the holistic brand profile formed by these algorithms. If you don't manage this process, your digital image can become bifurcated, or even disintegrate into dozens of contradictory versions. Let's look at the key metrics that determine whether AI will recommend your product.

1. Semantic proximity and vector associations

Large language models make decisions based on vector associations. Although these vectors aren’t directly observable, their impact is reflected in how brands appear in AI-generated responses.

Repeated associations with ideas like reliability or innovation strengthen a brand’s presence in training and retrieval data, making it more likely to surface in relevant AI results

This is where SE Visible comes in. It measures how visible and well-understood your brand is by next-generation search algorithms by examining the contexts in which the brand appears, the attributes it’s linked to, and the queries or intents it shows up alongside.

How to influence this metric

To strengthen these associations, create content where your brand appears naturally alongside the concepts you want to be known for. This isn’t about keyword stuffing, but about context and intent. Publish expert articles, case studies, and press releases that show your product solving specific problems using the right terminology. For example, if your goal is to be associated with security, focus your case studies on data protection to build a strong and consistent semantic connection.

2. Share of Model (SoM) 

This metric is similar to the classic Share of Voice, but applies to generative responses. It shows how often the AI ​​mentions a brand in response to category queries (e.g., "Name the best CRM for small businesses"). AI models are more likely to reference entities that appear consistently in widely cited or high-quality sources, as patterns in the training data and retrieval sources influence output probabilities.

How to influence this metric

Actively work with collections and ratings ("Top 10 services..."). Listings on reputable industry platforms (G2, Capterra, industry media) signal to the algorithm that the brand is a market leader. The more independent sources include a company in their lists, the higher its SoM.

3. Sentiment and precision of attributes

Neural networks are excellent at recognizing the sentiment of text and focusing on user experiences. Just like prospective buyers themselves, over 80% of whom read reviews first before deciding to purchase. If a brand is frequently mentioned in a negative context, AI outputs may reflect that sentiment, which can influence how the brand appears in recommendations or summaries. Accuracy is also important: does the model understand what you're selling?

How to influence metrics: Conduct a review audit. Negative reviews need to be addressed, but it's even more important to ensure consistency in product descriptions. Information on the website, social media, and catalogs should be identical. This helps the algorithm accurately calibrate product characteristics.

4. Authority of sources

AI models tend to prioritize patterns from reputable and widely cited sources, while mentions from low-quality or unreliable websites have less influence on outputs, especially in retrieval-augmented systems. Training samples are filtered, and priority is given to resources with a high level of trust (EEAT).

How to influence the metric

Focus on getting mentions from Tier-1 media and recognized industry experts. A single interview on a reputable portal will boost your brand's vector weight more than a massive backlink campaign.

5. Technical clarity

AI, despite its complexity, still relies on machines' ability to understand code and data structures. To become part of the Knowledge Graph, business information must be structured and machine-readable. Structured data at this stage helps AI and search systems accurately parse and verify key information about your brand and products. This is the state when a website's technical infrastructure allows neural networks to accurately index the essence of a brand, its products, and the relationships between them.

How to influence metrics

Use Schema.org to mark up everything from the "About Us" page to product cards and FAQs. A clear data structure helps AI quickly verify facts about a company, transforming it from an abstract set of words into a verified entity with confirmed characteristics.

Conclusion

We'll have to accept the fact that over 60%  of US adults already trust AI when searching, and neural networks have become a mirror reflecting what's written about businesses online. They do this through the prism of mathematical analysis and uncover everything that seemed unimportant.

AI Brand Performance is simply becoming the new canon of working with data. But it's not much different from the traditional approach.

Influencing search results is only possible through a comprehensive approach: developing the right semantic links, working on the authority of sources, and ensuring the technical accessibility of data. Companies that learn to manage these metrics today will have most of  user attention in the era of conversational search.