Evertune
FAQs

Currently: ChatGPT, ChatGPT Search, Gemini, Gemini Search, Google AI Mode, Google AI Overviews, MetaAI, Claude, Perplexity, DeepSeek. We regularly add new models.

Currently: ChatGPT, ChatGPT Search, Gemini, Gemini Search, Google AI Mode, Google AI Overviews, MetaAI, Claude, Perplexity, CoPilot and DeepSeek. We regularly add new models.

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• AI Model Insights (Prompt Volumes, AI Usage)
• Brand Monitoring (Word Association, AI Brand Index, Consumer Preferences)
• AI Website Optimization (Site Audit)
• Content Strategy (AI Education Brief, Content Analytics)

How AI models describe or mention a brand without the brand’s name in the prompt. For example, if a user prompts an AI model “What is the best luxury SUV?” and the AI model responds with “BMW”. The brand was not mentioned in the prompt, but the AI model mentioned the brand. Evertune’s AI Brand Index and Consumer Preferences reports analyze unaided awareness.T, ChatGPT Search, Gemini, Gemini Search, Google AI Mode, Google AI Overviews, MetaAI, Claude, Perplexity, DeepSeek. We regularly add new models.

How AI models describe a brand when the brand’s name is included in the prompt. Evertune’s Word Association report analyzes aided awareness by asking the AI models to describe specific brands within a product category. For example, “how would you describe the car brand BMW?”

Different versions of the same model produce different results because newer versions are usually bigger, trained on more recent information, and built with improved techniques. Even though they might seem similar, each version has learned different patterns from different data, so they naturally respond differently to the same prompts.

While citations provide useful guidance, they should be treated as indicators rather than definitive proof. LLM citations aren't always accurate and may reflect the model's built-in preferences, so they can sometimes reinforce incorrect conclusions rather than validate them. Additionally, due to AI models probabilistic nature, citations can vary across responses. Therefore at Evertune we advise clients to focus on appearing in a diverse set of authoritative and AI friendly content as much as possible and using our AI Education Score to help prioritize where to focus.

Absolutely. Evertune can generate an AI Brand Index report for any product category. This allows you to see AI preferences for different products, audiences and price points. For example you could see what brands AI recommends for "SUVs" or get more specific with "luxury SUVs" or even call out product features and price points like "3 row luxury SUVs under $100k".

Over 80% of AI prompts are unique—meaning they’ve never been seen before. That’s because people tend to write prompts the same way they’d talk to another person:

• They include detailed context
• They ask multi-part questions
• They use natural, conversational language

For example, two people might ask the same thing in completely different ways:

• “Write a summary of the Porsche Taycan for a buyer choosing between it and the Tesla Model S.”
• “Compare Porsche’s electric lineup to Tesla’s—focus on range and price.”

Same intent, different words. That’s why Evertune’s Prompt Volumes doesn’t just scan for granular keywords—we cluster prompts into broader intents and topics, giving you a more complete and accurate measure of user activity.

Yes, just enter a specific audience—like "soccer moms in Texas"—in the product category field, and Evertune will generate insights by asking the models for results tailored to that group.

Yes. Evertune can detect and report brand mentions in AI outputs even when no URL or citation is included.

They are, which is why Evertune’s platform tracks and updates reports regularly. Our methodology adapts in real time as new model versions are released or retrained. We also allow you to compare how your brand shows up on different model versions (e.g. Chat GPT 4 vs. Chat GPT 5).

AI models can sometimes distinguish sponsored from organic content, but it depends on how clearly it's marked. If labeled with tags like "sponsored" or "advertisement," such content can be filtered out during dataset curation. However, when content is not clearly labeled - especially if it takes the form of high-quality guides, reports, or whitepapers - it may still influence the model, particularly if it comes from a recognized brand or authoritative source.

Today, most consumer-facing AI is text-first and typically learns from text derivatives such as video transcripts, captions, or OCR-extracted text from images, rather than directly from the video or images. Sometimes you may see YouTube URLs listed in our Sources report. This means that the AI model is reading the title and description of the video, rather than watching the actual video. 

As AI models evolve, it’s important to remember that access to training data depends on the AI model’s parent company. For example, Google can use YouTube video data to train its AI models, Meta can use Instagram data to train Llama, and OpenAI has licensing deals with platforms like Reddit. Unless there's a direct agreement, most social media, video content, or in-app content is not included in training sets.

In traditional search, users typically enter in keywords + "near me" without typing in their location as their browser typically has location settings enabled. When using AI models, users tend to include their location in the prompt. If you want to track a product category that differs greatly across regions, we recommend creating unique Evertune trackers for each key location + product category you want to track.

Yes, personalization is evolving. For example, in April 2025, OpenAI expanded ChatGPT's memory capabilities, allowing it to retain facts about a user across sessions (such as name, preferences, or prior context). This enables more personalized answers. Other assistants are developing similar features. However, unless explicitly shared or remembered, models don't automatically bias results by age, location, or preferences. If your brand is hyper local or age specific, we recommend creating multiple trackers in our platform to compare across personas (i.e. clothing for women vs. clothing for girls) or locations (i.e. northeast credit unions vs. southeast credit unions).

ChatGPT goes through two phases of training. Pre-training is where it is trained on a massive dataset of text sources, including publicly available internet content, literature, and sometimes less transparent sources like Reddit or even public social. Once pre-training establishes the core capabilities, ChatGPT goes through post-training, where it is fine-tuned to learn what a “good” answer is and how to generate responses that are more relevant, accurate, and helpful. If ChatGPT does not have the answer within its pre-existing knowledge, it will retrieve information from indexed websites in real time and incorporate that information into its response, also known as RAG (retrieval-augmented generation). 

OpenAI periodically releases new versions with updated training data and improved techniques, which is why different versions like ChatGPT-4 and ChatGPT-5 can produce different results—each has learned from different datasets and training methods.

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No, ChatGPT doesn't give identical answers to everyone. This is because AI models are predictive by nature. This variability is why Evertune tests millions of prompts - to understand how AI models respond at scale and identify consistent patterns in brand recommendations.

Monitoring brand visibility in AI requires tracking how often and where your brand appears in AI-generated responses across multiple models and contexts. Here's how:

Use an Generative Engine Optimization (GEO) platform like Evertune:

• Track your AI Brand Score to measure the probability of AI models including your brand in responses, weighted by position
• Monitor Visibility Score to measure the percentage of responses mentioning your brand
• Analyze Average Position to see where your brand ranks in AI recommendations

Key monitoring strategies:

• Test across multiple AI models (ChatGPT, Claude, Gemini, Perplexity, etc.) since each has different training data
• Use unaided prompts (questions that don't mention your brand name) to measure true brand awareness
• Track thousands of prompt variations to understand patterns, not just individual responses
• Monitor over time, as AI model perceptions typically shift over 3-6 month periods
• Compare your performance against competitors in your category

What to track:

• Frequency of brand mentions
• Position/ranking in AI responses
• Consumer preference attributes AI associates with your brand
• Sentiment and words that AI uses when describing your brand
• Citations and sources AI uses when mentioning your brand
• Performance across different product categories and audience segments

GEO (Generative Engine Optimization), AEO (AI Engine Optimization), and AI Search Optimization are all terms used interchangeably to describe the practice of optimizing your online presence and content to improve how your brand appears in AI-generated responses. These practices focus specifically on influencing how generative AI models perceive, understand, and recommend your brand.

llm.txt is a proposed standard file that websites can use to provide structured information specifically formatted for AI models and large language models (LLMs) to read and understand.

The llm.txt file serves as a machine-readable summary of a website's key information, making it easier for AI models to accurately represent the site's content, products, or services when generating responses. While not yet universally adopted, llm.txt files represent the growing recognition that brands need to actively educate AI models about their offerings, just as they've historically optimized for traditional search engines.

In the context of AI-generated responses, a citation is a reference to a source that the AI model used to generate information in its response. Citations typically appear as links, footnotes, or inline references that direct users to the original content. Citations are important for understanding and optimizing your brand's AI visibility because they show which content AI models are using to form their knowledge about your industry, brand or competitors.

While GEO (Generative Engine Optimization) and SEO (Search Engine Optimization) share similar goals—improving brand visibility—they optimize for fundamentally different systems and user experiences.

Key differences:

• SEO optimizes for search engine rankings; AEO optimizes for AI model knowledge and recommendations
• SEO focuses on keywords and backlinks; AEO focuses on comprehensive content that educates AI models
• SEO results appear as ranked links; AEO results appear as narrative recommendations within AI responses

LLM stands for Large Language Model—a type of artificial intelligence trained on massive amounts of text data to understand and generate human-like language. LLMs are trained by reading billions of words from the internet, books, articles, and other text sources. Through this training, they learn patterns in language, facts about the world, and how to generate coherent, contextually relevant responses. The "large" in Large Language Model refers to the enormous amount of data and computational power used in training.