AI Perception Glossary

The AI Perception Score measures how likely AI assistants like ChatGPT and Claude are to recommend your brand when users ask about your industry. A higher score means more visibility and recommendations from AI models.

The score is calculated by analyzing: - How often AI mentions your brand when asked about your industry - The sentiment (positive, neutral, or negative) of those mentions - Whether AI recommends you specifically or just mentions you - Your brand's authority signals that AI models recognize

Generative Engine Optimization (GEO) is the practice of optimizing your brand's presence for AI models. Just like SEO helps you rank higher in Google, GEO helps AI assistants like ChatGPT recommend you more often.

GEO involves: - Ensuring your brand information is accurate across the web - Building authority signals that AI models recognize - Creating content that AI can understand and cite - Getting listed in knowledge bases AI models trust (like Wikipedia and Wikidata)

Share of Voice measures what percentage of AI conversations about your industry include mentions of your brand compared to your competitors.

For example, if we ask ChatGPT 10 questions about "best CRM software" and your brand is mentioned in 3 of those responses, your Share of Voice is 30%.

A higher Share of Voice means: - More visibility when users ask AI for recommendations - Greater mindshare in AI-generated content - More potential customers discovering your brand through AI

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trust. Originally Google's quality framework, AI models also use these signals to determine which brands to recommend.

The four components: - **Experience**: First-hand experience with the topic - **Expertise**: Deep knowledge in a specific field - **Authoritativeness**: Recognition from others in the industry - **Trust**: Reliability and accuracy of information

AI models learn E-E-A-T signals from: - Customer reviews and testimonials - Industry awards and certifications - Press coverage and expert mentions - Quality of your content and website

A hallucination occurs when an AI model confidently states something incorrect about your brand. This could be wrong products, incorrect locations, outdated pricing, or completely fabricated information.

Common hallucination examples: - AI says you're based in New York when you're in Los Angeles - AI describes products you don't actually offer - AI mentions partnerships that don't exist - AI gives outdated information (old pricing, discontinued products)

Hallucinations matter because: - Users trust AI responses and may believe false information - Incorrect information can damage your reputation - It can lead to lost sales or confused customers

Preventing hallucinations: - Ensure accurate information across all web sources - Get listed in trusted databases like Wikidata - Keep your website and business listings up to date

A Knowledge Graph is a structured database of facts that AI models use as trusted sources of information. The most important knowledge graphs include:

- **Wikidata**: The structured data behind Wikipedia - **Google Knowledge Graph**: Powers Google's knowledge panels - **DBpedia**: Academic database of structured information

Why Knowledge Graphs matter for AI perception: - AI models prioritize information from these trusted sources - Being in a Knowledge Graph reduces hallucinations about your brand - It establishes your brand as a recognized entity - Information in Knowledge Graphs gets cited accurately

Getting into Knowledge Graphs: - Create or improve your Wikipedia article - Add your company to Wikidata with accurate information - Ensure consistency across all official sources - Use Schema.org markup on your website