GEO Score Methodology
Generative Engine Optimization (GEO) is defined as the practice of structuring digital content so that AI agents — including ChatGPT, Perplexity, Claude, and Google AI Overviews — can find, interpret, and cite it accurately. Unlike traditional SEO, GEO focuses on machine-readable structure, factual density, and semantic metadata.
According to Gartner (2025), 37% of consumers now use AI-powered search tools instead of traditional search engines. By 2027, Statista projects this figure will exceed 60%, fundamentally changing how businesses are discovered online.
The 7 Dimensions of the GEO Score
| Dimension | Weight | Description |
|---|---|---|
| Structure | 15% | Hierarchical headings, clear sections, logical content flow |
| Quantitative Data | 20% | Numbers, prices, metrics, percentages with units and context |
| Semantic Metadata | 15% | YAML frontmatter, meta tags, Open Graph, JSON-LD structured data |
| Citability | 25% | Fact-checkable statements, attributed data, definition patterns |
| Compactness | 10% | Information density — ratio of useful content to total tokens |
| Localization | 5% | Geographic and cultural relevance signals (language, currency, regions) |
| Discoverability | 10% | FAQ sections, tables, source references, canonical URLs, sameAs links |
BonjourAgent analyzes over 50 signals across 7 quality dimensions. Average scan time: 8 seconds. Average score improvement after optimization: +35 points. Supported languages: Portuguese, English, Spanish.
Traditional SEO focuses on keyword density and backlinks for Google ranking. GEO optimizes for structured data, fact-checkable statements, and machine-readable metadata that AI agents prioritize when generating responses. According to research from Princeton and Georgia Tech (2024), content with statistical data and citations is 40% more likely to be referenced by generative AI models.