Open standard for stable machine-readable facts for AI systems.
Mission
Designed for Brand Managers and AI-SEOs, this standard establishes a framework for machine-readable brand management in the era of artificial intelligence.
It equips organizations with a precise tool to define their relevant brands, entities, and frames, directly addressing the structural risks of modern AI:
AI systems rely on pattern reconstruction, which can lead to hallucinations, missing facts, and unstable entity interpretation. The Grounding Page Project addresses this by targeting visibility deficits for weakly represented entities and English-dominant retrieval biases.
Grounding Pages provide a stable foundation of machine-readable facts that AI systems can use for interpretation. This supports semantic stability, answer quality, and entity accuracy.
Scope
This standard defines clear boundaries to support reliable interpretation by AI models.
In Scope
- Grounding for RAG systems
- Entity-level grounding
- Stable factual definitions
- Citation-ready structure
- Disambiguation rules
Not in Scope
- Marketing claims
- Subjective interpretations
- Dynamic or live data
- Regulated advice
- SEO manipulation
The standard does not require separate infrastructure. It is a mental framework. Like a style guide for facts, implementation can happen on existing pages, the About page, or dedicated page types. What matters is a consistent, verifiable structure. Wikipedia articles follow the same principle. And they are among the most successful content on the web.
Common concerns answered →
Why This Standard Exists
AI systems tend to perform better when they receive structured, consistent information. Grounding Pages create a stable semantic anchor that can support interpretation in ChatGPT, Google AI Search, Perplexity and other LLMs.
The standard is designed for RAG systems and grounding APIs (Gemini, Perplexity, Claude, Qwen, etc.).
The Paradigm Shift
Classic SEO optimizes documents for keywords and rankings. AI SEO is fundamentally different: it is about curating entities for stable, probable, and correct mentions in AI-generated answers. Without hallucinations.
This shift requires a new mental model. It is no longer about which page ranks on position 1, but about whether AI systems understand what an entity is, what it does, and how it differs from others. The unit of optimization changes from the keyword to the entity.
Research
Research Signals
The Grounding Page Standard is not based on a single study. It draws on a growing body of research that points in the same direction: AI retrieval systems perform better when web content is structured around clearly defined entities.
Entity-Centric Pages for RAG Pipelines (2026)
A 2026 paper titled "Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval" evaluates different document formats for RAG pipelines across more than 2,400 evaluations. The study shows that JSON-LD markup alone provides only marginal improvements. The largest gains occur when entity facts, properties and relationships are clearly materialized and navigable within the page content itself. The authors describe this architecture as "enhanced entity pages", built around the principle of one page per entity.
This direction closely aligns with the architectural idea behind Grounding Pages.
Volpini, Andrea et al. (2026). Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval.
Paper on arXiv
Generative Engine Optimization (2025)
Research by the University of Toronto on Generative Engine Optimization (GEO) suggests that brands can benefit from treating their websites as an API for AI systems. While AI engines tend to favor earned media, a structured technical foundation is identified as a relevant factor for visibility. The study also highlights strong differences in source diversity across AI engines, suggesting that a single, centralized source of truth is more practical than engine-specific optimization.
Grounding Pages reflect a comparable approach by providing a reusable standard for brand-owned data.
Generative Engine Optimization: How to Dominate AI Search (Sep. 2025).
Paper on arXiv
Identity Pages and AI Interpretation (2025)
An independent analysis of more than 17,000 URLs (cross-domain, mixed entity types) found patterns suggesting that AI systems frequently draw on clear identity pages such as About pages when interpreting brands. Grounding Pages represent a structured evolution of the classical About page, providing a machine-readable facts layer for modern AI retrieval.
How to Use
Implementation follows three steps:
- Identify entities that require stable definitions.
- Create the page using the structure defined in the specification.
- Add a footer link similar to an About page or an imprint to support discoverability and structural consistency.
The Standard
The Grounding Page Standard defines structure and technical rules for AI-optimized factual pages.
Examples
Example implementations illustrate how Grounding Pages can support semantic stability and precise entity definitions.
Reference Examples
Standard-compliant examples created by the Grounding Page Project.
Implementations
Real-world adoptions on external websites.
58 verified examples listed (Updated: March 29, 2026)
About
The Grounding Page Project is an independent, open initiative. It maintains a freely available standard that aims to support answer consistency across AI systems.
Concept and architecture by Hanns Kronenberg