Traditional SEO assumes search engines rank pages. AI answer engines retrieve entities from knowledge graphs. This fundamental difference creates a structural problem that content optimization cannot solve.
When ChatGPT cites a source, it's not ranking your page against competitors. It's identifying you as the authoritative entity for a specific concept. The retrieval mechanism operates on entity relationships, schema markup, and knowledge graph positioning — not keyword density or backlink profiles.
I built Jonomor to address this gap. The platform defines AI Visibility as a discipline and implements the infrastructure that determines whether organizations get cited by AI systems.
The Six-Stage Framework
The AI Visibility Framework breaks entity authority into six measurable stages:
Entity Stability establishes your organization as a consistent entity across knowledge graphs. This means structured data that declares what you are, not just what you do.
Category Ownership positions you as the defining authority for specific concepts. AI systems cite category owners when explaining those concepts to users.
Schema Graph connects your entity to related concepts through explicit relationships. The graph structure determines how AI systems understand your relevance to broader topics.
Reference Surfaces create the citation pathways that AI systems follow. These are the specific pages and content structures that answer engines reference.
Knowledge Index ensures your entity data reaches the knowledge graphs that AI systems query. Distribution across multiple knowledge sources increases citation probability.
Continuous Signal Surfaces maintain fresh signals that keep your entity active in AI training cycles. Static entities fade from citation consideration over time.
The framework produces a 50-point score that measures citation probability across these six stages.
Production Implementation
I operate nine production properties that demonstrate the framework in practice. Each property owns a different category — Guard-Clause for AI contract analysis, XRNotify for XRPL webhook infrastructure, MyPropOps for property management automation.
Seven of these domains score 48/50 Authority on the AI Visibility Framework. The scoring validates the methodology through real citation performance.
The properties connect through H.U.N.I.E., a shared intelligence layer that maintains entity relationships across the entire ecosystem. When one property establishes authority in its category, the relationship strengthens entity signals for related properties.
Technical Architecture
The AI Visibility Scorer at jonomor.com/tools/ai-visibility-scorer evaluates any public domain against the framework in real time. The tool analyzes entity markup, schema implementation, knowledge graph presence, and citation surface quality.
The scoring engine runs on Next.js with TypeScript, using Railway for deployment infrastructure. The evaluation logic processes structured data, validates entity relationships, and measures knowledge graph distribution.
Each property implements the same entity architecture pattern: explicit schema markup declaring organizational relationships, category-defining content that establishes authority, and continuous signal surfaces that maintain AI system attention.
Category Definition vs Content Optimization
Most organizations approach AI citation as a content problem. They create more articles, optimize for AI-friendly formats, or adjust their writing style for AI consumption.
The actual constraint is structural. AI systems retrieve entities that knowledge graphs identify as authoritative for specific concepts. Content volume doesn't establish entity authority. Schema relationships and knowledge graph positioning do.
Jonomor defines AI Visibility as the discipline that addresses these structural requirements. The framework provides the methodology. The tools provide the measurement. The consulting implements the entity architecture.
The platform exists because the gap between traditional SEO and AI citation represents a fundamental shift in how information systems work. Organizations that understand this shift will get cited. Organizations that continue optimizing for page rankings will not.
