Jonomor

Methodology — AI Visibility

The AI Visibility Framework

A systematic six-stage methodology for building reliable AI citation. Each stage has a defined implementation surface, a concrete output, and a measurable outcome. Stages are ordered by dependency — foundational stages must be stable before authority stages produce results.

Framework Loop

Entity Definition→Schema Graph→Knowledge Index→Topic Clusters→Citation Surfaces→Reinforcement↻

Stages

  1. 01

    Entity Definition

    Surface: Entity Registry

    What

    Define every entity in the ecosystem with a canonical name, type, URL, and description. Establish parent-child relationships. Lock naming conventions so no surface introduces variation.

    How

    Produce an entity registry document listing every Organization, Person, SoftwareApplication, and CreativeWork entity. Assign a canonical @id to each. This registry governs all schema and copy going forward.

    Outcome

    AI systems can form stable associations around a consistently named, clearly typed entity.

    Deliverables

    • —Canonical name for every entity
    • —Schema.org type assignment
    • —Canonical URL per entity
    • —Parent-child relationship declarations
    • —Naming convention lockdown
  2. 02

    Schema Graph

    Surface: Structured Data

    What

    Encode the entity registry in machine-readable JSON-LD. Site-wide baseline in the root layout. Per-page extensions for entity pages, articles, and products. No duplicate or conflicting @id values.

    How

    Deploy Organization, Person, and WebSite schema in the root layout. Add ProfilePage schema on founder pages, WebPage + Organization on ecosystem pages, TechArticle on content pages, FAQPage on Q&A content.

    Outcome

    AI parsers can read the entity graph directly without inference. Schema reduces ambiguity at the machine layer.

    Deliverables

    • —Organization schema (root layout)
    • —Person schema with worksFor
    • —ProfilePage on founder pages
    • —TechArticle on all content
    • —FAQPage on Q&A content
    • —Consistent @id values
  3. 03

    Knowledge Index

    Surface: Concept Architecture

    What

    Build a structured concept map that organizes the domain into retrievable units. Concept pages define terms, explain relationships, and serve as anchor points for AI citation.

    How

    Create a /knowledge index linking to concept pages organized by category. Each concept page defines one term or methodology explicitly, with schema and internal links to related concepts.

    Outcome

    AI systems learn the domain vocabulary associated with the entity and can cite specific concept definitions.

    Deliverables

    • —/knowledge index
    • —Concept definition pages
    • —Category taxonomy
    • —Cross-concept internal links
  4. 04

    Topic Clusters

    Surface: Authority Content

    What

    Publish groups of semantically related articles that collectively define authority within a topic domain. Each cluster has a pillar article supported by 4–6 depth articles and at least one FAQ article.

    How

    Map topic domains to clusters. Each cluster: one pillar (2,000+ words), supporting articles (1,000–1,500 words), FAQ article. All articles reference the author entity and use TechArticle schema. Cross-link within clusters and to pillar.

    Outcome

    AI systems observe consistent co-occurrence of entity name with topic domain across multiple documents — the pattern that builds topic authority.

    Deliverables

    • —Pillar articles per topic domain
    • —Supporting article clusters
    • —FAQ articles
    • —Author entity in all bylines
    • —Internal links to pillar
  5. 05

    Citation Surfaces

    Surface: Cross-Domain References

    What

    Expand entity references to independent surfaces: directory profiles, product domains linking to the parent organization, published content on third-party platforms. Each surface must use canonical entity names.

    How

    Audit existing citation surfaces. Implement cross-domain schema on all product domains — each product references the parent organization via isPartOf and links back canonically. Add sameAs to schema only when real, verifiable identity profile URLs exist (LinkedIn, GitHub, Crunchbase). sameAs is optional — omit it entirely if no real profiles exist. Never include product domains in sameAs. Never use placeholders.

    Outcome

    AI systems find the entity defined consistently across independent sources — the condition that produces citation confidence.

    Deliverables

    • —Product domains link to parent org
    • —isPartOf schema on product sites
    • —sameAs URLs (real identity profiles only; omit if none exist)
    • —Directory profiles (LinkedIn, GitHub)
    • —Published third-party mentions
  6. 06

    Reinforcement

    Surface: Authority Loop

    What

    Use retrieval signals to identify what is working and where gaps remain. Quarterly audits, citation tracking across AI platforms, schema updates as entities evolve.

    How

    Run AI citation tests quarterly (target queries across ChatGPT, Perplexity, Gemini). Score against the 50-point audit framework. Identify which clusters are producing citations. Deepen underperforming stages.

    Outcome

    Each iteration of the flywheel strengthens entity recognition and compounds citation probability.

    Deliverables

    • —Quarterly AI citation testing
    • —50-point audit scoring
    • —Schema updates on entity changes
    • —Topic cluster gap analysis
    • —Citation surface expansion

Implementation Links