Interactive — Jonomor

The 50-Point AI Visibility Framework: Interactive Scoring Visualization

By Ali Morgan, Founder and AI Visibility Architect at Jonomor

AI answer engines do not rank pages. They retrieve entities. Whether your organization gets cited by ChatGPT, Perplexity, or Gemini depends on structural signals most companies have never measured. The AI Visibility Framework measures them.

The framework is a 50-point scoring system developed by Jonomor that evaluates an organization's readiness for AI retrieval across five categories. Each category contains individual checks scored on specific, documented criteria. The total score determines whether an organization is functionally invisible, structurally viable, or positioned as an authority in AI-generated answers.

The interactive visualization below breaks down every category, every check, and every scoring threshold. Tap any category to expand its checks. Tap any check to see the exact criteria. For the written breakdown, see the AI Visibility Framework overview.

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AI Visibility Framework

Six-stage, 50-point scoring methodology

0/50

AUTHORITY

Dominant AI retrieval position

AUTHORITY

42+

VIABLE

30+

INVISIBLE

▶

1

Entity Stability

5 checks

0/10

Whether the entity is clearly defined and identifiable to AI retrieval systems.

ES-1Canonical name consistent/2▾

Organization schema name matches page title and H1. Both required for full marks.

ES-2Dedicated entity page/2▾

Homepage links to /about, /team, /founder, or /ecosystem page. Link must exist from homepage.

ES-3Founder or operator identified/2▾

Person schema in JSON-LD. Byline without Person schema caps at 1 point.

ES-4Products listed with canonical names/2▾

Organization schema declares hasPart array. SoftwareApplication without hasPart caps at 1.

ES-5Parent-child relationships stated/2▾

Bidirectional: hasPart on Organization AND isPartOf on child entities. One direction caps at 1.

2

Category Ownership

6 checks

0/10

Content depth, authority signals, and topic coverage that establish category leadership.

CO-1Pillar article 1,500+ words/2▾

Homepage body text word count. 1,500+ = 2 pts. 800-1,499 = 1 pt. Under 800 = 0.

CO-2Cluster density, 5+ articles/2▾

Homepage links to /article, /blog, /post, /insight, or /guide paths. 5+ = 2 pts. 2-4 = 1 pt.

CO-3Entity names in content/1▾

Organization name from schema appears in homepage body text. Case-insensitive match.

CO-4Author bylines present/2▾

Both author byline AND Person schema required for full marks. Byline alone = 1 pt.

CO-5Content variety/2▾

4 content types checked: definition/glossary, FAQ, how-to/guide, articles. 3+ types = 2 pts.

CO-6FAQPage schema present/1▾

FAQPage schema exists in homepage JSON-LD. Binary check.

3

Schema Graph

6 checks

0/10

Machine-readable entity graph integrity via JSON-LD structured data.

SG-1Organization schema present/2▾

Organization type in JSON-LD. Binary check. Present = 2 pts, absent = 0.

SG-2Person schema present/2▾

Person type in JSON-LD. Most external sites fail here. Organization without Person = 0.

SG-3@id consistency/2▾

All @id values fully qualified URLs. No www vs non-www conflicts. Partial qualification = 1 pt.

SG-4Per-page schema types/2▾

TechArticle, FAQPage, SoftwareApplication, or CreativeWork present. Other types only = 1 pt.

SG-5Schema validates/1▾

All schema blocks have both @context and @type fields. Basic structural validation.

SG-6No synthetic dates/1▾

No datePublished or dateModified values injected. Clean temporal signals.

4

Knowledge Index

6 checks

0/10

Internal linking architecture that creates navigable knowledge topology for AI crawlers.

KI-1Articles link to pillar/2▾

Homepage links to /article, /blog, /insight, /knowledge, /definition paths. 3+ = 2 pts.

KI-2Pillar links to supporting/2▾

Total internal links on homepage. 10+ = 2 pts. 5-9 = 1 pt. Under 5 = 0.

KI-3Entity pages in navigation/2▾

Homepage links to /about, /ecosystem, /team, or /founder paths. Present = 2 pts.

KI-4Descriptive anchor text/2▾

Anchor text contains entity names or descriptive terms. Generic 'click here' = 1 pt.

KI-5Products linked from ecosystem/1▾

Homepage links to /product, /ecosystem, /tool, or /service paths.

KI-6No orphan pages/1▾

sitemap.xml exists and is accessible. Ensures full discoverability.

5

Continuous Signal Surfaces

7 checks

0/10

Cross-domain reinforcement, external presence, and third-party corroboration.

CS-1LinkedIn in sameAs/1▾

linkedin.com URL present in Organization or Person sameAs array.

CS-2GitHub in sameAs/1▾

github.com URL present in Organization or Person sameAs array.

CS-3Directory listing in sameAs/1▾

crunchbase.com or wikidata.org URL present in sameAs array.

CS-4Child sites reference parent/2▾

Schema publisher @id differs from own @id, referencing parent organization.

CS-5Parent site links to children/2▾

External links containing parent domain or 'ecosystem' anchor text detected.

CS-6Third-party mentions/2▾

Independent domain citations. Cannot be auto-verified. The critical gap for most organizations.

CS-72+ sameAs URLs/1▾

Total sameAs URLs across Organization and Person schema. Minimum 2 required.

30 checks · 5 categories · 50 points

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The Five Categories

The framework evaluates five categories. Each targets a different layer of the signal stack that AI engines process. Weakness in any single category can block retrieval regardless of strength elsewhere.

Entity Stability (10 points) measures whether AI systems can unambiguously identify the organization. It checks canonical naming consistency, dedicated entity pages, founder identification, product declarations, and parent-child entity relationships. Without stable entity identity, AI engines cannot confidently cite.
Category Ownership (10 points) evaluates topic authority depth. It checks pillar content length, cluster density, entity name presence in content, author bylines, content variety, and FAQ schema. Organizations with thin content consistently fail AI retrieval regardless of schema quality.
Schema Graph (10 points) assesses the machine-readable entity layer. It checks for Organization and Person schema, @id consistency across pages, per-page schema types, structural validation, and the absence of synthetic dates. Schema is how AI engines read entity declarations directly rather than inferring from unstructured text.
Knowledge Index (10 points) evaluates internal linking architecture. It checks article-to-pillar connections, homepage link density, entity architecture in navigation, descriptive anchor text, product discoverability, and sitemap presence. AI crawlers evaluate how content connects, not just what content exists.
Continuous Signal Surfaces (10 points) measures cross-domain reinforcement. It checks LinkedIn and GitHub in sameAs, directory listings, child-to-parent schema references, parent-to-child links, third-party mentions on independent domains, and sameAs URL volume. This is where most organizations fail. AI engines deprioritize self-published claims and weight independent corroboration.

Score Thresholds

The total score classifies organizations into tiers that predict AI retrieval behavior.

Authority (42-50): Dominant AI retrieval position. Entity signals, schema, content depth, and external corroboration all present. The organization appears reliably across ChatGPT, Perplexity, and Gemini when category queries are asked.
Viable (30-41): Visible but gaps remain. May appear in some AI responses but lacks consistency. Typically strong in one or two categories with structural gaps in others.
Invisible (0-29): Not reliably retrieved by AI engines. May rank on Google but does not appear in AI-generated answers. Fundamental entity architecture work required.

How this was validated: the framework was tested against 150 domains across six verticals: legal technology, financial technology, property technology, education technology, AI infrastructure, and digital marketing. 450 live extractions from ChatGPT, Perplexity, and Gemini verified that framework scores predict actual retrieval behavior. Domains scoring Authority tier appeared in AI-generated answers. Domains scoring Invisible did not. Full methodology and dataset are published in the State of AI Visibility report.

The Critical Gap: Third-Party Corroboration

Across the full dataset, Category 5 (Continuous Signal Surfaces) produced the widest variance. Organizations with strong entity architecture, solid schema, and deep content still failed AI retrieval when no independent domain mentioned them. AI engines treat self-published authority claims as insufficient. Third-party corroboration from press, expert platforms, directories, and industry publications is the single most important differentiator between cited and ignored organizations.

This finding is consistent across all six verticals. It is not industry-specific. It is structural. Closing the corroboration gap requires earned media, published data assets, expert platform responses, and directory presence on domains the organization does not control.

Score Your Domain Against All 50 Points

Jonomor's free AI Visibility Scorer runs the full framework evaluation on your domain automatically. Five categories. Thirty checks. Specific gap identification.

Run Your Free Scan at jonomor.com

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