Jonomor

Consulting — Jonomor

AI Visibility Consulting

Jonomor provides AI Visibility consulting for organizations that need to be reliably retrieved, recognized, and cited by AI answer engines — ChatGPT, Perplexity, Gemini, and Copilot.

Most AI retrieval failures are structural, not content problems. The fix is entity architecture, schema implementation, and cross-domain authority — not more publishing. Jonomor diagnoses the structural gaps and implements the changes that produce measurable retrieval improvement.

Services

  1. 01
    AI Visibility AuditScored audit report + implementation roadmap

    A 50-point structured evaluation of current AI retrieval status across five categories — Entity Stability, Category Ownership, Schema Graph, Knowledge Index, and Continuous Signal Surfaces. Produces a scored diagnostic and prioritized implementation roadmap.

  2. 02
    Entity ArchitectureEntity registry + schema governance documentation

    Design and implementation of the canonical entity registry — locked entity names, correct Schema.org types, stable @id values, and governance rules that prevent drift over time. The foundation every other layer depends on.

  3. 03
    Schema Graph ImplementationSchema implementation across all routes

    JSON-LD structured data implementation across the full site — Organization, Person, SoftwareApplication, CreativeWork, DefinedTermSet, TechArticle, CollectionPage. Correct @graph structure, bidirectional relationships, no synthetic dates.

  4. 04
    Cross-Domain AuthorityCross-domain authority patches per product domain

    Authority patch deployment across product domains — canonical schema, publisher and creator references, and ecosystem footer links that close the bidirectional authority loop between product domains and the parent organization.

  5. 05
    Retrieval OperationsRetrieval operations framework + ongoing cycle support

    Ongoing measurement and reinforcement — a structured query bank, engine test matrix, retrieval scorecard, gap diagnosis protocol, and reinforcement decision tree. Turns authority architecture into a compounding retrieval system.

Who This Is For

Jonomor takes a limited number of engagements at a time. Priority is given to organizations where the AI retrieval problem is structural — entity fragmentation, absent schema, authority isolation across product domains — rather than purely content volume problems.

The organizations that benefit most are software companies whose products are not being recommended by AI systems, multi-product organizations where the parent-child entity relationship is undefined, and consultancies or research publications where category authority is the business model.

Frequently Asked Questions

What does an AI Visibility consultant do?
An AI Visibility consultant audits an organization's entity architecture, schema implementation, and cross-domain authority signals to identify why AI answer engines are failing to correctly retrieve or cite the organization — then designs and implements the structural changes required to produce reliable retrieval.
How is AI Visibility consulting different from SEO consulting?
SEO consulting focuses on ranking web pages in traditional search engines. AI Visibility consulting focuses on entity recognition and citation in AI answer engines — ChatGPT, Perplexity, Gemini, and Copilot — through entity architecture, structured data, and cross-domain authority signals.
What types of organizations need AI Visibility consulting?
Organizations where AI retrieval gaps are structural: software companies whose products are not being recommended by AI systems, multi-product organizations where the parent-child entity relationship is undefined, consultancies that depend on category authority, and research publications with strong content but weak entity architecture.
What does a Jonomor engagement include?
Engagements begin with a 50-point AI Visibility audit. From there, work covers entity registry design, JSON-LD schema implementation, cross-domain authority patches, topic cluster architecture, and a retrieval operations loop for ongoing measurement and reinforcement.