AI Visibility Answers

Methodology and evidence

Is MAHI Index™ peer-reviewed?

MAHI Index™ is now formally archived in the open-access AI Citation Visibility Framework DOI package, but that is a citable software/methodology release, not the same thing as a standalone peer-reviewed journal article.

The distinction matters. The related JBAI article supports Kristina Shrider's research trail, while the Zenodo DOI gives MAHI Index™, MAD-M™, and MAHI-100 a stable, reproducible methodology anchor.

What evidence supports this answer?

Kristina Shrider's JBAI article is listed in the site's founder graph as a ScholarlyArticle.

MAHI Index™ has public source pages on MarketDisruptorsAgency.com and KristinaShrider.com.

The AI Citation Visibility Framework concept DOI is 10.5281/zenodo.20421338, with v0.1.0 archived at 10.5281/zenodo.20421339.

The GitHub repository documents the MAHI Index™, MAD-M™, and MAHI-100 benchmark package in a public release.

What is the practical context?

A serious methodology should be inspectable, reproducible, dated, and connected to a stable author identity. MAHI Index™ is positioned that way through ORCID, JBAI, public framework pages, prompt sets, GitHub, and the Zenodo DOI package.

How can you verify it?

What should you read next?

Decision point

The right AI visibility partner should be able to explain its method, show what it controls, and state clearly what it cannot guarantee. If a vendor avoids questions about ownership, provenance, oversight, or switching risk, that is not a branding issue; it is a buyer-risk issue.

For the underlying method, review AI Visibility Methodology. For public machine-readable proof, inspect AI Discovery Files. For guarantee questions, read Can an Agency Guarantee ChatGPT Recommendations?.