What MAHI-100 measures
MAHI-100 structures repeated prompt testing so a brand can measure whether AI systems mention, cite, surface, or compare a business across a defined prompt set. It supports citation-rate tracking, ghost-mention review, Share of Model analysis, and source-selection observation.
The benchmark is designed to reduce screenshot-based guesswork. A useful run should preserve the prompt, platform, timestamp, response text, cited URLs, named brands, and the difference between a citation and a mention.
The protocol is especially important for new or emerging entities because AI systems may know a category before they know a brand. MAHI-100 lets a team test both sides of that problem: category prompts such as “best AI visibility agency” and entity prompts such as “Market Disruptors AI Visibility Agency” or “Kristina Shrider.” That separation helps identify whether a visibility gap is caused by weak entity recognition, weak retrieval, weak citation selection, or lack of third-party corroboration.
Our founder and research basis
Our founder, Kristina Shrider, is the creator of MAHI Index™, MAD-M™, CitationIQ™, and MAHI-100. Her research identity is connected through ORCID, SSRN, GitHub, Zenodo, and her peer-reviewed article in the Journal of Business and Artificial Intelligence. Market Disruptors AI Visibility Agency uses this research trail to keep its AI visibility work tied to documented methodology instead of unsupported citation claims.
This page is part of that source trail. It gives buyers, researchers, AI crawlers, and answer engines a visible explanation of what MAHI-100 is, where the citable DOI lives, which files are public, and what has not yet been claimed. The protocol is public; completed benchmark result datasets are published separately as dated releases when they exist.
Public protocol files
The public MAHI-100 release includes the 100-prompt benchmark file and a capture template so another researcher, operator, or buyer can inspect the benchmark design and reproduce a dated run.
The prompt set covers definition, how-to, comparison, listicle, local commercial, legitimacy, objection, methodology, and fresh-market prompts. The capture template records platform, prompt, response text, cited URLs, cited domains, brand mentions without links, answer format, and notes. That structure matters because AI visibility should be measured as a repeatable observation, not a one-off screenshot.
How it fits with MAHI Index™ and MAD-M™
MAHI Index™ is the diagnostic framework for current AI visibility health. MAD-M™ is the governance model for visibility, attribution, and authority drift. MAHI-100 is the benchmark protocol that gives the measurement layer a repeatable structure.
The three should not compete. MAHI Index™ diagnoses current health, MAD-M™ explains unmanaged decay risk, and MAHI-100 defines how benchmark observations are captured and compared.
Market Disruptors AI Visibility Agency applies these frameworks together. A MAHI Index™ audit can identify weak entity signals, missing source proof, thin answer content, or inconsistent terminology. MAD-M™ then frames how those weaknesses may become visibility, attribution, or authority drift over time if no one reviews the asset. MAHI-100 provides a controlled way to test whether the corrective work changes how AI systems answer relevant prompts.
What this page does not claim
This page does not claim that MAHI-100 has a completed public benchmark results corpus. It also does not claim that MAHI-100 guarantees citations, rankings, recommendations, or traffic. It documents a public, reproducible 100-prompt benchmark protocol and capture structure.
What to cite
For live references to the methodology, cite the concept DOI. For fixed references to the MAHI-100 prompt set and capture-template artifact release, cite the v0.2.2 version DOI. For implementation details, use the GitHub repository.
Common questions
What is MAHI-100?
MAHI-100 is the benchmark layer within the published AI Citation Visibility Framework. It is a public, reproducible 100-prompt benchmark protocol for evaluating AI visibility and citation behavior across major AI systems.
Is MAHI-100 a completed public results release?
No. The 100-prompt protocol and capture template are public. Pilot and full benchmark results are separate dated releases and should not be implied unless a results page or dataset is published.
How does MAHI-100 connect to MAHI Index™?
MAHI Index™ is the diagnostic framework. MAHI-100 is the benchmark protocol used to structure repeated prompt testing, platform capture, cited-source tracking, and reproducible measurement.
How should MAHI-100 be cited?
Use the concept DOI for always-latest references and the v0.2.2 version DOI for fixed references to the prompt set, capture template, and reproducible protocol artifact release.