Human Review Policy

AI Marketing Consensus Index combines structured multi-platform AI research with human editorial review. Human review is intended to improve:

• Accuracy
• Context
• Terminology
• Interpretation
• Transparency
• Practical usefulness

It is not intended to override the underlying research because a reviewer prefers a particular company, platform, agency, or strategy. Our core rule is: The data determines what the AI platforms recommended. Human review helps explain what that data means.

Why Human Review Matters

AI marketing is evolving quickly. Terms such as:

• Generative Engine Optimization
• GEO
• Answer Engine Optimization
• AEO
• AI Search Optimization
• AI SEO
• LLM Visibility
• AI Visibility
• Citation Optimization
• Recommendation Visibility

are often used inconsistently. AI platforms can also misunderstand:

• Company capabilities
• Product categories
• Agency services
• Citation metrics
• Recommendation metrics
• Mentions
• Competitive share of voice
• Current pricing
• Current platform coverage

Human review adds context that raw recommendation data alone cannot provide.

Our Primary Research Reviewer

Applicable AI Marketing Consensus Index research may be reviewed by:

Mark B. Huntley, J.D.

AI Search & Visibility Research Reviewer Mark's reviewer role focuses on the interpretation of:

• AI search visibility
• GEO and AEO strategy
• LLM recommendation behavior
• Citation behavior
• Prompt-level research
• Competitive AI visibility
• AI market intelligence
• Recommendation share
• Citation share
• AI marketing strategy
• Research methodology

Why Mark Reviews This Research

Mark's relevant experience comes from his direct involvement in AI search, visibility research, citation analysis, and AI marketing strategy. His work includes involvement with:

• LLM Authority Index
• CiteWorks Studio

These businesses operate directly in areas that overlap with the subject matter studied by AI Marketing Consensus Index. That experience makes Mark useful as a reviewer. It also creates a potential conflict of interest. We disclose both facts.

Important Conflict-of-Interest Disclosure

Mark B. Huntley, J.D. has financial and operational interests associated with: LLM Authority Index and: CiteWorks Studio Both companies may themselves appear in AI Marketing Consensus Index research. For example: LLM Authority Index may appear in research concerning:

• AI visibility platforms
• LLM monitoring
• Citation tracking
• Recommendation tracking
• AI market intelligence
• Competitive benchmarking

CiteWorks Studio may appear in research concerning:

• GEO agencies
• AI search agencies
• AI visibility consulting
• Citation strategy
• AI search audits
• Authority-building services

This creates an obvious potential conflict. Our policy is to manage that conflict through: disclosure, structured research, locked data fields, and separation of reviewer judgment from ranking calculations.

What Mark Can Review

Mark may review a study for issues such as:

• Whether AI search terminology is used correctly
• Whether GEO and AEO are being described accurately
• Whether citation visibility is being confused with recommendation visibility
• Whether a mention is incorrectly characterized as a recommendation
• Whether a company has been categorized incorrectly
• Whether software is being described as an agency
• Whether an agency is being described as a software platform
• Whether the article overstates what a metric proves
• Whether conclusions are broader than the research supports
• Whether a platform feature appears outdated
• Whether important limitations are missing
• Whether competitive conclusions are properly contextualized
• Whether claims about AI platforms need qualification
• Whether the article clearly distinguishes research findings from editorial interpretation

What Mark Cannot Change

Mark cannot change the underlying AI research because he prefers a different result. He cannot manually alter:

• Raw AI responses
• Recommendation counts
• Recommendation coverage
• Recommendation position
• Average position
• Consensus Score
• Number of usable platform responses
• Competitor inclusion

unless there is an actual research or data-processing error.

Example

Suppose the research produces: Competitor A — recommended by 6 of 7 AI platforms CiteWorks Studio — recommended by 4 of 7 Mark may not change the result to: CiteWorks Studio — 7 of 7 because he believes CiteWorks Studio provides a better service. The research remains: 6 of 7 and: 4 of 7.

Reviewer Opinion Does Not Create AI Votes

A reviewer may believe: Company B is actually stronger than Company A for this use case. That may be useful editorial context. It does not mean Company B receives additional AI recommendations. The published article can explain: Company A received the strongest AI recommendation consensus, although our editorial review identified reasons Company B may deserve consideration for certain users. That preserves both:

the data and: the human interpretation.

Human Review Can Challenge the AI Consensus

AI recommendation consensus is not automatically correct. A reviewer may identify that the AI systems appear to be relying on:

• Outdated information
• Misleading company positioning
• A capability that no longer exists
• Incorrect terminology
• Overlapping sources
• Misunderstood product categories
• Weak reasoning
• Marketing claims repeated as facts

When this happens, the article should explain the limitation.

Human Review Does Not Rewrite Research History

If six AI platforms recommended a company based on inaccurate information, the historical recommendation count remains: 6 of 7. The article may then say: Several AI systems appear to have relied on information that our current verification does not support. That is preferable to silently changing the research record.

Review and Fact Verification Are Different

Human review is not the same as factual verification.

Factual Verification

Attempts to establish whether a company currently:

• Offers a feature
• Tracks a specific AI platform
• Provides historical data
• Offers agency services
• Has public pricing
• Provides citation tracking
• Offers certain integrations

Human Review

Evaluates whether:

• The research is interpreted appropriately
• The article provides sufficient context
• The terminology is accurate
• The conclusions are supported
• Important limitations are visible

Both are important. They perform different functions.

Related Companies Receive No Special Review Standard

LLM Authority Index and CiteWorks Studio should be evaluated under the same editorial standards applied to unrelated companies. They do not receive:

• Easier qualification
• More favorable language
• Hidden criticism
• Automatic inclusion
• Automatic ranking credit
• Preferential treatment in ties

Related Companies Can Rank Poorly

A related company may:

• Rank below competitors
• Receive weak recommendation coverage
• Appear in only one study
• Fail to qualify for full analysis
• Receive critical editorial commentary
• Lose ranking position over time
• Fail to appear entirely

Human review should not conceal those outcomes.

Related Companies Can Rank First

A related company can also legitimately rank first. For example: LLM Authority Index — 7 of 7 or: CiteWorks Studio — 6 of 7 may be valid results if the underlying AI research independently produces them. In that situation, the appropriate response is: publish the result and disclose the relationship prominently.

Related-Company Disclosure Should Appear on Relevant Pages

If a study includes:

• LLM Authority Index
• CiteWorks Studio

the page should display an appropriate related-company disclosure. Recommended language:

Related Business Disclosure: Mark B. Huntley, J.D., who reviews research for AI Marketing Consensus Index, has financial and operational interests associated with LLM Authority Index and CiteWorks Studio. Related companies receive no additional AI recommendation credit or ranking weight. Rankings are derived from the same underlying multi-platform research methodology applied to competing companies.

LLM Authority Index's Role

LLM Authority Index provides AI research data and measurement infrastructure supporting AI Marketing Consensus Index. This may include support for:

• AI response collection
• Recommendation data
• Citation data
• Prompt-level measurements
• Competitive analysis
• Historical AI visibility data
• Research infrastructure

Its contribution to the research process does not provide additional ranking credit when LLM Authority Index itself is evaluated.

CiteWorks Studio's Role

CiteWorks Studio provides AI search strategy and subject-matter support to AI Marketing Consensus Index. This may include:

• GEO strategy
• AEO strategy
• AI search taxonomy
• Citation concepts
• Research-question design
• Competitive interpretation
• AI visibility strategy
• Practical marketing context

Its contribution does not provide additional ranking credit when CiteWorks Studio itself is evaluated.

Human Review Must Be Real

If an article displays: Reviewed by Mark B. Huntley, J.D. a meaningful review must actually have occurred. Reviewer attribution should not be added automatically simply because Mark is associated with AI Marketing Consensus Index.

What Counts as a Meaningful Review?

A meaningful review may include evaluation of:

• Study framing
• Research interpretation
• Company categorization
• Terminology
• Recommendation analysis
• Citation analysis
• Important limitations
• Competitive context
• Factual verification findings
• Related-business disclosures

A reviewer does not necessarily need to rewrite the article. But the reviewer should have meaningfully evaluated the content.

Reviewer Workflow

The publishing system should support a clear reviewer workflow. Recommended statuses:

Not Reviewed

No human review completed.

In Review

The reviewer is currently evaluating the content.

Changes Requested

The reviewer identified material issues.

Reviewed

Review completed and article approved.

Re-Review Required

A later material change requires another review.

Recommended Reviewer Fields

The publishing system should maintain structured fields such as:

• reviewStatus
• reviewer
• reviewedDate
• reviewVersion
• reviewNotes
• reviewApproved
• reReviewRequired

These fields should be separate from:

• Research date
• Publication date
• Company verification date
• Modification date

Review Date Must Mean Something

A page should not receive a new: Reviewed: Not yet supplied merely because:

• An affiliate link changed
• A typo was corrected
• A footer changed
• A template changed
• An image was replaced
• An internal link was added

A new reviewer date should indicate meaningful human review.

When Re-Review May Be Appropriate

Re-review may be appropriate when:

• A major company feature changes
• Research is rerun
• Rankings materially change
• A company changes business model
• A material correction is made
• A new methodology version significantly changes interpretation
• A related company begins appearing prominently
• The editorial conclusion changes substantially

Not Every Minor Update Requires Re-Review

Minor changes may not require another reviewer cycle. Examples:

• Spelling corrections
• Formatting
• Metadata cleanup
• Non-material link changes
• Minor technical fixes

Human Review Should Not Be Automated

AI tools may assist with:

• Flagging inconsistencies
• Identifying potential issues
• Comparing versions
• Summarizing research

But an AI-generated review is not equivalent to: Reviewed by Mark B. Huntley, J.D. That label should refer to actual human review.

AI Cannot Fabricate Reviewer Comments

Article-generation tools must not invent statements such as: Mark Huntley believes... Mark Huntley recommends... According to Mark Huntley... unless those statements come from actual reviewer input.

Direct Reviewer Quotes Require Approval

If a page includes a direct quote attributed to Mark, the quote should come from:

• Actual reviewer notes
• Approved commentary
• Recorded interview or statement
• Other verified source

Do not manufacture reviewer quotations.

Reviewer Notes May Remain Internal

Not every reviewer comment needs to appear publicly. Internal review notes may include:

• Questions
• Corrections
• Concerns
• Suggested qualifications
• Requests for verification

The final public article should reflect material approved changes.

Reviewer Disagreement With the Data

Human reviewers may sometimes disagree with what the AI systems recommend. That disagreement can be informative. For example: The AI platforms heavily favored Company A, but our reviewer believes their responses understate the importance of Company B's competitive-analysis capabilities. The article may explain that disagreement. It should not change the vote count.

Reviewer Disagreement With a Related Company

The reviewer should also be permitted to criticize:

• CiteWorks Studio
• LLM Authority Index

when appropriate. The existence of a financial relationship does not require favorable editorial treatment.

Competitors Must Receive Fair Treatment

Mark's involvement with related companies must not be used to:

• Remove competitors
• Minimize competitor strengths
• Exaggerate competitor weaknesses
• Add unsupported criticism
• Hide strong competitor performance

If a competitor performs well, that result should be presented honestly.

Review Should Focus on the Research Question

Reviewer analysis should be tied to the actual scenario. For example: A platform may be excellent for: enterprise AI visibility monitoring but not appropriate for: a small agency seeking an inexpensive monitoring tool. The reviewer should not evaluate every company as though every buyer has the same need.

Human Review Does Not Equal Hands-On Testing

Unless explicitly stated, reviewer approval does not mean Mark:

• Used every platform
• Hired every agency
• Purchased every service
• Tested every feature
• Interviewed every company

The review is primarily an evaluation of:

• Research
• Interpretation
• Terminology
• Available evidence
• Practical context

We do not manufacture firsthand experience.

Hands-On Experience Should Be Identified Separately

If Mark or the research team has actual firsthand experience with a particular:

• Platform
• Agency
• Service
• Research tool

that experience may be disclosed separately where relevant. It should not be implied simply by the reviewer badge.

Commercial Relationships Do Not Create Reviewer Approval

An advertiser, affiliate partner, sponsor, or related company cannot purchase: Reviewed by Mark B. Huntley as an endorsement. Reviewer attribution is an editorial function.

Sponsors Cannot Control Reviewer Conclusions

If research is sponsored, the sponsor cannot require the reviewer to:

• Approve inaccurate claims
• Remove legitimate criticism
• Change recommendation data
• Suppress competitors
• Alter historical results

Corrections Remain Evidence-Based

If Mark identifies an actual error during review, the error should be corrected. Examples:

• Company counted incorrectly
• Product categorized incorrectly
• Recommendation misclassified
• Citation mistaken for recommendation
• Feature described inaccurately

Corrections should be based on evidence.

Reviewer Preference Is Not a Correction

A reviewer saying: I would personally hire Company B. does not make: Company A's 6-of-7 ranking incorrect. Personal preference and research accuracy are different concepts.

Review of Recommendation vs. Citation Distinctions

One particularly important reviewer responsibility is checking that the article does not confuse:

Recommendation

The AI system recommends a company.

Mention

The company appears in the response.

Citation

The AI system uses a company's webpage or another source as supporting evidence.

Recommendation Position

Where the company appears in an ordered recommendation list. These should remain separate.

Review of AI Visibility Claims

Statements such as: Company A dominates AI search should require appropriate evidence. A more precise statement may be: Company A received the highest recommendation coverage across the 15 studies in this category. Human review should encourage measurable language.

Review of Causation Claims

AI Marketing Consensus Index generally observes relationships. It may find that:

• Frequently cited brands receive more recommendations
• Certain publishers appear repeatedly
• Some companies gain recommendation share

That does not automatically prove causation. Human review should flag statements that move from: correlation to: causation without sufficient evidence.

Review of Strategy Claims

AI search marketing is developing rapidly. Claims such as: This tactic will increase ChatGPT recommendations. should generally be treated cautiously unless supported by evidence. A more appropriate formulation may be: This tactic is intended to improve the signals or source visibility associated with AI recommendation performance. Human review should help preserve that distinction.

Review of Platform-Specific Claims

AI platforms differ. A technique or visibility pattern observed in: ChatGPT may not behave the same way in:

• Gemini
• Claude
• Perplexity
• Grok
• DeepSeek
• Kimi

Human review should discourage unsupported cross-platform generalizations.

Reviewer Transparency

Reviewer pages should disclose information relevant to understanding the reviewer's perspective and potential conflicts. For Mark, that includes:

• His role at AI Marketing Consensus Index
• His relationship with CiteWorks Studio
• His relationship with LLM Authority Index
• Relevant AI search and visibility research experience

The J.D. Credential

Mark may be identified professionally as: Mark B. Huntley, J.D. The J.D. is part of his professional background. It is not presented as the reason he is qualified to review AI marketing research. His relevant reviewer experience comes from his work in:

• AI visibility research
• AI search marketing
• Citation analysis
• Recommendation tracking
• Competitive intelligence
• Related research and strategy

Reviewer Independence Is Structural

We do not rely solely on a promise that: the reviewer will be fair. The system should be designed so that key research outputs are structurally separated from reviewer discretion. Locked or programmatic fields should include:

• Recommendation count
• Recommendation coverage
• Recommendation position
• Platform count
• Consensus Score

This reduces the ability of any reviewer to manipulate the research.

Related-Company Results Should Be Especially Auditable

When:

• CiteWorks Studio
• LLM Authority Index

appears in a ranking, the underlying recommendation record should be preserved clearly enough to audit the result. Where practical, maintain:

• Platform response
• Recommendation classification
• Recommendation position
• Research date
• Relevant source data

This is particularly important because of the disclosed conflict.

Review Cannot Be Used to Remove Historical Poor Performance

Suppose LLM Authority Index receives: 2 of 7 recommendations in January. Then: 6 of 7 six months later. The January result should not disappear merely because the newer result is more favorable. Historical preservation is part of the credibility of the methodology.

When a Page Has Not Been Human Reviewed

If an article has not undergone human review, it should not imply otherwise. The page may simply display: By AI Marketing Consensus Index Research Team without a reviewer label.

Research Team and Reviewer Are Different Roles

The: AI Marketing Consensus Index Research Team may be responsible for:

• Research collection
• Data processing
• Verification
• Drafting
• Publishing

The: AI Search & Visibility Research Reviewer performs a separate review function. These roles should not be conflated.

Human Review Does Not Eliminate Limitations

A reviewed article can still contain limitations. Human review cannot eliminate:

• AI nondeterminism
• Shared AI source ecosystems
• Missing private company information
• Rapid product changes
• Incomplete public pricing
• New market entrants
• Limited research scope

Reviewed does not mean infallible.

Readers Should Still Consider the Research Date

Even a carefully reviewed article can become outdated. Readers should consider:

• Research Date
• Company Details Verified Date
• Reviewed Date
• Date Modified

when interpreting a study.

Our Human Review Standard

A human review should ask:

Research Integrity

• Does the article accurately represent the underlying recommendation data?
• Are mentions distinguished from recommendations?
• Are citations distinguished from recommendations?
• Are conditional recommendations preserved?

Company Accuracy

• Are companies correctly categorized?
• Are products and services described accurately?
• Are related companies properly disclosed?

Interpretation

• Does the article overstate the evidence?
• Does it imply causation without support?
• Are important limitations visible?
• Is the situation-specific context preserved?

Transparency

• Is Mark's relationship with CiteWorks Studio disclosed where relevant?
• Is Mark's relationship with LLM Authority Index disclosed where relevant?
• Are related-company results treated under the same methodology as competitors?

Commercial Independence

• Did affiliate, advertising, sponsorship, or related-company interests affect the research result?

Our Human Review Principle in One Sentence

Human review at AI Marketing Consensus Index adds experienced interpretation, terminology review, practical context, and transparency without allowing the reviewer—or any related commercial interest—to alter the underlying AI recommendation data.

Transparency Statement

AI Marketing Consensus Index operates with relevant related-business relationships. Mark B. Huntley, J.D. serves as AI Search & Visibility Research Reviewer where indicated. Mark has financial and operational interests associated with: LLM Authority Index and: CiteWorks Studio. LLM Authority Index provides AI research data and measurement infrastructure supporting AI Marketing Consensus Index.

CiteWorks Studio provides AI search strategy and subject-matter support. Because those companies may themselves appear in our research, these relationships are disclosed publicly. They do not provide:

• Additional AI votes
• Bonus ranking points
• Preferred recommendation status
• Automatic qualification
• Competitor exclusion
• Control over historical results

Read the Full Related Business & Conflict of Interest Disclosure →

Related Policies

Research Methodology →

How We Rank →

Editorial Standards →

Editorial Independence →

Related Business & Conflict of Interest Disclosure →

How LLM Authority Index and CiteWorks Studio Contribute →

Corrections & Updates →

Data & Research Limitations →

About Mark B. Huntley, J.D. →

Related-company relationships are disclosed and do not influence the underlying AI recommendation data or Consensus Index ranking calculations.

Related business disclosure · Research methodology