Editorial Standards

AI Marketing Consensus Index publishes research about how leading artificial intelligence platforms recommend companies, agencies, software, and services for specific AI marketing needs. Our research covers areas including:

• AI Search Optimization
• Generative Engine Optimization
• Answer Engine Optimization
• AI visibility monitoring
• LLM recommendation tracking
• AI citation analysis
• AI search audits
• AI market intelligence
• Citation and authority building
• AI SEO
• Content optimization for AI search

The AI marketing industry is evolving quickly. Terminology is inconsistent. Company capabilities change. AI systems themselves change. And AI platforms can produce incorrect, outdated, or contradictory information. For those reasons, our editorial process is designed around several separate layers:

1. AI recommendation data
2. Factual verification
3. Editorial interpretation
4. Human research review
5. Commercial disclosure

These layers work together. They should not be confused with one another.

Our Core Editorial Principle

Our primary editorial rule is: Report what the research found, verify material facts separately, explain limitations honestly, disclose related business interests clearly, and do not manipulate the underlying result to benefit ourselves or a commercial partner. That means:

• AI recommendation counts come from the research
• Verified facts come from appropriate sources
• Editorial conclusions come from analysis
• Human review adds context
• Related-company relationships are disclosed
• Affiliate relationships do not create ranking credit
• Advertising does not determine research outcomes

We Start With the Marketing Problem

Our research begins with a defined business situation. Examples include:

• A SaaS company looking for a GEO agency
• An enterprise CMO needing an AI visibility platform
• A marketing team trying to track ChatGPT citations
• A company wanting an AI search audit
• An agency looking for white-label AI visibility tools
• A publisher trying to understand declining AI citation share
• A business looking for tools to optimize content for AI search

The use case should be specific enough that the resulting recommendations have practical meaning.

We Do Not Start With Companies We Want to Promote

Our research should not begin with:

Which of our related companies or commercial partners should appear in this article?

It should begin with:

Which companies do the AI systems independently recommend for this particular situation?

That distinction is fundamental.

Standard Open Research Does Not Seed Preferred Companies

For ordinary open recommendation studies, we should not insert: CiteWorks Studio LLM Authority Index or their competitors into the prompt. Instead of asking:

Which is better: CiteWorks Studio, Company A, Company B, or Company C?

we should ask:

Which AI search optimization agencies would you recommend for this situation, and why?

This allows the AI platforms to surface companies naturally. Named-company comparison studies are allowed when the research question itself is explicitly a comparison. Those studies should be labeled accordingly.

Research Data and Editorial Content Are Separate

AI Marketing Consensus Index distinguishes among several types of information.

Research Data

What the AI systems actually recommended.

Verified Information

What current primary or reliable sources support.

Editorial Analysis

What the research and verified facts may mean for the defined marketing situation.

Reviewer Context

Additional interpretation from the research reviewer.

Commercial Information

Affiliate, referral, sponsorship, ownership, or related-company relationships. These concepts may appear together on one page, but they should remain conceptually separate.

The AI Platforms Are Research Subjects

We study what AI platforms recommend. We do not treat their responses as automatically correct. An AI system can:

• Recommend an appropriate company
• Recommend an unsuitable company
• Cite outdated information
• Confuse one company with another
• Misstate product capabilities
• Invent pricing
• Confuse a citation with a recommendation
• Overstate a platform's AI coverage
• Mischaracterize GEO, AEO, or AI SEO
• Recommend a company for a service it does not actually provide

The fact that an AI system said something is evidence of: what that AI system said not necessarily evidence that: the statement is factually correct.

We Preserve the Original Recommendation Record

If an AI system recommends a company, we preserve that recommendation in the research dataset even when later verification identifies a factual problem. Example: Six AI systems recommend Platform A because they believe it tracks seven major AI platforms. Current verification later finds that Platform A actually tracks five. The correct editorial treatment is: Platform A still received six recommendations.

Then we explain: The supporting product claim was inaccurate or outdated.

We Do Not Fix AI Errors by Changing the Votes

If an AI system makes a factual mistake, we should not silently remove its recommendation simply because we disagree with the reasoning. The recommendation is part of the research record. The factual correction belongs in the verification and analysis layer.

Recommendation, Mention, Citation, and Comparison Are Different

This distinction is one of the most important editorial standards on the site.

Recommendation

The AI presents a company as an appropriate option for the use case.

Conditional Recommendation

The company is recommended only under certain circumstances.

Mention

The company appears in the response but is not necessarily recommended.

Citation

The AI references content from a source.

Comparison Reference

A company appears because it is being contrasted with another provider.

Negative Reference

The company is specifically discouraged or criticized. These should not automatically be treated as equivalent.

A Citation Is Not a Recommendation

Example: ChatGPT cites: Company A's research report while recommending: Company B's software. Company A received a citation. Company B received a recommendation. The article should not report that both companies received recommendation credit.

A Mention Is Not a Recommendation

If an AI response says: Unlike Company A, Company B offers enterprise competitive benchmarking. Company A has been mentioned. That does not necessarily mean Company A was recommended.

Conditional Recommendations Must Preserve the Condition

Example: Company A is worth considering for enterprise teams but may be excessive for a small business. That should not become: Company A is the best AI visibility platform. The qualification is part of the recommendation.

Recommendation Coverage Is the Primary Ranking Signal

Our core research methodology measures how consistently a company appears across AI platforms. If: 6 of 7 usable platforms recommend a company, its recommendation coverage is: 85.7%. This is the dominant signal in standard study-level rankings.

Recommendation Position Provides Secondary Context

If two companies receive the same number of recommendations, placement can help differentiate them. Example:

Company A

5 of 7 recommendationsAverage position: #1.8

Company B

5 of 7 recommendationsAverage position: #3.7 Company A demonstrated stronger placement. Recommendation position is therefore an important secondary measure.

We Allow Genuine Ties

If the research does not support a meaningful distinction between two companies, we do not need to invent one. We may report:

• A tie
• Near-tie
• Comparable recommendation strength

The purpose is to describe the data accurately, not manufacture ranking precision.

We Avoid Unsupported Superlatives

Words such as:

• Best
• Leading
• Most accurate
• Most comprehensive
• Most advanced
• Cheapest
• Most effective
• Highest ROI

should be used carefully. A page titled: Best AI Visibility Platforms means: the strongest companies under the published consensus methodology for the defined research scenarios. It does not mean one platform has been objectively proven superior in every dimension.

“Best” Must Be Tied to the Methodology

We prefer language such as: Highest recommendation coverage in this study or: #1 AI consensus result for this use case over broad unsupported claims such as: The best AI marketing company in the world.

We Verify Material Company and Product Claims

Where material to the analysis, we attempt to verify information such as:

• Product capabilities
• AI platforms monitored
• Citation tracking
• Recommendation tracking
• Prompt monitoring
• Competitor benchmarking
• Historical tracking
• Reporting capabilities
• GEO services
• AEO services
• AI search services
• Content optimization features
• Integrations
• Pricing where publicly available
• Target customer
• Agency services
• Current product availability

We Prefer Primary Sources for Product Facts

Preferred sources include:

• Official company product pages
• Official pricing pages
• Official documentation
• Official knowledge bases
• Official product announcements
• Official methodology pages
• Reliable third-party sources where primary information is unavailable

Company Marketing Is Not Independent Proof

A company may describe itself as:

• The leading GEO platform
• The most accurate AI visibility tool
• The #1 AI search agency
• The industry's most advanced citation platform

Those statements remain company claims unless independently supported. We should attribute promotional language rather than automatically adopting it.

We Distinguish Feature Claims From Performance Claims

A company may be able to verify: Our platform tracks ChatGPT, Gemini, and Perplexity. That is a feature claim. A statement such as: Our platform is the most accurate solution for tracking AI visibility. is a performance claim and requires stronger support.

Pricing Must Be Current and Qualified

AI marketing software pricing changes frequently. Agencies may use:

• Retainers
• Project pricing
• Custom proposals
• Enterprise contracts
• Usage-based pricing

If pricing is discussed, it should be:

• Dated
• Sourced where practical
• Clearly described
• Qualified when custom

We should not present old promotional pricing as current pricing.

AI Platform Coverage Requires Care

A vendor may claim to monitor: ChatGPT but that could mean different things. It may track:

• Consumer ChatGPT
• Search-enabled ChatGPT
• Selected prompts
• API responses
• A proprietary simulation
• Multiple model variants

Where the distinction matters, we should avoid assuming these are identical.

Consumer Interface and API Results Are Not Necessarily Identical

A company may collect AI responses through:

• Consumer interfaces
• APIs
• Search-enabled modes
• Automated browser sessions
• Third-party data providers

These environments can produce different results. Where material, our editorial content should explain the research environment rather than implying all outputs are interchangeable.

GEO, AEO, AI SEO, and AI Search Optimization Are Not Perfect Synonyms

The AI marketing industry uses overlapping terminology. Common terms include:

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

Companies may use these terms differently. We should avoid pretending that the industry has one universally accepted definition. Our Research Definitions & Terminology page should provide the site's working definitions.

AI Visibility Is Not One Metric

AI visibility can refer to:

• Mentions
• Recommendations
• Citation frequency
• Citation coverage
• Recommendation position
• Prompt coverage
• Share of voice
• Competitor presence
• Platform coverage

We should not collapse all of these into one number without clearly explaining what the number measures.

Recommendation Share and Citation Share Are Different

A company can:

• Be frequently recommended but rarely cited
• Be frequently cited but rarely recommended
• Perform strongly on one platform and weakly on another

Those differences are often more interesting than one overall score.

We Avoid Unsupported Causal Claims

If a company gains AI visibility after publishing content, we should not automatically say: The content caused the visibility increase. Correlation is not proof of causation. Likewise: Company A gained citations after launching a Reddit campaign does not prove the Reddit campaign caused the change unless evidence supports that conclusion.

Case Studies Must Distinguish Observation From Causation

If we publish or discuss case studies, we should separate:

What changed

from:

Why we believe it changed

and:

What can actually be proven.

We Do Not Manufacture Firsthand Experience

AI Marketing Consensus Index should never claim:

• We used software we did not use
• We hired an agency we did not hire
• We interviewed a company we did not interview
• We ran a campaign we did not run
• We tested a feature we did not test
• We obtained results we did not obtain

“Tested” Must Mean Tested

If an article says: We tested Platform A there should be a real test. If we instead analyzed:

• AI recommendations
• Public documentation
• Product descriptions

we should say that.

AI May Assist Editorial Production

AI tools may assist with:

• Organizing research
• Summarizing structured responses
• Drafting
• Formatting
• Comparing results
• Identifying inconsistencies
• Extracting structured information

AI assistance does not make generated content authoritative.

AI Writing Tools Cannot Invent Research Metrics

The article-generation system must not invent:

• Recommendation counts
• Recommendation percentages
• Average positions
• Consensus Scores
• Platform counts
• Citation counts
• Research dates

Those values must come from structured research data.

Structured Research Data Is Authoritative

The editorial system should treat structured fields as locked. For example: If the database says: Company A — 5 of 7 an AI writing tool cannot decide that: Company A — 6 of 7 sounds better.

AI Cannot Invent Reviewer Comments

The article generator should not create statements such as: Mark Huntley believes… unless that comment actually came from Mark's approved reviewer notes or approved editorial material.

Human Review

Applicable AI Marketing Consensus Index research may be reviewed by:

Mark B. Huntley, J.D.

AI Search & Visibility Research Reviewer Mark's role may include reviewing:

• GEO/AEO terminology
• AI visibility methodology
• Recommendation interpretation
• Citation interpretation
• Competitive analysis
• AI search strategy
• Market-intelligence conclusions
• Unsupported causal claims
• Confusion among platforms, services, agencies, and tools

Mark's Review Does Not Create Recommendation Votes

Mark does not receive:

• A vote
• Bonus ranking weight
• The ability to manually change recommendation coverage

Human review adds: context not: additional consensus points.

Full Disclosure of Mark Huntley's Business Interests

Mark B. Huntley, J.D. has financial and operational interests associated with: CiteWorks Studio and: LLM Authority Index. Both companies operate in markets that AI Marketing Consensus Index researches. That relationship creates a potential conflict of interest. We disclose it openly.

LLM Authority Index's Role

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

• AI response collection
• Recommendation measurement
• Citation data
• Prompt-level research
• Competitive benchmarking
• Historical research
• Research infrastructure

LLM Authority Index may itself appear in relevant research. Its contribution to the research infrastructure does not provide additional recommendation credit.

CiteWorks Studio's Role

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

• GEO/AEO strategy
• AI search concepts
• Citation strategy
• Authority-building concepts
• Research taxonomy
• Research-question design input
• Practical AI marketing context

CiteWorks Studio may itself appear in research involving agencies and services. Its role does not provide additional ranking credit.

Related Companies Receive No Special Ranking Treatment

Neither LLM Authority Index nor CiteWorks Studio receives:

• Extra AI votes
• Bonus Consensus Score
• Preferred ranking position
• Automatic qualification
• Competitor removal
• Guaranteed inclusion

They are subject to the same underlying research methodology.

Related Companies Can Rank Poorly

If the data shows: Competitor A — 7 of 7 Competitor B — 5 of 7 CiteWorks Studio — 2 of 7 then that is the result. The relationship does not alter it.

Related Companies Can Receive Zero Recommendations

If no AI platform recommends a related company: 0 of 7 is a valid outcome. We do not manufacture recommendations.

Related Companies Can Rank First

Editorial independence works in both directions. If the data independently shows: LLM Authority Index — 7 of 7 or: CiteWorks Studio — 6 of 7 we do not suppress the result because the relationship exists. We publish it and disclose the relationship.

Mark Cannot Override Competitors

Mark's commercial interests do not allow him to:

• Remove competitors
• Reduce competitor votes
• Increase related-company votes
• Rewrite historical results
• Alter category rankings for commercial reasons

Competitors Must Remain Eligible

If an AI platform recommends:

• Profound
• Semrush
• Scrunch
• Otterly
• Surfer
• Writesonic
• Another GEO agency
• Another AI visibility platform

the recommendation belongs in the dataset. The site exists to measure the market, not protect related businesses.

Related-Company Disclosure Must Be Visible

When CiteWorks Studio or LLM Authority Index appears in a study or ranking, the page should display a visible: Related Company or equivalent disclosure. Readers should not need to discover the relationship by searching through legal pages.

Reviewer Attribution Must Reflect a Real Review

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

Reviewer Dates Must Be Real

A new reviewer date should mean meaningful review occurred. It should not automatically change because:

• A typo was corrected
• An affiliate link changed
• A template changed
• Internal links were updated

Research Before Monetization

Our preferred editorial sequence is: Define the marketing problem

Collect AI responses

Extract recommendations

Calculate consensus

Verify material facts

Create editorial analysis

Perform applicable human review

Publish

Apply commercial treatment where appropriate

Commercial Value May Influence Topic Selection

AI Marketing Consensus Index is a commercial publication. It is acceptable for:

• Search demand
• Buyer intent
• Affiliate potential
• Lead-generation value
• Market relevance
• Strategic importance

to influence which topics we choose to research. That is different from allowing commercial value to determine which company ranks first.

Related Businesses May Benefit From the Research

We do not pretend otherwise. If AI Marketing Consensus Index produces qualified traffic for:

• CiteWorks Studio
• LLM Authority Index

those businesses may benefit. That commercial benefit is one reason the relationship is disclosed prominently. It does not justify changing the data.

Affiliate Relationships Do Not Determine Rankings

AI Marketing Consensus Index may earn commissions or referral compensation from companies included in its content. Affiliate status does not determine:

• Inclusion
• Recommendation count
• Recommendation coverage
• Recommendation position
• Consensus Score
• Category ranking
• Editorial conclusion

Non-Affiliate Companies Can Rank First

A company does not need to compensate AI Marketing Consensus Index to rank highly. If the research supports a non-affiliate company as #1, it should remain #1.

Affiliate Companies Can Rank Poorly

An affiliate can:

• Rank below non-affiliates
• Receive low consensus
• Fail to qualify
• Receive critical analysis
• Lose visibility over time

Advertising Must Be Clearly Identified

Paid placements should be labeled clearly using language such as:

• Advertisement
• Sponsored
• Paid Placement
• Sponsored Content

Paid advertising should not be visually disguised as earned consensus ranking.

Advertisers Cannot Buy AI Votes

Advertising does not create:

• Recommendation votes
• Better recommendation position
• Higher Consensus Score
• Category leadership
• Historical gains

Sponsored Research Requires Disclosure

If an outside company financially supports a research project, that sponsorship should be disclosed. A sponsor may help define:

• Research subject
• Market
• Use case

It may not purchase:

• The winner
• Recommendation counts
• Competitor exclusion
• Editorial conclusion

Data Licensing Does Not Create Editorial Influence

AI Marketing Consensus Index may license:

• Research datasets
• Historical data
• Rankings
• Benchmark reports
• Market intelligence

A company purchasing data does not gain authority over the underlying research.

We Do Not Punish Companies for Commercial Decisions

Editorial independence works both ways. A company should not receive worse research treatment because it:

• Declines an affiliate relationship
• Declines sponsorship
• Stops advertising
• Refuses to buy data
• Competes with a related business

Corrections Are Available to Everyone

A company does not need to:

• Pay us
• Advertise
• Become an affiliate
• Purchase data

to report a factual error. Corrections should be evaluated based on evidence.

Disagreement Is Not Automatically an Error

A company may dislike:

• Its ranking
• Its recommendation count
• Competitor inclusion
• Editorial interpretation

That disagreement does not automatically establish a correction. A correction requires evidence that the published data or facts are materially wrong.

Correction, Update, and Rerun Are Different

Correction

We made an error. Examples:

• Wrong recommendation count
• Incorrect entity normalization
• Calculation error
• Wrong product fact

Update

The market changed. Examples:

• Pricing changed
• Product feature changed
• Agency services changed
• Platform coverage changed

Rerun

The AI research was conducted again. These concepts should remain separate.

We Preserve Historical Research

A new research cycle should not erase the old one. If: Company A — 6 of 7 in January and: Company A — 3 of 7 in July both results may be useful. Historical recommendation movement is part of the dataset.

Research Dates Must Be Meaningful

Maintain separate dates where appropriate for:

Research Date

When AI research occurred.

Date Published

When the article first appeared.

Details Verified Date

When material company/product information was checked.

Date Modified

When meaningful editorial content changed.

Reviewed Date

When applicable human review occurred.

We Do Not Artificially Refresh Dates

Minor edits such as:

• Punctuation
• Formatting
• Internal links
• Small copy changes

should not automatically make old research appear newly conducted.

Methodology Changes Should Be Documented

If we materially change:

• Platform set
• Extraction rules
• Recommendation definition
• Ranking formula
• Qualification threshold
• Category aggregation

the change should be documented. Where practical, methodology versions should be preserved.

AI Systems Can Share Sources

Cross-platform agreement does not necessarily mean several completely independent evidence ecosystems produced the same conclusion. AI platforms may rely on overlapping:

• Company sites
• Reviews
• Industry publications
• Research
• Comparison articles
• Social discussions

Therefore: cross-platform consensus is not the same as independent factual verification.

AI Citations Are Not Automatically Verification Sources

If an AI system cites a webpage, that citation is part of the research record. We may inspect it. But the existence of the citation does not automatically mean:

• The source is reliable
• The claim is current
• The claim is accurate

Verification remains separate.

We Avoid Citation Laundering

A claim does not become reliable merely because many websites repeat it. Where practical, important claims should be traced to:

• Original product documentation
• Original methodology
• Primary company information
• Other credible direct sources

rather than chains of sites copying one another.

Editorial Interpretation Must Follow the Evidence

A study should not conclude: Company A clearly dominates enterprise AI visibility if the data only shows: Company A received one additional recommendation in one small study. Conclusions should match the scale and quality of the evidence.

We Avoid Overgeneralizing Small Samples

A study using seven AI platforms is useful for measuring the defined research universe. It does not represent:

• Every AI model
• Every user
• Every prompt
• Every geography
• Every possible session

We should explain sample limitations appropriately.

AI Outputs Are Nondeterministic

The same AI system may produce different recommendations when asked the same question again. That variability is part of the technology. Our studies represent: research snapshots not deterministic laws.

Rankings Should Be Interpreted According to Their Research Date

AI marketing moves quickly. A ranking can change because:

• AI systems change
• Company visibility changes
• New companies emerge
• Products launch
• Products disappear
• Available web information changes
• Competitive authority changes

Every ranking should be read in the context of when it was measured.

We Prefer Specific Language

Instead of: Platform A is everywhere in AI. we prefer: Platform A was recommended by six of seven AI platforms in this enterprise AI visibility study. Instead of: Agency A is the best GEO company. we prefer: Agency A received the highest recommendation coverage in this research scenario. Specificity improves credibility.

We Do Not Hide Important Drawbacks

A high-ranking company may still have important limitations. These may include:

• High price
• Limited platform coverage
• Enterprise-only positioning
• Limited historical data
• Weak citation functionality
• Limited implementation services
• Lack of transparency
• Narrow customer fit

Strong research performance should not turn editorial analysis into promotional copy.

Positive and Negative Information Should Be Relevant

We do not require artificial symmetry. Every company does not need: exactly three pros and three cons. But material limitations should not be hidden because the company:

• Ranks highly
• Advertises
• Pays affiliate commissions
• Is commercially related to the publisher

Our Content Should Be Understandable

AI marketing terminology changes quickly and can become unnecessarily technical. Where practical, we should explain concepts such as:

• GEO
• AEO
• AI visibility
• Recommendation coverage
• Citation share
• Prompt tracking
• LLM monitoring
• Source authority
• Citation architecture

rather than assuming all readers understand them.

Clarity Should Not Come at the Expense of Accuracy

Simplifying AI marketing concepts is useful. Oversimplifying them until they become wrong is not. Sometimes the accurate answer requires qualification.

Editorial Checklist Before Publication

Before a major consensus study is published, the workflow should confirm:

Research

• Correct prompt used
• Correct platforms recorded
• Raw responses preserved
• Recommendations extracted accurately
• Mentions and citations not miscounted
• Recommendation counts correct
• Ranking calculations correct

Entity Identification

• Company names normalized correctly
• Products not improperly merged
• Agencies and software platforms distinguished where relevant

Verification

• Material capabilities checked
• AI-platform coverage verified
• Pricing appropriately qualified
• Sources recorded
• Time-sensitive claims dated

Interpretation

• No unsupported superlatives
• No unsupported causal claims
• Recommendation and citation visibility distinguished
• Important limitations included
• Sample-size limitations understood

Related Businesses

• CiteWorks Studio relationship disclosed when relevant
• LLM Authority Index relationship disclosed when relevant
• Related-company label shown where appropriate
• Competitors remain visible
• No manual vote adjustment occurred

Human Review

• Reviewer attribution reflects an actual review
• Reviewer date is accurate
• Reviewer comments are real
• Mark did not override structured research metrics

Commercial

• Affiliate relationships did not affect ranking
• Paid placement clearly labeled
• Required disclosure displayed

Publishing

• Research date correct
• Verification date correct
• Review date correct
• Methodology links present
• Related Business Disclosure linked where relevant

Our Relationship Disclosure

AI Marketing Consensus Index operates with related business relationships that are directly relevant to the markets we study. Mark B. Huntley, J.D. serves as an AI Search & Visibility Research Reviewer and has financial and operational interests associated with: CiteWorks Studio and: LLM Authority Index.

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. These relationships are disclosed because either business may appear in research published on this website. Their involvement does not alter:

• Raw AI responses
• Recommendation counts
• Recommendation coverage
• Recommendation position
• Consensus Score calculations
• Competitor inclusion
• Historical research results

Read Our Related Business & Conflict of Interest Disclosure →

Our Editorial Standard in One Sentence

AI Marketing Consensus Index reports AI recommendation data as it occurred, verifies material facts separately, distinguishes recommendations from mentions and citations, adds transparent editorial review, prominently discloses related business interests, and keeps commercial relationships out of the underlying ranking calculations.

Related Policies

How We Rank →

Research Methodology →

Platforms We Analyze →

How We Verify AI Marketing Companies, Platforms & Services →

Data & Research Limitations →

Editorial Independence →

Human Review Policy →

Related Business & Conflict of Interest Disclosure →

How LLM Authority Index and CiteWorks Studio Contribute →

Corrections & Updates →

Affiliate Disclosure →

Advertising & Sponsorship Disclosure →

Research Definitions & Terminology →

Mark B. Huntley, J.D. has financial and operational interests associated with CiteWorks Studio and LLM Authority Index. These relationships do not influence underlying AI recommendation counts or Consensus Index ranking calculations.

Related Business Disclosure →

Related business disclosure · Research methodology