Methodology

LLM Authority Index measures how brands are cited, compared, and recommended in AI-generated answers.

Our methodology is built to analyze the emerging layer of visibility that large language models now shape: which brands appear, which sources support them, and how competitors gain or lose authority across AI-driven discovery.

Rather than treating AI visibility as simple mention volume, we evaluate the structure behind recommendation outcomes. That includes brand presence, citation frequency, source influence, competitive share, and the patterns that determine when a brand is surfaced — or left out.

Methodology framework

Our analysis is based on four core layers:

1. Prompt Set Analysis

We evaluate commercially relevant prompts and category-specific questions designed to reflect how buyers discover, compare, and assess brands through AI-generated answers.

2. Response and Citation Extraction

We analyze model outputs to identify which brands appear, which sources are cited, and how authority is constructed within the response.

3. Competitive Benchmarking

We compare brand visibility against relevant competitors to understand share of presence, share of citation, and relative recommendation performance across the same query set.

4. Source Influence Mapping

We identify the domains, publishers, and referenced materials most associated with visibility and recommendation outcomes, revealing the source architecture shaping model trust.

What we measure

Our reporting is structured around five core dimensions:

1

Brand Presence

The rate at which a brand appears in relevant AI-generated responses.

2

Citation Visibility

The extent to which a brand is supported by cited or source-linked information within model outputs.

3

Competitive Share

A brand's relative visibility compared with peer brands across the same prompt environment.

4

Source Influence

The degree to which specific sources contribute to brand authority and recommendation patterns.

5

Recommendation Positioning

How a brand is framed in AI answers — including whether it is surfaced positively, comparatively, or not at all.

How to read the data

AI outputs are probabilistic and dynamic. Results can vary across models, prompts, retrieval conditions, and source availability. For that reason, LLM Authority Index is designed to measure repeatable patterns across structured analysis, not to overstate the importance of any single answer.

Our methodology is intended to identify durable signals: where authority is accumulating, where competitors are outperforming, which sources are shaping trust, and where the greatest opportunities exist to improve AI visibility over time.

Why this matters

As AI becomes a more important layer of discovery, brand performance will increasingly depend on more than search rankings or share of voice. It will depend on whether AI systems consistently recognize a brand as credible, relevant, and worth recommending.

That is the shift our methodology is built to measure.

LLM Authority Index helps organizations turn AI visibility into something measurable, benchmarkable, and actionable.

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