AI Market Discovery Research
Independent Benchmarking of Brand Visibility in AI-Assisted Discovery
The LLM Authority Index AI Market Discovery research program tracks how brands appear across commercially relevant AI-assisted recommendation, comparison, pricing, and discovery contexts.
Each industry benchmark is maintained on a single evergreen URL and updated as new measurements become available.
The research program separates:
- source-query collection;
- AI/search surface observations;
- tracked-brand relevance;
- commercial buyer intent;
- brand presence;
- recommendation validity;
- recommendation placement;
- sentiment;
- evidence-source patterns; and
- modeled AI opportunity.
This separation makes it possible to distinguish a simple brand mention from a meaningful recommendation outcome.
Research Architecture
Industry Benchmarks
Each industry page is the primary source of record for that category's:
- current standings;
- baseline;
- historical measurement record;
- tracked-brand comparison;
- recommendation movement;
- placement metrics;
- sentiment;
- source/evidence observations where available; and
- report-specific interpretation notes.
Example:
Kids & Family Graphic Apparel AI Market Discovery Index
Shared Methodology
AI Market Discovery Methodology
Defines:
- query selection;
- prompt-surface observations;
- collection scope;
- brand-relevance qualification;
- buyer-intent classification;
- recommendation coding;
- sentiment;
- source/citation analysis;
- QA; and
- longitudinal update rules.
Metric Definitions
AI Market Discovery Metric Definitions
Defines:
- valid recommendation coverage;
- top-three rate;
- rank-one rate;
- presence;
- qualified surface breadth;
- net sentiment;
- shortlist share;
- percentage-point movement; and
- denominator rules.
Research Standards
AI Market Discovery Research Standards
Defines:
- interpretation boundaries;
- small-count treatment;
- longitudinal integrity;
- entity naming;
- corrections;
- methodology changes;
- publication QA; and
- source-of-record rules.
Modeled AI Authority Value
Explains the role and limitations of the benchmark's comparative modeled-opportunity metric.
How an Industry Benchmark Is Built
At a high level:
- high-demand category queries are selected using search-demand data as a prioritization proxy;
- the query set is evaluated across the benchmark's AI/search surface universe;
- prompt-surface observations are screened for tracked-brand relevance;
- relevant observations are classified by commercial buyer intent;
- brand presence, recommendation validity, placement, sentiment, and source evidence are coded; and
- the resulting qualified benchmark set is used to calculate public metrics.
For current industry implementations, the upstream monthly collection can contain approximately 800 prompt-surface observations before qualification.
The qualified sample is intentionally smaller because the benchmark is designed to measure commercially meaningful AI discovery moments rather than every answer generated during collection.
Buyer-Intent Framework
The current public benchmark focuses on three commercial-intent classes.
Brand Recommendation
Users ask the AI/search system to recommend brands, products, stores, or options.
Pricing & Value
Price, affordability, value, discounts, or budget materially affects the evaluation.
Multi-Brand Comparison
The AI/search system compares competing brands, products, or alternatives directly.
Primary Benchmark Metrics
| Metric | What it answers |
|---|---|
| Raw mention presence rate | Is the brand appearing at all? |
| Valid recommendation coverage | Is the brand receiving valid recommendations? |
| Top-three recommendation rate | Is the brand appearing prominently? |
| Rank-one recommendation rate | Is the brand the first choice? |
| Net sentiment score | Is coded sentiment favorable? |
| Qualified surface breadth | Across how many tested surfaces do qualified observations appear? |
| Valid recommendation shortlist share | How often does the benchmark produce a valid recommendation shortlist? |
| Modeled AI Authority Value | What comparative modeled opportunity does the Index assign? |
See Metric Definitions for formulas and interpretation rules.
Evergreen Reporting
Industry benchmarks use a single URL rather than creating a new indexable page every month.
As the series grows, each page should preserve:
- the original baseline;
- current values;
- leadership changes;
- new highs and lows;
- major ranking shifts;
- persistent trends;
- reversals; and
- methodology annotations when relevant.
This makes each industry URL progressively more valuable as a longitudinal research source.