building brand authority signals for AI search citations — dataset

About this dataset

Answer engine optimization (AEO) studies how often AI answer engines cite a brand or source. This page is the frozen measurement export for building brand authority signals for AI search citations (measured 27 July 2026). It is primary evidence — re-derivable from the published methodology — not a dashboard summary. The full row freeze lives in the CSV/JSON downloads; the HTML table below is a short preview only.

Methodology

Each row is one direct answer-engine check. We asked the category question verbatim through the engine's live API and recorded whether the answer cited any sources — cited: true means at least one source URL; cited: false is a measured negative, not missing data. Measurement date: 27 July 2026.

Engines and sampling rules (including any variance / test-retest panel) are disclosed on the linked methodology page for “building brand authority signals for AI search citations.” This dataset page freezes the rows; it does not recompute the study narrative.

Full methodology

Key findings

Declarative facts computed from this freeze only — not a substitute for the study article.

Overall freeze

• In this freeze, 71 of 92 checks (77.2%) recorded at least one cited source.
• 21 of 92 frozen checks recorded no citations (negatives kept).
• This freeze covers 24 distinct queries.

Citation rates by engine

• chatgpt: 3 of 24 runs (12.5%) recorded ≥1 cited source.
• gemini: 24 of 24 runs (100%) recorded ≥1 cited source.
• perplexity: 44 of 44 runs (100%) recorded ≥1 cited source.

Summary statistics

Computed from the frozen rows and rollup sample counts. No figures are invented at render time.

• Rows92
• Distinct queries24
• Engineschatgpt, gemini, perplexity
• Checks with ≥1 citation71 of 92 (77.2%)
• Checks with no citations21
• Observation dates2026-07-27
• Queries in design24
• Checks recorded92
• Checks planned92
• Variance panel groups10

Query coverage

Distinct category questions present in this freeze.

Questions in this freeze

• how to build brand authority
• AEO and brand authority
• GEO and brand authority
• what signals build entity authority for AI search
• how Wikipedia and Wikidata affect brand authority in AI answers
• how many published mentions does a brand need to be AI-recognized
• does press coverage help AI search citations
• how do backlinks affect answer engine trust
• what role does founder personal brand play in AI citations
• how does consistent NAP data affect local AI answers
• does social proof influence which brands AI cites
• how long does it take to build AI search authority
• does publishing original research build brand authority faster
• how do case studies affect brand credibility in AI answers
• how does third-party validation affect AI citation likelihood
• what makes a brand look credible to an AI answer engine
• does winning industry awards help AI search visibility
• how does customer review volume affect brand authority
• how does being cited by competitor content affect authority
• what schema markup signals authority to AI crawlers
• does a Wikipedia page increase AI citation rate
• how do analysts and industry reports build brand trust for AI search
• what is the fastest way to become a recognized authority in a niche
• how does consistent publishing cadence affect topical authority

Column dictionary

What each field means when reading the CSV/JSON download. The cited column is the direct citation detector: true means at least one source URL; false means none were cited.

cited

Whether this check recorded at least one cited source URL (true) or none (false) — negatives are data.

query

The category question put to the answer engine.

engine

Which answer engine produced this observation (e.g. perplexity, chatgpt, gemini).

run_at

ISO timestamp when this engine check was recorded.

run_seq

Repeat-draw index within a same-day variance panel (1 = first draw).

source_urls

Pipe-separated full source URLs cited in the answer, when any.

check_group_id

Groups k-repeat draws of the same query on the same engine for test-retest.

source_domains

Pipe-separated domains cited in the answer, when any.

How to use this data

Use this freeze as primary evidence. Prefer the methodology for instrument rules and the study article for narrative claims.

1. Download the CSV or JSON for independent re-analysis — every row is frozen at measurement time.
2. Read the methodology page for sampling rules, engines, variance panels, and limitations.
3. Cite the study article for the narrative findings; cite this dataset when quoting row-level figures.
4. Treat cited: false (or empty source columns) as measured negatives, not missing data.

Measurement preview

Showing 8 of 92 rows with the scannable columns only (source URL lists stay in the download). Download CSV or JSON for the complete freeze.

Full freeze: CSV · JSON