is answer engine optimization a scam or a legitimate discipline — 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 is answer engine optimization a scam or a legitimate discipline (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 “is answer engine optimization a scam or a legitimate discipline.” 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, 73 of 91 checks (80.2%) recorded at least one cited source.
• 18 of 91 frozen checks recorded no citations (negatives kept).
• This freeze covers 24 distinct queries.

Citation rates by engine

• chatgpt: 6 of 24 runs (25%) recorded ≥1 cited source.
• gemini: 23 of 23 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.

• Rows91
• Distinct queries24
• Enginesperplexity, chatgpt, gemini
• Checks with ≥1 citation73 of 91 (80.2%)
• Checks with no citations18
• Observation dates2026-07-27
• Queries in design24
• Checks recorded91
• Checks planned92
• Variance panel groups10

Query coverage

Distinct category questions present in this freeze.

Questions in this freeze

• is answer engine optimization real or a fad
• is AEO a scam
• is generative engine optimization a real thing or marketing hype
• are AI visibility tools worth the money
• how do I know if an AEO agency is legitimate
• do AEO agencies actually deliver measurable results
• how much does a legitimate AEO service cost
• are AI citation tracking tools accurate
• how to spot a fake AEO agency
• does answer engine optimization actually work
• can AI visibility be faked or gamed
• how do I verify an AEO vendor's claims
• is AEO just rebranded SEO
• are AI search visibility claims exaggerated
• should I trust a vendor's AI citation reports
• how do I evaluate whether an AEO tool's data is real
• what red flags indicate an AEO scam
• do AEO case studies get faked
• how do I know if my AI citations are being tracked accurately
• is paying for AI visibility monitoring worth it
• is it possible to guarantee AI citations
• are there fake AI search optimization credentials
• how reputable are AI visibility SaaS platforms
• is llms.txt real or vaporware

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 91 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