We Measured Our Own Brand Six Times. Two Engines Returned Zero.

We measured our own brand six times between 29 June and 2 September 2026. It did not go well, and we are publishing it as it came out.

Measurement frame

The measurement tool is Rankie.ai. The last four scans ran across ChatGPT, Gemini and Google AI Overview. Each scan runs commercial-intent queries on those engines and records, for every response, whether the brand was mentioned, its position within the answer, sentiment and citation share.

The AI Visibility Score is a weighted average of four dimensions: mention 40%, position 30%, recommendation 20%, citation 10%.

Raw results

Totals across three deep scans, per engine:

Engine	Queries	Mentions	Margin
Gemini	1,075	zero	±0.5 pp
Google AI Overview	1,052	zero	±0.5 pp
ChatGPT	300 (31 Aug)	14.3%	±3.4 pp
ChatGPT	214 (2 Sep)	6.5%	±3.4 pp

We also mapped 50 demand themes: we lead in 0, contest 11, and 39 are completely open.

What we are not claiming

This is the most important section of the report.

We claim no trend on ChatGPT. The two measurements we hold (14.3% and 6.5%) come from scans of different types and are not comparable. We present the gap between them as neither an improvement nor a decline.

We have an older, higher Gemini figure. We do not use it, because it was measured with a different engine set.

General market claims — "brands doing GEO are N times more visible", "AI traffic converts at X" — do not appear here, because we cannot verify them with our own measurement.

The finding that is solid

The zeros are solid: across three scans on each of two engines, 2,127 queries produced not a single mention, with a half-point margin of error on those engines. That is not a result small samples explain away.

Comparability rule

Two scans are comparable only when they run the same engine set and the same scan type. The difference between the rates of two scans of different types cannot be read as improvement or decline. This rule applies to every case we publish.

Do not mix this with academic findings

Figures on the relative effect of GEO techniques (citation integration, statistical density, source attribution) belong to the Princeton study published at KDD 2024 — "GEO: Generative Engine Optimization", Aggarwal et al. Those numbers are peer-reviewed and reproducible; they should not be confused with our measurement.

The full report, with margins of error and scan records, is published at AI Visibility Benchmark Report 2026.