Data & Research
How we measure, in the open.
A measurement company's method should itself survive measurement. So we publish how the numbers are made, the definitions, the confidence, the sources, before we publish any number.
01/Methodology
The method, stated plainly.
What Share of Model measures
Share of Model is the share of category answers that name, cite, or trust a brand, read by engine, intent, language, and region. It is not a rank and not a vanity count: it only means something paired with confidence and correctness. We define the query universe first, then measure against it, so the denominator is explicit rather than implied.
Why every number carries a confidence interval
Answer engines are stochastic, ask twice, get two phrasings. A single reading is a sample, not a fact. We report every metric with a confidence interval and a sample size, and we treat a movement as real only when it clears its interval. The CI cuts both ways on purpose: it makes the number diligence-proof, and it makes it harder to oversell.
Source citation over source guessing
A grade without a source is an opinion. For each answer we trace which pages, partners, and threads appear to be teaching the model, and who owns them, so a recommendation points at a specific, checkable cause rather than a hunch.
02/The index
The India Share-of-Model index.
Our first public study: a quarterly, open read on which brands AI names across India's categories, engines, and languages, the scoreboard for the answer era, with its method attached. We're finalising the cadence and first release; we'd rather publish it late than publish it wrong.
No figures are published yet, this page describes the method, not results. Subscribe to Machine Readable to get the index the moment it's live.
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