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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