The AI Recommendation Index measures what AI assistants actually recommend when buyers ask. Every number on an Index page traces back to the process below — and every page discloses its own sample size and window.
Each category carries 6–8 buyer-shaped prompts: the questions real buyers type, like “best [category] for [audience]”, “[category] alternatives”, or “what should I use for [job]”.
Prompts are strictly neutral — they never contain product names, never steer toward or away from any vendor, and stay stable week to week so trends compare like with like.
Categories are measured across up to 5 engines via their official APIs, with web search enabled where the engine supports it:
Each category page shows per-engine shares separately — engines disagree, and that disagreement is part of the data. Not every category runs on every engine; the engines measured are listed per category.
Every active category runs weekly, with calls spread across the week to avoid time-of-day artifacts. Published numbers aggregate a 4-week rolling window; trends compare that window against the 4 weeks before it.
Categories with fewer than 20 sampled answers in the current window are marked “insufficient data” and publish no rankings — we publish stable numbers, not noise. Share changes within ±1 percentage point are reported as flat for the same reason.
recommendation_share = % of sampled answers in the window that recommend the product · avg position = mean rank when answers are ordered lists
Every product mentioned in an answer is extracted and resolved against a canonical registry, so “HubSpot”, “Hubspot CRM” and “hubspot.com” count as one entity. Resolution matches by domain first, then by name and known aliases; cited URLs serve as a confirmation signal.
Generic phrases (“CRM software”, “the tool”) are filtered and never become entities. Newly seen entities and suspected duplicates are auto-flagged for human review; confirmed duplicates are merged with their history re-pointed, and confirmed junk is removed from the rankings.
Orbator customers are badged on Index pages for disclosure. Customer status does not affect measurement — the prompts, sampling schedule, extraction, and ranking math are identical for every product, customer or not. Rankings cannot be bought, and no one can pay to be removed.
The Index exists because we sell AI-visibility tooling — that is exactly why the measurement itself has to be, and is, untouched by who pays us.
Every category is downloadable as CSV from its page and queryable via the free JSON API (/api/index/categories; docs at /developers). Free to use with attribution to orbator.io. Questions or corrections: support@orbator.io.
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