RadRank / methodology
Published, on purpose

How the RadRank number is made

RadRank measures how often AI assistants name your brand when people ask about your category. That is the whole product. This page explains exactly how the number is produced — and, just as importantly, what it is not. We publish it in full because transparency is the only honest form of neutrality. If you can read how the score is made, you can check our work.

01What AI Share-of-Voice actually means

AI Share-of-Voice (SoV) is the percentage of category prompts whose answers name you, relative to all brands named, measured per engine, then averaged across the engines that actually answered. For a single engine:

That is the entire calculation. No weighting by who pays us, no "adjusted" score, no proprietary multiplier. SoV is a count of real mentions in real answers, divided by total real mentions, expressed as a percent.

02The prompt set

RadRank ships a small, disclosed default prompt set for each category. The defaults are short (eight words or fewer), realistic (phrased the way a person actually asks an assistant), neutral (they ask the question; they do not lead toward an answer), and disclosed (the exact prompts we run are shown to you — nothing is hidden).

You can — and should — add your own real prompts. The closer the prompt set is to how your customers actually ask, the more useful the number. We do not claim the default set is exhaustive; we claim it is honest, repeatable, and the same for everyone in your category.

03The engines, and honest engine stamping

RadRank queries multiple AI engines. Not every engine answers every time — keys expire, rate limits hit, an engine declines a prompt. When that happens, we tell you.

If two engines answered, the page says two engines answered. This is a hard rule, not a preference.

04Citation extraction: only names that are really there

When we read an answer to find the brands it names, we surface only names that appear verbatim in the answer text.

Refused in code

We would rather show an honest gap than invent a number — or a name — that makes anyone look more or less beatable than they are. This is enforced in the extractor, not just promised in copy.

05Show the receipt

Every score links to its evidence. For each engine and each prompt you can see the exact prompt sent, the raw answer text the engine returned, and the brand names we extracted from it. Nothing about the score is asserted without the underlying answer you can read yourself. If a mention is in your count, you can point to the sentence that earned it.

06Sandbox / sample mode

If no engine keys are configured, or a daily spend cap is reached, RadRank does not invent a result to fill the gap. Instead it returns a clearly labeled sample (mode: 'sandbox'). Sample cards are visibly marked "sample" and fabricate no metric — there is no real-looking SoV or rank presented as a measurement. A sample shows you the shape of the report; it never pretends to be your report.

07The honest fault line: synthetic vs. real

This is the most important thing on the page, so we state it plainly. RadRank measures answers to a disclosed, synthetic prompt set. We do NOT claim access to a private stream of real user queries. We do not have a backdoor into ChatGPT's, Perplexity's, or anyone else's live traffic, and we will never imply that we do.

What RadRank gives you is a repeatable, transparent, apples-to-apples benchmark: the same prompts, the same extraction rule, the same engines, run on a cadence, for every brand in a category. That repeatability is the value. A number you can reproduce and audit is worth more than a number we ask you to trust.

In one sentence: RadRank counts, per engine and only across the engines that answered, the real brand mentions in real answers to disclosed prompts — shows you the receipts — and never invents a name, a number, or a query stream it does not have.

Next: read the Neutrality Pledge →  ·  or check your RadRank →