What the score can’t see
An assistant answering a buyer’s question isn’t weighing your site against a topic. It is looking for the page, or the part of a page, that answers that specific question well enough to use. Three things follow from that.
- Fit to the question. A broad guide and a short, precise answer are judged differently. Which questions your market actually asks, and which of your pages could answer them, is rarely what a content calendar assumes.
- Answers you can lift. If the useful part is buried under background, it is harder to use. An FAQ block is the cosmetic version of this fix. The real one is how each page is written.
- Reasons to be referenced. Original data, documented methods and honest limits give other people something to cite. What others say about you away from your own domain matters too, and no scanner sees it.
What we know, and what we don’t
Nobody outside the AI labs can show you exactly why an assistant cites one page over another. Studies find patterns, and patterns are not causes: popular sites tend to be cited more, and they also tend to have more of everything else. So we treat each content recommendation as a hypothesis. We say how much evidence stands behind it, and we measure before and after with the same set of questions.
Why we don’t publish the full method
We could write another ultimate guide. We’d rather not. Content work only pays off when it is matched to your market, your pages and your questions, and that is a conversation, not a checklist. The technical side, Citehound shows you for free. The content side we do with you.
How a content review works
- We write the question set for your market, in your customers’ language, starting from your site.
- We read your pages against those questions: what each page can answer, what it can’t, what’s missing.
- You get a prioritized editorial brief. If you want it measured, we run the same questions before and after and report what changed, including when nothing did.
We don’t promise citations. Nobody honestly can.