Retrieval, grounding and consensus: the three mechanisms deciding whether your product makes the shortlist inside an AI answer.
Assistants answering a product question usually run a retrieval step first, pulling a handful of candidate sources, then generate an answer grounded in those sources. Winning is a two-stage game: get retrieved, then get selected.
Retrieval favours pages that closely match the shape of the query. A page titled 'Best invoicing tools for freelancers (2026)' matches a buyer prompt far better than a homepage headline about 'reimagining finance'.
Selection favours consensus. When several independent sources describe you the same way, the model treats that description as safe to repeat. When sources conflict, it hedges or omits you.
There is also a recency effect. Dated, updated content is preferred for questions that imply a current state — pricing, feature comparisons, market share, 'best in 2026' phrasing.
The practical implication: publishing one great page is rarely enough. You need the page, the corroboration, and the structure that makes both machine-readable.
Free audit, 48-hour turnaround. No commitment.