Four checks before changing the page
Suppose an expert firm publishes the strongest analysis in its field, yet an AI answer credits a trade article instead. The visible result is clear. The cause is not. The firm's page may be hard to reach, poorly matched to the question, difficult to verify or simply absent from that particular run.
A useful audit separates those possibilities:
| Check | What to inspect | What the result tells you |
|---|---|---|
| Source availability | Could the platform reach and consider the relevant public source through the search path being tested? | Whether that source was usable through the observed route. |
| Answer fit | Does the page directly resolve the same buyer need at the right level of specificity? | Whether the page and question are genuinely matched. |
| Inspectable evidence | Can a reader identify the source, find the answer, examine its basis, see when it applies and reach the protected depth? | Whether the public claim carries the evidence needed to be understood and checked. |
| Repeated selection | Across comparable questions and runs, is the source used for relevant claims and placed in the intended role? | Whether the apparent loss is persistent enough to investigate. |
These are external checks. They describe what a firm can inspect around an AI answer, not a private sequence that every engine follows.
A citation has a role, not just a link
Passing one check does not settle the others. OpenAI and Google describe access conditions for their current search surfaces, but accessible pages are not automatically selected. A page can be crawlable yet answer the wrong question. A well-matched page can make its source or basis difficult to inspect. A strong source can still appear inconsistently across engines and runs.
The link itself also needs interpretation. It may support one factual sentence while the answer presents another firm as the authority. Research on generative-search verifiability has found citations that are incomplete or do not support the claim attached to them. Preserve the answer, the cited passage and the role given to the source together.
That is why one output remains an example rather than a position. Repeated measurement shows whether the same pattern survives natural question variants and ordinary run variation.
What a defensible audit must establish
A defensible audit must distinguish an access problem from a question mismatch, incomplete public evidence and unstable selection. Evidence from one layer cannot substitute for another: reachability does not establish answer fit, a strong answer does not establish repeated selection, and a visible citation does not establish the authority role given to the source.
The public framework establishes those distinctions. Delphic's operating method determines how the observations are captured, classified and combined into a question-source record, including when the evidence supports deeper diagnosis and when it does not.
Change only what the evidence justifies
A vague theory of “what AI likes” turns every citation loss into more content, more markup or more disclosure. This audit prevents that reflex. It locates the evidence gap before a valuable public source is changed.
Treatment comes later. A source-gap diagnosis must still compare the measured result with competing public evidence and distinguish a changeable weakness from structural advantage and ordinary variation. Citation stops being a mystical verdict without becoming a universal recipe.