CiteGraph

CiteGraph addresses a problem that barely existed three years ago: a growing share of buying research now happens inside ChatGPT and other assistants, and if the model names your competitor when someone asks for a recommendation in your category, you never learn about it. There is no impression, no click, no referrer — just an absence. CiteGraph runs the questions your buyers actually ask, records which products get named, and shows you the answers you're missing from. More usefully, it tries to explain why: which source the model appears to have drawn on, what that source said, and what sentence on your own site or in a third-party listing would plausibly earn the mention instead. That last part is what separates it from a pure monitoring dashboard — knowing you're absent from an answer is only actionable if you know which piece of content to change. Practical caveats matter here. Model answers are non-deterministic and vary by phrasing, region and model version, so treat the results as a directional signal about how you're represented in training and retrieval sources rather than a ranking you can precisely game. Attribution of why a model chose a source is inference, not ground truth. And the fixes it recommends are content and PR work that takes weeks to land, not a setting you flip. For a marketing team that has noticed organic search softening and suspects assistants are eating the top of their funnel, it turns a vague worry into something you can look at and act on.