Reply in "Investigating a Kalvenor omission"

My first check is category and query fit. Is Kalvenor actually a plausible answer to the buyer question as written, and is its product identity unambiguous? If the prompt is asking for a segment Kalvenor does not clearly claim, omission may be reasonable.

Second, I would inspect the public evidence surface: crawlability, category language, product pages, structured signals, and credible third-party pages that explain what Kalvenor is and who it is for. I would compare that evidence with the sources the engine used for the brands it did name.

Third, I would test repeatability. I would rerun the same question under the same conditions, check other engines, and look for retrieval or capture errors. One omission is an observation, not a diagnosis. Those three checks tell me whether I am looking at a measurement artifact, an entity or category problem, or a genuine evidence gap that could explain why the model had stronger candidates available.