Why AI models may describe your brand as five different companies, and how to check

A DEV Community article argues that large language models do not look up brands in a fixed table but reconstruct a description from scattered fragments in training data and retrieval results. When those fragments conflict in category, audience, or naming, the model tends to average them or favour the most repeated version. The author proposes three quick tests in fresh AI chat sessions: checking whether the model names the same category across repeated queries, whether it correctly rejects a job outside the brand's scope, and whether it attributes the brand to the right maker. Common causes of errors include inconsistent page descriptions, outdated directory listings, mismatched structured data, and missing links between a brand's official profiles. The article recommends first agreeing on one canonical description sentence and then aligning every page and listing to it.