How often do AI models update their knowledge about companies?

There are two clocks running. The slower clock is the training-data refresh: each major model is retrained or fine-tuned on cycles ranging from several months to a year or more, after which the baseline shifts to incorporate newer content. The faster clock is retrieval: any engine using RAG (Perplexity entirely, ChatGPT Search, Google AI Overviews, Gemini for many query types) pulls live web content at query time, so a new authoritative article can start influencing answers within hours. The two clocks interact: a training-data baseline that anchors a brand to outdated facts can be overridden by retrieval if the retrieval layer returns strong current sources, which is why source-layer work has more leverage than waiting for the next retraining.