Vector database
A vector database stores texts not as words but as numerical patterns of their meaning (embeddings). This lets an AI find the passage that fits by content — not the one with the same keywords. For AI visibility, clear meaning counts, not keyword density.
A vector database is a store that keeps content not as words but as so-called embeddings – sequences of numbers that represent the meaning of a text. Two passages with similar meaning sit close together in this database, even if they use completely different words.
This is exactly what AI answer machines use: when someone asks a question, the question too is translated into such a numerical pattern and the passage closest in content is retrieved. What matters is therefore not whether a text contains the exact search words, but whether it clearly carries the intended meaning.
For AI visibility this means: clearly worded, topically focused passages are found more readily than ones stuffed with keywords. Meaning beats keyword density.
Frequently asked questions.
How does an AI read my website — and what does that mean for my content?
An AI doesn't read your website top to bottom; it breaks it into individual text passages and, at the moment of the question, retrieves only the fitting ones (Retrieval-Augmented Generation, RAG). What matters is therefore not the whole page, but whether individual passages answer a concrete question on their own and machine-readably. Content loaded only via script or buried in long prose often doesn't reach the AI.
Has classic SEO become obsolete with AI search?
No. Google AI overviews mostly cite pages that already rank well classically. SEO remains the ticket in — AI optimisation (GEO) is the extension with citable answers, structure and visible authority.