What it means in practice
Picture an enormous library where the books are shelved not alphabetically but by how similar their subjects are. Embeddings do exactly this with text: they turn “dog” and “puppy” into numbers that sit close together in mathematical space. The AI therefore knows they are related concepts, even though they are spelled differently.
Why companies need to know this
- Intelligent search. A customer searches for “something for running” and, thanks to vectors, the shop finds them trainers even though the description never uses the word “running”.
- Custom company chatbots. This is the underlying technology (RAG) that lets AI answer questions from your internal PDF manuals and policies.
- More accurate analytics. It helps sort customer feedback by what it actually means rather than by keywords alone.
An example from practice
A law firm built itself an internal search engine. Thanks to embeddings, its lawyers find relevant older cases even when they type a loose description of the situation rather than exact legal terminology. In our company AI workshops we teach how to put this technology to work on your own company data.