What semantic search is and how it helps your business
Traditional search compares words: if the document does not contain the term you typed, it does not find it. Semantic search turns texts and questions into numerical vectors, called embeddings, that represent meaning. Texts with similar meanings end up close together, even if they use different words.
It is the engine behind many business assistants that answer questions about internal documents: first they find the relevant passages, then a model writes the answer. It often makes sense to combine it with classic search, because product codes, proper names and case numbers are found better by exact matching.
An example in a small and medium-sized business
At an electrical installation company, the technicians looked for solutions in old job notes, each written in their own style. With keyword search they found little. With semantic search, Fabio types "trip switch that goes off at night" and also finds the notes about "RCD that trips with no load".
The most common mistakes
- Dropping exact-word search altogether, which is still better for codes and acronyms.
- Indexing outdated documents together with valid ones, so the search returns old versions.
- Showing everyone all the results, instead of limiting them to the documents each person is allowed to see.
Why it matters to you
If the information is in your business but nobody can find it, semantic search is often the first useful step, even before a chatbot.
All the words of AI explained simply are in the glossary.
Frequently asked questions
Do I need a special database?
Usually you use a vector database, or an extension of the one you already have. For small archives the solutions are simple.
Does it work with documents in Italian?
Yes, if you choose an embedding model that handles Italian well. It is worth testing it on real questions from your colleagues.
Read next
What an embedding is and why it finds things by meaning
An embedding is a piece of text turned into a list of numbers that represents its meaning, so that texts with similar meanings get numbers that sit close together.
GlossaryWhat a vector database is and when you really need one
A vector database is a store built to hold embeddings and quickly find the ones closest to a question, that is, the texts most similar in meaning.
GlossaryWhat is RAG: AI that answers from your documents
RAG (Retrieval Augmented Generation) is a technique where, before answering, the AI searches your documents for the useful passages and uses them as the basis for its answer.
GlossaryWhat a knowledge base is and why AI needs one
A knowledge base is the organised set of your company's information, such as FAQs, procedures, product sheets and terms, from which an AI assistant takes its answers, kept up to date by someone responsible for it.
Want to understand what your business really needs, without the jargon? Let's talk.
Text and graphics created with the support of AI. Method and content are AIR's.