AI knowledge base (RAG): putting your own documents to work

An SME’s real advantage in the face of AI isn’t the public models — available to everyone — but its own documents: procedures, catalogues, contracts, support history. An AI knowledge base, often referred to by the acronym RAG (Retrieval-Augmented Generation), lets you put this material to work to get reliable, sourced answers. It’s a pillar of our AI solutions.

Without this layer, an AI assistant answers from general knowledge — sometimes plausible but wrong for your context. With it, it draws on your real information. Here’s how it works and what it changes.

What is an AI knowledge base (RAG)?

The principle is simple: before answering, the AI first searches for the relevant passages in your documents, then writes its answer from those excerpts. It’s the difference between an AI that “recites from memory” and an AI that “checks your archives before speaking”. In practice, your content is split up, indexed and stored (often in a structured format, such as JSON), so the right passage can be retrieved instantly when a question is asked.

Why it’s more reliable

Anchoring the AI to your documents brings three concrete benefits:

• Answers that are right for your business — prices, procedures and policies are yours, not a web average.
• Traceability — each answer can cite the source document, letting you verify and stay in control.
• Easy updates — correcting a piece of information means updating the source document, with nothing to retrain.

What concrete uses are there in an SME?

A knowledge base feeds several use cases at once. Internally, it acts as an assistant for teams: finding a contract clause, a procedure or the right version of a document in seconds. Externally, it powers a customer assistant able to answer from your real information — a use we detail in our article on the price and workings of an AI chatbot. It’s precisely this reliability that lets you build a trustworthy customer-service AI. The same base can therefore serve support, sales and onboarding.

In practice, the same building block answers very different questions depending on who’s asking: a salesperson looks for the current pricing terms, a new hire the steps for a task, a customer the details of a warranty. Instead of duplicating information across ten quickly outdated documents, you maintain a single source, and every answer stays consistent from one channel to the next. That’s what makes a well-kept base valuable.

Your data stays yours

A knowledge base doesn’t mean “sending the whole company” into a public model. Your documents are stored and queried within a perimeter you control; the AI only accesses them to formulate an answer. This control is essential, particularly in Switzerland where data protection is a sensitive subject. Confidentiality is designed into the architecture, not bolted on afterwards.

Where to start?

There’s no need to index everything at once. You start with a limited, high-value corpus — for example the support team’s frequently asked questions or a procedures manual — to validate answer quality. You then widen the scope document by document. The quality of the base matters more than its size: well-kept documents produce good answers; a messy corpus produces noise.