RAG stands for Retrieval-Augmented Generation. Instead of answering from what the model 'thinks', it first retrieves the relevant passages from your own documents and builds the answer on them. That gives precise answers with citations and far less risk of made-up replies.

How many documents can the system handle?

From a few handbooks to tens of thousands of documents across SharePoint, Google Drive, Notion, PDFs, and databases. The larger the set, the bigger the payoff from a good retrieval and indexing structure.

Can answers cite the source?

Yes. Each answer can show which documents and passages it is based on, so the user can click in and verify. That is often the whole difference between a knowledge assistant being used or ignored.

RAG · Knowledge Assistant

An AI that answers from your own documents.

Most AI answers are guesses based on general knowledge. With RAG, the AI first retrieves the right passages from your own documents and builds the answer on them - with citations, so you can trust it. Built for large document sets and GDPR-safe operation.

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What RAG does differently

Answers from your sources, not guesses

The AI looks up your own documents before it answers. That means precise answers about your products, prices, and procedures - not generic answers from the internet.

Citations you can check

Every answer points back to the documents it is based on. The user can click in and verify, and you avoid the made-up answers that otherwise destroy trust.

Scales to tens of thousands of documents

SharePoint, Google Drive, Notion, PDFs, and databases can be brought into one place. The larger the archive, the more valuable an AI that finds the right thing in seconds.

Where a knowledge assistant adds value

RAG is strongest when the answer has to be correct and traceable.

Knowledge work

Answers across thousands of cases

Advisers, lawyers, and specialists can query across past cases, notes, and rulesets and get answers with a reference to the source.

Support

Correct answers without digging through manuals

Support and customers get answers pulled straight from product documentation and FAQ, with a link to the right page.

Operations

Procedures and policies on demand

Staff find the right procedure, contract, or policy in natural language instead of searching through folders.

Questions & answers

How is this different from an internal AI assistant?

Large overlap. An internal assistant describes the solution from the employee's side. This page is about the engine behind it: RAG, which makes the answers precise and traceable - including when the AI is used externally toward customers.

How good does our documentation need to be first?

It does not need to be perfect. We start with the most important sources. Often the project reveals exactly where the documentation is messy, and we clean that up along the way.

Does it run GDPR-safe?

Yes. The solution can be built with access control, logging, and in some cases a model that runs locally, so sensitive documents never leave the building.

What does a RAG knowledge assistant cost?

A first version typically starts from around 35,000-80,000 DKK, depending on data volume, number of sources, and access-control requirements. You get a fixed quote.

What knowledge do your people look for every day?

Tell me which documents and systems hold the answers. I will propose a realistic first version of a knowledge assistant built on them.

Describe your documents