Your experts’ know-how, finally accessible to your whole team with AI

Traditional knowledge management relies on documents filed by hand and on everyone’s diligence to keep them up to date. With AI, you just ask a question to find information, a simple interview replaces writing a procedure, and outdated content is flagged automatically. AI does not replace organisation, however: it makes it easier to keep alive day to day.

RAG, for Retrieval Augmented Generation, is the technique that lets an AI assistant answer from your documents. When an employee asks a question, the assistant first searches for the relevant passages in your knowledge base, then writes its answer from them, citing its source. That is what lets it talk about your company accurately, and it is also why the quality of your documents matters so much. See also our RAG development agency page.

Four are generally distinguished:

• people, who agree to share what they know,
• methods, which make it possible to capture and update that knowledge,
• governance, with a designated owner for each topic,
• tools, of which AI is one.

In the projects we run, it is almost always the balance between these four pillars that makes the difference, far more than the choice of technology.

Three questions are enough for a first sort:

• Who is asked the same questions every week?
• Which activities would be blocked if one person left tomorrow?
• Which knowledge is written down nowhere?

The answers sketch a first map of your critical knowledge. That is what needs capturing first.

By valuing their role. An expert generally enjoys passing on knowledge, provided they feel it is recognised and useful. Involve them in the project from the start, name them as the go-to person on their topic, and show them the concrete effect of their contribution: the day people stop asking them the same question ten times, they see the benefit very quickly.

No, and that is often what puts companies off getting started. It is better to begin with a specific area, such as one department’s procedures or newcomers’ questions, make it reliable with the help of AI, then gradually extend it to other departments.

They exist, but they are well managed when anticipated. An assistant can give a plausible but wrong answer if information is missing or if two documents contradict each other: that is why it must always rely on your content and cite its source.

It can also expose sensitive information to people who should not see it, hence the importance of respecting access rights document by document. Finally, by trusting AI too much, people can forget to check what it suggests.

Taking the time to support your teams on this point is, for us, part of any successful project.

Depending on your needs, you can turn to an AI agency, a consulting firm or a knowledge management specialist:

This list is not exhaustive.

At Kreante, every project is priced on a custom quote, after a free assessment. The budget depends mainly on four factors:

After setup come the cost of using the AI models, hosting and maintenance, which depend mainly on the number of users.

A first scope, such as one department’s procedures, can be up and running in a few weeks. The timeline depends mainly on the state of your documents, your experts’ availability and the number of tools to connect. The base then grows as your teams ask questions.