Every day, companies produce videos, PDFs, internal notes, procedures, customer conversations, presentations… And the same question keeps coming back: how do we find information quickly—and more importantly, how do we leverage it with Artificial Intelligence?
That’s why more and more teams are looking to build their own AI-queryable knowledge bases. And if, on top of that, you can guarantee that your data stays on your territory by relying on a sovereign, online, simple, and secure solution, that’s a major advantage.
In this article, we’ll help you understand what you should really look at before choosing a tool.
Why owning your own AI knowledge base has become essential
Centralizing information is no longer a “nice-to-have”: it’s a massive time-saver, a way to reduce errors, and a safeguard against knowledge being lost—or locked away in personal folders.
An AI knowledge base enables your teams to:
- ask a question and get an immediate answer,
- capitalize on everything you’ve already produced,
- reduce duplication,
- make internal content more reliable,
- maintain a consistent editorial and narrative voice.
In other words: you turn your archives into usable resources, instead of forgotten documents.
Why data location matters when choosing a solution
Because data location and processing have become major strategic issues.
Choosing a solution hosted in France (or in Quebec for our friends in North America) means:
- built-in compliance with GDPR (and Law 25),
- reduced exposure to the U.S. Cloud Act,
- the assurance that your data doesn’t leave your territory,
- more transparency on how your content is used,
- support aligned with your needs and real-world organizational constraints.
When AI becomes a strategic tool, knowing where your data lives is a prerequisite.
Key criteria for choosing the right tool
Here’s what a strong solution should provide:
1. Easy, wide-ranging imports
YouTube videos, PDFs, audio, notes, screenshots, web pages… in just a few clicks.
2. Automatic indexing
Transcription, tags, vectorization, topic detection.
In practice, the approach usually relies on building a RAG (Retrieval-Augmented Generation), which combines information retrieval from a knowledge base with the capabilities of large language models (LLMs).
3. AI that answers strictly from your content
The key benefit of this method is that it drastically limits “hallucinations”: when the LLM can’t find the answer in the RAG, it doesn’t answer—unlike ChatGPT, which tends to try to satisfy the user even at the expense of accuracy. No mixing with the open web.
Answers must come only from your internal knowledge base.
4. Natural semantic search
You ask a question → you get a reliable, contextual answer.
5. Security and sovereignty
Hosting on your territory, no commercial use of your data, and no training of external models on your content.
narratheque.io: a complete solution ready to deploy—no installation or training required
The narratheque.io platform checks those boxes.
It enables you to:
- import all your content in just a few minutes,
- transcribe and automatically index it,
- ask questions directly to your archives,
- easily create content (articles, FAQs, scripts, internal documents),
- ensure your data stays on your territory—fully isolated and secure.
The tool fits small businesses, SMEs, mid-sized companies, and organizations that need something robust—without technical complexity.
Conclusion
Finding a solution to create and manage your own AI knowledge base is no longer a luxury: it’s a strategic lever to save time, align messaging, and prevent your company’s knowledge from disappearing.
If you want to test, in real terms, what this changes inside your organization, narratheque.io is available for free to get started.
