AI Knowledge Base (RAG) Implementation

Your knowledge. AI-accessible. Up-to-date.

From PDFs, Confluence, and SharePoint to your own retrieval pipeline.

Your internal knowledge becomes AI-accessible – from your documents, up-to-date, platform-independent. No more generic answers.

📖 Your knowledge, AI-accessible – platform-independent.

⏱️ Up-to-date answers – no outdated model knowledge.

📋 Including documentation & handover.

Futuristische grüne Gebäude neben einem leuchtenden Datenstrom, der zu einem digitalen Gehirn und vernetzten Mustern führt.

The Situation

Your company has knowledge – in PDFs, manuals, Confluence pages, SharePoint folders, Notion workspaces. But AI models don't know this knowledge. They respond based on their training – not your context. The result: generic answers instead of true business intelligence.

What you get

An AI that answers from your own content. We implement a Retrieval-Augmented Generation solution: Your documents are indexed, with each query, the relevant passages are loaded – and the AI responds with your knowledge, not with generic model output. Platform-independent, up-to-date, traceable.

Our Approach

We analyze your knowledge sources: Where is what? In what format? How often does it change? Then we build the retrieval pipeline – from indexing to the query layer. Whether PDF library, Confluence, SharePoint, or Notion: we connect what's relevant.

Who it's for

Companies with a large internal knowledge base – documentation, guidelines, manuals, product information – who want to make this knowledge AI-accessible.
Particularly strong when timeliness and source accuracy are crucial.

Not suitable for

Companies without a structured knowledge base or with purely process-based requirements.
For that, there's workflow automation or API & system integration.