Knowledge management: your corporate knowledge, queryable
Make documents, wikis and repositories searchable with AI – as a RAG system on your own infrastructure so that corporate knowledge stays in-house. Use knowledge instead of searching for it: information is often scattered across different systems, hard to find or outdated – a RAG-capable AI assistant on reliable, GDPR-compliant, C5-attested infrastructure solves this.
- Powerful language model with context processing – analyses complex internal documents precisely, generates context-based results and links company-specific knowledge.
- Ease of use as a success factor – queries in natural language, quick access to the information needed without technical knowledge.
- Data sovereignty through self-hosting – complete control over sensitive company data, maximum data protection compliance, independence from external providers.
- Professional infrastructure – high-performance GPU hardware, cloud with C5 Type 1 attestation, GDPR-compliant processing, minimised maintenance and operating costs.
From a mountain of documents to an answer
The knowledge is there – it’s just scattered. RAG systems make it searchable without sending it to third-party clouds.
Knowledge indexed
Documents from file repositories and wikis are migrated as embeddings to a vector database on NVMe volumes – the source files remain in S3.
Generated replies
A self-hosted language model generates responses with source citations – running on a dedicated GPU, accessible internally.
Internal matters remain internal
Contracts, manuals and project knowledge never leave your VPC – this is the key difference compared to cloud-based AI services.
Rights respected
Access rights from the source systems carry over into the search – everyone can only see what they are authorised to view.
The advantages of AI-supported knowledge management
Retrieval-augmented generation (RAG) is an AI approach in which the language model, during execution, draws not only on its training knowledge but additionally uses information from a dedicated knowledge base – flexibly filled with your own documentation, for precise, context-related answers.
Automated data capture & organisation
AI captures and categorises information in real time – sources from repositories, wikis and drives become embeddings in the vector database, the originals sit in the AI data lake on S3 Object Storage.
Fast access to relevant content
AI-supported chatbots deliver answers with source references instantly, without manual searching – LLM inference runs on dedicated cloud GPUs with constant response times.
Personalised knowledge delivery
Information is tailored to employee roles and needs – access rights of the source systems carry through to the search, so everyone finds only what they are allowed to see.
Proactive knowledge distribution
Automatic identification and closing of knowledge gaps – the assistant recognises where documentation is missing or outdated and keeps corporate knowledge current.
Cost savings & optimised decision-making
Less manual effort for data maintenance and training – and well-founded, data-driven decisions through AI-supported analysis of your own knowledge base.
Effective onboarding & continuous learning
AI eases the onboarding of new employees with personalised training content and keeps knowledge up to date through automated updates.
Become an in-house knowledge assistant in a matter of weeks
Open-source components such as LangChain, Qdrant and vLLM integrate with the RAG stack on the centron infrastructure: an ingest pipeline in Kubernetes, a vector database on volumes, and inference on GPUs. The result: an in-house search and answer engine that the legal department can approve.
- Open building blocks – LangChain, Qdrant, vLLM and others
- Sources linked – Folders, wikis, drives
- Answer with supporting evidence – Source reference for each answer
- Preservation of rights – Permissions remain in effect
The right centron products
Customers typically implement this use case using these building blocks – which can be combined and expanded at any time.
- RTX A4000 from €92.59 per month
- Quadro RTX 6000 from €170.83 · A100 from €489.47 · RTX 6000 Ada from €858.19 per month
- Dedicated, not shared
- No minimum term
- Can be expanded whilst in operation
- Snapshot-compatible
- Independent of the VM
- S3-compatible API
- Free traffic
- Unlimited scalability
How much does AI-powered knowledge management cost at centron?
Knowledge management with AI: Make corporate knowledge searchable via RAG – on your own infrastructure, GDPR-compliant, with data remaining in-house. The core component is Cloud GPU from €92.59 per month – billed by the hour, with no minimum contract term. This is supplemented, as required, by Volumes and S3 Object Storage. Hosting is provided in compliance with the GDPR in centron’s own certified to ISO 27001 on the basis of IT-Grundschutz data centres, which hold BSI C5:2020 Type 1 certification. New accounts receive a €200 starting credit.
| Building block | Price |
|---|---|
| Cloud GPU | from €92.59 per month |
| Volumes | from €0.05 per month |
| S3 Object Storage | from €5.00 per month |
Frequently Asked Questions
Why should companies use AI for their knowledge management?
What is a cloud GPU?
How does our knowledge find its way into the system – and how is it kept up to date?
What are some examples of configurations?
How much does it cost to get started?
Can this be implemented in a way that complies with the GDPR?
Why is a BSI C5 attestation important for AI knowledge management?
What advantages does object storage offer as a storage solution for AI applications?
How does AI improve onboarding and learning in companies?
How transparent are the costs for centron cloud GPUs?
Can I rent servers from centron and which options are there?
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