Retrieval-Augmented Generation (RAG) is essential for accurate, context-aware Agentic AI. This guide presents a fully sovereign, secure, and high-performance RAG architecture built on Exoscale SKS and open-source components.
Business Challenge Many Organisations want to build internal AI capabilities but lack a proven, governed approach to creating and scaling a sovereign digital workforce.
UNLOCK FULL USE CASE + PDFExecutive Summary / Key Takeaways
- Production-grade sovereign RAG architecture on Swiss infrastructure
- Advanced chunking, embedding, and retrieval strategies
- Hybrid vector + graph memory systems
- Enterprise security, access control, and audit capabilities
- Performance benchmarks and cost optimisation patterns
The Challenge
Data leaving Switzerland, high latency, limited control, and compliance risks with foreign RAG services.
Our Approach / Framework
A complete sovereign RAG stack with local embeddings, Qdrant vector store, hybrid search, and seamless LangGraph integration.
Technical Architecture
Ollama embeddings, Qdrant vector database, hybrid search/reranking, and secure ingestion pipelines on Swiss GPU infrastructure.
Implementation Guide
8-week roadmap covering foundation, core architecture build, integration, and optimisation.
Conclusion & Future Outlook
Sovereign RAG is the foundation for trustworthy Agentic AI. Running it on Swiss infrastructure provides performance, security, and regulatory peace of mind.
Key Takeaways
- Production-grade sovereign RAG architecture on Swiss infrastructure
- Advanced chunking, embedding, and retrieval strategies
- Hybrid vector + graph memory systems
- Enterprise security, access control, and audit capabilities
- Performance benchmarks and cost optimisation patterns
