Access to the latest medical literature and research data is essential for pharmaceutical innovation. This guide presents a fully sovereign RAG architecture optimised for life sciences, ensuring secure, accurate, and compliant knowledge retrieval.
Executive Summary / Key Takeaways
- Production-grade sovereign RAG on Swiss infrastructure
- Advanced medical document chunking and embedding strategies
- Hybrid vector + graph memory for complex biomedical relationships
- Enterprise security, access control, and audit capabilities
- Seamless integration with LangGraph agent workflows
The Challenge
Data leaving Switzerland, compliance risks with public RAG services, and difficulty retrieving accurate medical knowledge at scale.
Our Approach / Framework
Complete sovereign RAG stack with local embeddings, Qdrant vector store, hybrid search, and LangGraph integration tailored for life sciences.
Technical Architecture
Ollama embeddings, Qdrant vector database, biomedical-specific 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 in life sciences. Running it on Swiss infrastructure provides performance, security, and regulatory peace of mind.
