Implementing Retrieval-Augmented Generation (RAG) systems for enterprise knowledge management and intelligent Q&A with full data sovereignty.
The Challenge
The client needed an intelligent knowledge retrieval system but had strict data sovereignty requirements. Their sensitive internal documentation could not leave their infrastructure or be processed by third-party cloud AI services. They also struggled with employees spending excessive time searching through scattered documentation and internal knowledge bases.
Our Solution
We built a fully self-hosted RAG system using open-source models, deployed entirely on the client's own infrastructure. All data processing, embedding generation, and language model inference happen on-premise, ensuring no sensitive information is transmitted to external services. The system indexes internal documents into a vector database, retrieves the most relevant context for each query, and generates accurate, cited answers using a self-hosted open-source LLM.
Results
Employees now receive instant, accurate answers grounded in actual company data while maintaining complete data sovereignty. The system operates independently of external AI providers, giving the client full control over their data and AI capabilities.