# Challenges and Breakthroughs in RAG Implementation for On-Prem Industrial Copilot Assistants  
**Mostafa Gharieb, Sr. Research Engineer, Siemens**  
**Michael Schrapp, Global Head of Industrial AI Innovations, Siemens**

AI-Generated Summary of this Video  
Deep dive into Siemens' Industrial Copilot for operations with a special focus on integrating operational time-series data as industrial context, time-series generative search breakthroughs, and optimized agentic design to meet performance and quality-of-response metrics by shop floor target personas.

## Key Insights  
- **Integration of Time-Series Data**: Addressing operational challenges by leveraging time-series data within industrial contexts.  
- **Generative Search Breakthroughs**: Development of generative search capabilities that significantly enhance operational efficiencies.  
- **Optimized Agentic Design**: Focus on creating adaptable and smart systems that can optimize quality-of-response metrics for on-the-floor personnel.

### Future Directions  
- **Interoperability with MES Systems**: Siemens is actively working on connecting its copilot systems to Manufacturing Execution Systems (MES) for end-to-end process integration.  
- **Expanding Capabilities**: As operational challenges evolve, Siemens aims to further enhance the capability of its Industrial Copilot to address diverse needs across the manufacturing landscape.