Building Production-Grade GenAI Virtual Assistants

LLMs change every few months. Architecture should not. In this session, participants will be exposed to real-world architectural patterns, scaling strategies and hard-earned lessons from building modular, multi-LLM virtual assistant platforms. Whether you’re launching your first assistant or scaling an existing platform, this session offers field-tested strategies and actionable insights—with space for shared learning and peer exchange. Whether you’re launching your first assistant or scaling to thousands of concurrent users, this session provides proven architectural guidance and implementation insights. We will explore:

• How to design backend systems that remain flexible as LLMs evolve
• Why separating prompts from code is critical for long-term maintainability
• Practical RAG architectures using vector databases such as Pinecone
• Orchestration strategies with LangChain and alternative approaches
• Observability and prompt performance analytics with Phoenix
• Integrating STT/TTS for full voice-enabled experiences
• Managing cost, latency, drift and production reliability

This is not a theoretical overview — it’s a practical walkthrough of what works (and what breaks) when GenAI systems meet real users and enterprise requirements.