Delivering great AI chat experiences means building systems that can think—and respond—at the speed of human conversation. In this talk, we’ll explore how to design “conversation-fast” backends that power LLM and agent-based applications with real-time responsiveness and intelligence.
Through a live demo and open-source reference architecture, you’ll learn how to combine streaming ingestion, real-time analytics, and orchestration into a production-grade conversational AI system. The backend integrates:
- Postgres for transactional data
- Elasticsearch for retrieval
- ClickHouse for analytics
- Kafka-compatible platforms for streaming
- Temporal for orchestration
- A React frontend with MCP interfaces that connect LLMs directly to the app
You’ll also see how MooseStack, an open-source toolkit for building analytical backends, simplifies integrating streaming and OLAP capabilities directly into your app (in TypeScript or Python). Finally, we’ll walk through a cloud-deployed reference implementation, showcasing collaboration between StreamNative and FiveOneFour’s Boreal to achieve production-ready scalability.
What you’ll learn:
- How to architect low-latency systems that keep up with conversational AI
- Real-time data patterns for integrating chat, streaming, and analytics
- Practical techniques for deploying cloud-native AI chat backends
- How to use MooseStack to accelerate AI application development
If you’re building AI-powered chat systems or data-driven agent platforms, this session offers concrete, open-source patterns to make AI conversations feel natural—at real-time speed.
