Modern analytics experiences demand “conversation-fast” backends—systems that serve the requests of an agent or LLM in real time at the speed of a natural conversation.
In this talk, we’ll get deep into an open-source reference architecture for powering conversational AI and real-time analytics in user-facing applications. We’ll get hands-on in the code, and explore practical patterns for integrating streaming and analytical infrastructure into your web application, including AI chat systems.
We’ll focus on a reference architecture that integrates transactional (Postgres) and analytical engines (ClickHouse®), along with RedPanda for streaming, a React web app frontend, and MCP interfaces for enabling LLM chat within the app.
The whole analytics stack is defined in code using the MooseStack open source framework. https://docs.fiveonefour.com/moose
This architecture ensures the low-latency responsiveness users expect with analytics visualizations and conversational AI, and a modern, local-first developer experience that integrates naturally into your stack.