In this session you'll learn how to decouple your AI components for better scalability and resilience by leveraging SurrealDB native features to turn it into a reactive 'central nervous system' and trigger AI workflows in real-time as data changes.

Overview of how SurrealDB's native eventing features can act as the core of AI architectures, enabling reactive, real-time AI workflows that are triggered by data changes.

In the era of intelligent applications, the demand for scalable, real-time AI processing has never been higher. This session explores how leveraging an event-driven database like SurrealDB can transform your AI architecture. We will demonstrate how SurrealDB's native eventing features allow it to act as the central nervous system for your application, seamlessly integrating data with AI pipelines. Discover how to build resilient, loosely coupled systems that can trigger AI workflows—such as embedding generation, content summarization, and sentiment analysis—in real-time, directly in response to data changes. Leave this talk with a clear understanding of how to decouple your AI components and build smarter, more reactive applications.

In the era of intelligent applications, the demand for scalable, real-time AI processing has never been higher. This session explores how leveraging an event-driven database like SurrealDB can transform your AI architecture. We will demonstrate how SurrealDB's native eventing features allow it to act as the central nervous system for your application, seamlessly integrating data with AI pipelines. Discover how to build resilient, loosely coupled systems that can trigger AI workflows—such as embedding generation, content summarization, and sentiment analysis—in real-time, directly in response to data changes. Leave this talk with a clear understanding of how to decouple your AI components and build smarter, more reactive applications.

In this session, you’ll learn how to set up SurrealDB as both a graph and vector store—one connection, one system; use LangChain to ingest documents; use LLMs to infer keywords; tune retrieval (k, thresholds) and compare vector-only, graph-only, and intersected results.

In this session, you’ll learn how to: Set up SurrealDB as both a graph and vector store—one connection, one system; Use LangChain to ingest documents; Use LLMs to infer keywords; Tune retrieval (k, thresholds) and compare vector-only, graph-only, and intersected results. We’ll walk through an experiment: a chatbot answering questions over chat-style conversations, showing when vector retrieval wins, when lightweight graphs help, and how to handle tricky bits like time awareness.

In the rapidly evolving field of AI, most of the time is spent optimising. You are either maximising your accuracy, or minimising your latency. Join our live SurrealDB webinar where we'll be showing some LangChain components, testing some prompt engineering tricks, and identifying specific use-case challenges. We’ll walk through an experiment: a chatbot answering questions over chat-style conversations, showing when vector retrieval wins, when lightweight graphs help, and how to handle tricky bits like time awareness. In this session, you’ll learn how to: Set up SurrealDB as both a graph and vector store—one connection, one system; Use LangChain to ingest documents; Use LLMs to infer keywords; Tune retrieval (k, thresholds) and compare vector-only, graph-only, and intersected results

Learn how to combine graph and vector search to build smarter GenAI features using SurrealDB and LangChain, including how to analyse symptoms and suggest scheduling appointments based on semantic similarity and graph relationships.

A session showing how to build smarter AI-powered apps by combining SurrealDB's graph and vector capabilities with LangChain. We'll walk through a complete example: a chatbot that analyses symptoms and recommends appointment scheduling based on semantic similarity and structured graph relationships. Learn how to set up SurrealDB as both a graph and vector store in a single system, use LangChain to query structured knowledge alongside embeddings, chain together document ingestion, graph construction, and AI-driven Q&A, and deploy an architecture that scales from prototype to production.

Learn how to turn SurrealDB into a long-term memory layer for your LLM apps by combining graph data and vector embeddings to power richer context and better decisions. Store persistent memories with graph-linked facts; perform similarity search and structured reasoning in one query; use vector embeddings and graph hops inside SurrealDB. This session walks through practical patterns and demonstrates how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.

In this session, we’ll show how to turn SurrealDB into a long-term memory layer for your LLM apps, combining graph and vector data to power richer context, better decisions. We’ll walk through practical patterns and show how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.

In this session, we’ll show how to turn SurrealDB into a long-term memory layer for your LLM apps, combining graph and vector data to power richer context, better decisions. We’ll walk through practical patterns and show how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.

An online session on building production-ready RAG apps using Python, SurrealDB, and Streamlit. Learn the fundamentals in pure Python before using a framework, how to manage multi-model data with SurrealDB, and how to build a front end for your RAG app with Streamlit.

Overview of building production-ready RAG applications using only Python, SurrealDB, and Streamlit; covers fundamentals in pure Python, managing multi-model data with SurrealDB, and building a front end for a RAG app with Streamlit.

Overview and live demonstration of collapsing traditional ETL steps into a single real-time system using SurrealDB, including modeling both structured and semantic data with native graph and vector support.

How do you detect suspicious activity across seemingly unrelated transactions?\n\nJoin us at the next SurrealDB London Meetup for a deep dive into how graph and vector capabilities can help financial institutions spot transaction fraud rings, surface anomalous behaviours, and enhance fraud detection efforts.\n\nIn this session, you’ll learn:\n\n How to model financial transactions and entities using SurrealDB’s graph features\n How a multi-model structure enhances pattern detection and fraud analysis\n* Why these capabilities matter in real-world FinServ systems\n\nWhether you’re working in fraud detection, anti-money laundering, risk analysis or data architecture, this meetup is built to equip you with ideas and tools for building smarter, more connected systems.\n\nDrinks and networking after the talk — we hope to see you there!