Golem XIV uses Neo4j as its core graph engine to dynamically extract, connect, and reason over data — showing how code can become a cognitive tool for metacognitive AI.

Adam Amara, cofounder & CEO at Turing Biosystems, discusses network-based modelling and multiomics integration on metabolic networks, multilayer networks to integrate mutiomics and clinical data with knowledge graphs, and the use of TuringDB.ai to analyse large biomedical knowledge graphs and build digital twins.

Explore how TigerGraph's high-performance, scalable graph database and analytics platform processes complex, interconnected datasets in real time. Perfect for use cases where understanding relationships and hidden patterns is critical—fraud detection, supply chain optimization, and advanced AI applications.

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

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.