Top tech companies have utilized graphs to power everything, from fraud detection systems to recommendation engines, and they are now finding their way into use cases across industries. This session will introduce the concept of graph analytics and the algorithms used to find hidden insights in data that enhance decision making, with context as king. Additionally, we will explore how Knowledge Graphs can significantly augment LLMs, particularly in the context of Retrieval Augmented Generation (RAG) systems.

How does a lean data science team at Lottery Corporation manage to avoid the common pitfall of AI/ML being stuck in the lab, have 90% of their models in production and drive substantial business outcomes? Join Dr. Chris Hillman and Stewart Campbell to learn how the current programmes of models are built for success, driving risk reduction, anomaly detection and better customer retention at scale. Then, hear about their plans to use LLM, RAG and agentic AI to drive hyper personalisation, business productivity and better investment decisions.

Enterprise-grade GenAI needs a unified data strategy for accurate, reliable results. Learn how knowledge graphs make structured and unstructured data AI-ready while enabling governance and transparency. See how GraphRAG (retrieval-augmented generation with knowledge graphs) drives real success: Learn how companies like Klarna have deployed GenAI to build chatbots grounded in knowledge graphs, improving productivity and trust, while a major gaming company achieved 10x faster insights. We’ll share real examples and practical steps for successful GenAI deployment.