Curious about the Global AI Community? Join this casual meetup to connect with AI enthusiasts worldwide. Learn how the Global AI Community helps you share ideas, build skills, and access resources—whether you're just starting out or deep in AI development. Meet Microsoft experts and community organizers, ask questions, and discover how to get involved in local meetups and global events. Stop by to ask questions and discover how easy it is to get involved and grow your AI network.

Connection Pods accommodate up to 15 people. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees.

In this talk, you will learn about GraphRAG, a technique that combines graph databases with generative AI to improve the quality of LLM-generated content. We will explore the terms Retrieval-Augmented Generation (RAG) and Context Engineering, and how GraphRAG can be used in both scenarios. The topic is aimed at Generative AI practitioners who are familiar with vector-based Retrieval-Augmented Generation (RAG) and would like to understand how the approach of GraphRAG can improve the quality of LLM-generated content.

This session presents the knowledge graph as a dynamic reasoning engine, not just a static data repository. Learn how to deploy autonomous AI agents that intelligently navigate the relationships within your connected data to discover profound insights. Leveraging GenAI and graph algorithms, this agentic approach moves beyond simple retrieval to create a verifiable foundation for AI systems that can reason and learn.

This session presents the knowledge graph as a dynamic reasoning engine, not just a static data repository. Learn how to deploy autonomous AI agents that intelligently navigate the relationships within your connected data to discover profound insights. Leveraging GenAI and graph algorithms, this agentic approach moves beyond simple retrieval to create a verifiable foundation for AI systems that can reason and learn.

Knowledge graphs are key to taking GenAI from proof of concept to production — making applications more reliable, transparent and secure. In this session we’ll show how to build one: connect siloed data, enrich it with semantics and structure it for GenAI. With real tools, examples and steps, you’ll see how graphs prepare enterprise data for AI and unlock faster, more trustworthy results.

ASKG is a knowledge graph of all the MCP servers on the internet, scraped and loaded into a Neo4j instance. It has its own MCP server that allows agents to put together agent calls for various data pipelines. ASKG is a project of the AIliance and you can contribute at github.com/oakstack/askg

As organizations scale GenAI from concept to production, they face challenges like ensuring accuracy, explaining responses, and connecting GenAI to unique knowledge. This session shows how GraphRAG combines knowledge graphs with retrieval-augmented generation to build GenAI apps grounded in enterprise data. 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.

Organisations adopting a Data Mesh framework often face challenges in ensuring regulatory compliance, transforming data assets into scalable products, and maintaining governance. Explore how NatWest addresses these complexities by integrating knowledge graphs with GenAI and LLMs to enhance data discovery, enforce governance policies, and accelerate product development. Learn how this approach strengthens regulatory data qualifications, automates metadata management, and delivers faster, more reliable insights— to build and scale AI-driven data products yielding a potential 10x efficiency gain.

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: a major gaming company achieved 10x faster insights, while Data2 cut workloads by 50%. Discover how knowledge graphs and GraphRAG create a foundation for trustworthy agentic AI systems across retail, healthcare, finance, and more.

Measuring the performance of VectorRAG vs GraphRAG in knowledge graph contexts.

Natural language is an ideal interface for many real time applications such as inventory tracking, patient journey, field sales, and other on-the-go situations. However, these real time applications also require up to date and accurate information, which necessitates a real time RAG architecture. In this session, we will demonstrate how you can build an accurate and up to date real time generative AI application using a combination of Dataflow and graph databases.

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Discover how to turn PDFs into interactive Knowledge Graphs in just minutes! In this demo, you’ll learn how to simplify your workflows, bring knowledge seamlessly into your apps, and even build your own chatbot.

Jesús Barrasa vous expliquera comment les graphes révèlent les connexions naturelles dans vos données d’entreprise et fournissent le contexte dont l’IA a besoin pour produire des résultats concrets. Découvrez comment des organisations de premier plan transforment la connaissance de l’entreprise en avantage concurrentiel, en favorisant des décisions intelligentes et en conduisant la transformation de l’entreprise.