Explorez comment la bases de données de graphes Neo4j transforme des secteurs variés en révélant des connexions insoupçonnées. De la détection de fraude à la gestion des connaissances, cette session met en lumière des cas d'usage éprouvés et des applications innovantes, démontrant la polyvalence et la puissance des graphes dans l'analyse de données complexes, mais également des cas d'usages auxquels vous n'auriez peut-être pas pensés ! Et si les graphes étaient faits pour vous?

Et si vos données devenaient vraiment intelligentes ?

Au croisement de l’IA générative, des agents autonomes et des graphes de connaissances, Neo4j révèle une nouvelle dimension de performance en assurant des réponses précises et contextualisées. En structurant les relations entre vos données et en intégrant RAG (Retrieval-Augmented Generation), Neo4j réduit les hallucinations des LLM, renforce la pertinence des réponses et décuple vos capacités de décision.

Venez découvrir comment cette alliance révolutionne les workflows IA, et pourquoi Neo4j devrait être le socle de votre stratégie IA.

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

Looking to improve the performance of Cypher queries or learn how to model graphs to support business use cases? A graph database like Neo4j can help. In fact, many enterprises are leveraging Neo4j to power their business-critical applications. This book offers practical and concise recipes on how and when to successfully leverage Neo4j into architectures. Authors Christophe Willemsen and Luanne Misquitta walk you through typical Neo4j implementation strategies from proof of concept to iterative improvements and, finally, to production readiness and beyond. By the end of this book, you should understand how to: Make practical decisions in the proof of concept stage to maximize value Revisit and revise your decisions when transitioning to production Configure and implement observability features for in-production data graphs Integrate graph databases into existing enterprise architectures

Dive into building applications that combine the power of Large Language Models (LLMs) with Neo4j knowledge graphs, Haystack, and Spring AI to deliver intelligent, data-driven recommendations and search outcomes. This book provides actionable insights and techniques to create scalable, robust solutions by leveraging the best-in-class frameworks and a real-world project-oriented approach. What this Book will help me do Understand how to use Neo4j to build knowledge graphs integrated with LLMs for enhanced data insights. Develop skills in creating intelligent search functionalities by combining Haystack and vector-based graph techniques. Learn to design and implement recommendation systems using LangChain4j and Spring AI frameworks. Acquire the ability to optimize graph data architectures for LLM-driven applications. Gain proficiency in deploying and managing applications on platforms like Google Cloud for scalability. Author(s) Ravindranatha Anthapu, a Principal Consultant at Neo4j, and Siddhant Agarwal, a Google Developer Expert in Generative AI, bring together their vast experience to offer practical implementations and cutting-edge techniques in this book. Their combined expertise in Neo4j, graph technology, and real-world AI applications makes them authoritative voices in the field. Who is it for? Designed for database developers and data scientists, this book caters to professionals aiming to leverage the transformational capabilities of knowledge graphs alongside LLMs. Readers should have a working knowledge of Python and Java as well as familiarity with Neo4j and the Cypher query language. If you're looking to enhance search or recommendation functionalities through state-of-the-art AI integrations, this book is for you.

Gain expert insights on building GenAI applications with GraphRAG and Knowledge Graphs. Dan McGrath, VP of Product at Neo4j, will share the power of these technologies, illustrated by the real-world success of Jin Foo, Head of Data & Analytics at Prospa. Hear directly from Prospa on how they use Knowledge Graphs to surface complex entity relationships across structured and unstructured data to achieve faster yet safer loan approvals and significant efficiency gains, including an 80% reduction in manual verification work, while establishing a foundation for future Agentic AI capabilities.

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.

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.

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.

Talk about agentic AI and MCP server approaches in building a personalized travel experience using Neo4j.

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.

Entdecke, wie GraphRAG, Neo4j und KI Agenten zusammenarbeiten, um intelligentere Datenabfragen und dynamische KI-Anwendungen zu ermöglichen. Beginnend mit einer Einführung, zeigen wir dann eine spannende Live-Demo mit praxisnahen Einblicken in GraphRAG-Workflows mit Neo4j.

Paul Horn will showcase the neo4rs Rust driver integration into the Rust ecosystem, how it compares to an official product driver, and future plans.