Build smarter, more explainable AI systems using the power of graphs.

Graph-Powered AI with Neo4j is a practical, end-to-end guide for developers, data engineers, and AI practitioners who want to design intelligent systems that understand context, relationships, and meaning-not just data points.

Modern AI struggles with trust, explainability, and real-world complexity. Graphs solve these problems by modeling how things are connected. This book shows you how to combine knowledge graphs, graph algorithms, machine learning, vector search, and generative AI into a single, production-ready architecture using Neo4j.

You'll move step by step from foundational graph concepts to advanced, real-world applications, learning how to design systems that are scalable, explainable, and resilient.

Inside, you'll learn how to:

• Model and build knowledge graphs that power intelligent behavior

• Use Neo4j and Cypher effectively for AI-driven workloads

• Apply graph algorithms and the Neo4j Graph Data Science library

• Engineer graph-based features and generate embeddings

• Build graph-powered recommendation systems, fraud detection, and search

• Integrate graphs with vector search and generative AI using RAG patterns

• Deploy, tune, secure, and govern graph-powered AI systems in production

• Design future-ready architectures aligned with emerging Graph AI trends

Every concept is explained clearly and reinforced with complete, runnable examples-no external repositories, no hidden dependencies, and no diagrams required. The focus is on action, clarity, and correctness.

Whether you're building AI-driven products, modern search systems, or intelligent enterprise platforms, Graph-Powered AI with Neo4j gives you the tools and architectural mindset to move beyond black-box AI and build systems that reason, adapt, and earn trust.

If you want AI that understands relationships, this book is your blueprint.