Emil Eifrem on Neo4j, Graph Databases, Connected Data & Graph-Native AI | Ep 08

🎙️ Future of Data and AI Podcast: Episode 08 with Emil Eifrem

Before graph databases were everywhere…
Before knowledge graphs became essential to AI…
Before LLMs, embeddings, and RAG entered the conversation…
There was one simple, stubborn idea: data makes more sense when you understand the relationships.

In this engaging and insightful episode, Emil Eifrem (Co-Founder & CEO | Neo4j) shares the story behind building Neo4j — the graph database platform that quietly reshaped how the world models and reasons over data. Emil takes us through the early days when graph databases felt risky, misunderstood, and years ahead of the market, and explains why connected data mirrors human thinking better than tables, rows, and columns ever could.

What you’ll discover:

🔹Why relationships became the foundation of Neo4j — and how treating them as first-class citizens changed everything.

🔹How property graph models preserve context, making complex systems easier to reason about and analyze.

🔹Unexpected ways graph databases are powering fraud detection, recommendations, cybersecurity, and enterprise knowledge.

🔹Why knowledge graphs quietly became essential to modern AI — long before most people noticed.

🔹How graphs and LLMs work together to ground AI systems in structure, meaning, and explainability.

🔹Insights on building deep infrastructure technology with patience, conviction, and long-term vision.

This is more than a conversation about AI, graphs, or databases. It’s a look at how intelligence — human or artificial — depends on connections, context, and understanding.

📌 If you’ve ever wondered:

Why AI sometimes feels confident but wrong

How machines can reason instead of just predict

Why some technologies take years before the world catches up

…this episode will change how you think about data, AI, and connected systems.

🔹A must-listen for: data scientists, AI practitioners, knowledge graph enthusiasts, graph database users, enterprise architects, AI researchers, and anyone curious about how connected data powers modern AI.

Prepare to walk away inspired.

Keywords: Emil Eifrem, Neo4j, graph databases, knowledge graphs, property graph model, AI reasoning, connected data, enterprise AI, graph analytics, fraud detection AI, AI infrastructure, LLM applications, RAG, embeddings, AI explainability, building data platforms, AI strategy, human-like reasoning, data science insights, graph AI, Neo4j interview, AI podcast, Future of Data and AI Podcast

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