Michael Hunger of Neo4j, joins Simon Maple to unpack how graph databases inject structure, intent, and traceability into modern AI systems.
On the docket:
- why relationships in data encode intent
- the black-box problem in vector based RAG
- why devs should build their own MCP server
AI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development.
Connect with us here:
- Michael Hunger- https://www.linkedin.com/in/jexpde/
- Simon Maple- https://www.linkedin.com/in/simonmaple/
- Tessl- https://www.linkedin.com/company/tesslio/
- AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/
(00:00) Trailer
(01:03) Introduction & Neo4j Origins
(03:02) Persisting Relationships for High-Performance Queries
(04:00) Modeling Business Intent & Key Use Cases
(05:00) Fraud Detection at Scale with Graph Algorithms
(06:11) Graph-Enhanced RAG vs. Vector-Only Retrieval
(09:02) Explainability & Drill-Down Evaluation in RAG
(13:05) Fusing Structured & Unstructured Data for Context
(15:00) MCP for Developer Productivity: Schema-to-Code & API Wrapping
(21:16) Security & Sandboxing Best Practices for MCP
(29:08) MCP Server Recommendations & Outro
