Munich Datageeks September Edition

What changed in this room? Spatial memory for physical AI on a Neo4j graph by Jordi Spranger

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[Berlin] Graphs Gone Wild

Bring your coding agent. Pick an idea. Build something. Graphs Gone Wild is for people experimenting with AI agents, memory, documents, graphs, MCP, and new developer tooling.

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Daytona, Neo4j, Mistral & SGLang AI Builders – Paris, September 2026

AI engineering is moving beyond individual models toward full-stack systems that bring together models, inference, data, and infrastructure. Join Mistral, Neo4j, Daytona, and SGLang for an evening of practical conversations about what it takes to build and run modern AI… Read more →

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Vom Datenzugriff zur Datenfusion – Besitzverhältnisse mit Knowledge Graphs analysieren

Worum es geht Finanzrelevante Daten aus Zoll, Polizei, Finanzverwaltung und Registern liegen in unterschiedlichen Systemen und Formaten vor. Abweichende Namen, Adressen oder Kennungen erschweren es, Personen, Unternehmen und Beteiligungsstrukturen zuverlässig zusammenzuführen – mehr Datenzugriff allein löst das nicht. In diesem… Read more →

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Reusable skills and procedural memory with graph engineering | Europe

Join us on October 6th to hear how graph engineering enables AI agents to reliably execute reusable skills with portable procedural memory that improves governance and performance.

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Bayer Built a Knowledge Graph That Predicts Heart Failure Drug Targets

Bayer’s R&D team fused cardiac imaging with 18 biological databases in a Neo4j knowledge graph, identifying druggable gene targets across three major cardiovascular diseases and surfacing existing medications as repurposing candidates.

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Turn complex documents into scalable knowledge graphs | Europe

Learn how to turn semi-structured documents into knowledge graphs. Hear how AI agents can navigate complex information with deterministic graph traversal and GraphRAG workflows.

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Road to NODES | GenAI and Graph Foundations: Building GraphRAG with Neo4j

Build knowledge graphs from unstructured documents and structured data, enrich them with embeddings, and use Neo4j vector indexes to power similarity search. Through hands-on exercises, implement vector-based, vector-plus-Cypher, and text-to-Cypher retriever patterns, then assemble them into a working conversational agent… Read more →

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Neo4j + GraphAware: A new chapter in intelligence analysis | Europe

Join us on September 22 to learn how GraphAware and the Western Australia Police Force are rethinking intelligence analysis with evolving capabilities that uncover relationships and keep citizens safe.

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