May 21
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NeoArbi uses Neo4j and AI agents to power a real-time crypto intelligence platform - covering arbitrage detection, market insights, blockchain forensics and regulatory analysis. By modeling exchanges, assets and transactions as a connected graph, it uncovers pricing gaps, patterns, anomalies and risk signals across the crypto ecosystem. AI agents add context by learning from historical behavior and market dynamics, enabling deeper insights and continuous monitoring. This session explores the graph data model, key Cypher patterns and the agent architecture behind these capabilities - and shows how graph-based intelligence connects market, compliance and risk insights in one unified view. Guest: Arthur Namias de Crasto ( https://www.linkedin.com/in/arthur-namias-de-crasto-49613414/ ) The AI Memory Landscape: https://neo4j.com/videos/nodes-ai-2026-the-ai-agent-memory-landscape/ Neo4j MCP: https://neo4j.com/developer/genai-ecosystem/model-context-protocol-mcp/ Datashare ICIJ: https://datashare.icij.org/ Neodash: https://neo4j.com/docs/neodash-commercial/current/ Aura Agents: https://neo4j.com/developer/genai-ecosystem/aura-agent/ Create Context Graphs: https://neo4j.com/blog/genai/introducing-create-context-graph/ #neo4j #graphdatabase #agenticai #knowledgelayer #crypto #trading #genai #knowledgegraph