#Graph Databases
5 articles with this tag
LLM Provenance: Tracking Data Origins with Graffiti
Daniel Chalef of Zep AI discusses the critical challenge of provenance in LLM-generated data and how the Graffiti framework addresses it through temporal graph modeling.
AI Assistants Need Graph Memory, Not Just More Tokens
Stephen Chin of Neo4j discusses how graph databases offer superior memory solutions for AI assistants compared to traditional file storage or vector databases.
AI for Financial Compliance & Fraud Detection
Varsha Shah, Enterprise Technical Architect, discusses an AI framework for financial compliance and fraud detection, highlighting its three-component architecture and performance metrics.
Neo4j's Zach Blumenfeld on AI Agents and Decision Traces
Neo4j's Zach Blumenfeld explains why AI agents need decision traces and how context graphs, powered by Neo4j, can provide the necessary memory and reasoning capabilities for more accurate and accountable AI.
Neo4j CEO Emil Eifrem on Graph Databases and AI
Neo4j CEO Emil Eifrem discusses the symbiotic relationship between graph databases and AI, highlighting how relational context is crucial for modern AI applications.
