This Week in Neo4j: NODES AI, Context, Text2Cypher, Fraud Detection and more

Senior Developer Marketing Manager

February 20, 2026

1 min read

Welcome to This Week in Neo4j, your fix for news from the world of graph databases!

This edition spotlights why AI agents hit the “Context Wall” without structured enterprise knowledge, dives into practical Text2Cypher techniques, and shows how real-time graph architectures can power fast fraud detection while cutting false positives.

NODES AI, our global graph-and-AI event, is taking place April 15, and the full agenda with themes like Context Graph, GraphRAG, Agents and AI in Production – register now! Road to NODES workshop series already kicking things off in March!

Happy Graphing,

Alexander Erdl

COMING UP!

Livestream: Neo4j Aura Agent: Production-Ready, Ontology-Driven AI Agents, Neo4j Live: Rethinking Data: A Data Scientist’s First Dive into GQLs on February 24 & Going Meta: S03E06 on March 3
Conferences: Find us at Microsoft AI Tour, London on February 24, CDAO UK, London on February 25-26, Devnexus, Atlanta on March 4-6, AgentCon, New York City on March 9, Gartner D&A, Orlando on March 9-11 & HIMSS26, Las Vegas on March 9-12
Meetup: Meet us in Munich, DE on February 25, Berlin, DE & San Francisco, US on February 26, San Francisco, US on February 27, Fukuoka, JP on February 28, Amsterdam, NL on March 3 & London, UK on March 5
All Neo4j Events: Webinars and More
GraphSummit Series: Transform Your Enterprise with Graph and GenAI – Next Stop: Copenhagen on March 10

FEATURED COMMUNITY MEMBER: Shaurya Agrawal

Shaurya Agrawal is a Data & Analytics leader with 25+ years of experience driving transformative initiatives across Tech/SaaS, E-commerce, and FinTech. With expertise in AI/ML, Enterprise Data Architecture, and BI, he’s led impactful projects, creating customer-centric solutions and modernising data platforms.

Connect with him on LinkedIn.

Shaurya is a featured speaker for NODES AI. In his talk “Lakehouse + Graph: Delta Lake, Unity Catalog and Neo4j for Governed AI Pipelines”, he will demonstrate how to pair Delta Lake’s bronze/silver/gold architecture with Neo4j to build governed AI pipelines that track lineage, derive graph features and power GraphRAG, while keeping RBAC, audit logs and data contracts synchronised across both systems.

NODES AI: Agenda now Live

The full agenda for NODES AI 2026, our free virtual conference on April 15, is now live! So browse sessions and plan your day. This year’s program goes deep on building more intelligent AI with better context through live technical sessions that explore knowledge graph, GraphRAG, agent memory design and lessons from deploying AI in production.

To help you prepare, we’re also running the Road to NODES workshop series: free two-hour, hands-on online workshops designed to sharpen your graph-powered AI skills ahead of the main event.

CONTEXT: The Context Wall: Why AI Agents Fail Without Enterprise Context

Firat Tekiner breaks down the Context Wall, the idea that AI agents can’t make reliable decisions without access to rich, structured enterprise context such as organisational hierarchies, business rules, and historical data. He argues that without knowledge graphs or similar structured context stores, agents suffer hallucinations and inconsistent behaviour, and he illustrates how integrating graph-modelled context dramatically improves reliability and operational accuracy in real-world workflows.

TEXT2CYPHER: Byte-Sized Cypher Series

This guide by Alex Gilmore walks through Text2Cypher, where LLMs are used to translate natural-language questions into valid Cypher queries that retrieve structured graph answers from Neo4j. The extensive guide covers prompt design, execution strategies, error handling and evaluation techniques to make Text2Cypher more reliable and context-aware in real applications.

FRAUD DETECTION: Beyond Relational: Architecting a Real-time Graph Database for Sub-100ms Fraud Detection (and Slashing False Positives by 20%)

This article by Shubham Gupta shows how moving beyond relational databases to a real-time graph architecture with Neo4j and Apache Kafka lets you detect complex fraud rings via multi-hop relationship traversals in <100 ms, while dramatically cutting false positives and latency. By modeling users, accounts, devices, and interactions as connected graph entities and streaming events into Neo4j, the approach delivers real-time pattern matching that outperforms traditional SQL-based fraud systems.

CONTINUOUS LEARNING

GraphAcademy: Learn how to use GenAI and LLMs to convert unstructured data into knowledge graphs in “Building Knowledge Graphs with LLMs“
Learn on Your Schedule: Go deeper into graph technology on Neo4j’s On-Demand webinar library
Workshops: Join our virtual classrooms workshops from Fundamentals to GenAI
New Webinar: Is Your AI Brain-Dead? Knowledge Graphs Provide the Nervous System – Americas, Europe, Middle East & Africa, Asia Pacific