Building a graph workload?
- ▸ Fraud detection / identity graph needs multi-hop traversal at production scale — Postgres recursive CTEs are slow, and the team is debating Neo4j Community vs Enterprise plus GDS.
- ▸ Knowledge graph for RAG — retrieval needs to traverse semantic relationships, not just nearest-neighbour vectors, and the architecture call hasn't been made between Neo4j + vectors and a pure vector DB.
- ▸ Cypher learning curve — the team is fluent in SQL and uncertain whether Cypher's graph-first model justifies the ramp time for the workload at hand.
JusDB Neo4j specialists design, deploy, and operate graph workloads. See Neo4j consulting →
Neo4j Graph Database Services
In short: Neo4j is the leading native graph database, storing data as nodes, relationships, and properties rather than tables. Its index-free adjacency makes multi-hop relationship traversals fast, queried with the Cypher language. It powers fraud detection, recommendation engines, knowledge graphs, and identity graphs, with the Graph Data Science library for in-database algorithms.
Native graph storage with index-free adjacency, Cypher query language designed for traversal, Graph Data Science library for in-database algorithms, and AuraDB managed-cloud across AWS, Azure, GCP.
Neo4j service paths
Neo4j Consulting
Graph model design, Cypher query strategy, GDS algorithm selection, AuraDB sizing, fraud and knowledge-graph architectures — written advisory deliverables.
Talk to a Neo4j Engineer
Scoping call for graph design, migration from RDBMS, GDS pipeline design, or production Neo4j operations on AuraDB or self-managed K8s.
What we build with Neo4j
From graph model design to production GDS pipelines — end-to-end Neo4j expertise.