The Platform includes the Neo4j Graph Data Science Library – the leading enterprise-ready analytics workspace for graph data – the graph visualisation and exploration tool Bloom, the Cypher query language, and numerous tools, integrations and connectors to help developers and data scientists build graph-based solutions with ease.

At the core of the Neo4j Graph Data Platform is the Neo4j Graph Database, a native graph data store built from the ground up, to leverage not only data but also data relationships. Unlike other types of databases, Neo4j connects data as it’s stored, enabling queries never before imagined, at speeds never thought possible. 

Neo4j’s cloud service, AuraDB, is now available free for small projects, with no credit card required.

Clients:  Dooloo, Current, Allianz Benelux, Comcast, eBay, Adobe, Volvo Cars, Caterpillar, Tourism Media, Swiss International Air Lines.


Digital platforms: Cross-platform software

Versions: Cloud/On-Premise 

Use cases

  • E-Health creates knowledge network solution to help patients with chronic pain (Dooloo)

Problem: Nearly one person in five suffers from chronic pain, a complex, multi-factorial, multi-dimensional disease whose symptoms are individual and subjective. Often disabling, it is also frequently associated with numerous comorbidities that worsen the prognosis and rule out overly standardised treatment.

Solution: The team turned to an approach that is increasingly used in medical research: creating knowledge graphs that assemble and organise large quantities of data relevant for solving complex problems. Knowledge graphs support open-ended queries and are ripe for advanced analytics using AI and ML.

Result: Through an iterative process, the team coordinated the various points of view of the participants and created a property graph model in Neo4j that enabled them to import the data and rapidly build a prototype.

  • Modern Banking Products Fueled by a User-Centric Approach (Current)

Problem: Traditional banks organise their products around accounts. This means, for a single household, bank products and divisions – such as home equity lines of credit, checking accounts, credit cards, and more – might compete against each other. 

Solution: The  total data model for the core banking engine was modelled directly in Neo4j. The main use case for the company was being able to quickly traverse to different data elements, and to encode the data model as a primitive directly into the database. 

Result: Current’s first product changes the way teens bank and learn about money. Traditionally, a parent opens an account for their teen. That account-centred structure is inflexible. 

Contrast that with Current’s Teen Banking, where a parent adds a product and connects a teen. “We map that into Neo4j by creating user nodes, and then connecting relationships between that user and the products they can access,” said Marshall. “This led to a completely new type of account structure that is far more flexible and more adaptable to the modern family.”


Neo4j is the only enterprise-strength graph database that combines native graph storage, advanced security, scalable speed-optimised architecture, and ACID compliance to ensure predictability and integrity of relationship-based queries.

That’s why it’s deployed by hundreds of Fortune 500 companies, government agencies, and NGOs.