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Solution partner

Neo4j

Neo4j powers 3 source-linked AI deployments documented in AIUseCaseHub, across 2 industries and 3 countries. Documented deployments include RAG.

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Use Cases

3

Industries

2

Countries

3

Hyperscaler mix

Filter Neo4j's implementations by cloud provider evidence.

How Neo4j builds AI

Build / Buy / Compose across this partner's documented cases

BuildBuyComposeMixed

3 of 3 cases classified (100%) · Compare all use-case types

Reported outcomes

1 case reports measurable results

+20%

Revenue & growth

median · 1 metric

Medians of results published in Neo4j cases, normalized for comparability. See all benchmarks →

All Use Cases (3)

Alibaba

TNG Digital scales fintech infrastructure and adds AI-powered moderation using Alibaba Cloud PAI and Edge AI

TNG Digital is a Malaysia-based fintech and eWallet provider that expanded beyond payments into investments, loans, remittances, and insurance while serving more than 22 million verified users and over 2 million merchants.The company used Alibaba Cloud to strengthen scalable transaction infrastructure, improve disaster recovery, support fraud mitigation and credit scoring, and deploy AI-powered content moderation in real time.

FinanceMalaysia
GCP

BenchSci decodes disease biology with Neo4j and Google Cloud to accelerate drug discovery

BenchSci transforms fragmented biomedical evidence into a graph-native model of disease biology, empowering 9 of the top 10 pharmaceutical companies to de-risk and accelerate discovery.BenchSci’s ASCEND platform combines large language models with the Biological Evidence Knowledge Graph (BEKG), an experimentally grounded knowledge system that integrates open-access literature, closed-access publications, and proprietary pharmaceutical datasets.Its LENS extraction engine uses Google Cloud Vertex AI and Gemini models to interpret scientific papers with contextual awareness and validate claims against associated imagery before materializing structured assertions into Neo4j.

PharmaCanada
RAG

Fortune 500 Financial Services Company Enhances Fraud Detection with Neo4j and Amazon SageMaker on AWS

A Fortune 500 financial services company faced challenges in fraud detection due to slow, inefficient queries with their previous Microsoft SQL Server relational database, impacting analysts' ability to meet SLAs and detect fraudulent activity efficiently.To solve this, the company deployed Neo4j Enterprise Edition on AWS to enable real-time graph data analysis and visualization, providing faster, more expansive fraud pattern recognition with scalability for billions of nodes and relationships.They also integrated Neo4j with Amazon SageMaker machine learning to enrich fraud detection models, improving suspicious pattern identification.The deployment included a multi-region disaster recovery setup on AWS ensuring high availability and security with role-based access control combined with AWS security features.The solution cut manual review times by half, doubled reviewed transactions daily, uncovered previously undetected suspicious clusters, and enabled instant stopping of fraudulent transactions saving the company thousands daily.

FinanceUnited States