The Power of 'AI' in Every Graph Database
AGE was under development since 2019 by a team of engineers at Bitnine Global Inc. The project, originally born out of AgensGraph, a multi-model graph database fork of PostgreSQL, was donated to the Apache Software Foundation and entered incubation in April 2020. Since May 2022, Apache AGE® is a Top-Level Project of the Apache Software Foundation.
About
Transforming Data with AI: The AGEDB Mission
AGEDB merges AI innovation with data clarity. In a data-saturated world, we leverage AI to streamline complexity into actionable insights, transforming data management with our advanced DBMS solutions.
Pioneering Hybrid Technology
It isn't just a name, it's a promise. We pioneer hybrid database technology, bridging relational databases with graph insights.
Innovating with AI & Open Source
We're pioneering with AI at our core, enhancing relational databases with graph insights. As leaders in the Apache AGE® open-source project.
Simplifying Complexity
In the fast-paced business world, AGEDB streamlines data management with our advanced technology.
Use Cases
Graph AI-Enhanced FDS
Challenge: The rise in sophisticated financial frauds, especially using counterfeit bank accounts, has outpaced traditional FDS (Fraud Detection System) capabilities, making it harder to identify and analyze fraudulent transactions efficiently.
Solution: AGEDB introduces a cutting-edge Graph FDS, combining rules and machine learning with graph analysis to pinpoint complex fraud patterns with unprecedented accuracy. This approach redefines fraud detection, identifying fake accounts by exploring intricate transaction relationships.
Smart Grid Intelligence
Challenge: KEPCO KDN faced the hurdle of efficiently managing nationwide smart grid power meters (AMI) and the associated high false positive rates in fault detection, leading to impractical dispatch frequencies and increased operational costs.
Solution: With the deployment of AgensGraph, KEPCO KDN now visualizes the entire AMI landscape as an interconnected graph, allowing for precise root cause analysis of faults through advanced LCA graph techniques. This approach has significantly reduced unnecessary dispatches, enabling faster fault resolution and substantial cost savings.
Cybersecurity based on Graph-AI
Challenge: In the realm of cybersecurity, the landscape is continuously evolving with complex attack patterns like advanced persistent threats, pattern modulation, and multi-vector attacks, which overwhelm traditional table-structured data systems.
Solution: AGEDB's innovative Graph-Based Cyber Threat Intelligence (CTI) system, developed for KISA, integrates diverse data types from multiple channels to construct a relational analysis powerhouse. This allows for: