AI-Augmented Graph Databases for Judicial Case Management: A Scalable AWS-Powered Framework for Relationship Analysis and Decision Support

Abstract

The objective of this study is to modernise court case management through an AI aided graph database that operates in AWS technologies. Amazon Neptune organises intricate legal connections, while SageMaker and Comprehend make it possible to expose predictive insights and information extraction through NLP. GNNs ease the finding of hidden patterns in an easier way, all to speed up the processing and increase the decision accuracy. The query latency is less than 200 ms with AI enhanced query performance. It is also architected based on privacy and security using AI. Data extraction time is 30% quicker and the operation cost is slashed by 25%. However, it shows how ethics and acting at scale in the judicial system can be integrated through the use of AI.