International Journal of Advanced Research in Computer and Communication Engineering

Abstract: AI-based cloud systems are expected to have a positive effect in enabling efficient automated processing of standard legal documents. Legal technology (LegalTech) tools aim to increase efficiency in automated legal services such as e-discovery and legal-billing review by classifying, extracting, comparing, and summarizing information. These document-specific tasks rely on supervised-computing models that require large-scale datasets for training and performance evaluation. Cloud-based services based on multi-task and multi-lingual-large-pretrained transformer models are proposed for supporting the automation of common LegalTech tasks, including contract analysis, abbreviation, e-discovery, and litigation support.
LegalTech service providers usually offer platform-as-a-service or software-as-a-service solutions to support the e-discovery process—all of which require compliance with legal and ethical regulations. Therefore, deployment of AI services must guarantee not only satisfactory accuracy and performance metrics but also issues such as data governance, bias determination, mitigation procedures, accountability assignment, and ethical compliance of usage. Concentrating on the architectures that provide these services, the availability of the AI models for Cloud APIs covering the required tasks is paramount.

Keywords: AI-Based LegalTech Systems, Cloud Legal Document Automation, Automated Legal Services, E-Discovery Analytics, Legal Billing Review Automation, Contract Analysis AI, Legal Text Classification, Information Extraction In Law, Multi-Task Transformer Models, Multilingual Legal AI, Cloud-Native Legal Platforms, LegalTech SaaS And PaaS, Supervised Learning For Legal Data, Legal Data Governance, Bias Detection And Mitigation, Ethical AI In Legal Services, Regulatory Compliance In LegalTech, Accountability In AI Systems, Cloud APIs For Legal AI, Scalable Legal Analytics.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412158