Modern enterprises struggle with inefficient manual document processing, leading to productivity losses and compliance risks. In this research , we introduce the creation of an archival and retrieval of the document to an end endpoint by means of a ML driven pipeline. Specifically, we are merging these two components (ingest, pre_join, class) and (metadata generate and search) from NLP and Computer Vision.

Mainframe systems remain the backbone of critical industries such as banking, healthcare, and government, handling vast amounts of transactional data with unmatched reliability. However, traditional mainframe software delivery methods—often manual and sequential—lag behind modern DevOps practices, creating bottlenecks in agility, deployment speed, and innovation.

Hype r-automation, the strategic convergence of advanced technologies such as artificial intelligence (AI), robotic process automation (RPA), and the Internet of Things (IoT), is reshaping supply chain and quality management across industries. This paper examines the deployment and implications of Hyper-automation within the Consumer Packaged Goods (CPG) and Healthcare sectors, focusing on its influence on operational efficiency, quality control, and scalability.

Authorisation systems in financial services are under growing pressure to strike the right balance between strong fraud control and minimal user friction. Rule-based authorisation models typically rely on a set of threshold-based static rules, which are often stiff and slow to react to shifts in fraud patterns or a variety of customer behaviour. It introduces a new framework which combines generative AI to forge synthetic customer personas that span a broad spectrum of spend behaviours and risk aversions.

The rapid evolution of payment card fraud techniques necessitates advanced detection frameworks capable of adapting to emerging threats with minimal delay. Most of these systems have problems detecting unknown fraud patterns and allow much inaccurate positive detection. New hybrid architecture has been proposed using Large Language Models (LLMs), Graph Neural Networks (GNNs) and context together, which helps to detect fraud for the card authorization process promptly and reliably. The LLM section can extract meaningful metadata through using advanced methods, while the GNN component dynamically models transactional relationships and propagates risk scores across entities such as customers, merchants, and transactions.

The emergence of large language models (LLMs) such as GPT-4, Claude, and PaLM 2 has introduced transformative capabilities into modern cybersecurity operations. Leveraging advanced natural language processing, code synthesis, and real-time summarization, LLMs are increasingly embedded within Security Operations Centers (SOCs) to augment threat detection, automate event analysis, and support incident response. This review systematically explores the application of LLMs in log analysis, anomaly detection, SOC automation, and cyber threat intelligence, drawing on recent implementations, benchmarks, and case studies.

The growing adoption of encryption protocols such as TLS 1.3, QUIC, and DNS-over-HTTPS has limited the effectiveness of traditional deep packet inspection, challenging conventional methods of network traffic analysis. In response, machine learning (ML) has emerged as a powerful alternative, enabling the analysis of encrypted and obfuscated traffic through side-channel features, flow metadata, and behavioral patterns. This review systematically examines the evolution of ML-based techniques for secure network traffic analysis, covering supervised flow classification, anomaly detection, and encrypted threat inference.