ABSTRACT:
This study provides Cognistream, a revolutionary predictive framework that aims to transform transactional intelligence for high-velocity digital firms functioning in dynamic, multichannel environments. The study aims to meet the growing need for intelligent, scalable, and real-time decision-making in revenue-critical business operations. The proposed system combines predictive analytics, real-time pattern identification, and anomaly-aware processing to allow for seamless orchestration of transactions, billing behaviors, and consumer engagement flows. A modular prototype was created and tested with simulated datasets representing telecommunications, digital retail, and public sector settings. The results show a 38% increase in prediction accuracy for billing anomalies, a 45% reduction in processing latency, and a noticeable improvement in customer response alignment. This study presents a novel architecture that differs from traditional rule-based systems by allowing for self-evolving transactional cognition a significant step forward in intelligent enterprise automation.
Cite this article:
Vivek Ghulaxe. Cognistream: A Predictive Framework for Transactional Intelligence in High-Velocity Digital Enterprises. Research Journal of Engineering and Technology. 2025; 16(3):115-6. doi: 10.52711/2321-581X.2025.00011
Cite(Electronic):
Vivek Ghulaxe. Cognistream: A Predictive Framework for Transactional Intelligence in High-Velocity Digital Enterprises. Research Journal of Engineering and Technology. 2025; 16(3):115-6. doi: 10.52711/2321-581X.2025.00011 Available on: https://rjetonline.com/AbstractView.aspx?PID=2025-16-3-3
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