Intelligent Communication Analytics : An Intelligent RPA-Cloud Based Framework for Automated Processing and Mining for Communication
Das, Biplob (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025081924005
https://urn.fi/URN:NBN:fi:amk-2025081924005
Tiivistelmä
Organizations are increasingly adopting digital transformation strategies, making the integration of Robotic Process Automation (RPA), cloud computing, and Intelligent Document Processing (IDP) essential for improving operational efficiency and enabling data-driven decisionmaking. This thesis addresses the challenge of efficiently processing unstructured communication data such as emails, chat messages, and digital documents by proposing a novel cloud-native intelligent automation framework. The research problem centers on reducing manual effort and improving accuracy in communication analytics while maintaining scalability and compliance in enterprise environments.
The theoretical framework combines concepts from intelligent process automation, Natural Language Processing (NLP), and Large Language Models (LLMs). The proposed solution integrates advanced AI methods with low-code/no-code tools to make automation deployment accessible to non-technical users. Built on UiPath’s Communications Mining capabilities, the system automatically extracts, classifies, and analyzes communication content, identifying intents, entities, and sentiments. Transformer-based architectures and contextual retrieval techniques are employed to enhance accuracy and scalability. The study uses case-based evaluation and benchmarking against the Everest Group PEAK Matrix® Assessment 2024 to validate the framework.
The results demonstrate a 94% reduction in processing time, 99% accuracy in data extraction, and a 73% decrease in manual effort compared to traditional approaches. The cloud-native architecture ensures GDPR compliance, robust data security, and seamless scalability across enterprise use cases. These findings highlight the framework’s potential to significantly modernize enterprise communication processes through advanced intelligent automation.
The theoretical framework combines concepts from intelligent process automation, Natural Language Processing (NLP), and Large Language Models (LLMs). The proposed solution integrates advanced AI methods with low-code/no-code tools to make automation deployment accessible to non-technical users. Built on UiPath’s Communications Mining capabilities, the system automatically extracts, classifies, and analyzes communication content, identifying intents, entities, and sentiments. Transformer-based architectures and contextual retrieval techniques are employed to enhance accuracy and scalability. The study uses case-based evaluation and benchmarking against the Everest Group PEAK Matrix® Assessment 2024 to validate the framework.
The results demonstrate a 94% reduction in processing time, 99% accuracy in data extraction, and a 73% decrease in manual effort compared to traditional approaches. The cloud-native architecture ensures GDPR compliance, robust data security, and seamless scalability across enterprise use cases. These findings highlight the framework’s potential to significantly modernize enterprise communication processes through advanced intelligent automation.