Improving Customer Service Using Intelligent Document Processing Systems Powered by Machine Learning and Natural Language Processing in Large Enterprise Environments
Keywords:
Intelligent Document Processing, Machine Learning, Natural Language Processing, Customer Service, Enterprise Automation, Text Analytics, Cognitive Systems, Document ManagementAbstract
In the evolving digital economy, large enterprises are turning to intelligent document processing (IDP) to optimize customer service. By leveraging machine learning and natural language processing, IDP systems automate unstructured document handling, reduce response time, and enhance service accuracy. This paper investigates the impact of these technologies exploring implementation challenges, success stories, and technological foundations. Two diagrams illustrate workflow and integration, while two tables compare traditional vs. intelligent systems and benchmark performance metrics.
References
Moreno, A., & Redondo, T. (2016). Text analytics: The convergence of big data and artificial intelligence. IJIMAI. PDF
Vaddepalli, R.K. (2022). Streaming vs. batch at scale: How Snowflake’s real-time processing stacks up against on-premises data warehouses. ISCSITR-International Journal of Cloud Computing (ISCSITR-IJCC), 3(1), 9–26. https://doi.org/10.63397/ISCSITR-IJCC_2022_03_01_002
Rapolu, H.K. (2020). Optimizing Single-Page Applications with AngularJS. Journal of Advances in Developmental Research, 11(1), 1–6. https://doi.org/10.5281/zenodo.14916830
Müller, O., Junglas, I., & Brocke, J. (2016). Utilizing big data analytics for information systems research. European Journal of Information Systems. https://doi.org/10.1057/ejis.2016.2
Rapolu, H.K. (2021). Building Microservices with CQRS Axon Framework – Challenges and Best Practices. International Journal of Core Engineering & Management, 6(11), 393–398. https://doi.org/10.5281/zenodo.15061645
Ittoo, A., & van den Bosch, A. (2016). Text analytics in industry. Computers in Industry, 78, 96–107. https://doi.org/10.1016/j.compind.2015.10.005
Mustafi, J. (2016). Natural Language Processing and Machine Learning for Big Data. Springer.
Hardeniya, N., et al. (2016). Natural Language Processing: Python and NLTK. Packt Publishing.
Vaddepalli, R.K. (2021). Who’s responsible when algorithms fail? Comparing accountability under GDPR, CCPA, and other data laws. IACSE - International Journal of Computer Technology (IACSE-IJCT), 2(1), 8–23.
Rapolu, H.K. (2021). Streaming Data Ingestion into BigQuery Using StreamSets. International Journal of Leading Research Publication, 2(4), 1–4. https://doi.org/10.5281/zenodo.14945749
Palangi, H., et al. (2016). Deep sentence embedding using LSTM. IEEE Transactions on Audio, Speech, and Language Processing.
Vaddepalli, R.K. (2022). Measuring AWS’s dual impact on SME innovation: Beyond just efficiency. International Journal of Computer Science and Engineering Research and Development (IJCSERD), 12(1), 161–175. https://doi.org/10.63519/IJCSERD_12_01_012
Kaur, A., & Chopra, D. (2016). Comparison of text mining tools. International Conference on Computing for Sustainable Global Development.
Vaddepalli, R.K. (2021). Adaptive AI-driven data integration: Navigating regulatory challenges in healthcare, finance, retail, and logistics. QIT Press - International Journal of Artificial Intelligence and Machine Learning Research and Development (QITP-IJAIMLRD), 2(1), 8–21. https://doi.org/10.63374/QITP-IJAIMLRD_02_01_002
Joshi, K. P., et al. (2016). ALDA: Cognitive Assistant for Legal Document Analytics. AAAI Fall Symposium.
Vaddepalli, R.K. (2022). Making AI explainable for stronger distributed systems: Clear machine learning for smarter fault tolerance. International Journal of Science and Research (IJSR), 11(12), 1505–1511.
Vaddepalli, R.K. (2021). Breaking the mold: Why data-sharing challenges differ in strictly regulated vs. innovation-focused public-private partnerships. International Journal of Computer Engineering and Technology (IJCET), 12(2), 96–112. https://doi.org/10.34218/IJCET_12_02_011
Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130–134. https://doi.org/10.52783/jes.9328
Rapolu, H.K. (2021). Comparative Analysis of SOAP and REST for Enterprise-Level Applications. Journal of Advances in Developmental Research, 12(2), 1–5. https://doi.org/10.5281/zenodo.14916859
Giudice, D. L., et al. (2016). How AI will change software development and applications. Forrester Research.
Kolbjørnsrud, V., Amico, R., & Thomas, R. J. (2016). The promise of artificial intelligence. Accenture. PDF
Vaddepalli, R.K. (2022). Can AI outsmart threshold alerts? A hybrid machine learning approach for smarter anomaly detection in Azure data pipelines. International Journal of Information Technology and Management Information Systems (IJITMIS), 13(1), 170–184. https://doi.org/10.34218/IJITMIS_13_01_015
Rapolu, H.K. (2021). Automated Testing in Java – Comparative Analysis of Automated Testing Tools. International Journal of Innovative Research and Creative Technology, 7(6), 1–5. https://doi.org/10.5281/zenodo.14883117
Vaddepalli, R.K. (2021). Adaptive XAI narratives for dynamic fraud detection: Keeping AI explanations clear as models evolve. International Journal of Science and Research (IJSR), 10(9), 1819–1825.
