Context-Aware Enterprise LLM Architecture for Trusted Customer Intelligence and Generative AI Applications in CRM Platforms
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P116Keywords:
Context-Aware Computing, Enterprise LLM, Customer Intelligence, CRM Platforms, Retrieval-Augmented Generation, Trustworthy AI, Generative AI, Privacy-Preserving Analytics, Explainable AI, Enterprise ArchitectureAbstract
Customer relationship management (CRM) platforms increasingly depend on artificial intelligence to convert fragmented customer interactions into actionable intelligence. However, conventional CRM analytics pipelines typically separate predictive modeling, knowledge retrieval, governance, and human decision support into loosely connected components. This separation becomes problematic when large language models (LLMs) are introduced into enterprise environments, because generative systems can produce fluent but unsupported content, expose sensitive information, amplify biased patterns, and misalign recommendations with business context. This paper proposes a context-aware enterprise LLM architecture for trusted customer intelligence and generative AI applications in CRM platforms. The proposed framework integrates contextual signal modeling, retrieval-augmented generation, enterprise knowledge grounding, privacy-preserving data governance, explainability, human-in-the-loop control, and continuous monitoring. The architecture is designed to support customer service automation, sales enablement, churn-risk interpretation, next-best-action recommendation, knowledge-assisted agent support, and executive customer intelligence. Methodologically, the paper develops a layered conceptual model and an evaluation framework that jointly assesses factuality, contextual relevance, privacy risk, fairness, latency, business utility, and governance compliance. The analytical discussion shows that enterprise LLM value in CRM depends less on model scale alone and more on the alignment among customer context, authoritative enterprise knowledge, operational workflows, and trustworthy control mechanisms. The paper contributes a structured reference architecture and a multidimensional performance framework for deploying LLM-enabled CRM capabilities in high-accountability organizations.
References
[1] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: A method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA, USA, 2002, pp. 311–318, doi: 10.3115/1073083.1073135.
[2] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems 33, 2020, pp. 1877–1901.
[3] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, 2019, pp. 3982–3992, doi: 10.18653/v1/D19-1410.
[4] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30, Long Beach, CA, USA, 2017, pp. 5998–6008.
[5] Joint Task Force, Security and Privacy Controls for Information Systems and Organizations, NIST Special Publication 800-53, Revision 5. Gaithersburg, MD, USA: National Institute of Standards and Technology, 2020, doi: 10.6028/NIST.SP.800-53r5.
[6] E. W. T. Ngai, L. Xiu, and D. C. K. Chau, “Application of data mining techniques in customer relationship management: A literature review and classification,” Expert Systems with Applications, vol. 36, no. 2, pp. 2592–2602, 2009, doi: 10.1016/j.eswa.2008.02.021.
[7] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems 33, 2020, pp. 9459–9474.
[8] I. J. Chen and K. Popovich, “Understanding customer relationship management: People, process and technology,” Business Process Management Journal, vol. 9, no. 5, pp. 672–688, 2003, doi: 10.1108/14637150310496758.
[9] M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should I trust you?’: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 1135–1144, doi: 10.1145/2939672.2939778.
[10] H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.
[11] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2019, pp. 4171–4186, doi: 10.18653/v1/N19-1423.
[12] R. S. Winer, “A framework for customer relationship management,” California Management Review, vol. 43, no. 4, pp. 89–105, 2001, doi: 10.2307/41166102.
[13] C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, 2006, Lecture Notes in Computer Science, vol. 3876, pp. 265–284, doi: 10.1007/11681878_14.
[14] V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W. Yih, “Dense passage retrieval for open-domain question answering,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online, 2020, pp. 6769–6781, doi: 10.18653/v1/2020.emnlp-main.550.
[15] D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J.-F. Crespo, and D. Dennison, “Hidden technical debt in machine learning systems,” in Advances in Neural Information Processing Systems 28, Montreal, QC, Canada, 2015, pp. 2503–2511.
[16] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020.
[17] G. Adomavicius and A. Tuzhilin, “Context-aware recommender systems,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston, MA, USA: Springer, 2011, pp. 217–253, doi: 10.1007/978-0-387-85820-3_7.
[18] S. Amershi, A. Begel, C. Bird, R. DeLine, H. Gall, E. Kamar, N. Nagappan, B. Nushi, and T. Zimmermann, “Software engineering for machine learning: A case study,” in Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice, Montreal, QC, Canada, 2019, pp. 291–300, doi: 10.1109/ICSE-SEIP.2019.00042.
[19] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30, Long Beach, CA, USA, 2017, pp. 4768–4777.
[20] K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konečný, S. Mazzocchi, H. B. McMahan, T. Van Overveldt, D. Petrou, D. Ramage, and J. Roselander, “Towards federated learning at scale: System design,” in Proceedings of the 2nd SysML Conference, Palo Alto, CA, USA, 2019.
[21] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
[22] K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang, “REALM: Retrieval-augmented language model pre-training,” in Proceedings of the 37th International Conference on Machine Learning, PMLR, vol. 119, 2020, pp. 3929–3938.
[23] European Parliament and Council of the European Union, “Regulation (EU) 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC,” Official Journal of the European Union, L 119, pp. 1–88, May 4, 2016.
[24] S. Barocas, M. Hardt, and A. Narayanan, Fairness and Machine Learning: Limitations and Opportunities. Available: https://fairmlbook.org, 2019.
