Intelligent Enterprise Platforms for RAG LLM Workflows and Cloud Native Data Lineage and Automation
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Abstract
Intelligent enterprise platforms are redefining how organizations operationalize Retrieval-Augmented Generation (RAG) workflows, large language models (LLMs), and cloud-native data ecosystems. By integrating scalable vector databases, knowledge graphs, and real-time data pipelines with containerized microservices and Kubernetes orchestration, enterprises can build secure, context-aware AI systems that deliver accurate, explainable, and domain-specific insights. RAG-based architectures enhance LLM performance by grounding generative outputs in trusted enterprise data, improving reliability, compliance, and decision quality.
Cloud-native data lineage and automation frameworks further strengthen governance and operational transparency. Automated metadata tracking, data provenance management, and policy-driven orchestration enable end-to-end visibility across data ingestion, transformation, model training, and inference workflows. Intelligent DevOps and LLMOps pipelines ensure continuous integration, monitoring, and optimization of AI services while maintaining security and regulatory alignment. Together, these platforms establish autonomous, scalable, and auditable enterprise systems capable of delivering real-time analytics, intelligent automation, and sustainable digital transformation
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1. Gangina, P. (2025). The role of cloud architecture in shaping a sustainable technology future. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12827–12833.
2. Chennamsetty, C. S. (2025). Bridging design and development: Building a generative AI platform for automated code generation. International Journal of Computer Technology and Electronics Communication, 8(2), 10420–10432.
3. Gaddapuri, N. S. (2021). Big data storage observation system. Power System Protection and Control, 49(2), 7–19.
4. Gurajapu, A., & Garimella, V. (2025). Serverless vs. containerized workloads: Comparative performance and cost under bursty telecom traffic. International Journal of Computer Technology and Electronics Communication (IJCTECE), 8(1), 10085–10088.
5. Panchakarla, S. K. (2025). Personalized mobile engagement in global hospitality: A unified framework for guest communication compliance. Journal of Computational Analysis and Applications, 34(7).
6. Sriramoju, S. (2025). Architecting scalable API-led integrations between CRM and ERP platforms in financial enterprises. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(4), 10303–10311.
7. Thakran, V. (2025, June). An Analysis of Machine Learning Solutions for Precise Forecasting of Oil and Gas Pipeline. In 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) (pp. 1-6). IEEE.
8. Ponugoti, M. (2024). AI-driven microservice architectures: Enhancing compliance and decision intelligence in cloud environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14869–14880.
9. Natta, P. K. (2024). Designing trustworthy AI systems for mission-critical enterprise operations. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13828–13838. https://doi.org/10.15662/IJFIST.2024.0706003
10. Rajasekharan, R. (2025). Automation and DevOps in database management: Advancing efficiency, reliability, and innovation in modern data ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(4), 10284–10292.
11. Kumar, R., & Panda, M. R. (2022). Benchmarking Hallucination Detection in LLMs for Regulatory Applications Using SelfCheckGPT. Journal of Artificial Intelligence & Machine Learning Studies, 6, 149-181.
12. Devi, C., Inampudi, R. K., & Vijayaboopathy, V. (2025). Federated data-mesh quality scoring with Great Expectations and Apache Atlas lineage. Journal of Knowledge Learning and Science Technology, 4(2), 92–101.
13. Kamadi, S. (2025). Machine learning and AI architecture: A comprehensive framework for production-grade intelligent systems. World Journal of Advanced Research and Reviews, 27(1), 2789–2799.
14. Surisetty, L. S. (2025). AI-driven compliance: Using data science to ensure fair pricing and policy alignment in healthcare systems. International Journal of Computer Technology and Electronics Communication, 8(1), 10069–10084.
15. Mudunuri, P. R. (2025). Socio-technical impacts of automation in regulated scientific organizations. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(3), 16488–16498.
16. Bathina, S. (2025). Composable commerce architectures: Building agile retail systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12226–12231.
17. Mulla, F. A. (2024). The mobile revolution during COVID-19: A technical analysis of application evolution. International Journal for Multidisciplinary Research (IJFMR), 6(6), Article 33494. https://www.researchgate.net/profile/Farooq-Mulla/publication/389208652_The_Mobile_Revolution_During_COVID-19_A_Technical_Analysis_of_Application_Evolution/links/67b8addf207c0c20fa910e38/The-Mobile-Revolution-During-COVID-19-A-Technical-Analysis-of-Application-Evolution.pdf
18. Chintalapudi, S. (2025). From backend to business: Fullstack architectures for self-serve RAG and LLM workflows. International Journal of Research Publications in Engineering Technology and Management (IJRPETM), 8(3), 12121–12132.