Automated Feature Pipelines for LLM-Based Executive Intelligence Systems in Hybrid Cloud Environments
Srinivas Chippagiri, Prasad Gadiraju, Ramesh Somayajula, Prudhvi Naayini, Balaji Krishnan · 2025
The rise in complexity of executive decisionmaking calls for smart solutions that can deal with a lot of information from different data sources. Automation helps in extracting needed features from data and transforms them for machine learning which eases decision-making. It looks at how to integrate automated feature pipelines into LLM-based executive intelligence systems used in hybrid cloud systems. Our proposed framework lets you take advantage of LLMs alongside automated data organizing, choosing of features and their modification, designed for hybrid cloud deployment. Cloud scalability in our process guarantees data privacy, security and the highest level of efficiency. The proposed solution, tested through experiments, showed that it improves automation, lessens manual efforts and boosts the performance of the system. We also offer a collection of performance benchmarks that show the results of our hybrid cloud approach against the usual methods when vital tasks are performed using different cloud systems. It has been shown that using such methods can make executive decision-making more rational and efficient even for large-scale situations.