OceanFront: An LLM-Orchestrated Cloud-Native Framework for Real-Time Ocean Intelligence

The world’s oceans play a fundamental role in regulating global climate and supporting marine ecosystems, yet effective real-time monitoring and forecasting remain limited by fragmented data pipelines and static analytical systems. Traditional ocean observation platforms primarily rely on offline processing and manual interpretation, restricting their ability to deliver timely, actionable insights in rapidly evolving environmental conditions. This paper presents OceanFront, a cloud-native, LLM-augmented ocean intelligence system designed to provide real-time monitoring, predictive analytics, and natural language– driven decision support for oceanographic applications. The system ingests live buoy telemetry through distributed streaming pipelines, preprocesses and stores spatio-temporal data in a scalable cloud backend, and performs continuous forecasting using an ensemble of machine learning models, including Random Forest, Gradient Boosting, and Long Short-Term Memory networks. A Groq-powered large language model enables users to interact with the platform through natural language queries, autonomously orchestrating data retrieval and model execution. Predictions and insights are delivered through interactive dashboards and web-based interfaces. System-level evaluation demonstrates that OceanFront enables scalable, real-time, and user-accessible ocean forecasting, highlighting its potential for climate research, disaster preparedness, and marine decision support.