The artificial intelligence is transforming maritime anomaly analysis by moving beyond isolated alerts to a more contextual understanding of events at sea. Developed in collaboration with Windward, the system combines geospatial intelligence with generative AI to help analysts quickly understand unusual vessel behavior such as unexpected route deviations, suspicious activity spikes, or unexplained stops. Instead of manually collecting data from multiple sources, the AI automatically builds a complete situational picture.
A key feature of the solution is its agentic multi-step workflow powered by large language models on Amazon Bedrock and orchestrated through AWS Step Functions. Once an anomaly is detected, the system gathers metadata such as location, time, vessel type, and alert category. It then pulls in contextual information from news feeds, web search, and weather APIs using separate AWS Lambda functions. This allows the AI to connect a vessel anomaly with possible external causes like storms, regional conflict, or port disruptions.
Another advanced capability is its self-reflection logic. After retrieving initial data, the AI checks whether the information is sufficient to explain the anomaly. If gaps remain, it automatically generates additional search queries and collects more relevant data. The system then ranks and filters sources using reranking models and LLM-based scoring to keep only the most relevant results. Finally, it produces a concise report summarizing potential causes, risks, and implications, while also citing the data sources for analyst verification.
Overall, the article shows how generative AI is improving maritime intelligence by reducing investigation time and enhancing decision-making. What previously took analysts hours of manual work can now be done much faster with AI-driven contextual reports. This helps both expert and non-expert users better understand maritime risks and respond more effectively to suspicious activity at sea.
