Developing a full-stack Multi-Source Intelligence (MSI) system for processing diverse data types such as open-source intelligence (OSINT) and signals intelligence (SIGINT) involves several architectural and engineering considerations to ensure scalability, predictability, and real-time integration. The system's data ingestion layer must handle both structured and unstructured data using tools like Apache NiFi and Airflow, while normalizing and indexing data for efficient processing and retrieval. Entity resolution and knowledge graph construction are crucial for linking fragmented data into coherent intelligence outputs, using techniques like fuzzy matching and probabilistic models. Natural Language Processing (NLP) pipelines are essential for analyzing open-source content, while real-time tactical intelligence relies on Apache Kafka for millisecond-latency processing and secure delivery across devices. Anomaly detection and behavioral modeling leverage machine learning to identify potential threats, and a fusion layer integrates various intelligence sources for comprehensive situational awareness. The system also incorporates predictive modeling to forecast adversarial actions, utilizing advanced sequence modeling techniques. Overall, the intelligence infrastructure must be cloud-native, AI-augmented, and capable of evolving rapidly in response to increasingly sophisticated adversaries.