SAIP is a domain-specific ontology platform that transforms expert knowledge into structured organizational intelligence for operational decision-making. The platform models expert knowledge as formal ontologies and links them with organizational data to construct knowledge graphs that reflect real-world operational contexts. The platform defines domain concepts, attributes, and relationships to create machine-interpretable structures. It captures operational logic, judgment criteria, and exceptional rules to build knowledge graphs that represent how work is performed in actual operations. SAIP addresses organizational challenges by integrating expert knowledge with data structures, processes, and domain expertise. The platform consists of three main components: ontology modeling that structures expert knowledge into consistent assets, knowledge graph construction that connects real data to reveal semantic relationships and patterns, and a reasoning engine that interprets data through predefined concepts and rules. The reasoning engine analyzes how condition changes affect outcomes and supports root-cause analysis, alternative evaluation, and outcome prediction. SAIP provides transparent reasoning and explainable insights throughout the decision-making process. The platform presents the rationale behind conclusions, including applied data, rules, and relationships. It supports knowledge-based automation for repetitive or analytically demanding tasks. Use cases include emergency management systems that integrate natural hazard data, geographic information, and operational inputs for situational understanding and resource allocation. Additional implementations span retail trend analysis, manufacturing knowledge platforms, and cybersecurity threat intelligence applications.