Abstract
The increasing deployment of artificial intelligence (AI) across heterogeneous operational environments has created a need for infrastructure capable of interpreting context, integrating semantic information, and supporting decisions under computational and resource constraints. Conventional AI infrastructures frequently emphasize model performance and scalability while treating contextual interpretation, computational efficiency, and sustainability as relatively independent concerns. This research develops a conceptual framework for Context-Aware Semantic AI Infrastructure (CASAI) that integrates semantic representation, contextual reasoning, adaptive model selection, resource-aware inference, and decision-oriented orchestration. The proposed framework is theoretically grounded in efficient neural computation, model compression, quantization, knowledge distillation, tensor factorization, and reduced-order modeling. The methodology synthesizes the provided literature to define an architecture in which contextual information dynamically influences model selection, inference precision, computational allocation, and decision confidence. The framework further incorporates sustainability as an infrastructure-level objective rather than merely an environmental reporting measure. The analysis indicates that context-aware adaptation can reduce unnecessary computational complexity while preserving decision utility when semantic relevance and resource constraints are jointly considered. The study also identifies important trade-offs between compression, accuracy, contextual fidelity, and interpretability. The resulting architecture provides a research-oriented foundation for intelligent decision systems capable of adapting computational behavior to changing operational conditions while maintaining semantic consistency and sustainability objectives.