Strategic and analytical foundations for ALFRESCO, a semiotic AI framework for cultural image interpretation

This thesis examines and defines the development conditions for ALFRESCO, an artificial intelligence framework designed for the cultural and semiotic interpretation of images. The goal is to identify technological opportunities, innovation spaces, and design trajectories that support the system’s realization. The research maps the technological landscape through a systematic patent and competitor analysis in the domains of computer vision, semantic representation, and computational semiotics, identifying gaps and evolutionary directions related to symbolic modeling, interpretable multimodal integration, and culturally adaptive systems. In parallel, a focus group with experts and potential stakeholders explores usage expectations, value drivers, and perceived risks, informing the system’s functional positioning and usage identity. The results highlight three main development directions: integration of symbolic and neural approaches for visual meaning representation, visual explanation tools that make interpretive processes transparent, and applications oriented toward brand intelligence and cultural image analysis. Based on the evidence collected, a preliminary design direction for a future MVP and a development roadmap are defined, aligned with technological opportunities, user needs, and the competitive context. The thesis contributes by integrating analytical tools and co-interpretation processes to structure innovation pathways in the field of computational culture, providing a strategic and design foundation for the evolution of semiotic AI systems.