Technological Surveillance is a strategic information search and analysis methodology aimed at transforming dispersed data into actionable knowledge, thereby supporting low-risk and evidence-based decision-making processes. In a rapidly evolving and uncertain technological landscape, staying ahead of emerging trends and identifying innovation opportunities enables organizations to maintain competitive advantages and foster adaptive transformation. Innovation-oriented change enhances system responsiveness to environmental challenges, promoting inclusive benefits for all stakeholders. In healthcare systems, decision-making platforms increasingly rely on intelligent mechanisms that integrate technological surveillance tools to promote innovation. One such tool is a custom-built search engine, specifically implemented for the systematic updating of medical imaging equipment and the identification of emerging resources for Clinical Engineering services. This search engine facilitates the detection of both tangible (e.g., diagnostic devices) and intangible (e.g., software tools, training programs) innovations by analyzing technological trends relevant to biomedical services. The system serves as a catalyst for continuous technological renewal, informing procurement strategies, guiding educational curricula for engineering staff, and aligning Clinical Engineering priorities with global innovation trajectories. Alert tuning to provide optimal results remains a major challenge, as user needs and requirements are expert-dependent and context-specific. As an innovative aspect, the engine incorporates Fuzzy Logic predicates to manage uncertainty in the classification and prioritization of technological alerts. These fuzzy predicates enable nuanced evaluations of alert relevance, urgency, and contextual impact, particularly useful during periods of reduced operational load or budget constraints. The alerts are dynamically adapted based on expert-defined requirements and integrated into public procurement processes, enhancing transparency and strategic alignment. By embedding fuzzy inference mechanisms into the surveillance architecture, the system transforms raw data into semantically enriched knowledge, strengthening the Clinical Engineering service’s capacity for proactive and intelligent decision-making. Additionally, the system features an alert rescoring technique that further reduces the number of alerts. The quantification and classification of alerts are performed using Fuzzy Logic predicates. The system integrates fuzzy logic to classify and prioritize technological alerts, supporting decision-making in public procurement processes. Each expert’s requirements are used to adapt alerts via Fuzzy Logic. This search engine helps the service stay up-to-date with tangible and intangible new resources and supports the decision-making process during periods of reduced workload.