## 1 Introduction
AI systems should be designed and trained using diverse and representative datasets, and their performance should be rigorously evaluated across different demographic groups to identify and mitigate any potential biases. Moreover, explainability and interpretability of AI decision-making processes are essential to fostering trust and ensuring accountability. While the Cross-Industry Standard Process for Data Mining (CRISP-DM) has been widely adopted across various domains, including public sector applications such as labor inspection data analysis [13], and automated complaint classification has been explored in e-commerce contexts [14], the integration of semantic clustering using sentence embeddings within the CRISP-DM framework for public sector complaint management remains under-explored. Our study distinguishes itself by employing BERT-based sentence embeddings to semantically cluster complaint descriptions, enabling the identification of latent issue categories beyond traditional keyword-based methods. Furthermore, we operationalize these insights through a real-time dashboard, facilitating proactive monitoring and decision support. To our knowledge, this is one of the first studies to combine semantic clustering,advanced classification techniques, and real-time operational integration within the context of public sector informatics complaint management. To address these challenges, this paper presents an AI-driven approach that leverages both data mining and text mining techniques to standardize and classify informatics complaints in a scalable and semantically aware manner. A key contribution of this work lies in the practical integration of BERT-based sentence embeddings and semantic clustering within the CRISP-DM framework to support complaint management in a real-world public administration context. While the core components--BERT embeddings, K-Means clustering, and Random Forest classification--are established methods, their combined application to unstructured informatics complaints enables the harmonization of varied user expressions referring to similar technical problems. More importantly, the framework translates these insights into a real-time dashboard that supports proactive remediation and operational decision-making. This end-to-end implementation demonstrates how off-the-shelf components can be orchestrated effectively to address long-standing bottlenecks in digital service delivery, offering a replicable model for other public sector settings. Unlike previous work, which often focuses separately on classification or clustering, our approach offers a full end-to-end complaint management framework: from semantic understanding to real-time monitoring and proactive remediation, validated with real-world data from a public sector agency. This study thus contributes not only technically, but also operationally, to the digital transformation of public service delivery. The main contributions of this work are threefold: (1) the development of a methodology for harmonizing complaint topics through natural language processing and pattern recognition; (2) the deployment of a machine learning pipeline that transforms raw interaction logs into actionable insights; and (3) the evaluation of the framework through a real-world case study conducted in a local Portuguese government agency, focusing on the automation of error handling in equipment data import processes. The findings highlight the potential for proactive digital service management in public administration. Figure 1, shows the major work concept. The concept of proactive complaint management refers to the ability of a system to anticipate, detect, and resolve issues before they escalate into formal user complaints. In contrast to traditional reactive approaches--where user reports trigger support interventions--proactive systems leverage predictive analytics and semantic pattern recognition to identify anomalies, recurring issues, or latent user dissatisfaction embedded in operational data such as logs and free-text submissions.