Madeira's IT directorate (DRI) deployed a BERT + Random Forest triage system on 12,843 support tickets (2022-2024), reaching 83% accuracy, cutting repeat complaint categories 27% and raising classification efficiency 32%, per a peer-reviewed case study funded by Portugal's PRR.
Direção Regional de Informática (DRI), Governo Regional da Madeira — with ISTAR-IUL and VIPA (Funchal)
Period
2022-2025
Region (NUTS)
PT30
Keywords
public administration, IT service management, NLP, complaint triage
Context
Portugal's regional public services see growing volume and complexity of IT support requests (locally called PATs) submitted through the PatOnline and PATS systems used by the Autonomous Region of Madeira's government, with ambiguous language, inconsistent terminology and fragmented reporting across departments making conventional keyword-based triage unreliable. The Direcao Regional de Informatica (DRI), working with researchers from ISTAR-IUL (Instituto Universitario de Lisboa) and the VIPA laboratory in Funchal, built an AI pipeline combining multilingual BERT sentence embeddings, K-Means clustering into four complaint categories, a Random Forest classifier for automated categorisation, and Isolation Forest for anomaly detection, with SHAP values for interpretability. The project was funded under Portugal's Recovery and Resilience Plan (PRR).
Results
Evaluated on 12,843 complaint/ticket records drawn from 7,615 user interaction logs between January 2022 and December 2024, the system reached 83% classification accuracy (F1-score 0.80) and processed each ticket in 0.21 seconds end-to-end. In the six months after deployment, repeated complaint categories fell by 27% and classification efficiency rose by 32% (clustering silhouette score 0.47). The results come from a single regional agency and a specific ticketing context; the authors note that applying the approach elsewhere would likely require domain-specific fine-tuning or adaptive retraining, and no independent replication outside Madeira has yet been published.
Implementation
Indicative cost
Low (< €50k) — No budget figure is published; classified as low cost given the narrow single-department scope delivered through an academic research partnership under PRR co-funding.
Time to results
Medium (1–3 years) — Data and evaluation span January 2022 to December 2024 (about three years), including six months of post-deployment measurement.
Staffing & skills
Direcao Regional de Informatica (DRI), Governo Regional da Madeira, ISTAR-IUL (Instituto Universitario de Lisboa) researchers, VIPA laboratory, Funchal
Conditions for success
Combining multiple ML techniques (BERT embeddings, K-Means clustering, Random Forest, Isolation Forest) with SHAP interpretability
Sustained government-university partnership
PRR funding to support the initial build
Common failure modes
Authors note the model was evaluated in a single agency/context and would likely need domain-specific fine-tuning or retraining to transfer elsewhere
No independent replication outside Madeira has yet been published
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