Artificial Intelligence and Data Analytics

Our work covers the complete AI development process, from problem formulation and data preparation to baseline construction, model training, evaluation, validation, and integration into reports, dashboards, or software interfaces.

Areas of work

 
• Machine learning and deep learning.

 
• Classification, regression, and forecasting.

 
• Time-series and sequence analysis.

 
• Anomaly and early-fault detection.

 
• Sensor and SCADA data processing.

 
• State, degradation, and health estimation.

 
• Multimodal, tabular, and image data.

Evaluation criteria

 
• Classification: Accuracy, Precision, Recall, F1-score, Sensitivity, Specificity, ROC-AUC, and PR-AUC.

 
• Regression: MAE, MSE, RMSE, R², and residual analysis.

 
• Forecasting: MAE, RMSE, MAPE or sMAPE, and horizon-dependent performance.

 
• Anomaly detection: detection rate, false-alarm rate, detection delay, Precision, Recall, and CARE when appropriate.

 
• Uncertainty: coverage probability, calibration, prediction-interval width, and confidence intervals.

 
• Operational models: inference time, resource use, robustness, and constraint violations.

Validation can include leakage-safe data splitting, temporal or asset-based evaluation, interpretable baselines, robustness testing, sensitivity analysis, and reproducible experiment documentation.