AI Quality Inspection & Predictive Maintenance in Singapore

This programme is eligible for SFEC (S$10,000 per employer), ETSS (up to 90% for SMEs), and PSG (up to 50% for pre-approved AI solutions).

All AI workflows comply with PDPA obligations including data protection, consent management, and mandatory breach notification within 3 calendar days.

Yes. All materials and facilitation are in English, Singapore's primary business language.

Starting point: 500-1000 images per defect type (good parts plus defect examples). AI can work with less using transfer learning, but more data improves accuracy. We teach data collection strategies, image augmentation, and active learning to accelerate model training.

Yes, basic predictive maintenance can use existing data (run hours, cycle counts, historical failures). Adding low-cost vibration or temperature sensors dramatically improves accuracy. We teach phased sensor deployment strategies matching budget and ROI priorities.

AI models require calibration to balance sensitivity (catching all defects) and specificity (minimising false positives). We teach threshold tuning, human-in-the-loop review for edge cases, and continuous model improvement. Expect 4-8 weeks of refinement post-deployment.

Trust builds through accuracy and transparency. We teach explainable AI showing what data drives predictions (e.g., rising vibration, temperature anomaly). Start with low-risk equipment, prove ROI, then expand. Champion-challenger testing accelerates adoption.