AI-driven quality inspection reduced defect escape rate by 74%

The manufacturer's quality control process relied on manual visual inspection at the end of production lines — a process that was slow, inconsistent across shifts and facilities, and allowed an unacceptable defect escape rate to reach customers. Previous attempts to implement automated inspection had failed because the solutions were built as standalone technology projects without redesigning the surrounding workflow. Production line operators viewed the technology as a threat rather than a tool, and the systems generated so many false positives that inspectors learned to ignore them.

We took a fundamentally different approach. Instead of starting with the technology, we started with the workflow. We spent three weeks on the production floor across four facilities, mapping the actual inspection process — not the documented one — and identifying where AI could augment human judgment rather than replace it.

We designed a hybrid inspection workflow where AI handles the initial screen (detecting surface defects, dimensional deviations, and assembly anomalies) and human inspectors focus on the judgment calls that require experience and context. The AI system flags anomalies with confidence scores, and inspectors can override and provide feedback that continuously improves the model.

We deployed the system at a single facility first, iterated based on operator feedback for six weeks, and then rolled out across the remaining 11 facilities. In parallel, we built a predictive maintenance model using sensor data from production equipment to anticipate failures before they cause unplanned downtime.