Nuvbudh's Quality AI analyses your inspection data, batch records and process parameters to find the exact causes of rejections — not just count them.
Adding more inspectors catches rejections after they happen. The real cost is in rework, scrap, and the repeat batches that fail for the same hidden reason your team never fully pinned down.
AI cross-references rejection data with machine settings, shift timings, raw material batches, and operator records to find what's actually driving defects.
Identifies which defect types are increasing, which machines or shifts produce more rejects, and what process changes correlate with better quality.
Flags when process parameters drift outside the range that historically leads to rejections — before the bad batch is complete.
Simple summary: top 3 rejection causes this week, which corrective actions worked, and what to focus on next. No statistics degree needed to read it.
Connects your incoming material quality records to downstream rejections — so you know which supplier batches are causing your scrap.
Flags batches likely to generate complaints based on patterns in past customer returns — before they ship out.
Correlate dimensional rejections with tool wear, coolant temperature, and raw material hardness variations.
Link sink marks, warping, and flash to injection parameters, mould temperature cycles, and material moisture levels.
Identify solder defect clusters by operator, time of day, humidity levels, and paste batch — often invisible to manual review.
Map defect frequency to specific machines, thread batches, and operator shifts to target retraining and maintenance precisely.
Share 3 months of your rejection data in the demo. We'll run a live analysis and show you patterns your team hasn't seen yet.