Computer vision that doesn't miss what humans do.
A machine vision system using deep learning models trained on your defect library to inspect products in real time at line speed, at accuracy and consistency levels manual inspection cannot match. Every inspection record linked to unit, batch, shift and line parameters.
Trained on your defects. Not a generic dataset.
The defect library is built from your own historical defect samples, which means accuracy is measured against the specific failure modes your line produces, not a benchmark dataset.
- Accuracy>99.2% on trained defect classes
- SpeedMatched to line speed · no production slowdown
- Defect libraryBuilt from your own historical samples
- InferenceEdge · sub-50ms decision latency
- Line controlReal-time rejection signal integration
- Pattern analyticsDefect clusters by shift, machine, input batch
- TraceabilityUnit serial · batch · shift · line parameters
- SectorsAuto · PCB · pharma · textile · FMCG · defence
SOLDER BRIDGE · 0.971
MISSING CAP · 0.944
OK
SN·A4-019822 · BATCH B-3391 · SHIFT B
VerdictREJECT
- ✕Solder bridge · J40.971
- ✕Missing cap · C120.944
- ✓Dimensional · within tol0.998
- ✓Label · OCR match0.996
THROUGHPUT · 1,840 U/HRLATENCY · 42msYIELD · 24H · 97.6%
FIG · 01 / Inline inspection · station operator view// LIVE EDGE INFERENCE