The problem
we solve
Production generates evidence continuously: process parameters, measurements, alarms, inspection images, test reports, defects and rework records. Yet when quality starts to drift, this information is often analysed separately and only after a problem has already occurred.
Finding the cause of a defect can mean manually reconstructing what happened across machines, batches and production steps. Visual inspection depends on repeatability at line speed. Test results accumulate in reports that are difficult to compare at scale. Predictive signals remain hidden inside data that was originally collected simply to document the process. The challenge is to turn all of this into a system that can detect what is changing, explain what is driving it and act early enough to matter.
Use cases
_Defect diagnostics
Connecting production conditions to defects, rework and final quality outcomes.
- Root-cause analysis: process variables and production events associated with recurring defect patterns.
- Predictive quality: quality outcomes and rework risk estimated before final inspection.
- Defect intelligence: historical defects grouped and analysed to surface recurring mechanisms behind them.
_Visual inspection
Using computer vision to detect defects directly from images acquired during production and assembly.
- Automated defect detection: visual anomalies identified consistently across components and production batches.
- Fine-grained inspection: small defects in surfaces, soldering, assembly or geometry recognised beyond simple pass/fail classification.
- Production integration: models designed to fit the latency and hardware constraints of the inspection station itself.
_Test report analytics
Turning large volumes of production and validation reports into structured, searchable evidence.
- Automated extraction: measurements, outcomes and test conditions captured directly from heterogeneous reports.
- Cross-report analysis: recurring failures, deviations and relevant historical cases identified across large report archives.
- Traceable insight: every extracted result connected back to the test and document that produced it.
How we do it
#Computer Vision
#Adaptive AI
#Predictive Intelligence
Production data is heterogeneous by nature. A single quality outcome can depend on time-series signals, machine settings, material properties, inspection images, test results and events that happened several process steps earlier. Analysing any one of these in isolation misses part of the system.
These connect through predictive models and time-series learning, which capture relationships across process variables and production history; computer vision, which turns visual inspection into structured quality information; and root-cause and anomaly analysis, which identify which signals are associated with a deviation and when it began. Where data arrives continuously, models process it incrementally, updating as new data arrives without rebuilding the full analysis each time. The result is a quality system that moves upstream: from documenting defects after they occur to recognising their precursors, tracing their causes and improving the process that produced them.
Deployed.
Measured.
Proven.
TEST REPORT ANALYTICS
70%
Efficiency gain in time per resource as report volume and complexity grow.
DEFECT DIAGNOSTICS
40x
Faster root-cause diagnosis of production defects.