Real-time issue & solution

How a AI engineer actually solves it

Not a highlight reel. Each case follows one engagement as it happened — the brief that was wrong, what two days on site revealed, what shipped first, what got automated, the number that wasn’t good, and what it earned.

Germany Featured

The €2.1M inspection bottleneck a model didn't fix

"Our scrap rate is too high. Build us an AI that spots defects on the line."

A supplier asked for a defect-detection model. Eighteen weeks later the model was the third most valuable thing delivered. This is how the engineer worked out what the problem actually was, what got automated, and what it earned.

Sector: Automotive tier-2 manufacturing
Company: ≈480 employees, three plants in Baden-Württemberg
Engagement: 18 weeks — 6 on site, the rest remote

−94% Historical defect lookup

−97% Warranty claim → batch trace

Consistent Inspection recall, 10 of 11 defect families

10 milestones · read the engagement →

This is the job the program trains for

Twenty weeks of learning to do exactly what these cases describe — find the real problem, ship something useful early, automate the expensive manual work, and report the number that isn’t good.

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