Case Study · Manufacturing
Vision-based defect detection for a manufacturer
A custom computer-vision inspection system caught defects manual checks missed, cut escapes to customers, and freed inspectors for high-judgment work.
- Client
- A precision parts manufacturer, 3 lines
- Market
- Remote, US
- Timeline
- 11 weeks to launch
Anonymized and illustrative of a typical engagement.
- −72%
- defect escapes to customers
- 3x
- inspection throughput
- 100%
- of units inspected, not sampled
- 11 wks
- to production
01 / The challenge
Where the time was going
- 01Quality relied on human spot-checks and sampling. Inspectors were skilled but could not examine every unit at line speed, so subtle defects slipped through and reached customers as costly returns and reputation damage.
- 02Sampling meant entire batches could ship with a recurring defect before anyone noticed the pattern.
- 03The best inspectors were spending their time on repetitive visual checks rather than root-cause work.
02 / The build
What we shipped
We built a custom vision system that inspects every unit at line speed and routes uncertain cases to people.
- 01Custom defect modelA vision model trained on the manufacturer's own parts and defect types, not a generic detector.
- 02Edge inferenceInference runs on the line at speed so every unit is inspected without slowing production.
- 03Human-in-the-loopBorderline cases are routed to an inspector, whose decisions feed back into the model.
- 04Pattern alerts and MES integrationRecurring defects trigger alerts and feed the manufacturing execution system so root causes get caught fast.
03 / The results
What changed
Quality went from sampled to total.
Defect escapes to customers fell 72% because every unit is now inspected rather than a sample, and inspection throughput tripled. Recurring defects are caught as patterns early, and inspectors moved from repetitive checking to root-cause analysis.
- −72%
- defect escapes
- 100%
- units inspected
- 3x
- inspection throughput
“We went from checking samples to checking everything, and our inspectors finally work on why defects happen.”
Quality Manager, manufacturer
04 / The stack
Built with, and what you own
The manufacturer owns the trained model, the edge deployment, and the dashboards, integrated with their MES.
- Computer Vision SolutionsGive your business the power of sight with AI that analyzes images and video for actionable insights.
- Computer Vision & Vision ModelsDeploy custom vision AI for image recognition, object detection, visual inspection, and video analytics tailored to your business needs.
- AI Quality Control & InspectionEnsure consistent product and service quality with AI that inspects, measures, and reports in real-time.
05 / FAQs
Questions about this build
Will it slow the line?
No. Inference runs at the edge at line speed, so every unit is inspected without reducing throughput.
What about new or rare defects?
Borderline and novel cases route to an inspector, and their decisions retrain the model so coverage improves over time.
06 / More
Other builds
Want a result like this for your team?
Name the work that is costing you the most time. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.