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Case study | Artificial Intelligence
ForgeLine — Manufacturer AI Readiness & Vision QC
Scoring the Line, Then Shipping Visual Inspection
01
Overview
A multi-site manufacturer wanted “AI on the line” without knowing which stations had usable images, lighting, or owners. Suraket assessed readiness station by station, then delivered a computer-vision quality-check at the one line that could support it.
Stations assessed
14
False scrap reduced by
32%
Inspection cycle time cut by
41%
02
Challenge
Corporate had bought cameras. Plants still used paper defect logs. Some lines had no stable lighting. Others had no labeled defect set. A single enterprise AI program would have failed in the first plant that was not ready.
03
Solution
Suraket scored each station for image quality, labeling, network, and a human override path. Four stations were marked not ready. One packaging line had enough history to train and evaluate. We delivered a vision QC station with reject reasons, a supervisor override, and a weekly quality review. The readiness report stayed the backlog for the next three stations.
Customer
Multi-site Manufacturer
Industry
Artificial Intelligence
Services delivered
AI Readiness Assessment
Machine Learning & Computer Vision
Edge + Cloud Inference
Line Integration
QA Against Labeled Defects
Operator Training
Built with
Computer Vision · AWS
04
The results
14
stations scored
1
line in production
32%
fewer false scrap events
41%
faster inspection cycle
✓
Labeled defect set the next plant can reuse
05
Services delivered
AI Readiness Assessment
Machine Learning & Computer Vision
Edge + Cloud Inference
Line Integration
QA Against Labeled Defects
Operator Training
06
Technology stack
Artificial Intelligence
Computer VisionDefect classifiersEvaluation set
Edge
On-line inferenceCamera calibration
Cloud
AWSModel registry
Integrations
PLC / line signalsMES events
Customer
Multi-site Manufacturer
Readiness first. Vision QC only where the line could actually support it.
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