Case Study
Computer Vision Quality Control
AI-powered defect detection for precision manufacturing
Eliama Agency — Research Study
“This analysis demonstrated how computer vision could catch defects invisible to human inspectors while transforming our quality economics.”
Problem
Precision manufacturers using manual visual inspection achieve only 75-80% defect detection rates, leading to costly recalls and customer complaints. Human inspectors face fatigue, inconsistency, and limited throughput during extended shifts.
CONSEQUENCE
When systems drift, the business pays twice: once in delivery speed, and again in operational risk. Roadmaps become guesses, reliability becomes a negotiation, and teams burn cycles on work that should not exist.
The longer this persists, the more expensive it becomes to correct—because every release adds new coupling, new assumptions, and new fragility.
Solution
We researched computer vision systems with custom-trained ML models inspecting 100% of parts at production speed. Analysis covered surface defect detection, dimensional variance measurement, and assembly error identification with accuracy exceeding human capability.
Outcome
Research indicates potential 99%+ defect detection rates, 80-90% reduction in customer returns, 5-10x inspection speed increase, and 45-55% quality control cost reduction. System operates 24/7 with consistent accuracy.
Stats
- Delivery
- Predictable
- Risk
- Reduced
- Ownership
- Restored
- System
- Durable
Clear scope, stable plans, controlled change.
Operational failure modes identified and removed.
Teams can operate and extend safely post-handover.
Built to survive change, not just ship once.