Catch defects in real time
Identify problems on the line before they become wasted batches or recalls.
Case Study · Quality Intelligence
How a global pharmaceutical manufacturer used computer vision to catch defects in real time, cutting waste and downtime on its production lines.
The challenge
A few production mix-ups a year were costing a Fortune 500 pharmaceutical manufacturer millions of dollars in wasted batches and recall risk.
Quality control depended on manual visual checks, repeated several times a day along each production line. The process was labor-intensive and error-prone, and it kept skilled employees away from higher-value work.

Identify problems on the line before they become wasted batches or recalls.
Reduce the errors and delays tied to manual inspection.
Automate routine checks so people can focus on higher-value tasks.
The solution
AI Collaborator took the client from proof of concept to a scalable inspection roadmap, drawing on specialists in Germany, Belgium, Italy, and the U.S.:
End-to-end testing showed a faster, more affordable approach than alternatives proposed by top research institutions.
Machine vision was integrated with the client’s existing cameras and production systems, keeping disruption to a minimum.
Working with on-site teams, algorithms were customized for each production environment to maximize accuracy and reliability.
Advanced image recognition flags defects as they happen, cutting inspection error rates.
A clear plan for expanding across facilities with minimal overhead and a defined return on investment.
The results
Real-time inspection gave the client earlier warning on production issues and fewer costly surprises:
A sharp reduction in wasted batches and the costs that come with them.
Fewer disruptions and faster decisions when issues appear.
Labor-intensive inspection steps are automated, and staff are redeployed to higher-impact work.
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