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AI Computer Vision in the Pharmaceutical Industry

  • Writer: Shubham Darwatkar
    Shubham Darwatkar
  • Aug 8
  • 2 min read

Precision that Saves Lives


In pharmaceuticals, precision and compliance are non-negotiable. A smudged expiry date, a misprinted label, or a cracked blister pack doesn’t just risk a customer complaint, it risks lives. Add to that the intense scrutiny of regulators and the costs of non-compliance, and the stakes couldn’t be higher.


For years, inspection teams did their best. But with lines moving fast and fatigue setting in, mistakes were inevitable. That’s why leading pharmaceutical manufacturers are turning to Eaglai Detect.


Various pills on a light gray background, some circled in red or green. Pills vary in color, shape, and size, appearing evenly spaced.
Defect Detection in Pharma

The Challenge in the Pharmaceutical Industry


Even top-tier pharma companies struggled with:

  • Label misprints and expiry date smudges

  • Seal integrity failures

  • Pill shape anomalies and micro-defects

  • Manual inspection slowing throughput (5–7 sec per unit)

  • Fines averaging $5–10M per compliance breach


Implementing AI in Pharmaceutical Industry : Eaglai Detect


Enter Eaglai Detect - a system built to deliver medical-grade accuracy to integrate AI in the pharmaceutical industry. The deployment included:


  • Multi-angle smart cameras covering all surfaces of blister packs, vials, and tablets

  • OCR models for validating expiry dates and batch codes

  • Deep learning algorithms trained on thousands of good vs defective packaging samples

  • Adaptive lighting to catch micro-defects invisible in standard conditions


The system automated over 90% of quality checks previously handled manually, without slowing production lines.


Close-up of pink pills in blister packs on a blurred background, suggesting a pharmaceutical or medical theme.

How It Worked | Operational Flow of the AI System


  • Image capture and classification in ~2 milliseconds per frame

  • Synthetic data generation to train models for rare anomaly detection

  • Edge AI for on-site processing, ensuring compliance and reducing privacy risks

  • Central dashboards tracking every inspected unit for traceability


The Results


The improvement was clear:

  • Inspection speed improved from ~6 seconds to ~1.5 seconds per unit

  • Error rate dropped to just 0.05% (from ~1.2%)

  • Compliance incidents reduced to near-zero

  • Annual recall costs cut by about 80%, saving roughly $15 million per year

  • Full ROI achieved in less than 12 months


Pills spilling from orange bottles onto a stone surface. Pills are pink, red, and white, heart-shaped. Background is dark and blurred.

The Takeaway

In pharma, AI vision is about more than efficiency. It’s a lifeline for compliance, accuracy, and patient safety, three things no pharmaceutical company can compromise on.


 
 
 
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