PHARMACEUTICALS//
Authenticate Every Dose Without Breaking the Seal
Mineral City AI enables non-destructive verification of sealed pharmaceutical products, helping manufacturers confirm authenticity, completeness, and packaging integrity without disrupting sterility, throughput, or regulatory workflows.
The Problem
Dangerous, unregulated drugs are often ineffective and can lead to serious health consequences like organ failure, infection, and death. Counterfeit pharmaceuticals are responsible for over 1 million deaths globally each year.
Our Solution
Mineral City's proprietary XR Vision software analyzes scans of sealed bottles, boxes, and blister packs to confirm product presence, detect anomalies, and support quality control, helping prevent counterfeit or diverted medications from entering the supply chain.
The Impact
- Ensure patient safety
- Meet strict regulatory standards
- Reduce recall risk
- Protect brand credibility
Client Snapshot
Global top-10 pharma manufacturer producing sterile oncology vials across multiple fill-finish sites.
Challenge
Counterfeit diversion and line-side quality risk were rising across key markets. The client needed a non-destructive, line-speed way to validate unit-level authenticity and packaging integrity—without opening secondary packaging or slowing throughput.
Mineral City AI Solution
Mineral City AI deployed its radiographic AI authentication platform to analyze X-ray images of sealed units. The model learns material/geometry “signatures”—stoppers, crimps, lyo-cake morphology, fill presence, and insert layouts—then compares each unit against a SKU-specific reference library.
- Non-destructive: No package breach; maintains sterility and chain of custody.
- Line-speed compatible: Works with leading X-ray scanner OEMs via lightweight API.
- SKU library: Tuned models for oncology vials, vaccines, and lyophilized products.
Auditability: Unit-level decisioning with image evidence for QA/QC and investigations.
Implementation
Started at one fill-finish line, then expanded to multi-site deployment. Training used manufacturer-approved reference sets and a controlled set of known out-of-spec exemplars to harden the model.
Outcomes
- Improved detection of anomalous units and packaging inconsistencies before secondary pack-out.
- Fewer false alarms versus manual visual checks, with consistent, documented decisions.
- Release confidence increased—QA had a defensible, image-backed trail per unit and lot.
- Global readiness—standardized model profiles replicated across sites with minimal re-validation.
Why it matters
(for investors & partners)
- Risk reduction across high-value SKUs with minimal operational friction.
- Scalable moat: Library growth compounds model accuracy over time.
- Partner leverage: Seamless with existing X-ray hardware and MES/LIMS systems.