Compliance & Validation — Not documented by Atlas
Ask the vendor: Which compliance certifications (HIPAA, 21 CFR Part 11, SOC 2, GxP) do you hold?
SEA Vision
Vision inspection and AI-based quality control systems for pharmaceutical packaging and production lines.
SEA Vision, founded in 1995 and headquartered in Pavia, Italy, develops vision inspection systems and software for pharmaceutical packaging lines — including AI-based defect detection and line-level data tools — deployed with packaging machinery worldwide. The company has been wholly owned by packaging machinery group Marchesini Group since 2022 and continues to operate under its own brand. Its systems are equipment-adjacent: evaluation is typically tied to specific lines and machinery rather than a standalone software purchase.
- Headquarters
- Pavia, Italy
- Founded
- 1995
- Deployment options
- on-premise
- Website
- https://seavision-group.com
- https://www.linkedin.com/company/seavisiongroup
Categories
AI Use Cases
- Manufacturing Quality Control
Vendor Scorecard
50% — Partial coverage. Coverage score — how completely Atlas documents SEA Vision across 7 buyer-evaluation areas. Reflects available Atlas data, not solution quality.
Deployment & Hosting — Documented
on premise
Pricing Transparency — Not documented by Atlas
Ask the vendor: What is your pricing model, and what drives total cost of ownership?
Implementation & Onboarding — Not documented by Atlas
Ask the vendor: What is a realistic implementation timeline, and what onboarding support is included?
Organization & Role Fit — Documented
Mid-Size Biotech, Large Pharma, Top 20 Pharma, IT / Digital, Data Management
Solution Scope — Documented
Manufacturing Quality Control
Company Viability — Partially documented
Private, Founded 1995
Always ask the vendor
Not captured in Atlas data — confirm directly. Excluded from the score above.
Data Rights & IP
Who owns model outputs? Is our proprietary data used to train models others can access, and where does our data reside?
Model Credibility & Explainability
Can you share validation studies or benchmarks, explain how the model reaches its conclusions, and describe how bias and drift are monitored?
Questions to Ask This Vendor
- How does AI-based inspection performance compare with rule-based systems on our defect classes?
- What false-reject rates do reference customers see at production speed?
- How does the software integrate with non-Marchesini packaging machinery?
- What validation documentation is provided for GMP line qualification?