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Aizon Trust Center Pharmaceutical manufacturers and CDMOs operate in some of the most tightly regulated environments in the world. Aizon was built specifically for this reality. Our platform maintains GxP integrity from the point of data capture through every insight it surfaces, and our security and compliance posture reflects the standards your regulators, your customers, and your patients require. What follows is the evidence. Accreditations Quality & Security Certifications In Progress ISO 42001 Expected Q4 2026 Data Privacy Technology Partners AWS Advanced Technology Partner Regulatory Compliance Whitepapers PDF Aizon Platform — FDA 21 CFR Part 11 & EU GMP Annex 11 Compliance Download whitepaper → PDF Aizon Execute — FDA 21 CFR Part 11 & EU GMP Annex 11 Compliance Download whitepaper → Ethics and Integrity PDF Aizon Code of Conduct Download code of conduct → Frequently Asked Questions Can AI be used in a GxP-regulated manufacturing environment? Yes — when it's built for GxP from the start. The FDA's 2023 discussion paper on AI/ML in drug manufacturing, the EMA's 2024 Reflection Paper on AI in the medicinal product lifecycle, and the joint FDA–EMA guiding principles of good AI practice (January 2026), which cover the use of AI in manufacturing all establish clear pathways under risk-based governance, human oversight, and lifecycle controls. Aizon was designed against this framework: purpose-built for pharmaceutical and CDMO manufacturing — not adapted from a horizontal industrial platform — with GxP integrity enforced from the point of data capture and batch-centric contextualization across MES, LIMS, historians, and quality systems. How are AI models validated and governed in production? Model validation is led by your team, before any model is deployed to production — consistent with GAMP 5 and current FDA/EMA expectations for AI/ML in GMP environments. Aizon provides the supporting evidence (model cards, training data lineage, validation methodology documentation) and governs everything after validation: versioned lifecycle, continuous drift monitoring, human-in-the-loop review, and controlled retirement when a model's performance degrades. Every prediction is timestamped, attributed to a model version, and treated as GxP data. How does Aizon maintain ALCOA++ data integrity? Integrity is enforced at the point of capture, not retrofitted in the cloud. Every data point is captured with GMP envelope metadata (what, who, when, where, why) and cryptographic integrity controls at the edge — making the record tamper-evident. Data is stored as raw, never-overwritten binary in a single-tenant environment, with native audit trails and electronic signatures designed to support 21 CFR Part 11 compliance applied to review and approval workflows. Is Aizon ready for FDA and EMA inspection? Aizon holds ISO 9001, ISO 27001, ISO 27017, and SOC 2 certifications, with ISO 42001 (AI Management System) expected by end of 2026. The platform supports compliance with 21 CFR Part 11 and EU GMP Annex 11, is designed against GAMP 5 risk-based principles, and is aligned with ICH Q8–Q12 product lifecycle expectations and with the joint FDA–EMA guiding principles of good AI practice (January 2026), which cover the use of AI in manufacturing. Predict generates structured records throughout the AI lifecycle — model certification status, monitoring summaries, drift records, and full audit logs — designed to support regulatory inspection and customer-led submission preparation. Where does our manufacturing data live, and who can access it? Your data lives in a dedicated, single-tenant AWS environment — your own private instance, with no commingling with other Aizon customers. Aizon staff cannot access your data without your express written permission. European customers can deploy in Dublin or Frankfurt AWS regions for GDPR-compliant data residency, and Aizon is working with AWS on European sovereign hosting to address the CLOUD Act for EU-based pharma and CDMO buyers. All data is exportable via RESTful API. get started Next-Level Pharma Manufacturing Request a demo