AI you shipped — kept working, kept safe, kept validated.
Model monitoring, drift response, HITL queue operations, eval refresh, prompt & template governance, and Annex 22 assurance upkeep.
AI managed services keep deployed AI systems fit for use: drift monitoring, periodic re-evaluation against the held-out set, model change control, incident handling and registry upkeep. These are continuous obligations under the draft EU Annex 22, and they are the ones most likely to lapse once the project that delivered the system has closed.
Outcomes you can expect
- Drift response SLA
- HITL queue operating
- Assurance upkeep
AI Managed Services is delivered by our managed services practice. Where it saves time, the team uses our products: GxP Copilot for validation lifecycle work and TraceDraft for clinical documentation.
Explore related capabilities: Validation Managed Services, Clinical Managed Services, Regulatory Managed Services,
AI Managed Services: Production AI Operations for Regulated Environments
Deploying an AI system to production is not the endpoint of an AI programme — it is the beginning of an ongoing operational and compliance obligation. AI models in production require monitoring for performance drift, human-in-the-loop queues require staffing and throughput management, evaluation frameworks require periodic refresh as the deployment population evolves, and Annex 22 assurance requires ongoing documentation and audit trail maintenance.
Cybroscape's AI Managed Services delivers: model drift monitoring (data drift statistics reviewed weekly; output quality metrics monthly; drift threshold breach notification within 24 hours; remediation plan proposed within five business days); HITL queue operations with domain-qualified reviewers (average queue wait time under four business hours for standard priority; under two business hours for urgent); quarterly evaluation refresh; prompt library governance with version control and change control; and Annex 22 documentation maintenance updated quarterly.
Incident Management and Annex 22 Assurance Upkeep
AI system incidents in production — unexpected model behaviour, HITL queue SLA breach, drift threshold breach, prompt failure — require defined incident management with escalation, investigation, and communication procedures. For GxP-regulated AI systems, incidents affecting the integrity of regulated data or the validity of a GxP decision are logged as quality events. Our incident SLA: Critical incidents (patient safety or regulatory submission integrity affected) — immediate escalation, 2-hour response, fallback to manual process within 4 hours; High — 4-hour response, root cause assessment within 24 hours; Medium — 8-hour response; Low — 24-hour response.
As EU GMP Annex 22 on artificial intelligence is finalised, GxP AI systems will be subject to specific documentation and monitoring requirements that must be maintained as ongoing compliance obligations. Under AI managed services, the AI system validation documentation — model cards, training data provenance, performance qualification results, drift monitoring records, HITL protocol documents — is maintained as a controlled documentation set updated quarterly and reviewed at each annual system review, designed to satisfy a GMP inspection at any point in the system's production lifecycle.
Sources
Primary documents referred to on this page, from their official publishers.
- EudraLex Volume 4: EU Good Manufacturing Practice guidelines · European Commission
- Consultation on EU GMP Chapter 4, Annex 11 and draft Annex 22 (Artificial Intelligence) · European Commission
See it on your own system.
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