We start with a focused conversation to understand your organization, your current AI ambitions, and the regulatory context you operate in. This session helps us identify stakeholders, existing pain points, and the boundaries of your GxP environment before any technical work begins.

We map your specific compliance and operational needs against your AI use case, identifying gaps, risks, and opportunities. This step ensures that every recommendation we make later is grounded in your actual business and regulatory reality, not a generic template.

We conduct a structured review of your existing Quality Management System to understand how AI-related processes, data flows, and controls currently fit - or don't. This gives us a clear baseline for designing a governance framework that integrates smoothly rather than creating duplicate or conflicting structures.

Based on the review, we design a tailored AI governance framework aligned with relevant standards such as ISO 42001 and applicable GxP guidance. The framework covers risk classification, documentation requirements, oversight roles, and validation strategy specific to your use case.

We integrate the new framework directly into your existing QMS, ensuring traceability, audit-readiness, and alignment with your current SOPs and change control processes. This step is designed to minimize disruption while maximizing regulatory defensibility.

We run a scoped pilot project applying the framework to a real use case, giving you concrete, evidence-based results before wider rollout. This approach lets you validate the framework's effectiveness with manageable risk and clear success metrics.

Every engagement starts with a conversation. Let's discuss where your organization stands and how a structured, risk-based approach can move you forward.