NSF uses an Azure AI-based, agentic solution to streamline highly regulated pharmaceutical audits that used to be largely manual. The process brings together several Azure services:

• Azure Document Intelligence scans and verifies that tens of thousands of required documents are present.
• Azure OpenAI models and Model Context Protocol (MCP) tools automatically sort documents into the correct, internationally regulated folder structures.
• Azure Blob Storage and Azure Cosmos DB support structured data storage and automated version tracking.
• Azure OpenAI models generate first-draft summaries that NSF experts then review and refine.

This setup has had a measurable impact:

• Average audit duration dropped from 4–6 weeks to about 2 weeks — a reduction of roughly 50% or more in turnaround time.
• The AI tool delivers what NSF describes as “100% truth value” in its summaries, with staff mainly making cosmetic or style edits.
• Scientists and auditors spend less time on repetitive document handling and more time on higher-value work, such as developing regulatory strategies.

By reimagining the audit workflow with Azure AI, NSF is able to help clients bring new medications and therapies to market faster, while maintaining rigorous compliance and review standards.

The Microsoft Cloud Accelerate Factory is a no-cost service that provides expert support to organizations looking to kickstart Azure projects. NSF had tried several times to build an AI-driven audit tool on its own before engaging this team. With the Factory’s help, NSF:

• Received a working AI proof of concept in just 12 weeks, even though they had originally budgeted a full year for development.
• Co-designed a custom, agentic solution that integrates Azure Document Intelligence, Azure OpenAI, MCP Servers, and other Azure services.
• Upskilled internal technologists by involving them directly in the build process, helping them gain prompt engineering skills and a deeper understanding of the technology.

This collaboration did more than deliver a single tool. It helped NSF rethink its broader AI strategy by:

• Providing a reusable blueprint that can be adapted to other business units and product types (for example, medical devices, dietary supplements, and water safety).
• Showing how structured data and Azure AI can be combined to create repeatable, secure workflows across the organization.

As a result, NSF now plans to duplicate and customize the original solution across multiple audit domains, using the same Azure foundations established with the Cloud Accelerate Factory team.

NSF operates in a highly regulated environment with sensitive medical data and intellectual property, so security and compliance are central to its Azure AI approach. Key measures include:

• Private data environment: Data is maintained in a private tenant within SharePoint and then funneled into Azure Blob Storage. The entire audit workflow runs inside the Azure Cloud, so data is not exposed externally.
• Strict access control: NSF uses Microsoft Entra ID and Azure role-based access control (RBAC) to ensure that only authorized users can access specific data and tools.
• Private connectivity: All connections to and within NSF’s applications use private links to reduce risk and limit exposure.
• Controlled AI interactions: Azure Model Context Protocol (MCP) Servers manage how language models interact with external tools and data sources, allowing NSF to decide how networked or isolated each AI solution should be.

NSF also benefits from the interconnected Microsoft ecosystem:

• The organization uses Microsoft 365 Copilot in a closed environment for broader AI use across the business.
• The customizable nature of Azure AI lets NSF create secure linkages between multiple agentic solutions that draw on the same structured data, while still maintaining strong boundaries where needed.

This combination of private cloud infrastructure, granular access control, and managed AI connectivity helps NSF minimize risk, maintain trust in its brand, and stay aligned with regulatory expectations while it scales AI across its auditing operations.