Normalized claim
Audit turnaround time: 50% decrease
“reduced audit turnaround time by half—or more”
NSF accelerates regulated medical audits using agentic AI with Azure Document Intelligence and Azure OpenAI
Use case typeLegal workflow automationUpdated Jun 13, 2026
NSF, a nonprofit scientific and regulatory auditing organization, needed to organize, verify, summarize, and synthesize tens of thousands of documents across country-specific rules for medical audits. The solution uses Azure Document Intelligence, Azure OpenAI, Azure Model Context Protocol (MCP) tools and servers, Azure Blob Storage, Azure Python SDK, Azure Cosmos DB, Microsoft Entra ID, and Azure RBAC to automate document validation, structured sorting, version tracking, and summary drafting. Staff review and refine the AI-generated summaries, while the workflow remains inside Azure cloud controls and private tenant security. The implementation reduced audit turnaround time from 4–6 weeks to about 2 weeks and was reported to deliver near-perfect accuracy for the proof of concept.
- Organization
- NSF
- Industry
- Healthcare
- Location
- United States
- Published
- June 2026
Planned next steps
- The source says the pilot could deliver: Accelerated regulated audit turnaround.
- The source says this outcome is planned: Reduced human error in audits.
- The source says this outcome is planned: Enabled scaling to other audit types.
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Audit turnaround time: 67% decrease
“The average audit used to take four to six weeks, staff using the AI solution now need only two weeks”
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- NSF
- Provider
- Microsoft
- Maturity
- PoC
- Linked source
- Microsoft Customer Stories
The implementation reduced audit turnaround time from 4–6 weeks to about 2 weeks and was reported to deliver near-perfect accuracy for the proof of concept
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Document Processing
- 2Audit Automation
- 3Workflow Automation
- Built an agentic Azure AI workflow with Azure Document Intelligence to verify required document components.
- Used Azure OpenAI and MCP tools to sort documents into a regulated structure and generate summary drafts.
- Used Azure Blob Storage, Azure Cosmos DB, Azure Python SDK, Microsoft Entra ID, and Azure RBAC for storage, version tracking, and access control.
- The Microsoft Cloud Accelerate Factory helped deliver a proof of concept in 12 weeks.
- Halved or better audit turnaround time from 4–6 weeks to around 2 weeks.
- The tool reportedly delivered 100% truth value with only cosmetic/style edits.
Architecture
NSF built an agentic Azure AI workflow that ingests documents from a private SharePoint tenant into Azure Blob Storage, uses Azure Document Intelligence to verify required components, uses Azure OpenAI and Azure Model Context Protocol (MCP) tools to sort data into a regulated structure, uses Azure Python SDK and Azure Cosmos DB to automate document version tracking, and uses Azure Document Intelligence plus Azure OpenAI to draft audit summaries for human review. The workflow is secured with Microsoft Entra ID and Azure RBAC and runs within the Azure cloud.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 13, 2026Publisher: MicrosoftEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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