SharePoint - Prodigy Integration and Automation

Common Integration Use Cases Between SharePoint and Prodigy

1. SharePoint document libraries as the source of truth for annotation projects

Data flow: SharePoint → Prodigy

Teams can store source documents, images, PDFs, and transcripts in SharePoint document libraries and push selected content into Prodigy for labeling. This is useful when business users already manage regulated or sensitive content in SharePoint and data science teams need controlled access to approved files for model training.

• Legal, HR, or compliance teams keep master content in SharePoint
• AI teams pull only approved files into Prodigy for annotation
• Version control in SharePoint ensures labeling is based on the correct document set

Business value: Reduces duplicate file storage, improves governance, and prevents annotation teams from working on outdated or unauthorized content.

2. Annotated datasets returned to SharePoint for review and audit

Data flow: Prodigy → SharePoint

After labeling is completed in Prodigy, exported annotations, review reports, and dataset snapshots can be saved back to SharePoint for business review, audit, and retention. This supports traceability for regulated use cases where stakeholders need to see what was labeled, when, and by whom.

• Store final annotation files in a controlled SharePoint library
• Attach review notes, approval status, and sign-off records
• Maintain an audit trail for model training inputs

Business value: Improves compliance, supports model governance, and gives non technical stakeholders visibility into training data preparation.

3. SharePoint based intake workflow for labeling requests

Data flow: SharePoint → Prodigy

Business teams can submit labeling requests through a SharePoint list or form, including project details, data source links, label schema, priority, and due date. A workflow can then route approved requests to Prodigy projects for annotation setup.

• Marketing, operations, or compliance teams submit AI labeling requests in SharePoint
• Data science team reviews and approves the request
• Prodigy project is created with the correct task configuration

Business value: Creates a structured intake process, reduces ad hoc requests, and improves prioritization across AI initiatives.

4. Domain expert review of annotations through SharePoint collaboration spaces

Data flow: Prodigy → SharePoint

Annotated samples, edge cases, and review summaries can be published to SharePoint team sites for subject matter experts to validate labels. This is especially useful when domain experts are not regular Prodigy users but need to approve taxonomy decisions or resolve ambiguous cases.

• Prodigy exports uncertain or disputed examples
• SharePoint hosts review pages, comments, and decision logs
• Experts provide feedback that informs relabeling or schema updates

Business value: Improves label quality, speeds up expert review, and makes collaboration easier for non technical reviewers.

5. Controlled access to sensitive training data for external partners

Data flow: SharePoint → Prodigy

Organizations working with vendors, contractors, or external annotators can use SharePoint permissions to control which files are exposed to Prodigy and which outputs are returned. This is valuable when training data contains confidential customer records, internal documents, or proprietary product information.

• SharePoint manages access by project, department, or partner
• Only approved datasets are passed into Prodigy
• Completed annotations are returned to a restricted SharePoint location

Business value: Strengthens data security, simplifies partner collaboration, and supports least privilege access practices.

6. Active learning feedback loop for continuously improving model training sets

Data flow: Prodigy → SharePoint → Prodigy

Prodigy can identify uncertain samples that need human review, and those samples can be published to a SharePoint queue for business experts to inspect. Once reviewed, the decisions can be sent back into Prodigy to refine the labeling model and improve future sample selection.

• Prodigy flags high uncertainty items
• SharePoint acts as the review queue and collaboration layer
• Approved labels are fed back into Prodigy for retraining

Business value: Reduces labeling effort, improves model accuracy faster, and creates a repeatable human in the loop workflow.

7. Enterprise knowledge base for label definitions and annotation standards

Data flow: SharePoint → Prodigy

SharePoint can serve as the central repository for labeling guidelines, taxonomy definitions, examples, and policy documents that Prodigy annotators reference during project setup and execution. This helps ensure consistent labeling across teams and projects.

• Store annotation playbooks, label dictionaries, and edge case guidance in SharePoint
• Link Prodigy projects to the latest approved documentation
• Update standards centrally when business rules change

Business value: Improves annotation consistency, reduces training time for annotators, and supports scalable AI operations across departments.

8. Project status reporting and operational dashboards

Data flow: Prodigy → SharePoint

Annotation progress, throughput, quality metrics, and backlog status from Prodigy can be published into SharePoint pages or lists for leadership reporting. This gives program managers and business owners a simple way to monitor AI project delivery without logging into the annotation tool.

• Track number of items labeled, reviewed, and pending
• Display quality metrics and turnaround times in SharePoint
• Use SharePoint as the reporting layer for cross functional stakeholders

Business value: Improves transparency, supports project governance, and helps teams manage AI delivery against business timelines.