Prodigy - Glean Integration and Automation

Common Integration Use Cases Between Prodigy and Glean

1. AI Training Data Discovery from Enterprise Knowledge

Flow: Glean ? Prodigy

Use Glean to help data scientists and subject matter experts quickly find relevant internal documents, policies, support tickets, product specs, and research notes that contain source material for model training. Those documents can then be sent into Prodigy as candidate datasets for annotation.

• Speeds up dataset sourcing for NLP and document understanding projects
• Reduces manual searching across shared drives, wikis, and ticketing systems
• Improves label quality by grounding annotation in approved internal content

2. Subject Matter Expert Review and Labeling Assignment

Flow: Glean ? Prodigy

When Prodigy identifies items that need expert review, Glean can surface the right internal experts based on their documents, authored content, team membership, and prior activity. This helps route labeling tasks to the most relevant reviewers for legal, medical, engineering, or operations data.

• Shortens time to find qualified reviewers
• Improves annotation consistency for specialized domains
• Supports distributed labeling across business teams

3. Annotation Guidelines and Policy Retrieval During Labeling

Flow: Glean ? Prodigy

Annotators working in Prodigy can use Glean to retrieve the latest labeling guidelines, policy documents, taxonomy definitions, and edge-case examples while they work. This is especially useful when label rules change frequently or are maintained across multiple internal systems.

• Reduces labeling errors caused by outdated instructions
• Keeps annotators aligned with current business rules
• Minimizes back-and-forth with project managers and domain leads

4. Active Learning Prioritization Using Business Context

Flow: Glean ? Prodigy

Prodigy can use enterprise context discovered through Glean to prioritize which unlabeled examples should be reviewed next. For example, if Glean identifies a surge in support cases, compliance incidents, or product feedback around a specific topic, those records can be prioritized in Prodigy for faster model improvement.

• Aligns labeling effort with current business priorities
• Improves model performance on high-impact topics sooner
• Helps AI teams respond faster to emerging operational issues

5. Labeling Output Published Back to Enterprise Search

Flow: Prodigy ? Glean

Once Prodigy produces labeled datasets, taxonomy mappings, or annotation summaries, those outputs can be indexed in Glean so business users can search and reuse them. This creates a searchable record of training data definitions, labeling decisions, and model development artifacts.

• Improves transparency across AI and business teams
• Prevents duplicate labeling efforts
• Supports auditability for regulated or high-risk use cases

6. Model Error Analysis Linked to Source Knowledge

Flow: Prodigy ? Glean

When Prodigy is used to review model mistakes or uncertain predictions, those error cases can be linked to related internal knowledge in Glean. Teams can quickly investigate whether the issue is caused by ambiguous policy language, missing documentation, or inconsistent terminology.

• Speeds root-cause analysis for model failures
• Helps teams distinguish data issues from model issues
• Supports continuous improvement of both models and business content

7. Cross-Team AI Project Collaboration Hub

Flow: Bi-directional

Glean can act as the enterprise discovery layer for AI projects, while Prodigy serves as the execution layer for annotation. Teams can search for project documentation, label definitions, and stakeholder notes in Glean, then move selected datasets or tasks into Prodigy for labeling. Completed outputs can be pushed back to Glean for broader visibility.

• Creates a shared workflow between data science, operations, and domain teams
• Improves project handoffs and reduces information silos
• Makes AI development artifacts easier to find and govern