Prodigy - Axiell Integration and Automation

Common Integration Use Cases Between Prodigy and Axiell

1. AI-Assisted Metadata Tagging for Collection Records

Data flow: Axiell ? Prodigy ? Axiell

Collection records, object descriptions, and archival metadata from Axiell can be exported to Prodigy for annotation by curators, archivists, or subject experts. Prodigy can be used to label entities, themes, periods, materials, locations, and subject categories, then push the validated labels back into Axiell as enriched metadata.

Business value: Improves catalog consistency, reduces manual tagging effort, and accelerates the enrichment of large legacy collections for search and discovery.

2. Automated Image Classification for Digital Asset Libraries

Data flow: Axiell ? Prodigy ? Axiell

Digitized images of artworks, artifacts, manuscripts, or exhibition materials stored in Axiell can be sent to Prodigy to train image classification models. These models can identify object types, visual attributes, condition issues, or digitization quality flags, with results written back to Axiell for asset organization and review workflows.

Business value: Speeds up image cataloging, supports quality control, and helps institutions manage growing digital collections more efficiently.

3. Entity Recognition for Names, Places, and Events in Archival Text

Data flow: Axiell ? Prodigy ? Axiell

Archival descriptions, finding aids, correspondence, and OCR text from Axiell can be routed into Prodigy to annotate people, organizations, places, dates, and event references. The resulting labeled data can train NLP models that automatically extract entities from future records and improve indexing in Axiell.

Business value: Reduces backlogs in archival description, improves discoverability, and supports more accurate cross-referencing across collections.

4. Curator-in-the-Loop Model Training for Specialized Taxonomies

Data flow: Bi-directional

When institutions use custom taxonomies or domain-specific vocabularies, Axiell can provide the source records while Prodigy captures curator feedback on ambiguous classifications. The labeled examples can be used to train models that suggest controlled vocabulary terms, which are then reviewed and approved before being stored in Axiell.

Business value: Preserves expert oversight while reducing repetitive classification work and improving taxonomy consistency across departments.

5. Prioritizing Records for Enrichment Using Active Learning

Data flow: Axiell ? Prodigy

Axiell can supply batches of under-described or high-value records to Prodigy, where active learning identifies the most informative items for human review first. This is especially useful for large backlogs, rare collections, or records with incomplete metadata that need targeted enrichment.

Business value: Maximizes the impact of limited expert time and helps institutions improve the most valuable records first.

6. Quality Review of Digitization and Preservation Metadata

Data flow: Axiell ? Prodigy ? Axiell

Preservation metadata, digitization logs, and scanned asset records from Axiell can be labeled in Prodigy to identify issues such as missing fields, format anomalies, damaged files, or inconsistent preservation descriptions. The trained models can then flag problematic records in Axiell before publication or long-term preservation.

Business value: Improves data quality, reduces preservation risk, and supports more reliable digital stewardship processes.

7. Preparing Training Data for Public Discovery and Search Enhancements

Data flow: Axiell ? Prodigy ? Axiell

Institutions can export public-facing collection descriptions from Axiell into Prodigy to label search intent, topical themes, or relevance categories. This data can train models that improve search suggestions, faceted browsing, or automated recommendation features within public discovery portals connected to Axiell.

Business value: Enhances visitor experience, improves search relevance, and increases engagement with digital collections.

8. Workflow Support for Cross-Functional Review and Approval

Data flow: Bi-directional

Collection managers, archivists, and AI teams can use Axiell as the system of record and Prodigy as the annotation workspace for review cycles. Records can move from Axiell to Prodigy for labeling, then return to Axiell with approval status, enriched fields, or exception flags for final validation and publication.

Business value: Creates a structured collaboration model between technical and curatorial teams, shortens review cycles, and ensures governance over collection data changes.