Every piece of content tagged in real time, related content suggested automatically.
Content creators see suggested tags and labels that reflect your organisation's content hierarchy. Druid Learning's real-time tagging layer automatically categorises new content as it enters your workflow — generating metadata, topic tags, and related content suggestions without any editorial overhead.
Automated tagging from ingestion to publication
As soon as content enters your system, Druid Learning's pipeline runs automatically — no manual triggers, no editorial bottlenecks.
- 01
Ingest
New content — articles, video, audio, documents — enters Druid Learning through your existing CMS, DAM, or API integration. No change to your publishing workflow is required.
- 02
Auto-tag
Each asset is automatically labelled with topic tags, categories, entities, sentiment, and custom taxonomy fields aligned to your organisation's content hierarchy. Tags are applied consistently across historical and new content.
- 03
Suggest related content
Druid Learning's vector search connects newly tagged content to related articles, assets, and archive material — surfacing suggestions for linking, recirculation, and audience engagement.
- 04
Production tools
AI-assisted content production tools — including intelligent summarisation of lengthy text, video, and audio, and editable metadata so your team can review and refine labels within their existing workflow.
Built for content-heavy organisations
Newsrooms
Tag every story automatically at the point of publication. Surface related archive material instantly. Free editorial staff from manual metadata tasks entirely.
Broadcast & audio media
Automatically transcribe and tag audio and video content — making broadcast archives fully searchable and connecting new episodes to related historical content.
Publishers
Apply consistent tagging across new and backlist titles. Generate intelligent summaries for catalogue entries. Connect related titles across your full content library.
Digital content platforms
Enrich every asset with rich metadata at the point of upload — improving discoverability, powering recommendation engines, and feeding downstream analytics.
Proven in production
Public broadcasting · United States
Decades of audio and editorial content made searchable and production-ready
NPR worked with Druid Learning to bring structure and intelligence to a vast archive of audio content, transcripts, and editorial assets. Automated tagging and summarisation created a unified, searchable content layer — enabling producers and editorial teams to surface relevant material instantly.
Read the full case studyStop manually tagging content.
See how Druid Learning automates your content tagging workflow from day one.