A publisher was pulling editorial content from multiple wire services and internal systems, each with its own schema and metadata format. Getting that content out to the web CMS, the print system, and social channels meant manual reformatting and asset creation. Two editorial FTEs spent their days on plumbing instead of journalism.
An ingestion pipeline that normalises content from every source into a single schema, applies editorial filtering rules (by topic, region, and rights), then runs LLM processing to generate summaries and headlines for each channel. Content that lacks visuals gets images generated automatically. The enriched package routes to each destination system without manual intervention.
flowchart LR W1[Wire service 1] --> NORM[Normalise<br>schema] W2[Wire service 2] --> NORM INT[Internal CMS] --> NORM NORM --> FILT[Filter] FILT --> LLM[LLM enrichment<br>summaries + headlines] LLM --> IMG[Image<br>generation] IMG --> CMS[Web CMS] IMG --> PRINT[Print] IMG --> SOC[Social]
We monitor every wire feed for availability and schema changes. If an upstream format drifts, we catch it before it reaches the CMS. We also run quality guardrails on LLM output, a dead letter queue for content that fails validation, and daily volume anomaly detection to flag drops before the editorial team notices.
Two FTEs moved back to editorial work. Content reaches all channels within minutes instead of hours. AI-generated assets are clearly flagged for editorial review before publication, so the team keeps control without the manual bottleneck.
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