Media & Content

Scale Content Operations
with AI

Moderate user-generated content across 5 queues simultaneously, enrich articles with SEO metadata, categorise news feeds into taxonomies, and translate content into 4 languages in parallel. All streaming, all real time.

<10ms
p99 latency
4
content pipelines
5-way
moderation routing

Content Pipelines

Four Production-Ready Pipelines

From moderation to translation, each pipeline processes content at streaming speed.

HIGHLIGHTED

Content Moderation

7 nodes · 5 moderation queues

Stream user-generated content, classify with AI into 5 moderation categories (safe, needs-review, hate-speech, spam, NSFW), and route each category to its dedicated moderation queue. Handles thousands of posts per minute with consistent classification quality.

kafka-source prompt-template json-extract llm-router
safe | review | hate | spam | nsfw
5-Way Content Routing

The LLM Router node uses AI to classify content into exactly 5 output ports, each feeding a dedicated moderation queue. Safe content flows through automatically; flagged content is held for human review. No hardcoded rules — the AI adapts to evolving content patterns.

SEO Content Enrichment

8 nodes

Ingest published articles, extract key topics with AI, generate meta descriptions, suggest internal links, produce structured data markup (schema.org), and store enriched metadata. Automates the SEO workflow that typically takes hours per article.

file-source field-mapper prompt-template json-extract
prompt-template json-extract field-mapper imap-sink
SEO automation | Schema.org markup | Topic extraction

News Feed Categorisation

13 nodes

Process incoming news articles from multiple RSS/Kafka feeds, deduplicate, extract entities and topics with AI, assign to a multi-level taxonomy (politics, sports, tech, business, entertainment), generate summaries, and route to category-specific content queues for editorial review.

kafka-source field-mapper filter-dedup prompt-template json-extract
prompt-template json-extract llm-router
politics | sports | tech | business | entertainment
Multi-level taxonomy | Deduplication | Entity extraction

Multi-Language Translation

9 nodes · 4 languages

Take published content and translate it into 4 target languages (French, German, Spanish, Japanese) in parallel using separate AI prompt chains per language. Each translation preserves formatting, handles domain-specific terminology, and stores results in language-specific maps.

file-source field-mapper prompt-template if-lang-check
fr-sink | de-sink | es-sink | ja-sink & audit-sink
4-language parallel | Domain-aware | Format-preserving

Ready to Scale Content Ops?

See AI-powered content pipelines running live. Book a 30-minute demo with our team.