AI Content Creation Engine
Marketing AutomationNDA Client
Multi-path self-checking content system that researches, drafts, and QA checks at scale
52 nodesDual LLM3 content typesBuilt-in QA passLive research on every pieceStyle rubric enforced automatically
Content teams producing at scale struggle with consistency, research quality, and QA. Manual writing is slow, outsourced content is generic, and there is no system that checks its own output before delivery.
A self-checking AI content engine that classifies each brief, routes it through the right pipeline, researches live sources via Tavily, drafts section by section against a Google Docs style rubric, and runs a built-in QA pass before the content is delivered.
- 01Brief is received via chat message trigger
- 02Classifier node determines content type: long-form article, landing page, or short-form copy
- 03Switch node routes the brief to the correct pipeline
- →Long-form articles, landing pages, and short-form copy handled in one workflow
- →Live research integrated into every piece via Tavily
- →Built-in QA pass catches issues before content reaches the team
- →Dual LLM architecture with OpenAI and Claude running in parallel
- →52 nodes across multiple content paths in a single workflow
- →Self-checking system with built-in QA before delivery
stack
n8nOpenAIClaudeTavilyGoogle Docs