Automated Content Workflow

A content team was producing 3–5 long-form articles per week. Each piece required a researcher to gather sources, a writer to draft, an editor to review, and a publisher to format and post. The bottleneck was the handoff between research and writing — raw notes sat in a shared doc for days before anyone touched them.

The solution was to automate the entire middle layer: from raw research input to a structured, publish-ready draft — with human review as the only manual step.

• Built an n8n workflow triggered by a new row in an Airtable research base — researchers simply fill in a form with topic, sources, and key points.
• The workflow preprocesses the raw notes, structures them into a prompt template, and sends to GPT-4 with a detailed system prompt defining tone, format, and SEO requirements.
• Generated drafts are automatically formatted with headings, meta descriptions, and image placeholders, then pushed to the CMS via API.
• A Slack notification alerts the editor that a draft is ready for review — the only human touchpoint in the pipeline.
• Currently extending the workflow to include automated internal linking suggestions and keyword density analysis.

The core pipeline is live and processing articles. Production time per piece has dropped from ~4 hours to under 20 minutes. The team is now publishing 3x more content with the same headcount.

Currently in progress: adding a quality-scoring layer that evaluates drafts against readability and SEO benchmarks before sending to the editor, further reducing revision cycles.