I automate parts of my SEO workflow with n8n so I can publish more, faster, and without cutting corners. I built a process that takes topics from a Google Sheet, runs research, drafts full posts, creates images, and writes the finished article back to the Sheet ready for publishing. The goal is predictable, repeatable output that you can test and improve. This guide shows how I wire that together, what I check, and how I monitor results.
Start with a single Google Sheet as the control panel. I use columns called topic, seedkeyword, intentnote, title, slug, draft, imageurl, publishurl, status and lastchecked. Keep the sheet tidy. Use a small batch of five topics to begin. In n8n I trigger the workflow on a schedule or from a webhook that reads one row at a time. The first nodes run SERP research. I call a search API or a scraping node to pull top results, headings and common questions. Save those raw snippets back to the Sheet under intentnote. Use another node to run keyword extraction. I pass the topic, seed keyword and intent_note into a prompt template that asks the model to create an outline with H2 headings, a meta description and a target word count, typically 1,200 to 1,800 words for organic content. For content generation I use an LLM node (OpenAI-compatible or OpenRouter for Claude Sonnet 4.5) with explicit instructions: tone neutral, include target keyword X times, use natural language, produce at least 1,200 words. Keep the generation temperature low, around 0.2 to 0.4, for consistency. Store the raw draft in the draft column.
Add a humanisation and quality control stage. I run a second pass that enforces structure: intro under 60 words, at least three H2s, a conclusion that restates the intent without repetition, and a reading grade check. Use a grammar node to fix obvious mistakes. I also run an AI-detection node or a human-readability filter to flag content that reads too robotic. If it fails the checks, set status to needsedit and stop. If it passes, call an image generator or an image prompt node and upload to ImgBB or another host. Save the returned image URL in imageurl. Next, build slug and SEO meta fields. Use a slugify node on the title. Use a simple function node to ensure title length and meta description length meet common guidelines: title 50–60 characters, meta 120–155 characters. Push the final draft to an output sheet row. Optionally, use the same workflow to auto-create a post in a CMS via its API. I keep publishing separate until the content has passed a quick human spot-check.
Measure what matters and make it visible in the Sheet. I add fields for impressions, clicks, avgposition, pageviews and published_date. Hook the workflow to Google Search Console and GA4 APIs to pull those metrics daily. Track the change over time in the Sheet so you can spot content that either takes off or languishes. I treat the first four weeks as the test window. Look for CTR and ranking movement. If average position improves and clicks grow, the draft is doing its job. If a page gets impressions but low CTR, rewrite titles and meta descriptions, then republish. If a page gets clicks but no time on page, fix structure and add internal links.
Iterate quickly and with limits. I run batches of five to ten posts per run, then check results for one to four weeks. Use simple A/B variations for titles by creating two title candidates in the Sheet and running a short experiment where one is live for two weeks and then swapped. Use the data columns to track which title version had better CTR. For content generation prompts, keep a version history. Note which prompt produced the best organic performance. Small prompt changes produce measurable differences over weeks. When a change clearly improves metrics, copy that prompt into the template for future runs.
Use automation to remove grunt work, not judgement. I automate research, first drafts, image creation, basic SEO fields and metric collection. I do not automate final editorial sign-off until the draft passes structural and readability checks. That step matters for topical accuracy and brand voice. If you publish blindly, you will save time and waste traffic. If you publish with a tight control loop, you cut waste and scale what works.
Practical settings that save time. Keep temperature low for drafting. Set a minimum word count for long-form SEO pieces. Use explicit prompt instructions to include LSI terms and answer common search questions. Build a single column in the Sheet for publishstatus that accepts values draft, needsedit, ready, scheduled, live. Use that field as the gate between automation and publishing. Schedule the workflow to run daily for new rows and weekly for metric pulls. Start small and measure.
The last result should be clear: a repeatable pipeline where Google Sheets acts as the single source of truth, n8n runs the heavy lifting, and you focus on the edits that move metrics. Track impressions, clicks and average position for the first month, iterate on titles and prompts, and scale on the formats that show clear gains. My automation reduced the time to publish a polished SEO post to under an hour of human work per article once the pipeline was stable. Use that approach and adapt the steps above to your tools and risk tolerance.
