SEO Article Creation Automation with Make and AI

From a list of keywords to a stack of SEO-ready WordPress drafts — end-to-end SEO article creation, fully automated with Make.com and ChatGPT

Content marketing at scale runs into the same wall every time: the production pipeline is too manual. Research takes hours per article. Drafting takes more. Formatting, internal linking, meta tags, WordPress upload — every step adds friction and every step is a place where a team slows down. The teams that win at SEO content in 2026 have one thing in common: they have industrialized this pipeline. This project is exactly that industrialization, built as a Make.com scenario that can produce high-quality, SEO-optimized draft articles in bulk, with no human touching a keyboard until final review.

What the automation does, end to end

Step 1 — Keyword list ingestion A Google Sheets (or Google Drive CSV) document serves as the control panel. Each row contains a query, the target WordPress site, and a few meta fields (category, target persona, internal links to reinforce). The scenario reads this sheet row by row, allowing the client to queue up dozens or hundreds of topics at once.

Step 2 — Live SERP extraction For each query, Make.com fires an HTTP request that retrieves the top 4 organic Google.fr results. This is the critical research layer — the same thing a human writer would do manually to understand what Google currently rewards for that query. A Text Parser module extracts the structural information from each competing page (headings, bullet points, semantic entities, FAQ blocks).

Step 3 — Prompt assembly and AI drafting via ChatGPT The extracted SERP data is injected into a carefully engineered prompt sent to OpenAI. The prompt enforces:

• A clear H1 with the primary keyword placed naturally.
• A structured outline (H2/H3) that covers what the top competitors cover, plus differentiators.
• Intro, body, and conclusion with coherent transitions.
• Internal linking placeholders aimed at the client's existing content clusters.
• SEO metadata: meta title and meta description matching the target query's intent.

The result is not a "generic GPT blog post" but a researched, competitor-aware, SEO-structured draft.

Step 4 — Formatting and cleanup The raw AI output is passed through Text Parser and custom transformation modules that clean Markdown, fix stray code fences, normalize heading levels, and prepare the HTML expected by WordPress.

Step 5 — Publication as draft on the target WordPress The formatted article is pushed to the correct WordPress site via its REST API, with the right category, author, tags, featured image placeholder, and meta fields. It lands as a draft, ready for a human editor's 10-minute review pass before publication — not as an auto-published black box.

The engineering choices that make it production-grade

• Per-site configuration: a single scenario can publish to multiple WordPress sites based on the sheet row's target_site column. One control panel, many blogs.
• Error handling: each module has retries and graceful-failure branches. Failed articles are logged back into the sheet with the reason, never silently lost.
• Cost control: prompts are tuned for token efficiency, and SERP HTTP calls are cached within a session so re-runs don't rack up bills.
• Quality gates: a minimum word-count check and a keyword-density check are built in before the draft is sent to WordPress — anything below the threshold is flagged for human attention rather than published.
• Audit trail: every article run writes back to the Google Sheet with the WP draft URL, token usage, and runtime, so the client has a full log of what was produced and at what cost.

Why this matters for a real business

• Production speed: what used to take 4–6 hours per article (research + draft + format + upload) now takes minutes of machine time plus a short human edit.
• Consistency: every article follows the same SEO template, reducing on-page SEO variance across the blog.
• Scalability: the same setup handles 5 articles a week or 50 — only the keyword sheet changes.
• Editor focus: the human team stops writing from a blank page and starts editing pre-structured drafts, which is a much better use of expensive editorial time.

Technology stack

• Make.com as the orchestration layer (scenarios, routers, error handlers).
• Google Drive / Google Sheets as the control panel for the editorial pipeline.
• HTTP Request modules for live SERP extraction.
• Text Parser for structural extraction and cleanup.
• OpenAI (ChatGPT) for research-aware article generation.
• WordPress REST API for direct publication.

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