Revolutionize SEO Workflows with AI Agents 🚀


Publishing the same post across six or ten platforms shouldn’t mean wasting hours copying, pasting, and reformatting. That’s busywork—and it kills momentum. In this session we lay out a simple automation that turns one piece of content into many, pushing it to WordPress, Medium, Tumblr, Google Sites, Strikingly, and more with almost no manual work. The system keeps your brand voice intact, saves serious time, and gives you a repeatable workflow you can refine and scale.

Table of Contents

Why this workflow works

We focus on one primary rule: publish once, multiply many. If we keep a single source of truth for our content — ideally something with an RSS feed — we can use automation to feed every other platform. The automation does the heavy lifting: it reads new posts, sends them to an AI model that’s been primed with industry entity data, rewrites the post while keeping the structure, and republishes copies across our target platforms.

This approach saves time, keeps tone consistent, and lets us improve the prompts and entity data over time so the output gets better without redoing work for each client or niche.

Tools we use

  • n8n — our automation engine (it runs the triggers, connectors, and AI calls).
  • ChatGPT / OpenAI — the rewrite engine. We prime it and pass the post to it.
  • Blogger, Medium, WordPress, Tumblr, Google Sites, Strikingly, etc. — target platforms where we publish rewritten copies.
  • RSS feeds — the easiest trigger for detecting new posts from the source blog.
  • Entity database — a simple file or prompt block that lists industry terms, products, locations, and phrase variants to guide rewrites.

High-level workflow

  1. Pick a primary content source. Prefer a platform with an RSS feed.
  2. Set up an n8n workflow with an RSS or direct API trigger to detect new posts.
  3. Send the new post into a ChatGPT call. Include a prompt that tells the model how to rewrite the article and include the entity data.
  4. Keep the article structure intact (headings, paragraphs) but swap in entity variants and rephrase sentences.
  5. Push the rewritten output to each target platform via API or publish connectors in n8n.
  6. Optionally, run the rewritten output back to the original site as a different format (summary, excerpt, or alternate language).

Detailed n8n workflow example

We build the workflow in n8n as a chain of nodes. Here’s a simple node layout with short explanations:

  • Trigger node (RSS or HTTP): Watches the RSS feed or platform API for new posts.
  • Transform node: Cleans and extracts the title, body, tags, publish date, images, and metadata.
  • AI node (OpenAI ChatGPT): Sends the post content plus a rewrite prompt and our entity list. Receives the rewritten content.
  • Formatter node: Ensures the rewritten HTML is valid for the target platform and adds any metadata (canonical, author).
  • Publish nodes: One node per target platform (Medium, WordPress.com, Tumblr, Google Sites, Strikingly). Each node posts the rewritten version using the platform API or an available connector.
  • Logging node: Saves success/failure, stores URLs and status, and alerts us on errors.

Once the workflow is active, every new post triggers the process end-to-end. We can schedule runs, limit frequency, or add gates (manual approval) if we want a human check.

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How we prime ChatGPT with entity data

Priming means giving the model a short database of terms, phrases, and examples it should use when rewriting. We add this data inside the prompt or as a system instruction so the model knows industry context.

Example entity list items:

  • Service types (e.g., “AC repair”, “emergency HVAC service”).
  • Location names (city neighborhoods, suburbs).
  • Common product names and brand variants.
  • Approved tone words and phrase templates (friendly, local, professional).
  • Examples of finished articles so it mimics structure and phrasing.

We keep the entity data short and focused. For one industry, this might be a single prompt block. For many industries, it’s a separate priming set for each one. That’s why we recommend specializing in one industry — it keeps the list compact and the model output consistent.

Sample prompt pattern

We use a few fixed instructions each time we call the model. The pattern looks like this:

  • System instruction: Give the model the voice and content rules (short paragraphs, local tone, use entity variants).
  • Entity block: Insert the list of entities and examples.
  • Rewrite instruction: Tell the model to keep heading structure, rewrite sentences, swap in entity variants, and avoid verbatim duplication.
  • Output format: Ask for clean HTML with headings and images preserved or mapped.

Here’s a short example prompt (we keep it simple so it’s easy to reuse):

Rewrite the post below. Keep H1/H2/H3 structure. Use short paragraphs. Replace product names and locations using the entity list. Keep meaning the same but rephrase sentences to avoid duplication. Output valid HTML.

Publishing rules and SEO notes

We pay attention to a few SEO details so this automated system doesn’t cause problems:

  • Canonical link: On syndicated copies, include a canonical tag pointing to the original post if the platform supports it. This signals search engines where the master copy lives.
  • Meta description and titles: Let the AI create unique meta descriptions and slightly varied titles to reduce exact duplication.
  • Structured data: Preserve schema markup where possible — especially for local business pages.
  • Images: Rehost or ensure images are accessible and include alt text variations.

When we keep the structure but vary entities and phrasing, we reduce duplicate-content risk and help search engines understand the relation between copies.

Testing and refining

We don’t flip the automation on and forget it forever. Instead, we run tests:

  1. Start with a slow cadence — only new posts are pushed after manual approval.
  2. Check a few rewrites for tone, accuracy, and entity usage.
  3. Improve the prompt or entity list when the model makes repeated mistakes.
  4. Log errors and set alerts for publishing failures.

Over time, the model learns the style through better prompts and example outputs. We keep a short folder of “good outputs” to copy into the prompt as examples when needed.

Why focus on one industry

We recommend starting with a single industry for all automation work. If we try to cover many industries at once, we have to build separate prompts, entity lists, and tests for each — it becomes a mess. By focusing on one industry, we can:

  • Build a tight entity list that the model uses well.
  • Make small improvements across every post and see consistent gains.
  • Scale the workflow faster because we reuse the same automation across many client sites in that niche.

We don’t say you can’t work across niches. We just say if speed and lower friction matter, do one industry first.

Common platform connection tips

  • WordPress: Use the REST API or XML-RPC (for older installs). Ensure authentication keys are safe in n8n.
  • Medium: Use the Medium API and format posts to their expected structure.
  • Tumblr: Use OAuth and the create post endpoint. For microblogs, reduce length.
  • Google Sites / Strikingly: Many site builders have limited APIs — consider using their webhooks or a CMS that can push to them, or publish via an intermediate service that supports the site builder.
  • Images: Upload images separately if the platform needs a hosted URL, or use the same image host across platforms.

Troubleshooting

If something breaks, here’s how we fix it:

  • Check API keys and refresh tokens first.
  • Inspect the AI output for invalid HTML or disallowed tags.
  • Look at rate limits on the publishing platform and add retries in n8n.
  • Have a fail-safe that flags post failures for manual review instead of auto-publishing repeated bad posts.

Conclusion

We can stop spending hours manually copying content to ten platforms. With a single source and a short automation chain in n8n, we let AI rewrite posts for each platform while keeping the structure intact. We prime the model with a small set of entities, check a few outputs, and then let the system run. Focus on one industry to make the setup simple and fast to improve. Over time, small prompt tweaks and better entity lists multiply into large time savings and consistent content across our publishing network.

FAQ

Will this cause duplicate content issues?

Not if we vary wording, titles, meta descriptions, and include canonical tags when possible. The AI rewrite should keep meaning but change phrasing and entity mentions so search engines see each as a related but distinct piece.

How much does it cost to run?

Cost depends on your AI provider, the number of requests, and the publishing platforms. n8n itself can run self-hosted at low cost. AI usage is the main variable; we optimize prompts to reduce token use.

Can we use this for many industries?

Yes, but we recommend building one industry’s workflow first. For many industries, create separate prompt templates and entity lists so the model stays accurate.

Do we need to be a developer to set this up?

A basic setup needs some technical steps: connecting APIs and building nodes in n8n. But there are many tutorials and templates online. We can start simple and add automation steps as we learn.

What about content quality?

Quality improves with better prompts and examples. Start with manual review, then reduce oversight as outputs reach the desired level. Keep a short list of example articles the model should mimic.

Where can we learn more about building these workflows?

Search for “n8n AI agent automation” and similar terms on video platforms or documentation sites. There are many step-by-step guides that show connectors and example flows to copy and adapt.

We’re ready to set this up and make branded content publishing automatic. Small steps, repeatable process, and steady improvement — that’s how we get more done with less effort.