Building an AI-Powered SEO Anomaly Detector with n8n

One of the most tedious parts of managing SEO at scale is catching problems early. A page drops 30% in clicks, but you don’t notice for two weeks because it’s buried in a dashboard with 10,000 other pages. By then, the damage is done.

I wanted something that would tap me on the shoulder the moment something looked off. Not just flag the problem, but tell me why it’s happening and what to do about it. So I built it.

The architecture

The system runs on three layers:

Data layer: A scheduled n8n workflow pulls Google Search Console data via the API every morning. It stores 90 days of rolling data: clicks, impressions, CTR, and position at the query-page level.

Detection layer: This is where it gets interesting. Most people track rankings. That’s fine, but it misses the real signal. What I track instead is CTR relative to position, using my site’s own historical click curve as the baseline, not industry averages (those are useless, every site’s curve is different depending on brand strength, SERP features, and query type).

The system calculates the expected CTR for each position based on 60 days of the site’s own data, then flags any query-page combination where actual CTR is more than 30% below the expected curve for that position. A page ranking #3 with a 2% CTR when your curve says it should be getting 6%? That’s not a ranking problem. That’s a SERP problem. Something on the results page is stealing your clicks, and that’s a very different fix than “write more content.”

Intelligence layer: The flagged anomalies get sent to Claude’s API with the page content and competing SERP data. The LLM analyzes what might have changed (content freshness, search intent shift, new competitors) and suggests specific content updates.

What I learned

The hardest part wasn’t the AI. It was defining “anomaly” in a way that doesn’t cry wolf every day. Seasonal patterns, weekend dips, news cycles: there’s a lot of noise in search data. The statistical approach with rolling baselines handles most of it, but I still had to add manual exclusion rules for known seasonal queries.

The LLM suggestions are good about 70% of the time. The other 30%, it’s making reasonable guesses that don’t account for context only a human would know (like a product being discontinued, or a competitor running a massive PPC campaign).

What’s next: from alerts to actions

The current system tells me something is wrong. The next version will tell me exactly what to do about it, with the content ready to go.

Here’s the plan:

SERP feature analysis: For every flagged query, the system will pull live SERP data via a SERP API and identify what’s actually on the results page: featured snippets, People Also Ask, video carousels, AI Overviews, knowledge panels. If your CTR is tanking at position #2, it’s probably not because your content is bad. It’s because a featured snippet or an AI Overview is sitting above you absorbing the clicks. You need to know which one before you can fix it.

Competitive snippet analysis: The LLM will analyze the current snippet holder’s content format: is it a paragraph, a list, a table, a step-by-step? Then it cross-references that against your page’s current content and structured data. The output is the exact content block you need to add to your page to compete for that snippet, including the schema markup if applicable. Not a vague recommendation. An actual block of content you can drop in.

Metadata generation: For every flagged page, the system will generate three title tag and meta description variants, each optimized for the specific SERP features present on that query. If there’s a featured snippet, the title leans into the question format. If there’s an AI Overview, the description focuses on the unique angle your page offers that the overview doesn’t cover.

Test queue: Everything gets pushed into a Google Sheet as a structured test queue: the page URL, the flagged query, the current title and description, the three variants, the recommended content block, and before/after tracking dates. Pick a variant, implement it, and the system tracks whether CTR recovers over the following 30 days.

The bigger point

Most people using AI for SEO build reactive monitoring loops. Dashboards that light up red when something drops. Alerts that tell you what you already suspected. That’s useful, but it’s table stakes.

What I’m trying to build is something different: automations that create compounding value, not just notifications. Systems where the output isn’t “hey, this broke” but “here’s the fix, here’s the content, here’s the test, go.” Every cycle the system runs, it produces work that moves the needle, not just information that sits in a spreadsheet.

The AI tools we have now are capable enough to do this. The bottleneck isn’t the technology. It’s our willingness to think beyond monitoring and start building systems that actually do the work. The creative thinking is the hard part. The execution, increasingly, isn’t.

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If you’re an SEO working with n8n, I’m happy to share the workflow template. Reach out.