Automating Content Creation: What Ahrefs Actually Did

Everyone loves to talk about automating content, but too often those conversations float off into the clouds—either hand-waving about the future or, worse, chasing quantity at the expense of quality. Ahrefs, the SEO software folks you probably know, decided to go another route. Si Quan Ong, with Ryan Law reviewing, lays out exactly how they built real automations that tackle the boring, repetitive side of content marketing—without lowering their standards or polluting the web.

Here’s the crux: Ahrefs built tools for themselves, like the Data Refresh Hub, to make updating data-heavy articles faster and less painful. They didn’t try to replace people’s creativity. Instead, their automation does the heavy lifting: pulling fresh data, weeding out irrelevant bits, updating charts, and teeing up drafts in WordPress for real editors to polish.

As Tim Soulo, Ahrefs’ CMO, put it: "The AI marketing platform we needed didn’t exist. So we built it."

Inside Ahrefs’ Automation Workflow: What’s New and How It Works

This all started because Ahrefs ran into the same headache you get updating long lists—top Google searches, trending topics, that sort of thing. Once your content library gets big, refreshing everything by hand every month is a slog. Their answer: automate the whole workflow, but set guardrails so quality doesn’t slip.

Their Data Refresh Hub now runs monthly through a checklist:

  • Grabs new data for 12 datasets using Ahrefs’ own Keywords Explorer and internal systems.
  • Kicks out NSFW or off-topic results and checks each entry fits the right bucket.
  • Rebuilds tables and charts with the latest numbers.
  • Pushes the updated copy into WordPress drafts.
  • Pings the assigned editor for a final human check before anything goes live.

It’s not just a technical flex. Ong breaks down how Ahrefs made sure their automation wasn’t brittle:

  1. Pick a job worth automating: Zero in on tasks you repeat and want to offload—like updating data tables.
  2. Write down every step: Spell out exactly how you do it, including all those weird exceptions. Ong even suggests having the AI interview you to surface hidden rules.
  3. Connect to the right sources: Use trusted APIs and internal databases rather than letting a language model guess.
  4. Show examples of good and bad: Supply clear examples, plus why you’d accept or reject each, so the system learns your standards.
  5. Test on a single article: Run a trial to catch any issues before rolling out wide.
  6. Iterate and scale up: Once it’s stable, expand to more content and layer in improvements.

They went with Letaido as their automation platform because it meshes with their data, supports several AI models, manages hosting and credentials, and writes directly to WordPress. Ong’s quick to add: you can use whatever tool fits, as long as it can tie together your data, AI, and publishing stack. Just expect to get your hands dirty connecting the dots.

Why This Matters: Practical Impact for Content Teams

If you’re responsible for a growing pile of content—solo, at an agency, or on a team—Ahrefs’ process is a blueprint for using automation to save real time, without letting standards slip. The goal isn’t to spit out endless generic posts, but to automate the repetitive chores that slow you down.

Let’s look at evergreen content. Google and its AI helpers reward fresh updates, so maintaining up-to-date articles translates to more search traffic. But slogging through dozens or hundreds of manual updates is exactly the kind of job people put off. Automating this means your content stays fresh, rankings don’t dip, and your people can focus on smarter work.

This approach also sidesteps the usual AI headaches—like made-up facts or stale info—thanks to human review and a rules-based system for what’s allowed to go out the door.

How to Apply These Lessons in Your Own Business

If you’re thinking about automating part of your content workflow, here’s the playbook Ahrefs followed:

  • Spot a repetitive, high-value content task (updating stats, building lists, refreshing product info, etc.).
  • Document each step—don’t skip the edge cases or shortcuts you take.
  • Hook up the right data sources—think APIs, databases, or internal docs, not just public search or generic AI.
  • Give concrete examples of good and bad outputs and state your reasoning.
  • Start by testing on one piece to surface problems early.
  • Keep a human in review mode—especially for tricky or high-impact changes.

This can work whether you’re using Letaido, another platform, or something built in-house. The core ideas—be specific, start small, use reliable data, and review the results—apply almost anywhere.

My Take: Automation Is About Focus, Not Volume

What I like about Ahrefs’ approach is that they’re not chasing output for output’s sake. They’re targeting the tedious but necessary jobs, letting people focus on what really moves the needle. That means more consistent, reliable content, and editors still make the final call.

The catch: this setup takes technical chops. You’ll need API access, some data wrangling, and the right tools. That’s not always out-of-the-box for smaller teams. There’s also the risk of trusting your workflow too much and letting errors through—especially when things get weird. That’s why Ahrefs insists on human review and detailed rejection logs.

One thing I’m still watching: can these workflows ever handle opinion-heavy or creative content? Crunching data and updating tables is one thing; writing sharp analysis is another. For now, focus automation on the routine, not the remarkable.

The Bottom Line

Ahrefs’ guide proves you can automate content processes the right way—pick repeatable tasks, spell out your rules, use trustworthy data, and keep humans in the loop. The real win isn’t volume; it’s freeing up your team’s brainpower for work that actually matters.

If you want the technical details, the full breakdown by Si Quan Ong is at Ahrefs.