Most “AI writes your blog” setups fail in the same way: not with bad style, but with a confidently written falsehood. For most niches that is embarrassing. For an insurance website it is a real problem — a wrong sentence about a legal obligation or the scope of cover can mislead a reader.
This is how I designed an AI agent for gopoistenie.sk, a Slovak insurance website (motor liability, comprehensive cover, GAP, home and travel insurance). It prepares one article every week — and it is built so that a lack of facts is a reason to stop, not a reason to improvise.
The brief was not “generate articles”
The client needed regular content, because an insurance site lives on search: people look up what liability insurance covers or how GAP works, and if your site does not answer, someone else's will. But the real requirements were these:
- Choose topics from real data, not from whatever the model comes up with.
- Write nothing that is not backed by a verified source.
- Never repeat topics the site already covers.
- Publish nothing automatically — a person always has the final word.
One weekly run, step by step
The pipeline starts by itself every Monday at 6:00 with nobody involved:
- Data analysis. It reads Google Search Console and Google Analytics: which pages people read, which queries bring them in, where content is missing.
- Topic choice. Based on that data, the model picks one topic inside the site's areas — with a check against everything already published, so no duplicates.
- Research from an allow-list of sources only. Laws on slov-lex.sk, government bodies, the regulator (the National Bank of Slovakia) and insurers' own websites. Nothing else.
- The stop rule. If there are not enough confirmed facts for the topic, the article is not written and the topic is dropped. No “fill the gap with general knowledge”.
- Writing. Claude writes the article in Slovak strictly from the collected facts.
- Independent review. A separate AI step checks the draft for factual errors, outdated data, invented references and consistency with the sources. A draft with problems goes back for rework automatically.
- Draft, not post. The checked article is saved to WordPress as a draft.
Three design decisions that matter more than the prompt
1. An allow-list beats “be accurate”
Telling a model to “only use reliable sources” does not work. Giving it a fixed list of sources and refusing to proceed without them does. The research step is the place where hallucinations are prevented — the writing step only has to stay inside the material it was given.
2. Dropping a topic is a valid outcome
Most pipelines treat “no article this week” as a failure, so they are built to always produce something. Here it is a normal, expected result. That single decision removes the pressure that makes generated text drift into made-up specifics.
3. The writer is not its own proofreader
The model that wrote the text is not the step that approves it. A separate review step compares the draft with the sources before it is saved at all — just as with people, an author should not be their own editor.
What stayed with a person
Publishing. The site owner reads the finished article in a dashboard and clicks Publish or Reject. Nothing else is required — but without that click, nothing reaches the site.
That is a deliberate decision, not a technical limitation. The site is responsible for what it publishes, and that responsibility should not be handed to an automaton. The agent saves the work of finding a topic, gathering material and writing; the decision stays with a human.
The result
The project is delivered and running every week. The client left a 5/5 review on the independent Slovak freelance portal Jaspravim.sk (in Slovak, translated): “Maksym built an AI agent that drafts blog articles for regular publishing. A professional approach, prompt communication, and the work was finished quickly with no delays.”
Takeaways if you are building something similar
- Put your effort into where facts come from, not into prompt wording.
- Make “not enough data → stop” an explicit, first-class path.
- Separate generation and verification into different steps.
- Keep a human approval step wherever being wrong has a cost.
The same pattern — data-driven topic, verified sources, separate check, human approval — works in any field where accuracy matters: finance, legal, health, technical documentation.
Originally published as a case study on nexflow.sk. I build AI agents and automations for small businesses in Slovakia — see also AI agent vs chatbot vs automation: what your company actually needs.
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