Every time we publish a post here, the same manual work follows: rewrite it as a LinkedIn post, sketch an infographic, remember to actually post it. We automated that — same dogfooding approach as our lead-intake agent, pointed at our own content pipeline this time.
Writing the blog post is the easy part. Turning it into a LinkedIn post in our voice, then briefing an infographic to go with it, is the step that quietly gets skipped when we're busy — which is most weeks. Nothing about that work needs a human doing the first draft; it just needs someone reviewing it before it goes out.
An n8n workflow, triggered by a webhook, that takes a blog post's title, URL, and content, then runs two things in parallel through Google Gemini: one call drafts a LinkedIn post in our specific voice — honest, no-hype, bullet points over buzzwords — and the other writes an image-generation prompt for a matching infographic and hands it to a dedicated Gemini image model to actually render. Both results get combined and emailed to us as a draft, never posted automatically.
Five steps, unattended until the review step:
The text side — the LinkedIn post draft — worked cleanly from the first real test. The image step took longer: getting an AI image model to reliably produce an on-brand infographic, within the limits of a standard API plan, took a few rounds of real-world testing to get right. That's a normal part of building something like this properly, and it's exactly the kind of groundwork we do before handing an automation off to a client rather than shipping the first version that technically runs.
This is now our standard post-publish step for every future blog post — including the one you're reading, which got the same treatment. It's a small, unglamorous automation, which is exactly the kind of task we think AI automation is best at.
