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Violeta Maftei is a content marketer. While running test prompts for a course, Perplexity named her client's brand in an answer, even though nobody had done any work to put it there. So she built her first automation to see what would happen with effort behind it. 🧵

The client is a small Romanian firm that sells lightning and surge protection equipment. It has been selling for three years, grows on referrals, and does content marketing in bursts. AI search wasn't on the owner's radar at all.

There's even data on what gets a LinkedIn post cited. Scrunch found technical details raise the odds by 77% and named entities by 33%, while Unicode bold formatting lowers them by 58% on ChatGPT. A small firm with deep technical expertise is well positioned for that kind of post.

The stack runs on three tools, each with one job. AirOps orchestrates the workflow and holds the company's Brand Kit and Knowledge Bases. Peec AI tracks which AI prompts mention the brand and which don't. Buffer supplies the LinkedIn engagement data and is where the finished briefs land.

Every weekly run opens by checking what's already in motion. Through Buffer's API, the agent sees what's drafted or queued so it doesn't create duplicate ideas, then pulls reactions, comments, impressions, and reach for published posts. Posts that performed well become signals for follow-ups.