HARPA AI Automation Suite

In mid-2024, I approached the Head of AI Research at Webselenese with a self-built prototype — a HARPA AI command capable of drafting full articles in a single session. Manually prompting ChatGPT (as most did back then) couldn't scale up well: first drafts were slow, fact-checking was inconsistent, and each content vertical required its own research methodology. What started as a personal side project led to my transition from content writer to AI Automations Specialist, joining the AI Research team to build this into a production system.

Over the following 1.5 years, I built and continuously maintained a suite of HARPA AI commands that became the backbone of the company's content workflow. These were not off-the-shelf automations — each was purpose-built, integrating APIs and webhooks tailored to precise editorial requirements. The result: first drafts that previously took hours could be produced in a single session, and monthly content output increased significantly across multiple verticals. I also built supporting Make.com scenarios that handled API logging, server monitoring, and document pipeline automation across the operation.

A key aspect of these HARPA commands was the 'human in the loop'. From the outset, we knew that AI alone cannot draft high-quality content. It needs an expert writer's judgement and review. So each command was crafted to ensure that writers & editors review the AI output and correct it before feeding it into the next command in the chain. This avoids the classic problem of 'Bad Input = Bad Output', as feeding an AI's mistakes into another AI command would only exponentially compound problems.

• Detailed Research Command — Automated the full competitive research and analysis (CR&A) workflow: competitors, SEO data, search intent, and audience behavior.
• Simple Research Command — A lighter, faster version for teams needing quicker research without full competitive analysis.
• AI Writer Command — Generated complete article drafts from finished research briefs in a single session, flexible across multiple verticals.
• Fact Checker Command — Detected factual inaccuracies, typos, and common writing errors to streamline the editing process.
• Additional commands and supporting automations built throughout tenure as the team's needs evolved.
• Formally recommended by Ren Sayer, Head of AI Research, Webselenese.