What I like most about AirOps is how it helps move AI from ad-hoc prompting into structured, scalable workflows. The Content Engineering program gave me a practical way to connect content, data, AI, automation, and human judgment in a cohesive process. I especially appreciate the emphasis on building repeatable systems grounded in real context, with quality controls and measurable outcomes, rather than simply generating more content.

The Writing Rules playbook made a real difference in how I write. I can now put out articles that consistently match my voice and intent, and even with the required HILT review, the whole process still saves me time. The initial setup takes time to get right, and I'd tell anyone starting out to expect that. But it's worth the investment: the quality of everything I create depends on how well that foundation is built. Setting up integrations was easy, on the other hand. I connected AirOps to Google Analytics and Search Console, so I could bring performance data straight into my workflows instead of copying it over by hand. Quill has been just as valuable: it's a solid AI agent for adjusting my playbooks and grids, catching gaps, and suggesting improvements I wouldn't have caught on my own.

The platform is evolving constantly, but I find Quill, the smart AI assistant, to be the most helpful part, enabling non-techies like myself to actually build AI agents through a conversation. I also LOVE the MCP feature.