AirOps uses Claude and the Claude Agent SDK to power AI search visibility, content generation, and evaluation at scale
Most AI models struggle to simultaneously satisfy brand voice, information gain, and search performance requirements, often falling short on quality or performing inconsistently as prompts grow more complex. AirOps' early workflow builder also required human involvement at every step—identifying underperforming pages, deciding actions, triggering workflows, and verifying output—making it impossible to scale to thousands of pieces of content across multiple brands.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Human triggers content workflow
A human identifies which page is underperforming, decides what to do, and triggers the workflow.
AirOps 5x'd its revenue and doubled internal productivity, with engineering moving from prototype to production in weeks after adopting the Claude Agent SDK. Customer results include Chime achieving a 3x citation increase with an 89% time reduction in content creation, Carta 4x-ing quarterly top-of-funnel output, and LegalZoom cutting Reddit response workflows from 48 hours to under 30 minutes. Internally, the team reached a consensus 2x productivity increase with individual savings of 20 to 40 hours per week.
What failed first
Previous orchestration frameworks were brittle—setup decisions sometimes required full refactors, and testing different configurations took enormous effort, with time to quality at 60 hours.