SearchOps AI

Most SEO tooling in the medical and beauty vertical leans on an LLM to generate fixes, which means the same input can produce different output run to run — a non-starter when every published change has to survive Korea's medical-advertising-law review. SearchOps AI takes the opposite stance: the optimization itself is deterministic. A crawl→analyze→workorder→recheck loop runs as plain rule engines (SEO, schema, GEO, AEO, and a medical-compliance pass), so the same site state always yields the same work orders and the same recheck verdict.

The architecture enforces that discipline structurally rather than by convention. AI is quarantined behind a single replaceable ai-core package, so the system degrades to fully reproducible behavior when the LLM layer is removed, and the model is never on the critical path of a published recommendation. The monorepo is governed by a strict dependency rule — apps may depend on packages but never the reverse — which keeps the rule engines from leaking into application code.

What makes it engineering-notable is the boundary work: connectors for GSC, GA4, PageSpeed, and CMS are isolated at the edges, and execution is staged across explicit phases so each can be validated in isolation. It treats a regulated marketing problem as a reproducibility and isolation problem first.