An AI-Powered SEO Operating System
One connected place for research, audits, reporting, and content work, with AI handling the repetitive analysis so people can focus on the judgment calls.
Problem
Most search teams work across a dozen disconnected tools. Data gets stuck in silos, insights arrive late, and skilled people spend their time on busywork like exporting, reformatting, and manually checking for regressions instead of the strategy they are good at.
Approach
I mapped out every recurring workflow, then designed a single layer to sit above the tools. Connectors normalize the data, an AI layer summarizes and flags anomalies, dashboards surface what needs a person, and automations handle the next step.
Architecture
Ingestion connectors (SERP, crawl, analytics, backlinks, AI-visibility) → normalized data store → AI analysis & evaluation layer → dashboards + alerts → automation triggers. Each layer is modular, so new data sources and new AI models drop in without reworking the whole thing.
In practice
# Flag ranking drops and let AI take a first pass at the cause
for page in tracked_pages:
delta = rank_delta(page, window="7d")
if delta.dropped(threshold=5):
cause = llm.classify(page.signals) # tech? content? SERP shift?
alerts.push(page, delta, cause)Outcome
Reporting that used to take hours now takes minutes. Technical regressions get caught automatically. And because every new workflow adds to the shared model, it keeps getting more useful the more it is used.
Lessons learned
Start from the workflow, not the tool. Keep the AI layer easy to swap out, since models change all the time. And put in the work early on a clean data model, because everything else gets easier once the foundation is solid.