Build automated SEO dashboards, rank-tracking pipelines, and client reports using n8n, Google Sheets, and Data Studio.
Find low-CTR pages, rewrite their titles and meta descriptions with an LLM under strict guardrails, ship the changes, and measure the CTR lift — a full Python pipeline.
Position-only tracking is blind to AI Overviews, featured snippets, and PAA. Build a Python pipeline that monitors which SERP features appear for your keywords, who owns them, and what changes over time.
Detect keyword cannibalization programmatically from GSC API data, score each conflict by recoverable traffic, and decide what to consolidate or differentiate.
Build a scheduled Python pipeline that monitors Core Web Vitals with the CrUX and PageSpeed Insights APIs and alerts you in Slack the moment LCP, INP, or CLS go red.
A code-level teardown of the GSC, SEMrush, and Ahrefs APIs for automated rank tracking: quota math, data shape, cost at scale, and the hybrid build that wins.
Stop eyeballing the Search Console queries report. Build a Python pipeline that scores every GSC query by recoverable clicks and hands you a ranked SEO action queue.
DuckDB, BigQuery, and pandas benchmarked on the same 25M-row GSC export. Which engine wins for which SEO workload, with working code and a decision framework.
Pipe Google Search Console’s Bulk Data Export into BigQuery and a self-updating Looker Studio dashboard. Full architecture, SQL views with correct weighted position, a 16-month backfill script, and cost controls.
A working Python pipeline that pulls GA4 organic sessions, strips out weekly seasonality with STL, scores residuals with robust statistics, and pings Slack the moment an organic traffic drop becomes statistically real.
A code-first Python + n8n pipeline that tracks whether ChatGPT, Perplexity, and Google AI Overviews cite your site, so you can trend AI visibility like keyword rankings.