A practitioner’s comparison of n8n and Microsoft Power Automate for SEO automation: cost, API handling, custom code, bulk workloads, and which to choose in 2026.
Was it Google or was it you? Build a Python pipeline that pulls Search Console data, detects statistically real changes with changepoint detection, and attributes them to confirmed Google algorithm updates.
Author signals are structured and auditable. Build a Python pipeline that crawls your site, verifies bylines, Person schema, and sameAs links, and ranks pages to fix.
Google restricted FAQ rich results and deprecated HowTo in 2023. Here is a data-driven reassessment of which schema types are still worth automating in 2026 — and a decision framework for emitting only what Google actually renders.
Build a Python quality gate that separates genuinely thin and doorway pages from thin-looking pages that rank, using cheap structural signals first and an LLM judge only on the ambiguous middle.
Build a Python pipeline that extracts entities with spaCy and Google Cloud NLP, computes an entity gap against the SERP, and turns salience scores into content decisions.
Approximate Google’s Quality Rater Guidelines at scale: a Python pipeline with a QRG-mapped rubric, an evidence-bound LLM prompt, kappa calibration, and a refresh queue ranked by quality gap times traffic.
A Python audit that harvests parameterized URLs, classifies them into pagination, sort, and filter buckets, checks canonical and robots handling, and ranks the crawl traps inflating your index.
A Python pipeline to audit canonical tags at scale: catch conflicting signals, canonical chains, non-indexable targets, normalization mismatches and sitemap disagreements before they cost you rankings.
Build a Python keyword research pipeline that harvests seeds, expands with Google Autocomplete, mines People Also Ask, and scores opportunity on your own terms.