Raw HTML and rendered DOM often tell search engines two different stories. Build a Python + Playwright pipeline that diffs titles, canonicals, meta robots, content, and internal links — and scores each divergence by real SEO impact.
A three-stage Python pipeline — regex rules, fuzzy slug matching, and content embeddings — that builds site-migration redirect maps automatically and routes only the genuinely ambiguous URLs to human review.
llms.txt promised to make sites AI-readable. Eighteen months in, does anything actually fetch it? We check the log evidence, run the cost-benefit math, and build a Python generator that keeps yours current automatically.
A Python pipeline to isolate AI crawler hits in your server logs, verify authenticity against vendor IP ranges (20-35% are impostors), and turn measured behavior into a defensible robots.txt and CDN policy.
A working-code teardown of the three ways SEO teams collect Google SERPs at scale in 2026 – SerpAPI, Bright Data SERP API, and self-hosted Playwright – compared on cost, reliability, and maintenance.
A production Python pipeline that pulls images from the WordPress REST API, generates context-aware alt text with a vision LLM, and writes it back — with guardrails so you never overwrite good human-written descriptions.
A working Python pipeline that crawls your site, embeds every page, and ranks contextual internal-link suggestions by cosine similarity — with guardrails and an approval queue.
A repeatable Python pipeline that turns the live SERP into a structured editorial brief — heading consensus, entity checklist, question targets and word-count band — with guardrails against scaled-content abuse.
Keyword research is where most SEO programs quietly leak time. A mid-sized site exports 4,000 queries from Search Console, and someone spends two days dragging rows around a spreadsheet, guessing which queries belong on the same page. The output is subjective, unrepeatable, and stale the moment traffic shifts. The fix isn’t a better spreadsheet —…
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.