A redesigned website can look right while pagination, canonicals or indexing rules have changed underneath. Those details need acceptance tests before launch.

For each page type, define:

What crawlers should be able to reach.

Which URLs should be eligible for indexing.

What links, content and metadata the page should return.

Who checks the implementation and what counts as a pass.

One example: if you rely on a page’s noindex directive, blocking that page in robots.txt prevents Google from reading it.

I put together a practical guide to turning technical SEO requirements into a specification developers and QA can actually use.

Projects and client work: NikoCodes

This is a useful acceptance-test framing, Niko. I use the same approach for small launches: map the critical page types, check navigation and canonical/indexing behavior, then give the developer a short evidence pack. If a team needs a quick independent pass before launch, my...

Content teams often spend hours copying ideas from a web form into a spreadsheet, then drafting a post, and finally chasing a reviewer for sign off. Each handoff adds delay, creates version confusion, and makes it hard to measure ROI on content production.

The workflow I built starts with a Google Form that feeds directly into a Google Sheet tracker. n8n reads new rows, sends the topic to GPT 4o mini which improves the headline and writes a full SEO optimized article. The draft is emailed to a reviewer with a simple approval link. If feedback arrives, the same AI revises the copy and resends it. Once approved, a Google Doc is created, the sheet is updated with the link and status, and the whole process respects Google API rate limits and retries on transient errors. The result is a reliable pipeline that turns a raw idea into a published post with minimal manual steps.

What methods do you use to capture reviewer feedback in automated content pipelines, and which metrics help you decide when an AI generated draft is ready for publication?