What SeoGeo Tech is

SeoGeo Tech focuses on the overlap between technical SEO and Generative Engine Optimization. Traditional SEO still matters: pages need to be crawlable, indexable, internally linked, and useful for searchers. GEO adds another layer: the same page should be easy for AI-assisted search systems and human readers to extract, verify, and summarize without losing the original meaning.

The site is not an agency brochure or a collection of generic SEO tips. The goal is to publish practical pages that help readers make better decisions about page structure, metadata, schema, canonical URLs, internal links, Search Console data, and content refresh workflows.

Who creates the content

SeoGeo Tech is published by the SeoGeo Tech Editorial Team. The team name is used consistently across the site so readers can identify who is responsible for the publication. Editorial questions, correction requests, and source suggestions can be sent to [email protected].

The site may use AI tools during planning, drafting, editing, or quality review, but the publication is responsible for the final page. AI assistance is used to improve clarity and efficiency, not to replace first-hand testing or editorial review. A useful article still needs human judgment: checking whether a recommendation is technically accurate, whether a claim is too broad, whether an example is realistic, and whether the page gives readers enough context to apply the idea safely.

Who this site is for

SeoGeo Tech is designed for developers, technical SEOs, independent publishers, and content teams maintaining search-visible websites. The guides assume basic familiarity with websites and focus on practical implementation, not introductory SEO concepts. If you need to inspect a page, adjust a template, or decide what to update next, the site is for you.

Why I built SeoGeo Tech

I created SeoGeo Tech because I wanted a place to document practical SEO and GEO experiments instead of publishing generic optimization advice. Many articles begin with a real implementation question, a Search Console observation, or a publishing workflow that I wanted to improve on this site.

Over time, those notes became guides that could help other builders facing similar problems. The site is still small, which is useful: it forces the work to stay close to real pages, real constraints, and real maintenance decisions rather than abstract best practices.

Read the full story behind SeoGeo Tech →

Our research approach

Many articles begin with questions that arise while building or maintaining SeoGeo Tech itself. Rather than publishing theoretical recommendations first, we usually test ideas on our own pages, inspect source code, compare Search Console data, review structured data output, and document what we observe.

Some experiments lead to useful workflows. Others simply show where common assumptions break down. When we use our own site as an example, we explicitly say so. Screenshots, Search Console observations, live URL checks, or source-code notes are used as supporting evidence, not as proof of rankings, AI citations, or guaranteed search visibility. The goal is not to prove universal ranking rules, but to document implementation decisions that other builders can inspect and adapt.

What we cover

SeoGeo Tech primarily covers:

  • Technical SEO for crawlable, maintainable, readable websites
  • Generative Engine Optimization for extractable and citable content
  • Structured data, semantic HTML, and content architecture
  • Internal linking and source-page planning
  • Publishing workflows and refresh checklists
  • Search visibility measurement

The library is intentionally narrow. SeoGeo Tech is about how pages are built, interpreted, refreshed, and measured. Many guides published here originate from real implementation work rather than hypothetical examples.

What we avoid

We avoid invented case studies, anonymous performance claims, copied summaries without added value, and pages created only to target small keyword variations. We also avoid claiming that a checklist, schema type, internal link, or AI prompt can guarantee rankings, AI Overview inclusion, AI Mode visibility, or citations in generated answers.

Examples are intentionally generic unless they describe this site’s own implementation. Code snippets are meant to show the shape of a solution, not a universal rule. When a recommendation depends on the CMS, framework, or hosting setup, the guide should say so.

Keeping content current

Search changes quickly, especially around AI-assisted results and reporting inside Google Search Console. Older articles are periodically reviewed when the topic changes, when screenshots become outdated, or when new observations make the original explanation incomplete.

When a guide receives substantial technical updates, new screenshots, implementation notes, or revised recommendations, the page’s updated date may change. Minor wording, formatting, typo, or layout edits normally do not change the published date.

Feedback welcome

Constructive feedback helps keep SeoGeo Tech accurate and useful. We welcome:

  • Correction requests
  • Broken examples or outdated screenshots
  • Source suggestions
  • Technical feedback

For corrections, source suggestions, or editorial questions, contact [email protected]. If you are reporting an error, please include the page URL and the specific sentence, section, screenshot, or code example that needs review.