SEO/AEO Article Review Assistant

Problem It Solves

Content drafts are often reused by search, AI summaries, and internal knowledge bases. The usual gap is not missing keywords alone: the title sounds keyword-friendly, but the user intent is unclear; H2 headings read like table-of-contents labels instead of query answers; internal links and facts are weak, leaving AI systems with little citable substance. seo-article-review is aimed at SEO/AEO review, turning 'will this be cited by AI?' into inspectable dimensions rather than a generic 'optimize more' note.

How It Works

It accepts inputs such as materialText, materialUrl, and materialFile, then uses parameters like goal, audience, targetKeyword, and searchIntent to build review context. The core outputs include summary, diagnostic conclusions, action recommendations, and reusable deliverables. For engineers, the point is not keyword stuffing; it is organizing H1/H2, target keywords, internal link markers, brand tone, and compliance boundaries into an actionable checklist: identify the search intent, check whether the title and body answer the same question, then assign priority and next drafting steps. With public article links or draft files, it can operate at quick health check, standard diagnosis, or deep rewrite depth.

Boundaries

It is not a generic marketing copywriter, nor a replacement for keyword databases, crawlers, technical SEO audits, or legal review. If goals, audience, core queries, or accessible links are missing, conclusions may be generic. Keep original H1/H2, internal link markers, and mandatory facts visible so the review does not drift into a final publish-ready rewrite. It is most useful after a draft has taken shape, for structured review, prioritized edits, and delivery summaries.