SEO Analysis Checker.com

Methodology

How the scores are calculated

Generated from the engine at build time · 61 checks in single-page mode

The short version

A report runs 61 checks against one page. Each check passes or fails. Failures are weighted by severity, each category gets a score from its weighted pass rate, and the overall score is a weighted average of the categories. The “fix these first” list is every failed check sorted by severity weight × category weight. There is no machine learning, no curve and no per-site adjustment: the same page always produces the same score.

What runs in a single-page check

The engine is the one behind deepseoanalysis.com’s full-site audits. Single-page mode drops the checks that only make sense across a crawl (orphan pages, duplicate titles across pages, click depth, broken external links) and adds a handful of live probes that a crawler cannot do cheaply: host-variant redirects, compression, security headers, mixed content, favicon and social tags. Core Web Vitals come from the PageSpeed Insights API after the main report is done, so they never delay it.

CategoryChecksCriticalWarningInfoWeight in overall
Technical SEO18410425%
On-Page1329222%
Structured Data413015%
Links100115%
AI Readiness503215%
CMS & Platform20114515%

Weights are relative: only the categories that actually ran on your page are included, and the overall score is normalised by the sum of their weights. CMS checks, for example, only run when a platform is detected.

Severity weights

The formula

category score  = 100 × (1 − Σ weight(failed checks) / Σ weight(all applicable checks))
overall score   = Σ (category score × category weight) / Σ category weight
fix priority    = severity weight × category weight   (highest first, top 5 shown)

A category with no applicable checks scores 100 and is excluded from the average. Checks that cannot be evaluated on a page (for example, the question-heading ratio when the page has fewer than three headings) are marked not applicable and do not count against you.

The AI-visibility (GEO) score

The AI-visibility score is separate from the SEO score and answers a different question: how likely is this page to be fetched, understood and cited by answer engines such as ChatGPT, Perplexity and Google’s AI Overviews. It is a weighted sum of six factors, each scored 0–100:

FactorWeightWhat it measures
llms.txt signal22%Whether the site serves an llms.txt file and whether it is well formed.
AI crawler access18%Whether robots.txt allows the AI crawlers we probe (GPTBot, ClaudeBot, Claude-Web, PerplexityBot, Google-Extended, CCBot, Bytespider).
Structured data completeness18%Presence of Organization and WebSite schema on the homepage, and Article, FAQ, Breadcrumb or HowTo schema on the page.
Answer structure and chunkability18%Heading density, question-style headings, a summary near the top, and lists or tables an answer engine can lift.
Entity clarity13%sameAs links, consistent naming between title, schema and visible text, an author, and about or contact pages.
Freshness signals11%Published or modified dates in schema and in visible text.

Factors score 80 or above as “good”, 50–79 as “warning” and below 50 as “poor”. The weights reflect what answer engines can and cannot do today: they cannot cite a page their crawler is blocked from, and they lift structured, question-shaped content far more readily than prose. Read more in the AI visibility guide.

What the score is not

Every check, explained

Each check has a plain-language explainer covering what it tests, why it matters and how to fix it on the common platforms: browse the check explainers. If you think a check is wrong, tell us via the about page and we will fix the engine.