The audit follows a controlled sequence designed to find representative pages without wandering across the web. It reads only publicly available content and does not store an account or website history.
1. Validate and protect
The server normalizes the submitted address, requires HTTP or HTTPS, resolves the hostname, and refuses localhost, private networks, credentials, suspicious ports, oversized responses, and non-HTML files. Redirects are followed manually and revalidated to reduce server-side request forgery risk.
2. Discover important pages
The crawler checks robots.txt and sitemap.xml, then combines useful sitemap entries with internal homepage links. It favors pages whose paths suggest services, products, guidance, company information, policies, and support. The free scan stops at 20 unique URLs on the same hostname.
3. Extract readable signals
For every successful page, the analyzer records titles, descriptions, headings, canonical tags, robots directives, JSON-LD types, question patterns, contact details, evidence markers, images, links, and readable text. Failed responses remain visible rather than silently disappearing.
4. Score and prioritize
Ten checks combine the observed signals into scores and plain-language findings. The report then proposes questions, prompts, pages, schema improvements, trust additions, and a roadmap ordered by likely impact.
How page selection stays focused
A useful free website audit needs representative coverage without attempting to mirror an entire site. AI Search Helper begins with the submitted homepage, reads discoverable sitemap locations, and evaluates descriptive internal links. It prioritizes paths that commonly represent services, products, company information, resources, guidance, FAQs, case studies, contact details, and policies. Tracking fragments and obvious duplicate URLs are removed, common non-HTML file types are skipped, and the queue stops when the scan reaches 20 pages. This sample is broad enough to reveal common information architecture patterns while remaining fast and respectful.
What the analyzer extracts from each page
For each successful HTML response, the analyzer records the page title, meta description, headings, readable text, canonical reference, meta robots value, internal and external links, image and alt-text patterns, contact signals, dates, question phrases, and JSON-LD schema types. It also records failed responses instead of silently omitting them. This creates a traceable connection between a recommendation and the public evidence available during the scan. JavaScript-only content, personalized experiences, authenticated areas, and facts that require industry expertise may not be visible to the automated parser.
How scores are calculated and interpreted
Each visibility score combines several observable indicators into a directional value from zero to 100. Technical crawlability gives weight to successful access, sitemap and robots signals, indexability, and stable pages. AEO and LLM readiness consider question coverage, entity clarity, structured meaning, citations, topical depth, and trust. The score is deliberately not described as a probability of ranking or citation. Use it to compare areas within the same report, prioritize the lowest-supported signals, and document improvement over consistent scans—not as proof of how an independent model will answer a particular prompt.
From automated findings to human review
The final roadmap groups work into critical, high, medium, and low priority recommendations. Before implementation, confirm each finding against the live page, business facts, editorial policy, and platform documentation. A developer should review crawl directives and schema changes; a subject expert should verify claims and suggested answers; and an editor should check clarity, duplication, tone, and internal links. This review step prevents a technically valid recommendation from introducing inaccurate structured data, unsupported statistics, unnecessary pages, or repetitive FAQs.