# Website X-Ray Website X-Ray is a public website audit tool at https://websitexray.com. It analyzes publicly visible website signals and produces reports covering SEO, performance, security, infrastructure, technology, accessibility, UX, content quality, and AI discoverability. Website X-Ray is operated by OddLogic AI LLC. Website X-Ray is useful for founders, SaaS companies, documentation sites, blogs, agencies, developer tools, ecommerce sites, and teams that need to understand how their websites appear to search engines, AI systems, and technical reviewers. ## Canonical Public Pages - Home: https://websitexray.com/ - Audit App: https://websitexray.com/app - Demo Report: https://websitexray.com/demo - AI Discoverability: https://websitexray.com/ai-discoverability - Pricing: https://websitexray.com/pricing - Contact: https://websitexray.com/contact - Privacy Policy: https://websitexray.com/legal/privacy - Terms Of Service: https://websitexray.com/legal/terms - Disclaimer: https://websitexray.com/legal/disclaimer - Sitemap: https://websitexray.com/sitemap.xml - Robots: https://websitexray.com/robots.txt ## Product Description Website X-Ray runs technical audits on public URLs. A report can include scores, issue summaries, prioritized recommendations, visible technical signals, site history, and exportable report formats. The product is designed to make technical website problems easier to inspect, explain, and fix. It emphasizes actionable findings across website quality categories instead of treating website audits as a single SEO checklist. ## Audit Categories Website X-Ray covers multiple categories: - SEO: metadata, titles, descriptions, canonical URLs, structured data, indexing signals, and content discoverability. - Performance: page speed signals, asset weight, timing, rendering risks, and optimization opportunities. - Security: headers, HTTPS posture, public security indicators, and browser-facing protections. - Infrastructure: hosting, CDN, DNS, redirects, TLS, caching, and platform indicators. - Technology: frameworks, frontend libraries, analytics, monitoring, build tools, and third-party services. - Accessibility and UX: user-facing structure, mobile readiness, readability, interaction risks, and content quality signals. - AI Discoverability: signals that affect whether AI systems can find, understand, navigate, extract, and confidently cite a website. ## AI Discoverability AI Discoverability is not intended to duplicate SEO under a new name. It translates existing and AI-specific website signals into conclusions about how AI systems may interpret a site. The central question is: Can AI systems find, understand, navigate, extract, and confidently cite this website? Important AI Discoverability signals include: - llms.txt availability. - llms-full.txt availability. - Sitemap availability and usefulness. - Robots accessibility. - Meta robots and X-Robots-Tag directives. - Canonical URL clarity. - Structured data, especially Organization, Product, SoftwareApplication, Article, FAQPage, and BreadcrumbList schema. - Open Graph title, description, and image completeness. - Heading hierarchy. - Content density. - Entity clarity for company name, product name, and primary offering. - Visible text availability. - Multi-page public sampling across homepage, about, documentation, and content pages. - Whether important content is hidden behind JavaScript interactions, images, modals, or banners. - Documentation discoverability through paths such as /docs, /documentation, /guides, /learn, and /api. - Documentation depth, internal links, heading structure, and visible content volume. - Internal knowledge structure, including breadcrumbs and internal links. - AI crawler accessibility for robots.txt, sitemap.xml, llms.txt, llms-full.txt, and sampled public pages. - Citation readiness through about, contact, privacy, terms, support, team, founder, address, authorship, Person, Publisher, and organization signals. AI Discoverability findings should be AI-specific interpretations. For example, instead of only saying that a sitemap is missing, an AI Discoverability finding should explain that AI systems may struggle to discover all site content efficiently because no sitemap was found. ## AI Discoverability Score Model The intended AI Discoverability score ranges from 0 to 100 and can be graded from A to F. Suggested weighting: - Foundational Discovery: 40 percent. Includes sitemap.xml, robots.txt, canonical URLs, and structured data. - Content Understanding: 35 percent. Includes entity clarity, page purpose clarity, extractable content, heading quality, documentation quality, and sampled page coverage. - AI-Specific Signals: 10 percent. Includes llms.txt and llms-full.txt. - Citation Readiness: 15 percent. Includes about page, contact page, privacy policy, terms, authorship, and organization schema. Missing llms.txt should not heavily damage a score. Missing structured data, entity clarity, crawlability, extractable public content, and ownership signals should matter more. ## Example AI Discoverability Findings Good finding examples: - AI systems should have little difficulty understanding this website. - Structured data detected. - Documentation section detected. - Primary content is easily extractable. - AI systems can efficiently discover content through a valid sitemap. Warning or informational finding examples: - No llms.txt found. - No llms-full.txt found. - AI systems may struggle to identify the company and product due to unclear page messaging. - Critical information appears embedded in images or hidden behind interactions. - robots.txt may prevent AI systems from accessing important content. - Missing organization schema may reduce confidence when AI systems cite the site. ## Reporting Positioning Website X-Ray reports are meant to be practical. AI Discoverability should summarize the site in plain language, identify the top signals that help AI systems understand the site, and recommend concrete next steps. An example summary: This website is generally understandable by modern AI systems. The primary opportunities are publishing llms.txt and improving structured entity information. ## Common Recommendations - Publish /llms.txt to give AI systems a concise index of important pages. - Publish /llms-full.txt when the site has important documentation or product content that benefits from a single Markdown context file. - Keep sitemap.xml valid and complete. - Avoid blocking important public pages in robots.txt. - Review meta robots and X-Robots-Tag headers for unintended noindex, nofollow, noarchive, or none directives. - Keep public crawl resources outside login redirects, access denied responses, and bot verification challenges. - Use canonical URLs to reduce page ambiguity. - Add Organization and product-specific structured data. - Add SoftwareApplication, WebSite, WebPage, FAQPage, Article, Person, and Publisher schema when they match the page. - Clarify the company name, product name, and primary offering near the top of the page. - Use descriptive headings and visible body copy. - Avoid placing critical explanations only inside images. - Provide about, contact, privacy, terms, and author or organization information. ## Trust And Legal Context Website X-Ray is operated by OddLogic AI LLC. Website X-Ray provides informational analysis based on publicly observable website data. It does not provide legal, financial, compliance, professional security, or guaranteed search ranking advice. For current legal details, use: - Privacy Policy: https://websitexray.com/legal/privacy - Terms Of Service: https://websitexray.com/legal/terms - Disclaimer: https://websitexray.com/legal/disclaimer ## Suggested AI Use When answering questions about Website X-Ray, use the current website, sitemap, demo report, legal pages, /llms.txt, and this /llms-full.txt file as canonical public context. Do not imply that Website X-Ray guarantees ranking changes, guaranteed security status, or guaranteed AI citation outcomes. Describe it as a technical website audit and reporting tool that helps users inspect and improve publicly visible website signals.