web-ai-accessibility-analysis

Check web page accessibility for humans and AI agents by fetching both rendered content and raw HTML, evaluating against WCAG 2.1 AA, semantic HTML practices, and AI-readiness to produce a graded report. This dual-fetch method ensures comprehensive coverage of dynamic and static content.

AI-readability assessment evaluates how easily LLMs, crawlers, and assistive technologies can parse a web page by checking its semantic HTML and structured data. It runs alongside WCAG 2.1 AA checks to ensure content is machine-readable and logically structured for automated agents.

Audit a website against WCAG 2.1 AA standards by fetching the target URL through both rendered content and raw HTML methods. The analysis grades the page against WCAG criteria and outputs a structured report with actionable fixes and examples for product and engineering teams.

Yes, web accessibility analysis evaluates both rendered content via a headless browser and raw HTML via curl. This dual-fetch approach identifies issues in dynamically loaded elements and static semantic structure, ensuring comprehensive WCAG 2.1 AA and AI-readiness grading.

Generate an accessibility report with actionable fixes by running a dual-fetch page audit that grades against WCAG 2.1 AA and AI-readiness rules. It produces a structured report containing explicit fixes and examples tailored for product and engineering teams to implement.

Yes, you can use semantic HTML checks to improve your page for AI crawlers by analyzing the page's structured data and semantic structure. The analysis evaluates AI-readiness alongside standard accessibility to ensure LLMs and automated agents can parse the content effectively.