AI + Accessibility
Traditional accessibility checkers run rule-based WCAG tests. AI accessibility audits go further — using machine learning to predict user attention, assess cognitive load, detect context-dependent issues, and generate code-level fixes automatically. VertaaUX combines both approaches: deterministic WCAG 2.2 checks plus AI analysis across seven UX dimensions, delivering a comprehensive audit in under 60 seconds.
ML-powered saliency maps predict where users look, revealing hidden usability issues in page layout.
AI assesses visual complexity and information density to flag pages that overwhelm users.
AI generates HTML/CSS patches for accessibility issues with confidence scores and regression checks.
Deterministic rule checks cover all testable WCAG 2.2 AA success criteria with criterion-level mapping.
Provide the URL of the page you want to audit. VertaaUX accepts any publicly accessible web page.
VertaaUX runs WCAG 2.2 rule checks and AI models in parallel — analyzing DOM structure, color contrast, content clarity, and attention patterns.
Get a scored report with findings mapped to WCAG success criteria, severity levels, CSS selectors, and AI-generated fix suggestions.
Use AI-generated code patches to fix issues. Each patch includes a confidence score. Verify fixes in a sandboxed browser before deploying.
Set up scheduled audits or CI/CD integration to catch regressions. VertaaUX alerts you when accessibility scores drop.
An AI accessibility audit combines traditional rule-based WCAG compliance checks with machine learning models that detect usability issues invisible to rules alone. AI models predict user attention patterns, assess content readability semantically, estimate cognitive load, and generate code-level fix suggestions — going beyond the binary pass/fail of standard checkers.
AI improves accessibility testing in three ways: it detects context-dependent issues that rules miss (like poor heading hierarchy in complex layouts), predicts real-world usability problems (attention distribution, cognitive overload), and generates specific code patches with confidence scores. This reduces false positives while catching more real issues than rule-only tools.
VertaaUX is an AI-powered WCAG checker that combines deterministic rule checks with machine learning analysis. It tests against WCAG 2.2 Level AA, detects color contrast failures, missing alt text, keyboard issues, and ARIA errors — then adds AI layers for attention prediction, clarity assessment, and automated fix generation. Published accuracy benchmarks are available at vertaaux.ai/benchmarks.
VertaaUX generates automated accessibility fix patches using AI. Each patch includes a confidence score and targets specific HTML/CSS changes — color contrast adjustments, alt text suggestions, ARIA attribute additions, and keyboard navigation improvements. Patches can be verified in a sandboxed browser to confirm they resolve the issue without introducing regressions.
Enter any URL and get WCAG 2.2 compliance results plus AI-powered usability insights in under 60 seconds. No account required.