PAC (PDF Accessibility Checker) vs PDFAccessibility.ai

PAC (PDF Accessibility Checker) is a free Windows desktop application for assessing PDF accessibility. Its Free plan costs nothing and includes PDF/UA and WCAG checks, quality checks, and AI-assisted checks. PAC covers WCAG 2.2 and combines automated scans with manual review tools. It also provides remediation guidance for addressing accessibility issues. The application is made by PAC (PDF Accessibility Checker), founded in 2010 and headquartered in Zurich, Switzerland. Windows is the listed platform. Mobile testing, a browser extension, and CI/CD integration are not included among its capabilities. The stated product scope is PDF accessibility checking, with checks aimed at PDF/UA and WCAG requirements rather than broad application testing. For Windows users reviewing PDF files, the free plan brings automated analysis, reviewer-led assessment, quality checks, AI-assisted checks, and guidance for remediation into one desktop application. Its feature set is focused on document accessibility rather than multi-platform testing.

PDFAccessibility.ai combines PDF accessibility checks, automated remediation, manual editing, accessible export, and an API for structured PDF data. Its checks report issues against WCAG, PDF/UA, Section 508, ADA, and EN 301 549 expectations. Remediation can tag document structure, adjust reading order, and detect headings and tables. Users can review tags, heading levels, text, and image alt text when automation flags complex items. Batch workflows support file-specific actions and ZIP downloads. The Smart PDF RAG API returns JSON and Markdown with typed elements, tables, reading order, page references, and bounding boxes; its documented workflow uses asynchronous uploads followed by polling. API guidance recommends keeping keys server-side and sending them in an X-API-Key header. The free PDF Accessibility plan includes 10 accessible exports and retains files for seven days. Paid plans start at $29 a month. The privacy policy says uploads are not used to train public AI models, while noting that some processing may send necessary information to infrastructure or AI providers. Complex layouts and other difficult PDFs may need manual review.