Accessible PDFs, Intelligently Tagged

An accessible PDF is designed to work with assistive technologies such as screen readers, screen magnifiers, and voice-control tools used by people with disabilities. Accessible PDFs include semantic tags and a logical structure so assistive technologies can navigate headings, lists, tables, links, and form fields in the correct reading order. They also provide text alternatives for meaningful images and other non-text content.

That's what we're built for. Every document we return is structured so assistive technology can present content in the correct reading order, with headings, lists, tables, and image descriptions announced clearly. We run a validation check on every output and surface any issues before returning it to you — so you can see exactly where things stand and where manual review may still help.

You get two things: a remediated PDF rebuilt with the correct semantic structure, reading order, and image descriptions — and a validation report showing which PDF/UA-1 issues were resolved and which, if any, remain. Both files are available to download from your project dashboard so you can compare the original and the output and have a record of where things stand.

Manual remediation in Acrobat means tagging every element by hand — a process that requires deep accessibility expertise and can take hours on a complex document. We take care of this time-consuming process by incorporating automated fixes on the structural and technical requirements of an accessible PDF, and by harnessing AI to analyze your document for its full context in order to build the correct tagged structure and reading order. For organizations with more than a handful of documents, the difference in time and consistency is significant.

It covers both sides of what PDF/UA-1 actually requires. The standard has two categories of requirements: technical rules (does the structure exist, are annotations tagged, do images have alt text attributes) and contextual ones (is the reading order logically correct, are headings semantically accurate, do image descriptions actually describe the content). Our AI-powered pipeline handles the contextual side — analyzing the document to determine correct structure, reading order, and meaningful descriptions. veraPDF, the open-source validator jointly developed by the PDF Association and the Open Preservation Foundation, handles the structural/technical checks — and we use its output to drive fixes, not just report results. Every project surfaces a before/after comparison so you can see exactly which issues were resolved and any that remain.

Your files are encrypted in transit and at rest. Uploaded documents are processed and then automatically deleted from our servers within 7 days — we don't retain them beyond what's needed to return your output. We don't use your documents to train AI models or share them with third parties.

Each credit processes one page of a PDF. Subscribe to a monthly plan and get credits that roll over, or purchase add-on credits anytime you need more.