Documents are primarily written in two languages at once. There's the text — the words on the page. And there's the visual language — the conventions of size, weight, position, proximity, and spacing that tell you what those words mean structurally. Biggest text, centered, top of page? Title. Small italic text under an image? Caption. Indented block with a bullet? List item. This is a language that sighted people understand fluently without much thought.
Multimodal AI models — models that process both images and text — have an understanding of visual language and the coding knowledge to express it as semantic structure. This makes translating from a visual layout to accessible HTML a natural fit for what these models already do.
But having a model that can translate isn't the same as having a system that does translate reliably. A bilingual dictionary contains real knowledge, but it doesn't make you a translator. Translation requires architecture: knowing what you're translating from, what you're translating to, what counts as correct, and how to verify the output. That's what Equalify Reflow is.
Instead of trying to "fix" PDFs — a format designed for print fidelity where accessibility is bolted on after the fact — we extract the content and rebuild it in a format that is natively accessible.
That format is Markdown:
Equalify Reflow converts PDFs through a five-stage pipeline:
IBM Docling handles the first pass. It uses smaller, efficient models and whatever structural data already exists inside the PDF to produce a first-pass markdown version. This handles mechanical parsing — text blocks, tables, images, reading order — without burning expensive LLM calls on mechanical work. Gets you roughly 70% of the way there.
If the document is scanned (image-only), Docling applies OCR to extract the text before proceeding.
Before the AI processes a page, we need to understand what we're looking at. This stage analyzes the PDF's visual presentation alongside the semantic data pulled from Docling to classify the document type — is this a poster, an academic paper, a syllabus, a flyer?
The document type matters because it dynamically adjusts the prompt given to the model. A two-column academic paper needs different handling than a single-page event poster.
This stage also produces a structural map of the document: an outline of headings and sections, page-level attributes (layout type, content flags like images, tables, equations), footnote locations, and any elements that need special attention. All of this context is carried forward through the pipeline as a dossier that informs every downstream decision.
Headings come first because a valid heading hierarchy is the backbone of document accessibility. Get that right and everything else has a skeleton to hang on. The agent infers heading levels from visual signals: font size, weight, position, spacing — and reconciles them into a consistent hierarchy across the entire document.
This is where the core translation happens. Each page is given to a multimodal LLM as both an image and its current markdown interpretation. The model's job is to edit the markdown to make it match what the visual page communicates.
The model works through tool calls — each edit includes a reasoning explanation, giving us insight into how the model is interpreting the document. This reasoning trail is recorded in a change ledger for auditability.
Some of those tool calls spawn specialist sub-agents for tasks that need focused expertise:
The final pass brings all the individual markdown pages together into a single document and removes page boundaries — pages are a print metaphor, and on screens they're an obstacle. An AI examines the boundaries between pages and fixes artifacts from the paged presentation: words split across pages, tables or lists that were broken by a page break, footnotes relocated to their logical position, and other seams left over from the original layout.
The result is a reflowable, responsive document that adapts to any viewport, any device, any rendering context — accessible by construction.
Before any AI processing occurs, every document is scanned for personally identifiable information using Microsoft Presidio. If PII is detected — names, emails, phone numbers, SSNs — the document is held for human review before proceeding. The system is designed for course materials only, not student records.
Every edit made by the pipeline is recorded with:
human review mode, an administrator can inspect every change before the document is finalized.
The source code is not yet publicly available. We are currently in the UIC pilot phase and plan to open-source the full project under the AGPL license.
To learn more or request early access, visit the Getting Started guide or sign up as a partner.