AI + Accessibility
Responsible AI that helps accessibility scale.
Pallyfi helps teams explore AI-driven accessibility analysis, intelligent recommendations, monitoring workflows, and human-in-the-loop remediation.
AI with expert oversight
AI can accelerate accessibility work, but expert validation keeps quality, context, and user impact at the center.
Where AI can help
- Pattern detection across repeated UI issues.
- Suggested remediation paths for design and engineering teams.
- Content and alt text review workflows.
- Regression monitoring and accessibility trend reporting.
Where humans remain essential
- User impact interpretation and prioritization.
- Complex interaction testing and screen reader validation.
- Inclusive design judgment and product tradeoffs.
- Governance, policy, and accountability.
See it in the product
Pallyfi OnCall: 11 AI tools grounded in the real WCAG 2.2 spec.
The Pallyfi app pairs a real axe-core auditing engine with a retrieval system over the WCAG 2.2 spec and ARIA Authoring Practices, so responses cite the actual standard instead of guessing from training data, and every tool is built to acknowledge and engage with what you actually gave it, real code gets a real analysis, never a bare dismissal, and a snippet too small to say much about gets a clarifying question instead of a guess.
Explain issue
Plain-English breakdown of any accessibility issue: what's wrong, who it affects, and the WCAG criterion behind it.
Suggest fix
A corrected, developer-ready code snippet for a described issue, root cause and verification steps included.
Alt text generator
Upload an image and get purpose-aware alt text written directly from what the model sees, no manual description required.
Accessibility statement
A professional, ready-to-publish accessibility statement drafted for your organization and target conformance level.
Code auditor (Pallyfi Digger)
Pastes of HTML or JSX get a real axe-core scan plus AI review for issues automated testing alone can't catch.
ARIA reviewer
Checks roles, states, and properties against the ARIA specification and Authoring Practices Guide, flags invalid usage.
Screen reader simulator
Predicts exactly what NVDA, JAWS, and VoiceOver will announce for a piece of markup, before or after a fix.
Test case generator
Manual, automated (jest-axe, Playwright), and screen reader verification test cases for a feature or component.
Keyboard planner
A full keyboard interaction spec for a widget: key bindings, focus management, and the APG pattern it follows.
Issue prioritizer
Ranks a list of findings by user impact and effort, with a recommended sprint order.
Contrast checker
Mathematically verified WCAG contrast ratios with pre-computed passing color alternatives, never a guessed hex value.
AI woven through the whole workflow
AI isn't just a chat window, it's built into automation and remediation too.
Pallyfi's Accessibility Scanner and Pallyfi Digger aren't separate from the AI story, they're where it does its most practical work: turning a scan finding straight into a suggested fix, and a saved before/after comparison, without leaving the Issue Tracker.
Fix with Digger, from any tracked issue
One click on a scan finding calls the same Code Auditor engine for a plain-English explanation, WCAG citation, and corrected snippet, no copy-pasting into a separate tool.
Before/after fix comparison
Save an AI-suggested fix and compare it side by side with the original failing code, editable, and shareable with your team.
Hybrid, not guesswork
Every AI explanation is paired with a real, deterministic axe-core finding where one exists, the model explains and fixes, it doesn't invent violations.