AI Linter Flags Inaccessible Microcopy and Suggests Inclusive Alternatives

AI · 4 min read

The AI linter analyzes strings in context—form field labels, error messages, button text—and highlights issues that commonly trip up users of assistive technology: vague instructions, passive voice in critical alerts, and text that relies on visual cues alone. It links each suggestion to why a change matters (e.g., how screen readers announce focus) and provides tokenized alternatives that can be pulled into a design system.

Built with on-device inference and a privacy-oriented workflow, the tool is designed for integration in Figma plugins, IDEs, and CMS. It surfaces accessibility risk levels, from low (stylistic clarity) to high (missing semantic roles, ambiguous error recovery paths), and can generate ARIA-ready snippets and examples of improved copy for different locales.

Design leads who piloted the linter report a faster handoff between designers and content designers, and fewer iterations with engineering. The vendor also released a schema for storing microcopy with accessibility metadata (hint, role, expected input), enabling product teams to version-control copy alongside components in their design systems.