Traditionally, fixing accessibility issues was a slow, expensive process. You'd hire an auditor, wait weeks for a report, then spend months implementing fixes. The backlog grew faster than the fixes. AI is changing that equation fundamentally.
AI that reads your code
Modern AI models can read your actual source code, understand its structure, and generate targeted fixes. When xsbl identifies a missing alt text violation, it doesn't just tell you about it — it fetches the relevant source file from your GitHub repo, identifies the exact image element, uses AI Vision to understand what the image shows, and generates a contextually accurate alt text attribute.
The fix isn't generic. It's specific to your codebase, your component structure, and the actual visual content of the image. The AI understands JSX, Vue, Svelte, HTML — it generates fixes that match your existing code style.
Bulk fixes at scale
The real power shows at scale. A typical e-commerce site might have 200+ accessibility violations across dozens of pages. Manually fixing each one takes a developer hours of context-switching between the audit report, the codebase, and testing. With xsbl, you select the issues you want fixed, click one button, and get a single pull request with all the fixes applied — proper alt text, corrected ARIA roles, fixed color contrast values, semantic HTML replacements.
The PR includes a detailed description of every change, which WCAG criterion it addresses, and the impact level. Your team reviews and merges it like any other PR. The whole process takes minutes instead of weeks.
What AI can't replace
AI-generated fixes still need human review. The model might misinterpret an image's context, choose a suboptimal ARIA pattern, or make assumptions about your design intent. That's why xsbl creates pull requests instead of auto-committing — your team stays in control. AI handles the tedious, repetitive work. Humans handle the judgment calls.