There is a specific kind of exhaustion that comes from auditing a website for WCAG 2.1 compliance. You stare at the screen, squinting for missing alt tags, hunting for contrast ratios that are just barely acceptable, and manually typing out findings into a spreadsheet that will inevitably become outdated by the next sprint. I used to think this manual drudgery was the price of doing inclusive design. I was wrong. It was just inefficient.
I built accessibility-alt because I was tired of treating accessibility as a post-development checklist item. I wanted to see if I could shift the paradigm from "audit after build" to "inspect as you code." The result is a SaaS tool that lives entirely in your browser, requiring no heavy downloads or complex server setups. It’s a lightweight companion for developers who want to catch accessibility issues before they ship code.
The Local-First Advantage
One of the biggest hurdles in the accessibility tooling space is privacy and latency. Most existing solutions require you to upload HTML snippets to a cloud server or install heavy extensions that track your browsing habits. With accessibility-alt, I chose a different path. The core analysis engine runs locally in the browser using private on-device AI.
This decision wasn't just about marketing buzzwords; it was a technical necessity for speed and trust. When you paste a URL or upload a code snippet, the processing happens right there in your tab. Your proprietary code doesn't leave your machine for analysis. This means you can audit internal staging environments or sensitive client pages without worrying about data leakage. The feedback loop is instant because there is no network round-trip waiting for a cloud GPU to wake up. It feels native, fast, and secure.
Beyond the Alt Tag
The name might suggest a focus on image descriptions, but the tool does much more. While generating contextual alt text is a standout feature—using that private on-device AI to understand image context rather than just reading filenames—the scope is broader. It scans for ARIA misuse, keyboard trap detection, and color contrast failures.
I designed the interface to be non-intrusive. It doesn't clutter your IDE or your browser with pop-ups. Instead, it provides a clean, readable report that highlights exactly where the breakage is. For example, if you have a button that is only styled to look like a button but is semantically a div, the tool flags it immediately with a suggestion to switch to a <button> element or add the correct role. It’s not just about finding errors; it’s about understanding why they matter for screen reader users.
Why Build Another Tool?
The market is saturated with accessibility scanners. So why build another one? I found that most existing tools are either too expensive for small teams or too generic to be helpful for specific component libraries. Accessibility-alt is built for the modern developer workflow. It integrates seamlessly into the way we already work—copying code, pasting links, and iterating quickly.
By keeping the AI processing private and local, I’ve removed the friction of account setup for quick checks. You can open the tool, paste your code, and get actionable insights in seconds. It’s designed to be a "second pair of eyes" that you can consult whenever you’re unsure if your implementation is truly accessible. It’s not a replacement for human testing, but it is a powerful filter that catches the low-hanging fruit before it becomes a technical debt nightmare.
The Future of Inclusive Code
Building this tool has reinforced my belief that accessibility should be automated where possible and humanized where necessary. The private on-device AI handles the repetitive, pattern-based checks—like identifying decorative images versus informative ones—freeing up developers to focus on the nuanced user experience questions that machines can't answer.
I’m currently exploring how to extend this local-first approach to real-time DOM monitoring, which would allow developers to see accessibility scores update as they type in their code editor. It’s a challenging engineering problem, but one that could fundamentally change how we write inclusive code.
Have you integrated accessibility checks directly into your local development workflow, or do you still rely on post-deployment audits?
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