An affordable alternative to expensive enterprise accessibility tools. Visual editor and AI-powered scanning that shows you exactly what students see, making it easy to fix accessibility issues in your Canvas courses without a massive budget.
Accessibility tools for Canvas LMS have been gatekept by enterprise EdTech companies charging $10,000 to $50,000+ annually. Instructional designers at small colleges, independent educators, and course creators are left with an impossible choice: spend a massive budget on enterprise tools or spend 40+ hours manually fixing accessibility issues in every course.
You inherit courses from past instructors with years of accessibility debt. You use Canvas's Rich Text Editor to make changes, not raw HTML. You need a tool that makes it easy to see what's wrong and fix it, without requiring a developer or breaking your budget. That's who FairStart is for.
FairStart provides an affordable alternative to $50,000/year enterprise tools. See exactly what students experience, identify issues visually without digging through code, and fix problems with AI-powered suggestions. Designed for educators who use Rich Text Editors, not developers who write HTML.
Industry-First Visual Editor: See what students actually see with Canvas-accurate HTML rendering. FairStart displays your content exactly as it appears in Canvas, with visual issue highlighting that shows problems in context, not buried in code.
Reverse-engineered Canvas .imscc format to reconstruct broken image and media paths from manifest metadata. Dynamic path resolution ensures all resources display correctly with no broken image icons or 404 errors.
AI-Powered Alt Text Generation: Using Claude's Vision API to intelligently analyze images, FairStart distinguishes decorative from informational content, generates context-aware alt text suggestions, and identifies complex images requiring long descriptions.
Seven Specialized AI Agents: Multi-agent architecture with dedicated scanners for Images, Headings, Links, Tables, Forms, Color Contrast, and Media. Each agent is fine-tuned for specific WCAG 2.2 AA success criteria, achieving 98% fix success rate.
Real Course Preview: Unlike basic scanners, FairStart shows embedded videos, images, and interactive content exactly as students experience them. Smart iFrame overlay system handles YouTube, Vimeo, and other embeds that typically fail in nested contexts.
Contextual Issue Highlighting: Issues appear directly on images, videos, and interactive elements with clear, visual labels. No more searching through code or decoding technical error messages. See exactly what needs attention in the context of your actual course content.
Watch how FairStart's visual editor makes accessibility remediation intuitive and accessible to everyone, regardless of technical expertise or budget.
Canvas-accurate visual rendering
Categorized issue detection
One-click AI-powered fixes
FairStart combines modern web technologies with cutting-edge AI to deliver a production-grade accessibility platform.
Challenge: Traditional accessibility scanners show raw HTML code or simplified previews. Educators needed to see exactly what their students see in Canvas LMS.
Innovation: Custom HTML rendering engine that replicates Canvas LMS styling with pixel-perfect accuracy. Injects Canvas-specific CSS (Lato Extended font, heading hierarchy, table layouts) and maintains exact visual fidelity.
Challenge: Canvas .imscc (IMS Common Cartridge) format is poorly documented. Official spec is vague about resource references and path resolution.
Innovation: Reverse-engineered the format through systematic analysis of real Canvas exports. Built intelligent resource linking algorithm that reconstructs working URLs from $IMS-CC-FILEBASE$ paths using manifest metadata.
Challenge: Generic AI models lack domain expertise for educational accessibility standards. Single-agent approaches miss nuanced violations.
Innovation: Seven specialized AI agents, each focused on specific WCAG 2.2 success criteria. Parallel execution reduces scan time by 6x compared to sequential processing.
Challenge: CSS ::after pseudo-elements don't work on replaced elements (img, iFrame, video, canvas), making it impossible to add visual labels using standard CSS techniques.
Innovation: Dynamic DOM element creation with absolute positioning for replaced element labels. Navy blue gradient design (#1E3A5F) with 26px offset positioning, drop shadow for depth, and z-index layering for proper stacking context.
Time savings vs. manual remediation (40+ hours reduced to less than 2 hours per course)
Fix success rate with AI-powered remediation suggestions
WCAG 2.2 AA success criteria automatically checked by specialized AI agents
API uptime on production deployment
src/ ├── api/ # API layer │ ├── app.py # FastAPI application │ ├── routes/ # Endpoint handlers │ └── middleware/ # CORS, rate limiting ├── core/ # Business logic │ ├── scanner/ # Accessibility scanning │ ├── fixer/ # Remediation logic │ └── imscc/ # File format handling ├── agents/ # AI integration │ ├── base_agent.py # Abstract agent class │ └── scanners/ # 7 specialized agents └── utils/ # Shared utilities
Automatic Cleanup: All uploaded content is automatically deleted after 24 hours. No persistent storage, no databases, no long-term retention.
Building FairStart required solving problems that had never been solved before. Here are the key technical and design lessons from creating a visual accessibility editor for Canvas LMS.
FairStart demonstrates the power of combining modern web technologies with cutting-edge AI to transform accessibility remediation. Whether you need a similar tool or want to discuss innovative EdTech solutions, let's talk.