Accessibility Checker Tool

The Vendor is required to provide accessibility checker tool to develop an intelligent web accessibility checker tool that helps organizations ensure their websites comply with WCAG 2.0 and 2.1 (and future versions) and section 508 accessibility standards.
- The tool should automate accessibility scanning, provide actionable AI-driven recommendations, and offer interactive dashboards and reports for continuous improvement.
- Accessibility checker tool requirements
1. Standards compliance
• The tool must be SaaS (cloud hosted) application
• The tool must support:
• WCAG 2.0 and 2.1 (a, aa, AAA levels)
• Section 508 guidelines
• The tool must categorize the issues per different WCAG 2.0 and 2.1 levels (A, AA, AAA).
• The system must be extensible to support WCAG 2.2 and future updates.
• End-user should be able to select WCAG version and one or more levels and/or section 508 guidelines
• The tool must have configurable automated and summarized reports for reporting non accessibility compliance issues for the whole website or selected pages.
• The tool must be able to identify pages with broken links
• The tool must be able to identify and link to the related WCAG section per issue.
• The tool must be able to check not only accessibility issues but also the website quality
• The tool must be SOC 2 compliant
2. Website scanning
• Ability to crawl and scan public and authenticated (login-protected) web pages.
• Scheduled and on-demand scans.
• Support for scanning single-page applications (spas), dynamic content, and multilingual websites.
• Support for scanning content behind dynamic interactions, such as expandable menus, modals, and accordions.
• Capability to scan pages generated by JavaScript frameworks
• Ability to scan mobile-responsive views and device-specific renderings.
• Simulate or assist in evaluating how users navigate a website or web application using only a keyboard, without a mouse.
o Many users with disabilities, especially those with motor impairments or vision loss, rely solely on a keyboard (e.g., using the tab, Shift+Tab, enter, and arrow keys) to interact with digital content.
o WCAG and section 508 guidelines require that all interactive elements (like menus, buttons, forms, pop-ups, etc.) be fully accessible via keyboard.
• Identify and simulate page flows that involve client-side routing (e.g., in spas) to ensure accessibility is maintained across navigation events.
• Support for prioritization of scanned content based on page traffic data (e.g., integration with google analytics) to focus remediation efforts on high-impact pages.
3. AI-powered recommendations
• Generate context-aware suggestions for fixes using AI/ML (e.g., propose alt text for images based on image recognition and page context).
• Recommend proper aria attributes based on usage patterns.
• Provide code snippets or GitHub copilot-style suggestions for developers.
• Improve accuracy over time by learning from accepted or rejected recommendations.
• The tool must allow administrators to configure automated issue correction workflows for different types of accessibility issues.
• The system should support the following correction modes:
o Auto-correct immediately (e.g., for low-risk issues like missing alt text or incorrect heading levels).
o Flag and correct only after manual approval by a reviewer.
o Flag only for reporting purposes without making any corrections.
• Administrators must be able to define these modes per issue type or severity level.
• The system should log each correction action for auditability and rollback if needed.
• The tool must display confidence scores and explain ability metrics for each AI-generated recommendation to help users understand the rationale and trustworthiness of the fix.
• The tool must support domain-specific learning, enabling tailored recommendations based on the organization's design system, content style, and historical issue patterns.
• The AI engine should differentiate between editorial and content-level fixes (e.g., poor link text) and technical and code-level fixes (e.g., missing aria roles), providing targeted suggestions by user role (e.g., developer vs. Content author).
• The system should provide adaptive suggestions that evolve based on page type (e.g., homepage vs. product page), user journey stage, or language context.
• Support for integration with design systems or component libraries to map recurring issues to standardized component updates or global design fixes.
• AI models must be trainable on user-provided labeled data (e.g., annotated examples of past issues and fixes) to improve context-specific recommendations.
• The tool must provide recommendations not only for fixing issues but also for preventing them by alerting authors or developers at the point of content creation or design.