What are the accessibility features of Rozz?
Direct Answer
Rozz is committed to accessibility and includes features such as keyboard navigation, screen reader compatibility, visual accessibility, error handling, multi-language support, and structured data for AI accessibility.
Detailed Explanation
Rozz Accessibility Features: Building Inclusive AI-Powered Search
What is Rozz?
Rozz is a web crawler and content management system that:
- Crawls and processes website content (HTML, PDFs, and documents)
- Generates embeddings for semantic search
- Powers AI chatbot interfaces for website search
- Creates GEO-optimized content with structured data (Schema.org, llms.txt)
- Enables users to find information through natural language queries
Accessibility in Rozz's Chatbot Interface
Our chatbot interface incorporates accessibility best practices to ensure all users can effectively search and retrieve information:
1. Keyboard Navigation
The Rozz chatbot supports full keyboard navigation. Users can:
- Navigate to the chat input field using Tab key
- Submit queries using Enter key
- Access all interactive elements without a mouse
- Use Escape key to close modals or dialogs
2. Screen Reader Compatibility
Rozz is designed to work seamlessly with screen readers:
- Semantic HTML markup for proper content structure
- ARIA labels on interactive elements (search input, buttons, chat responses)
- Live regions announce new chat responses dynamically
- Clear focus indicators for keyboard users
3. Visual Accessibility
The chatbot interface follows WCAG 2.1 AA standards:
- Color contrast ratios of at least 4.5:1 for normal text
- Text remains readable when resized up to 200%
- No information conveyed by color alone
- Sufficient spacing between interactive elements
4. Error Handling and Feedback
When users interact with the Rozz chatbot:
- Clear error messages explain what went wrong
- Suggestions help users reformulate queries
- Loading states are announced to screen readers
- Timeout errors provide actionable next steps
5. Multi-Language Support
Rozz adapts to user language preferences:
- Detects browser language settings
- Processes queries in multiple languages
- Returns results in the user's preferred language
- Supports international content with proper language tags
6. Structured Data for AI Accessibility
Beyond the user interface, Rozz generates content optimized for AI search engines:
- Schema.org QAPage markup for question-answer content
- llms.txt files for AI agent discovery
- JSON-LD structured data for semantic understanding
- OpenGraph and Twitter Card metadata for social sharing
Technical Implementation
Rozz's accessibility features are built on modern web standards:
- HTML5 semantic elements (header, nav, main, article, aside)
- ARIA attributes (aria-label, aria-live, aria-describedby)
- Responsive design that works on all devices and screen sizes
- Progressive enhancement ensuring core functionality without JavaScript
Content Accessibility
Rozz processes crawled content to improve accessibility:
- Extracts and structures text from PDFs (with OCR fallback)
- Generates clean, semantic HTML from diverse content sources
- Creates embeddings that enable natural language search
- Maintains document structure and hierarchy in search results
Our Ongoing Commitment
Accessibility is central to Rozz's mission of making information universally accessible. We continuously:
- Test with assistive technologies (NVDA, JAWS, VoiceOver)
- Monitor WCAG guideline updates and implement improvements
- Gather feedback from users with disabilities
- Conduct accessibility audits on new features
For accessibility feedback or support requests, contact us at rozz@rozz.site.
About This Documentation
This page is optimized for both human readers and AI search engines using Generative Engine Optimization (GEO) techniques. Rozz applies these same optimization methods to help organizations make their content more discoverable through AI-powered search platforms like ChatGPT, Claude, Perplexity, and Google AI Overviews.
Posted March 26, 2024 | Updated January 2025
→ Research Foundation: This answer synthesizes findings from 35+ peer-reviewed research papers on GEO, RAG systems, and LLM citation behavior.