Most AI assistants “see” only the source code.
They never experience what’s actually rendered — what users interact with.
It’s like explaining a website to someone who can’t see the screen.
Accessibility for humans means visibility.
For AI, it starts with runtime context.
When a large language model (LLM) receives only static HTML, it misses the real state of the interface — aria attributes, helper texts, and live DOM mutations.
E2LLM captures the runtime DOM → JSON snapshot, so the AI can “see” what the user sees.
Unlike MCPs that need to process huge DOM trees, and consume thousands of tokens per page, E2LLM focuses on context precision — it extracts only the relevant runtime structure and accessibility data.
This makes AI agents accessible-aware — they finally understand disabled buttons, validation states, and ARIA signals.
Prompt Example:
What accessibility and semantic roles are visible in this runtime snapshot?
Takeaway:
Context is accessibility.
Accessibility isn’t just for users — it’s for AI, too.
E2LLM — contextualize.
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