AI Crawl Fidelity measures whether AI systems can successfully access, interpret, and reconstruct the institution’s digital surfaces. If pages block crawlers, rely heavily on JavaScript, or contain structural inconsistencies, AI models receive incomplete or corrupted information. This leads to broken entity graphs, missing signals, and reduced visibility across AI surfaces. High crawl fidelity ensures AI systems can fully retrieve the institution’s architecture — titles, descriptions, schema, canonicals, internal links, and identity markers — enabling stable interpretive reconstruction.
Related EEI Resources
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Ensure all core content renders server-side or is crawlable without JS execution.
Avoid content gates, forced modals, or interstitial elements that obstruct crawlers.
Confirm robots.txt allows access to all public-facing surfaces.
Provide clean, semantic HTML with correct hierarchy and minimal layout shifts.
Use paginated, crawlable URLs instead of infinite scroll or dynamic loaders.
Test retrieval using multiple model previews: GPT, Perplexity, Copilot, Gemini, and ERNIE.
Maintain consistent mobile/desktop rendering to avoid conflicting interpretations.
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Heavy reliance on JavaScript that prevents AI crawlers from loading key content.
Pages gated by modals, cookie walls, or dynamic loaders that hide structural elements.
Disallowed paths or overly restrictive robots.txt directives.
Missing server-side content fallback for dynamic or interactive components.
Infinite scroll or UI-only pagination with no crawlable links.
Inconsistent rendering between mobile and desktop versions.
Broken or incomplete HTML preventing models from parsing structure and schema.
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