Modern JavaScript sites often look perfect to humans—but AI systems frequently see something very different.
DataJelly generates an AI-friendly Markdown version of each page so AI crawlers can reliably read the real content, understand structure, and extract the important parts without getting distracted by UI noise.
If Markdown generation fails for any reason, DataJelly falls back to serving the normal HTML snapshot.
When DataJelly takes a snapshot of your domain, it saves the fully rendered HTML. From that HTML, it generates clean Markdown—this is what gets served to AI bots when the feature is enabled.
During normalization, DataJelly fixes a ton of structural issues, producing output that's significantly better than a default HTML-to-Markdown conversion.
Each Markdown snapshot starts with a small, consistent header so AI systems always know what they're reading:
Then the body content follows. This makes it easy for AI systems to quickly identify the page and its purpose before reading details.
To produce a clean AI view, DataJelly removes the content that hurts extraction quality:
The goal is simple: keep what humans came to read, remove what AI doesn't need.
After removing noise, DataJelly attempts to select the page's "real content root"—usually the main article or main content container. This improves consistency across frameworks and builders where the page HTML can include lots of layout wrappers.
Some sites put important user-facing text inside forms (especially signup CTAs).
Instead of dropping that content, DataJelly preserves the visible call-to-action text so it remains readable in the AI view.
AI extraction breaks down fast if links are messy or incomplete. DataJelly normalizes URLs so the Markdown view is reliable:
Relative links are converted into usable links whenever possible. Broken "local file" style URLs are corrected into normal web URLs.
The Markdown view keeps links clean and readable rather than packed with UI-only attributes.
If an image tag is missing a valid source, it's dropped rather than polluting the output.
Many modern sites use clickable cards (a link wrapping an image + headline + content). That structure can create ugly, nested, hard-to-read Markdown.
DataJelly applies fixes so "card-based" pages produce readable Markdown rather than one giant linked blob.
AI systems rely heavily on headings to understand structure. DataJelly normalizes headings so:
After conversion, DataJelly runs cleanup passes to fix common Markdown artifacts:
The result is a Markdown output that is both human-readable and LLM-friendly (structured, clean, and token-efficient).
Sometimes a page does not contain meaningful content after rendering, or it's not a normal HTML content page (for example, sitemap XML).
In those cases, DataJelly returns an empty Markdown result and uses the standard HTML snapshot instead. That ensures bots always get the best available representation.
It's a clean, structured Markdown version of your page that DataJelly generates specifically for AI crawlers, LLM-based agents, and answer engines—optimized for comprehension and citation.
Markdown is dramatically more token-efficient than HTML. It removes structural noise, making it easier for LLMs to understand content semantics and extract the information they need for answers and citations.
DataJelly falls back to serving the normal HTML snapshot. Bots always receive the best available representation of your page.
No. DataJelly normalizes links (making relative URLs absolute) and removes only broken images. Valid content links and images are preserved in the Markdown output.
Site chrome like headers, footers, navbars, and breadcrumbs are removed during noise filtering to produce a focused, content-only Markdown view.
Yes. DataJelly preserves visible call-to-action text even when it appears inside form elements, so your key messaging remains readable.
Yes. DataJelly renders your JavaScript app first to capture the full DOM, then generates the Markdown from that rendered output—framework agnostic.
AI systems rely heavily on headings for structure. Normalizing heading levels creates a consistent, scannable outline that improves summarization and retrieval accuracy.
Connect your domain and DataJelly will generate clean, token-efficient Markdown for every page—no code changes required.