Run a paired before/after experiment on any URL. See exactly how HTML structure affects LLM fact extraction — token cost, accuracy, and hallucination rate.
Large language models like GPT-4, Claude, and Gemini consume raw HTML when extracting facts from web pages. Bloated markup — inline styles, deeply nested divs, tracking scripts — inflates token counts and degrades extraction accuracy. The Extraction Lab quantifies this effect by comparing how an LLM processes your original page versus a structurally optimized twin with the same visible content but cleaner HTML.
The experiment is fully deterministic: no LLM API calls are made. Instead, we simulate extraction using token counting, structural analysis, and a fact-density heuristic to estimate accuracy, cost savings, and hallucination risk before and after optimization.
We fetch the page, create a structurally optimised twin, and compare extraction metrics side-by-side. Zero LLM API calls — fully deterministic.
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The Golden Semantic String is what an LLM actually reads: Title + Meta + H1 + H2s + first ~600 words of body content + JSON-LD entities. Everything else is noise.
Run a comprehensive crawl with template-level analysis, historical tracking, and automated remediation snippets.
Start Deep Audit →Our whitepaper documents the full methodology, case studies, and statistical proof that HTML structure causally determines LLM extraction accuracy.
Read the Whitepaper →