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                "level": 2,
                "text": "1. The question"
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                "level": 2,
                "text": "2. Measurement layers"
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                "text": "3. Hard separations"
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                "text": "4. Fail-closed extraction"
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                "text": "5. Coverage vs score"
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                "text": "6. Evidence contract"
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                "level": 2,
                "text": "7. Validity & regressions"
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                "text": "8. What INGEST does not claim"
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                "text": "INGEST overview"
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                "text": "Decision Traces"
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                "text": "Search system diagnostic"
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        "markdown": "---\ntitle: INGEST Methodology — Measurement Model | NRLC.ai for AI...\ndescription: How INGEST measures machine comprehension: crawler access, raw vs rendered DOM, structured data, atomic facts, parity, coverage, and evidence.\norganization: Neural Command LLC\ncanonical: https://nrlc.ai/en-us/tools/ingest/methodology/\n---\n\n## 1. The question\n\nSearch engines and AI systems do not see your brand the way a human does. They see bytes, redirects, HTML, scripts, structured data, and whatever survives policy and rendering. INGEST measures that substrate.\n\nThe product question is: can a machine attribute important claims cleanly?\n\n## 2. Measurement layers\n\n- Fetch & access — default request plus public crawler identities; status, redirects, body bytes, and challenge signals per agent.\n- Robots policy — matched group/rule and allow/disallow stay separate from HTTP usability. Unreadable robots.txt remains UNKNOWN.\n- Raw HTML vs rendered DOM — title, canonical, H1, links, JSON-LD, and visible text compared; JS-added content measured as dependency.\n- Structured data — JSON-LD parse validity is never treated as schema truth.\n- Atomic facts — propositions preserve subject, predicate, object, source text, assertion mode, polarity, and modality where detected. Interrogatives are not asserted facts.\n- Parity & entities — MATCHED, HUMAN_ONLY, MACHINE_ONLY, CONTRADICTORY, STALE, or AMBIGUOUS. Qualifier/scope differences do not auto-escalate to contradiction.\n## 3. Hard separations\n\n- Robots policy ≠ HTTP delivery ≠ content usability.\n- JSON parse OK ≠ schema correct.\n- Question ≠ claim.\n- Hash-only noise ≠ differential delivery.\n- UNKNOWN ≠ zero. Unmeasured components stay null.\n## 4. Fail-closed extraction\n\nWhen extraction is uncertain, INGEST prefers NO_FACT, UNKNOWN, AMBIGUOUS, or INFERRED over an explicit fact that required silent reconstruction.\n\n- Correct extraction\n- Honest under-extraction\n- Unsupported inference (rejected)\n## 5. Coverage vs score\n\nMeasurement coverage reports how many leaf components were actually measured. A composite machine-ingestion score may exist alongside incomplete coverage. A high score with unmeasured freshness or search interpretation does not imply those were observed.\n\n## 6. Evidence contract\n\nFindings and atomic facts carry inspectable provenance where available: source URL, representation type, selector or location, raw source text, extraction method and assertion mode, and confidence tier. If a claim cannot point back to evidence, it is not treated as trustworthy.\n\n## 7. Validity & regressions\n\nPermanent fixtures and unit/integration markers lock behavior (for example BCP-47 locale handling, robots group selection, 404 usability, and interrogative-not-fact extraction). Local and CI run npm run fixtures:validity in the INGEST lab. No measurement bug is fixed without a deterministic test.\n\n## 8. What INGEST does not claim\n\n- It does not reproduce private crawler internals.\n- It does not decide which conflicting assertion is “true.”\n- It does not treat cloaking labels as legal conclusions.\n- It does not turn marketing language into unsupported service facts.\n- It does not replace Search Console, SERP, or live AI-answer probing (deferred).\n\n\n---\n\nSource: https://nrlc.ai/en-us/tools/ingest/methodology/\nPublisher: Neural Command LLC\nLicense: Editorial use with attribution\n"
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