Run the instrument
Paste a page URL. INGEST fetches the page, compares the crawler response with the rendered page, checks structured data against the visible text, and shows the supported facts, entities, evidence, and contradictions.
What INGEST is
INGEST analyzes one URL for machine-understanding readiness. The question it answers is whether a machine can retrieve the page, read what rendering changes, parse structured data, extract supported facts, and keep the evidence.
Current checks: URL retrieval and crawler access, robots policy, raw HTML versus rendered DOM, JSON-LD parse versus schema truth, atomic facts, entity parity, and evidence provenance. Coverage can be reported when components were measured. The public product does not emit a recommendation list, an SEO score, or a citation forecast. Open-source SEO software is a different page: open SEO tools. The commercial work that follows an audit is AI Search Optimization.
How it works
- Fetch like a machine — raw HTML, crawler identities, robots policy, and rendered DOM, kept separate.
- Extract atomic facts — only propositions the source supports. Questions are not claims. Qualifiers survive.
- Compare representations — visible HTML vs JSON-LD vs other machine layers. Contradictions stay visible.
- Preserve evidence — every finding links back to source text, selector, and measurement confidence.
Example: live fact tension
Production pages often publish the same question with incompatible answers across layers:
- Sephora · Structured Offer: “InStock · ships free with Beauty Insider on orders $50+.”
- Sephora · Visible PDP: “Currently sold out online. Check your local store for availability.”
The same class of tension shows up across other surfaces INGEST is built to catch:
- Genentech — labeled usage excerpt vs a stale FAQ still citing a prior revision
- Car sales & rentals — JSON-LD daily rate excluding fees vs “all-in” hero copy
- Apartment rentals — structured rent that omits pet rent quoted on the leasing page
- Vacation stays — free cancellation in schema vs non-refundable visible terms
Scope may reconcile these. INGEST keeps both assertions and does not auto-pick a winner.
What the instrument measures
- Public crawler identities (access matrix)
- Raw HTML vs rendered DOM / JS dependency
- JSON-LD parse validity ≠ schema truth
- Atomic facts with assertion mode
- Human/machine parity classifications (MATCHED, HUMAN_ONLY, MACHINE_ONLY, CONTRADICTORY, STALE, AMBIGUOUS)
- robots.txt / sitemap / machine files
- Permanent regression fixtures and measurement-validity CI
Full contract: INGEST methodology.
Who it is for
Operators — see whether AI-facing answers can misstate offer, pricing, or policy from conflicting page layers.
Practitioners — audit crawler access, structured-data integrity, fact parity, and entity identity with permanent fixtures.
FAQ
What is INGEST?
INGEST analyzes a URL for AI-search and machine-understanding readiness. It checks crawler access, raw HTML against the rendered page, JSON-LD parse against schema truth, atomic facts, entity parity, and the evidence behind each finding. It is not an SEO score product and it does not publish recommendations or citation guarantees.
How is INGEST different from the Search System Diagnostic?
The public diagnostic on /diagnostics/ runs bounded discovery checks on a host. INGEST goes deeper on a URL: crawler access matrices, raw HTML vs rendered DOM, JSON-LD parse vs schema truth, atomic fact extraction, and human/machine parity with evidence.
Does INGEST decide which conflicting claim is true?
No. When visible copy and structured data disagree, INGEST keeps both assertions and classifies the tension. It does not auto-pick a winner.
Is INGEST a ranking or AI-citation guarantee?
No. It measures machine-readable substrate and fact trustworthiness. It does not claim rankings, traffic, or citation rates.