Citation and claim checks answer questions about a source or a generated answer. Kerynex addresses a narrower question: what did a bounded retrieval worker fetch, when did it arrive, and over what route? The architecture records the retrieval event; it does not determine whether a source is true, supports a claim, or was relied on by a model.
Kerynex is a pre-sale guide and sanitized reference implementation for engineers who want inspectable retrieval-event records: bounded, SSRF-safe fetching, fetch-time hashes, transport metadata, and explicit failure/escalation handling. Captured, not certified: a fetch-time record, not a verdict on the source.
When downstream systems rely on retrieved context, a broken citation is not an edge case. It surfaces in court sanctions and in statutory enforcement.
In published tests, 11–57% of AI-generated citations are fabricated across tested models: non-existent URLs, hallucinated DOIs, or real URLs pointing at unrelated content.
Cross-model citation-fabrication studies, 2024–2026 aggregates [B]Transparency duties under the EU AI Act fall in the tier carrying fines up to €15M / 3% of total worldwide annual turnover, in force since August 2, 2026.
EU AI Act Art. 99(4)(g), via EC AI Act Service Desk [A]Attorneys sanctioned by a federal court for filing ChatGPT-fabricated case law, submitted without verification of what the retrieval actually returned.
Mata v. Avianca, 678 F. Supp. 3d 443 (S.D.N.Y. 2023) [A]{
"raw_sha256": "sha256:d69204b4fa3f…",
"retrieved_at": "2026-08-24T14:02:11Z",
"http_status": 200,
"transport": "httpx",
"canonical_url": "https://example.com/doc.html",
"redirect_chain": ["https://example.com/doc"],
"elapsed_ms": 412,
"bytes_received": 18243
}
A pre-sale architectural guide plus a sanitized reference implementation. The validated internal pipeline exists; the buyer-facing package ships only after it is built and passes fresh-clone validation in its supported environment.
How to build a bounded retrieval layer that records what it fetched, with explicit limits and failure handling. It is not a compliance or legal guide.
A reference fetch worker with explicit IP egress guards, DNS rebinding mitigations, response-size ceilings, and strict domain policy enforced before any payload reaches memory.
A structured record for raw and normalized hashes, timestamps, routing identity, HTTP/content metadata, redirect history, and fetch outcome details.
Success, failure, escalation, and health classifications with local JSON records and aggregated escalation output. The pipeline records escalation events; it does not execute browser, crawler, Firecrawl, or OCR tiers.
Reliable retrieval is an active engineering space, and other projects attack adjacent parts of the problem:
Kerynex Artifact Relay teaches the retrieval-event boundary: SSRF-safe bounded fetch, SHA-256 artifact hashing, transport metadata, and explicit failure/escalation records. It does not certify a source, support a claim, or establish later record integrity.
This is paid early access through Gumroad. Here is the whole deal before you decide anything.
This is a pre-sale early-access bundle, not a finished product. You pay now; the bundle ships only after it is built and validated from a clean checkout in the supported Linux / Python 3.13 environment. If the validation gate misses its threshold, purchases are refunded in full to the original payment method. Educational product: not legal advice and not a compliance certification.