Evidence Model
What SeeLLM observes directly, what it samples, and what it infers.
Why the distinction matters
SeeLLM combines several kinds of evidence. They answer different questions and must not be treated as interchangeable. A crawler request proves access to a URL; it does not by itself prove that an AI answer cited, quoted, or sent a person from that page.
Evidence types
Crawler visit
An observed server-side request classified as an AI crawler using request metadata such as user agent, network information where available, headers, and behavior. User agents can be spoofed, so high-stakes decisions should use verified vendor IP data when the vendor publishes it.
AI retrieval
A user-directed or search-oriented fetch when the platform exposes a distinguishable request signal, such as ChatGPT-User or Claude-User. Retrieval is stronger evidence of live use than a training crawl, but it still is not proof of a visible citation.
Sampled citation
A monitored URL appeared as a source in a documented sample of generated answers. Citation checks are samples: results can vary by prompt, engine, geography, model, account, and time.
Quoted text
Text from a monitored page matched text in a sampled answer using SeeLLM's matching method. The result must include the matched passage, sample context, and confidence or matching rule.
AI referral
A human request reached the site with a detectable referrer from an AI product. Some clients strip referrers, so measured referrals are a lower bound rather than a complete count.
Crawled, not cited
Crawler evidence exists, but the page was absent from the stated citation sample. This identifies a gap worth investigating; it does not prove that the page is never cited.
Source gap
Another source appeared in the same sampled answer set where the monitored page did not. A source gap is tied to the recorded query set and observation window.
Provenance shown with every result
SeeLLM should identify whether evidence is observed, sampled, or inferred, together with its URL, platform, observation window, and collection method.
Known limits
- Crawler identities can be spoofed unless network ownership is verified.
- AI products can strip referral headers.
- Citation results are non-deterministic and must be sampled repeatedly.
- A crawler visit cannot establish how a model used the fetched content.
- Absence of observed evidence is not proof that no activity occurred.