# AI Claim Monitoring and Evidence Intelligence > Break AI answers into auditable claims with anchored quotes, factual verdicts, severity, contradiction receipts, and source attribution only when the response exposes reliable evidence. - Canonical: https://knitknot.ai/product/claim-intelligence/ - Category: AI claim monitoring, accuracy analysis, and evidence intelligence ## From answer to claim ledger KnitKnot segments each captured answer, extracts atomic checkable statements about the benchmarked company, and stores the claim text, response-derived quote, character span, polarity, factual verdict, severity, feature and competitor links, contradiction receipt, and supporting source when one can be attributed. The extraction is intentionally scoped to claims about the customer. Competitor-only statements, opinions, vague praise, and generic marketing language do not become factual accusations. ## Verdicts and receipts - Accurate: the claim matches the verified company brief. - Inaccurate: the claim directly contradicts a verified fact. - Fabricated: a rare, specific, checkable invention or impossible claim that contradicts the brief. - Unverifiable: the brief cannot confirm or refute it. This is the default when evidence is incomplete. An inaccurate or fabricated verdict must include a relevant receipt from the verified company brief and pass a separate contradiction check. If either check fails, KnitKnot downgrades the result to unverifiable. Absence from the facts ledger never proves a claim false. ## Company facts ledger The facts ledger stores current and historical company facts with kind, date, provenance, source URL, and confidence. Research-grounded and human-confirmed facts can ground claim validation. The system preserves superseded facts rather than rewriting history in place. ## Honest source attribution KnitKnot reads response-native citation signals through a deterministic ladder: nearby character offsets, numbered footnotes, inline links, or one unambiguous sole source. A claim binds only when the signal is close enough to its span. When no reliable signal exists, supporting-source attribution remains null. ## Derived scoring and consistent drill-downs Claim accuracy is derived from fact-checkable claims; unverifiable claims remain visible but do not enter its denominator. Competitive recommendation, feature verdicts, coverage, sentiment, and source balance are derived into a canonical evaluation record. Console, reports, and issue reconciliation read those same persisted rows. ## From repeated claim to Issue Claims belong to individual evaluations and can be replaced by a rescore. Repeated material problems are grouped into persistent Issues with deterministic signatures, evidence, reach, and activity over later benchmark runs. ## Frequently asked questions ### What counts as a claim? A claim is an atomic, checkable statement an AI answer makes about your company, such as a capability, limitation, identity fact, or comparison. Opinions, vague praise, and competitor-only statements are excluded from the factual claim ledger. ### What claim verdicts does KnitKnot use? Claims can be accurate, inaccurate, fabricated, or unverifiable. Unverifiable is the default when the available company brief is silent; absence from the facts ledger never proves a claim false. ### How does KnitKnot prevent false accusations? An inaccurate or fabricated verdict must carry a relevant fact receipt from the verified company brief and pass a separate contradiction check. Without that evidence, the claim is downgraded to unverifiable. ### Can KnitKnot trace every claim to a source? No, and it does not guess. A claim is linked only when the captured answer exposes a reliable citation signal, such as a nearby marker, inline link, character offset, or a single unambiguous source. Otherwise attribution is shown as unavailable. ### How is claim accuracy calculated? Accuracy uses claims that could be fact-checked: accurate, inaccurate, and fabricated claims. Unverifiable claims remain visible in the ledger but are excluded from the accuracy denominator. ### What happens when the same wrong claim appears again? Claim rows belong to individual evaluations. Repeated material problems are grouped into persistent Issues with a deterministic signature, evidence, reach, and run-over-run activity history. ## Related resources - AI Benchmarks: https://knitknot.ai/product/ai-benchmarks/ - Prompt Intelligence: https://knitknot.ai/product/prompt-intelligence/ - Issues: https://knitknot.ai/product/issues/ - Runs and scoring: https://knitknot.ai/docs/runs-and-scoring/ - Metrics reference: https://knitknot.ai/docs/metrics-reference/