Claim framing
Lenz converts a claim into a precise, testable statement before research begins, which helps reduce ambiguity and makes downstream verification more consistent.
Lenz is a fact-checking API and toolset for AI-generated content and other claims that need verification before they are published or acted on. The product is built to sit inside workflows, where it can screen output from AI systems, verify facts in drafts, or answer a factual question with cited evidence.
Its verification process is structured and auditable: a claim is framed, researched across independent sources, debated from both sides, reviewed by multiple models, and then returned as a verdict with citations and a reasoning trace. The site also presents Lenz as a drop-in layer for teams that want to block, publish, or escalate claims based on structured output rather than a manual review step.
Lenz converts a claim into a precise, testable statement before research begins, which helps reduce ambiguity and makes downstream verification more consistent.
The product searches multiple independent sources and reads the source pages themselves, extracting quotes and scoring evidence for authority, relevance, and recency.
Two models debate opposite positions, then rebut each other using only collected evidence, so disagreement is preserved instead of collapsed into a single average answer.
Three additional models review source reliability, logical support, and whether the wording and numbers match the evidence, adding a separate review layer before conclusion.
Verification returns a structured result with a truth classification, Lenz Score, citations, and a reasoning trace that can be audited or used in automated workflow branches.
The same core primitives are exposed through the API, SDKs, CLI, MCP, n8n, and Zapier, so the same verification logic can be used in different tools.
Use Lenz to check newsletter, blog, or product-update copy before it reaches subscribers or goes live, especially when a draft contains claims that need sourcing.
Verify enriched fields such as funding rounds, headcount, or tech stack data before importing records into a CRM or dataset.
Screen health, finance, or supplement copy where the full audit trail matters and a simple yes/no answer is not enough.
Add /assess as a pre-action check in agent workflows so the system can pause or continue based on whether a factual claim passes verification.
Use the /ask flow or an assistant integration to answer a factual question with grounded sources while working in tools like Claude, Cursor, or ChatGPT.
Lenz is designed as a fact-checking layer for AI pipelines. It can be used on claims generated by a product, or on a claim a person types in manually, and returns a sourced verdict with citations.
The site describes four API primitives: /extract, /assess, /verify, and /ask. The product also offers integrations through n8n, Zapier, MCP, CLI, and typed Python and TypeScript SDKs.
The verification flow shown on the site has five stages: framing, research, debate, panel review, and conclusion. The output includes a truthfulness label, a Lenz Score, citations, and reasoning trace.
The plans page shows a Free plan, plus Plus, Developer, Scale, and Enterprise options. Paid plans are self-serve and can be canceled any time, and the site says no card is required to start on the free plan.
Lenz is positioned for teams that want to prevent unsupported claims from shipping, including content publishing, data enrichment, customer support, regulated claims, and agent guardrails.
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