A public mental-health content platform serves a retrieval-first AI assistant (here called the assistant) to anonymous visitors who arrive carrying personal distress. The assistant is a consumer information tool, not a clinical or diagnostic system, and the deployment risk is specific: a fluent unsupported sentence reaches a vulnerable reader under the platform’s authority. This report describes the safety and reliability architecture that constrains that risk in production: an input safety classifier, retrieval-first tool use that answers only from a corpus of roughly 800 reviewed articles, sentence-level source-support checks applied before release, a partial-answer recovery step that returns the cited portion instead of refusing outright, and a clarifying question when retrieval is ambiguous. On a fixed 107-probe credentialed evaluation the strict unsupported-claim rate moved from 6.5% to 0.9% (1 of 107) across three hardening iterations while the refusal rate fell from 51.4% to 21.5%, the inverse of the usual safety-versus-coverage trade. Anonymous, account-free access is the positive-impact target of this deployment, and the measurement above treats safety and access as one goal rather than opposing ones. We report the governance choices that bound the deployment (the non-clinical boundary and how it is communicated, anonymous access, retrieval-restricted answering) and the honest limits: this is a consumer tool with no clinical-efficacy study, and 0.9% is one held-out measurement, not a guarantee on open traffic. The contribution is a documented deployment pattern for releasing a health-adjacent LLM to the public with the unsupported-claim rate held against a fixed gate, not a claim of clinical benefit.

Language-model-assisted pipelines emit theorems, experiment reports, and literature claims faster than any reviewer pool can check them. This paper argues that the knowledge base such pipelines demand is not a store of extracted facts but a claim ledger: a knowledge base whose entries pair each claim with the artifact that verifies it (a machine-checked proof, a rerunnable receipt, or a resolved forecast) and whose gates fail closed, blocking an unverified claim from entering the record rather than scoring it after the fact. We ground the argument in three mechanisms already running in one research operation: a twenty-two-gate manuscript-admission stack, a Lean 4 library in which the claim is the artifact, and a sealed-prediction registry with forced resolution. We then state what a community-scale claim ledger would require of its schema, its adjudication protocol, and its incentives. The evidence is a single operation and does not establish generalization; the contribution is the knowledge-base-construction framing and the fail-closed gate architecture.