Trust Veracity category guide
AI verification and release control for consequential work.
Trust Veracity verifies one AI-generated business artifact against authoritative evidence, calculations, and workflow rules before it is shared, filed, exported, or used downstream. AI verification establishes what the checks can support; release control turns that finding into Release, Hold, or Block.
The checkpoint
From generated output to a defensible decision.
AI output can scale faster than human review.
How it differs
Model evaluation measures how a model tends to perform across tests. Observability explains what happened in a system. Governance defines policies. Trust Veracity operates at a different point: it checks one concrete work product against external evidence and explicit requirements before that work affects the next system or decision.
What gets verified
Reports, financial models, investment memos, spreadsheets, regulatory filings, workpapers, underwriting summaries, operational reports, structured payloads, and other consequential business artifacts can be checked when the workflow has defined evidence and requirements.
How Trust Veracity works
Claims and calculations are mapped to relevant source records, applicable formulas or queries are recomputed, required conditions are tested, and unresolved exceptions remain visible. Release means configured checks pass; Hold means targeted review is needed; Block means a material contradiction or policy failure prevents downstream use.
Straight answers
Questions worth asking before you rely on a pass.
Does release control prove the entire output is correct?
No. It establishes only the properties covered by the configured evidence and checks. Scope, source quality, omissions, and unresolved judgment still matter.
What happens when the evidence is incomplete?
The work should not silently pass. Trust Veracity can return Hold or Block when a required relationship, source population, calculation, or rule cannot be established.
Is this the same as model evaluation or an LLM judge?
No. Model evaluation measures aggregate behavior, and an LLM judge asks a model to assess output. Trust Veracity checks one work product against external evidence and explicit requirements.
Can it replace a subject-matter reviewer?
No. It automates repeatable checks and directs attention to ambiguity, contradiction, missing support, and judgment that still requires an accountable person.
What does AI output validation mean here?
It is the practical act of checking one generated output. On this site, output validation is part of the broader AI verification and release-control workflow rather than a separate product category.