The authority layer for AI-generated work
AI can produce the work. Authority decides whether it moves.
Trust Veracity is the independent verification and release-control layer between machine-produced knowledge work and consequential action. It checks each work product against authoritative evidence and organizational requirements before it is shared, filed, exported, or used to make a decision.
AI-GENERATED FINANCE MEMOQ3 margin outlookClaim under review14%
Source evidence2 records attached
CalculationMismatch found · 23.7%
Business ruleFinancial reporting
Inspect sources & calculation traceMismatch found
FY2026.csvRevenue · $100.0M
Q3 planning workbookOperating income · $23.7M
Claimed value14.0%
→Verified value23.7%
$23.7m operating income ÷ $100.0m revenue = 23.7% verified margin
Operating income comes from the Q3 planning workbook; revenue comes from FY2026.csv.
Observed difference: 9.7 percentage points
Observed difference exceeds the configured limit.
BLOCKCorrect the claim before release.
Why this layer exists
Production is scaling faster than authority can be assigned.
Model reliability tells you how a system tends to perform. It does not establish whether this specific piece of work is correct, supported, and allowed to matter.
Where Trust Veracity fits
Reliability tells you what a system can do. Authority decides what work can do.
Trust Veracity works alongside model evaluation, observability, GRC, cybersecurity, and human review. Its question is distinct: has this specific machine-produced work earned organizational authority to affect the outside world?
| Category | Primary question | Trust Veracity's role |
|---|---|---|
| Model evaluation | How a system tends to behave across benchmarks or test sets | Checks whether one specific AI-generated work product is supported and ready to proceed. |
| Observability | What a model, agent or system did over time | Uses trace context where useful, but answers a different question: whether this work can proceed. |
| GRC and policy systems | What policies, controls and approvals govern the organization | Applies those requirements to a specific AI-generated work product at the release boundary. |
| Cybersecurity tooling | Whether systems, assets or data are exposed to security threats | Addresses a different control surface: whether the work has the support required to move downstream. |
| Human review | Who resolves exceptions and approves the next action | Keeps people accountable while making evidence, exceptions, and the authorization decision explicit. |
| Veracity | Whether this specific AI-generated work has earned authority to proceed | Returns Release / Hold / Block and keeps the verification record attached. |
Focused human judgment
Human judgment where it matters.
Trust Veracity automates repeatable checks, then directs attention to the parts of work where evidence is ambiguous, contradictory, incomplete, or judgment is required.
Every work product routed through Trust Veracity leaves a verification record: evidence, checks, unresolved issues, reviewer actions, and the final release decision.
Automate repeatable checks
Surface the exceptions that matter
Keep accountable judgment with people
Checks completeReleased
Q3 operating margin increased 23.7% relative to Q2
- Evidence checked
- Approved finance snapshot · Q2 and Q3 revenue and operating-income rows · current period
- Why it passed
- The source bindings are current and authoritative. Q2 margin recomputes to 27.9%, Q3 to 34.5%, and the relative increase recomputes to 23.7%, matching the artifact. All configured checks passed for the declared scope.
Release decision
Configured proof obligations are satisfied. This artifact may proceed downstream.
Illustrative review record. No customer or regulated data is shown.
Who Vera is
Vera is the verification interface inside Veracity.
Veracity uses Vera to surface what the AI-generated artifact says, what evidence supports it and what the configured checks allow before release. Vera is not the LLM that generated the artifact; Vera is the deterministic verifier experience applied to the artifact under review.
- Starts with the AI-generated artifact Vera begins with the artifact and the claims it makes.
- Surfaces evidence and checks Vera connects supported claims to source evidence and configured checks.
- Makes the decision legible Vera makes Release / Hold / Block clear before the artifact moves downstream.
Checking
Decision pending
Vera is evaluating the AI-generated artifact against its evidence and configured checks.
What Vera sees
The AI-generated output alongside its evidence and configured checks.
A plausible output is not release-ready until its claims satisfy the workflow's business rules and source evidence. Rules are configurable by regulatory and compliance requirements, companies, and other stakeholders.
Source-of-record evidence Business rules Release decision
AI-generated artifact
Q3 margin outlook
14%
Source records
Business rules
Decision
Block
Start with the work · Who Trust Veracity is for
For teams accountable for AI-generated work that has to hold up.
Choose the workflow closest to yours. We’ll show the evidence, rules, and release decision that matter for your workflow.
Financial servicesFinancial model or investment memo
Re-compute model math, leverage ratios, and covenant terms before capital deployment.
AccountingAccounting workpaper or tax provisionRecalculate tax provision formulas, revenue schedules, and ledger balances before filing or audit use.
LegalContract or regulatory filingMap extracted clauses and disclosures to authoritative statutory or contractual source text.
InsuranceUnderwriting or claims summaryCheck loss ratios, policy terms, and claims evidence before an underwriting or claims decision.
HealthcareHealthcare operational reportValidate operational summaries against approved evidence without requiring patient or protected health information.
Another workflowBring a different consequential work product.We can map the evidence and release requirements around your actual workflow.
Trust Veracity explained
Simple answers about AI work review.
What is Trust Veracity, in plain English?
It checks a specific piece of AI-generated work against the evidence and rules that apply to it, then records whether that work can move forward.
Does it guarantee that an AI output is correct?
No. It can establish only the properties that the configured evidence and checks actually test. It does not repair incorrect source data, resolve every ambiguous judgment, or prove that an entire artifact is universally correct.
What happens when the evidence is missing or incomplete?
The work should not silently pass. Trust Veracity can return Hold or Block when a required relationship, source population, calculation, or policy condition cannot be established. A missing proof is an unresolved condition, not evidence that the claim is true.
Does it replace human review?
No. It is intended to narrow human review to unresolved exceptions and judgments. If a workflow depends on interpretation, materiality, or authority that software cannot establish, a person still needs to make that decision.
Why are the AI provider’s citations not enough?
A citation can show what a system retrieved or named. It does not by itself establish that the source is authoritative and current, that the claim follows from it, that the calculation is reproducible, or that the work satisfies your release requirements. Those relationships still need to be checked.
Is this just another model evaluator or LLM judge?
No. Model evaluation asks how a system tends to perform across tests. An LLM judge asks a model to assess output. Trust Veracity focuses on a specific work product, external evidence, explicit checks, and the decision to Release, Hold, or Block it.
When is this not a good fit?
It is not a shortcut for work with no authoritative evidence, no repeatable checks, and no meaningful release requirement. It is also not a compliance certification, a security product, or a replacement for domain experts and accountable approvers.
Does it need sensitive or regulated data to be useful?
No data is required to explore the site or complete the fit assessment. A real deployment may involve sensitive business or regulated data depending on the workflow, so the evidence boundary, access controls, retention, and deployment requirements must be evaluated before use. We do not claim that a generic demo proves healthcare or regulatory compliance.
See how Vera works
Follow the release boundary from AI-generated artifact to evidence-backed decision.