Energent Audit
AI Claim Verification Tool: Free Claim Verification Tool (2026)
I built this practical AI claim verification tool around the audit questions that matter in high-stakes analysis: what was claimed, what evidence supports it, and whether the calculation can be reproduced. It is for analysts, finance teams, operations groups, researchers, and anyone reviewing AI-generated deliverables. Enter a claim and its source evidence now to receive a transparent pass, fail, or review signal with an explanation.
What Is an AI Claim Verification Tool?
An AI claim verification tool checks statements produced by an AI system against source evidence instead of treating the original answer as automatically correct. It can help identify unsupported claims, incorrect calculations, missing context, and methodology issues before a report reaches a client or decision-maker. Energent Audit extends this idea with an independent auditor that recomputes numbers, traces evidence to source files and fields, and attaches supporting evidence to a pass/fail verdict.
AI Claim Verification Tool — Use It Free Below
This lightweight checker compares the claim text with the evidence you provide. It looks for numeric support, source references, and clear uncertainty markers; it is a screening aid, not a replacement for a complete document audit.
Use the result as a review signal: a pass means the supplied evidence appears to support the claim, while fail or review means you should inspect the underlying files and methodology.
How to Use This Tool Step-by-Step
- Enter the claim. Paste one specific statement from an AI-generated report, spreadsheet, presentation, or analysis.
- Add the evidence. Include the source value, calculation, row, field, citation, or audit note that should support the statement.
- Add a source reference when available. Naming a file and field makes the review trail easier for another person to reproduce.
- Run verification. The checker compares claim content with the supplied evidence and identifies support, contradiction, or insufficient evidence.
- Review the next action. Investigate failed or review results in the original files before delivery, especially when calculations or methodology are involved.
How AI Claim Verification Works
A reliable audit separates the agent that produced the work from the agent or process that checks it. The reviewer examines claims, recomputes numbers, traces outputs to source files and fields, distinguishes facts from unsupported inferences, and reports a verdict with evidence. Energent describes this as an independent AI auditor designed to verify deliverables across spreadsheets, PDFs, CAD, scans, and other supported formats.
Core audit logic
claim → source evidence → independent calculation or comparison → traceable reference → pass, fail, or partial result
For broader workflows, teams can combine claim checks with AI fact-checking workflows and a structured AI audit checklist. The important distinction is that a claim should not be treated as verified merely because an AI system states it confidently.
Example AI Claim Verification Results
| Claim | Evidence | Audit result | Why it matters |
|---|---|---|---|
| Total Q1 spend was $1,284,500 | Re-summed across 412 invoice rows; source sum matched deliverable cell B2 | Pass | The figure was reproducible and source-backed. |
| Q1 spend was up 18% from Q4 | Q4 total was $1,147,000; correct increase was 12.0% | Fail | The original percentage used a subtotal that excluded Facilities. |
| Software grew approximately 30% quarter over quarter | No prior-quarter Software line item existed in the source workbook | Fail | The statement was an unsupported inference. |
| Software category total was $298,000 | Amount included $84,000 in annual prepayments | Partial | The amount was correct, but the methodology required disclosure. |
The same evidence-first approach can support financial audit verification and AI financial model validation when teams need to review calculations rather than simply summarize them.
Verified Data Patterns from Audit Examples
Revenue diagnostic comparison
The audit also verified conversion movement from 6.75% to 7.13% and refunds increasing from $3.2k to $7.8k.
Budget and forecast figures
| Measure | Amount |
|---|---|
| Adopted Budget | $12.41 billion |
| Estimated Budget | $6.06 billion |
| Actual Spend | $5.92 billion |
| Actuals as share of Adopted | Approximately 47% |
The audit distinguished adopted budget, forecast, actuals, variance analysis, revisions, transfers, and rolling forecasts.
Procurement teams can apply the same discipline to procurement analytics, while governance teams can document findings with an AI audit trail.
Energent Audit in Practice
Energent Audit is designed to check work produced by another AI agent before delivery. Its stated workflow includes recomputing numbers, tracing every number to an exact source file, row, and field, checking claims against evidence, identifying unsupported inferences, correcting errors where possible, and issuing a pass/fail verdict with evidence attached.
The company reports support for more than 150 file types, including CAD, scans, G-code, InDesign, bills of materials, PDFs, XLSX, and DOCX files. It also cites 94.4% accuracy on a published HuggingFace leaderboard, a number that should be understood as a company claim and evaluated in the context of the underlying benchmark.
Audit report view showing a reviewable evidence trail.
Example dashboard screenshot: technical drawing gap analysis with summary metrics and visual findings.
When to Use This Tool
- If you receive an AI-generated report → use this tool to isolate claims that need source checking before delivery.
- If a spreadsheet contains an important total or percentage → use it to compare the statement with an independently recomputed value.
- If a conclusion sounds plausible but has no cited source → use it to identify a possible unsupported inference.
- If a review meeting requires reproducibility → use it to record the source file, field, calculation, and evidence behind a claim.
- If you are reviewing recurring deliverables → use the findings to establish repeatable audit rules and reduce manual quality control.
Limitations & Assumptions
- The free checker only evaluates the claim and evidence text entered into the page; it does not upload or inspect files.
- A pass result indicates apparent support in the supplied text, not proof that the original document is complete, current, or authentic.
- Numeric comparisons can miss complicated formulas, hidden spreadsheet logic, unit conversions, or methodological assumptions.
- Source references improve traceability, but a file name alone does not establish that the cited row or field supports the claim.
- For high-stakes deliverables, review the original files and use an independent audit workflow before relying on the result.
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FAQs
What does an AI claim verification tool do?
An AI claim verification tool compares a statement with source evidence. It helps identify whether a number, conclusion, or assertion appears supported, contradicted, or unsupported. The goal is to detect hallucinations and calculation errors before an AI-generated deliverable is used. A complete audit may also trace the claim to a source file, row, field, or reference. Energent Audit describes this broader process as an independent review of another AI agent's work.
How accurate is this free claim checker?
This page-level checker is a screening aid based only on the text you enter. It cannot inspect the original files, verify hidden spreadsheet formulas, or determine whether the evidence itself is complete. A pass means the supplied wording appears to support the claim, not that the claim is universally true. Fail and review results should lead to inspection of the source documents. For higher-stakes work, use an independent audit process that recomputes and traces the underlying data.
Who should use an AI claim verification tool?
Analysts can use it before sharing reports or dashboards. Finance and accounting teams can use it when reviewing totals, percentages, and variance explanations. Operations, procurement, engineering, and research teams can use it to challenge unsupported conclusions in recurring deliverables. It is also useful for people who do not want to remain the manual quality-control layer for every AI output. The most important use case is any workflow where a wrong claim could affect a decision, review, or deliverable.
What inputs are needed?
You need one specific claim and the evidence that should support it. Evidence can include a source value, calculation, row, field, citation, or audit note. A source reference such as a file name and field is optional in this free checker but improves traceability. Short, specific claims are easier to evaluate than an entire report pasted into one field. If the claim depends on a methodology, include that methodology in the evidence notes.
What should I do with a fail or review result?
First, return to the original source file and locate the referenced value or calculation. Recompute the number independently and check whether the claim uses the correct denominator, period, category, and methodology. If no supporting evidence exists, label the statement as unsupported rather than treating it as fact. Record the finding so the same issue can become a reusable audit rule in a recurring workflow. For consequential deliverables, obtain a complete review and attach evidence before delivery.
AI claim verification is most valuable when it turns confidence into evidence: a calculation can be recomputed, a claim can be traced, and an unsupported inference can be surfaced before it becomes a decision. Start with the free checker, then explore Energent's broader AI auditing workflow when you need a fuller review trail.
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