Energent Audit independently recomputes, traces, and validates AI-produced work before delivery, returning a reviewable pass/fail verdict with supporting evidence.
Trusted by 100k+ companies across the globe.
Energent Audit is an independent AI auditor that checks work produced by another AI system before it reaches a user. It recomputes numbers, compares assertions with original source files, validates spreadsheets and other deliverables, identifies unsupported claims, fixes errors where possible, and attaches evidence to a pass/fail report. It is designed for analysts, finance and accounting teams, operations, procurement, engineering, research, and enterprise workflows where generated work must be defensible.
The auditor is separate from the AI that produced the deliverable, creating an independent check rather than asking the original system to approve its own work.
The audit retraces figures, verifies the work, and shows how the conclusion was built.
Every number can be traced to its source file, row, field, formula, or reference. The result is a reviewable audit report rather than an unexplained confidence score.
Full audit-report screenshot displayed without cropping.
A Q1 spend audit independently re-summed 412 invoice rows. The $1,284,500 total passed, while the claim that spend was up 18% failed because the correct change from $1,147,000 was 12.0%.
Energent Audit checks more than narrative text. In one RTL dashboard review, aggregations passed, but column reversal and chart coverage failed because a chart omitted the final row.
A revenue audit reconciled line-item sums with order prices, separated 6,075 organic-search sessions from 5,726 direct sessions, and flagged refund growth from $3.2k to $7.8k as a likely supplier-quality issue.
When required inputs are missing, the audit records an unsupported claim or evidence gap. For example, it refused to calculate ROA when total assets were not available.
Reduce manual quality control by reviewing flagged exceptions instead of checking every row or assertion yourself.
Trace every material number to the exact source file, row, field, formula, or reference.
Catch calculation errors such as wrong denominators, omitted rows, mismatched totals, and incorrect growth rates.
Validate complete deliverables including spreadsheets, charts, formulas, ranges, formatting, and output files.
Make unsupported claims visible when the source set cannot support a conclusion.
Create defensible work with a complete, cited, reproducible audit trail for review meetings.
Provide the AI-produced report, spreadsheet, dashboard, document, or other supported files with their source material.
What you see: Your original work enters the audit.
An independent agent checks calculations, assertions, source lineage, formulas, charts, and output completeness.
What you see: Evidence, exceptions, and corrected findings.
Receive a pass, partial, fail, unsupported-claim, or evidence-gap outcome with supporting details.
What you see: A report you can stand behind.
| Audit example | Verified | Flagged or failed | Verdict |
|---|---|---|---|
| Financial analysis | $1,284,500 across 412 rows; top vendor at $312,000 | 18% growth claim; correct increase was 12.0% | Mixed findings |
| RTL dashboard | Sheet property and data aggregations | Column reversal and chart off-by-one range | FAIL |
| Consulting savings | Six checks; savings model and baseline data | No failed checks reported | PASS |
| Revenue diagnostic | Line-item sums, IDs, conversion and revenue movements | Refund increase and likely inventory issue | Diagnostic flag |
The audit found gross revenue increased from $83.3k to $84.8k, while net revenue declined from $80.0k to $77.0k as refunds rose from $3.2k to $7.8k.
| Execution path | Savings per FTE | FTEs for $1M |
|---|---|---|
| Pure Elimination | $305,173 | ~3.3 |
| Internal Backfill | $203,449 | ~4.9 |
| AI Efficiency, 40% gain | $122,069 | ~8.2 |
The consulting savings audit reported six checks, six passes, and no partial or failed checks. It also validated a $37.62 BLS wage in July 2026 and an approximately $101,724 fully loaded internal FTE baseline.
AI financial analysis becomes more defensible when calculations, assumptions, and source figures are independently checked.
AI fact checking is especially useful when a generated answer contains many claims across several source documents.
AI audit trails preserve the chain from a final figure back to the supporting file, row, field, or formula.
AI budget planning can be reviewed for denominator errors, omitted categories, and differences between adopted, estimated, and actual spend.
AI revenue analysis can surface changes that are easy to miss when gross revenue rises but net revenue falls.
AI balance-sheet analysis should distinguish calculated ratios from metrics that cannot be derived because source inputs are missing.
AI sales forecasting benefits from explicit checks on historical windows, assumptions, formulas, and projection outputs.
AI ratio analysis can be checked against source financial statements instead of accepted as an unverified narrative.
“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”
| Decision dimension | Energent Audit | Manual review | Original producing AI |
|---|---|---|---|
| Independence | Separate auditor agent | Human reviewer | Same system may self-check |
| Source traceability | Files, rows, fields, formulas, references | Depends on reviewer process | Not inherently provided |
| Deliverable checks | Narrative, calculations, charts, ranges, formatting | Can be comprehensive but manual | Focused on producing the output |
| Unsupported claims | Explicitly identified as gaps | Depends on expertise and time | May remain in the answer |
| Verdict and evidence | Pass/fail with supporting evidence | Usually written or remembered separately | Usually not an independent verdict |
Clients worldwide
Supported file types
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Fewer hallucinations claimed
Automated quality control for AI outputs means using an independent checking system to verify work created by another AI system.
The checker recomputes numbers, compares claims with source documents, and validates the final deliverable.
It can trace figures to files, rows, fields, formulas, or other evidence rather than treating the generated answer as a black box.
Energent Audit also identifies unsupported claims and evidence gaps when the available sources cannot prove an assertion.
The result is a pass/fail-style audit report that helps a team focus human attention on what is flagged.
Energent Audit is intended for teams that rely on AI-generated analysis or automated deliverables.
Relevant users include analysts, finance and accounting teams, operations, procurement, engineering and CAD teams, research groups, and enterprise customers.
It is especially relevant when a wrong number, unsupported claim, or incomplete file could create review, operational, or financial risk.
The system can audit work produced by AI systems other than Energent, so adoption does not require replacing every existing workflow.
Users can review flagged exceptions instead of manually checking every row when the audit is complete.
Energent states that the platform supports more than 150 file types.
Examples provided include PDFs, XLSX, DOCX, scans, CAD, G-code, InDesign files, and bills of materials.
The audit can review calculations, narrative assertions, spreadsheets, charts, formulas, ranges, formatting, and output completeness.
One documented example checked an RTL Excel dashboard and found both a column-placement issue and an incomplete chart range.
The exact result depends on the files and source evidence supplied for the audit.
Yes, auditing another AI's work is one of the stated use cases for Energent Audit.
The auditor is designed to remain separate from the system that produced the original deliverable.
This separation creates an independent review step before the work reaches the end user.
The audit can recompute values, trace claims to sources, and identify defects in the deliverable itself.
This makes it suitable for workflows where teams use multiple AI tools and still need one evidence-backed quality-control layer.
Energent Audit distinguishes an unsupported claim from a verified result.
If the source set does not contain enough information to calculate or confirm an assertion, the audit records that limitation.
It can also report an evidence gap when source coverage or required fields are missing.
In one ratio audit, the system refused to invent a return-on-assets figure because total assets were not available.
This approach makes the boundary of the available evidence visible to reviewers instead of presenting an unsupported conclusion as fact.
The supplied information does not provide specific pricing details or trial terms.
Users can access the Energent product entry point through the Start Free button on this page.
Teams that need to discuss an enterprise workflow can use the Book Demo option.
A demonstration can help clarify how a particular set of spreadsheets, documents, or reports would be audited.
For current pricing and availability, use Energent's provided product or demo links rather than relying on an unspecified figure.
Verify the work, trace the evidence, and review only what needs your attention.