A live audit, start to verdict
A fresh agent retraces each AI-generated figure to its source, verifies how it was constructed, and produces a report that can be defended in a review meeting.
Independent AI audit for high-stakes analysis
AI Profitability Analysis Verification for Finance Teams Without Manual Rechecking
Energent Audit independently recomputes AI-generated financial analysis, traces every figure to its source, and returns evidence with a clear pass/fail verdict.
94.4%
Published leaderboard accuracy
3×
Fewer hallucinations claimed
150+
Supported file types
100,000+
Clients worldwide
Trusted by 100k+ companies across the globe.
What Is AI Profitability Analysis Verification?
AI profitability analysis verification is the independent checking of AI-produced financial conclusions against the original source documents and data. Energent Audit uses a separate agent to recompute metrics such as gross margin, EBITDA margin, profit margin, return on equity, and valuation multiples, then shows the source trail and identifies missing inputs. It is designed for finance, accounting, analytics, operations, and other teams that need analysis they can review and defend.
For adjacent workflows, teams can also explore AI revenue analysis verification, AI variance analysis auditing, and AI budget analysis verification.
See the Audit in Action
Evidence, not a black box
The audit attaches supporting evidence, highlights what passed or failed, and distinguishes independently verified figures from calculations blocked by missing source data.
What You Get
Recompute financial formulas independently instead of accepting reported results at face value.
Trace every figure to the source file, row, field, and reference used for checking.
Flag unsupported claims, incorrect calculations, missing inputs, and methodology differences.
Defend conclusions with a complete, cited, reproducible evidence trail.
Audit work produced by other AI systems as well as work produced within Energent.
Reuse repeating audit rules through persistent workflows so corrections can become permanent controls.
How It Works
Step 1
Submit the analysis
Provide the AI-generated deliverable and the source files used to create it.
You see the files and requested audit scope.
Step 2
Recompute and trace
An independent auditor checks calculations, assertions, sources, and evidence.
You see citations, formulas, exceptions, and missing data.
Step 3
Review the verdict
Receive a pass/fail result with supporting evidence and actionable findings.
You focus on flagged rows instead of checking everything.
Features
Core workflow features
Independent second-agent verification
Financial ratio recomputation
Source-file and field traceability
Pass/fail audit verdicts
Evidence attached to findings
Reliability & control
Hallucination detection before delivery
Missing-input identification
Unsupported-claim detection
Cross-agent auditing
Reusable workflows that retain audit rules
Integrations & export
Support for 150+ file types
PDF, XLSX, DOCX, scans, CAD, and G-code support
Stakeholder-ready outputs
White-label and brandable reporting
Reviewable audit trails for enterprise workflows
Profitability Verification Example
Super Retail Group ratio analysis
| Metric | Calculated | Reported | Result |
|---|---|---|---|
| Gross margin | 45.64% | 45.63% | Close match |
| EBITDA margin | 11.37% | 11.37% | Exact |
| Profit margin | 5.45% | 5.45% | Exact |
| Return on equity | 16.79% | 16.46% | Method differs |
| Return on assets | N/A | 7.13% | Assets missing |
Margin progression
Gross margin45.63%
EBITDA margin11.37%
Net profit margin5.45%
The audit identified a substantial decline from gross margin to net profit margin. It also noted approximately 1:1 debt-to-equity leverage and explained why ROA could not be independently calculated without total assets.
Additional verified financial metrics
| Metric | Formula or source basis | Result |
|---|---|---|
| Debt-to-equity | $1.24B / $1.32B | 93.53% |
| Net debt | $1.24B − $63.30M | $1.17B |
| P/E | $13.31 / ($221.80M / 225.83M shares) | 13.55 |
| Price-to-book | $13.31 / $5.851 | 2.27 |
| Price-to-sales | $3.01B / $4.07B | 0.74 |
Proof
- 412 invoice rows were independently re-summed, matching the spend deliverable to the cent.
- A spend audit corrected a reported 18% increase to the correct Q1 increase of 12.0%.
- A consulting savings audit received an overall PASS with 6 PASS, 0 PARTIAL, and 0 FAIL checks.
- A revenue diagnostic identified rising refunds from $3.2k to $7.8k and a product-quality or bad-inventory-batch signal.
- A dashboard verification separated two passing checks from two failures, including chart omissions caused by an off-by-one indexing error.
“You can see exactly which source file the number came from, the field it was extracted from, and the reference it was checked against. Not a black box—a traceable chain.”
Audit buyer-facing feedback
financial analysis auditReview AI-generated financial conclusions with source-grounded evidence.
AI 10-K analysis verificationApply the same verification mindset to complex financial statements and disclosures.
Comparison
| Energent.ai | Generic Alternative A | Generic Alternative B |
|---|---|---|
| Independent second agent checks the original AI output | Not specified | Not specified |
| Recomputes and traces figures to file, row, and field | Not specified | Not specified |
| Pass/fail verdict with supporting evidence | Not specified | Not specified |
| Flags unavailable source inputs and methodology differences | Not specified | Not specified |
| Supports 150+ file types and reusable workflows | Not specified | Not specified |
Credentials & Key Stats
94.4%
Accuracy on a published HuggingFace leaderboard, according to the company
30%
More accurate than the listed second-place alternative in the company comparison
3×
Fewer hallucinations in public evaluations, according to the company
150+
File types supported, including CAD, scans, G-code, PDFs, and spreadsheets
AI budget planning
balance sheet analysis
FAQs
Finance and accounting teams can use it when AI-generated profitability analysis will influence a decision, review, or deliverable. It is also relevant to analysts, operations teams, procurement groups, research teams, and enterprise users handling high-stakes documents. The workflow is designed for people who need more than a plausible answer because they need to show how the answer was produced. It is especially useful when multiple source files, formulas, or assumptions must be checked together. Energent Audit can also audit work created by another AI system.
Energent Audit uses an independent AI auditor separate from the agent that performed the original work. It recomputes figures such as gross margin, EBITDA margin, profit margin, and return on equity from the supplied source data. It traces each figure to the relevant file, row, field, and reference. The audit then compares the independently calculated result with the reported result. Where the source data is incomplete, the report identifies the limitation instead of presenting an unsupported calculation as verified.
Energent states that it supports more than 150 file types. Examples include CAD files, scans, G-code, InDesign files, bills of materials, PDFs, XLSX files, and DOCX files. The audit can use the original source documents alongside the AI-generated deliverable. This allows the system to check both the conclusion and the evidence chain behind it. The exact audit result still depends on whether the required source inputs are available and readable.
Yes. The stated purpose of Energent Audit is to check deliverables produced by other AI agents, not only outputs produced by Energent. The independent auditor is separate from the agent that created the work, which gives the verification process a different checking role. A shipped sample task is explicitly described as auditing another AI’s work. The auditor can recompute numbers, trace assertions, identify errors, and issue a verdict. The quality of the check remains dependent on the source files and assumptions supplied for the audit.
No audit can independently verify information that is absent from the supplied sources. In the Super Retail Group example, total assets were missing, so reported return on assets could not be independently calculated. Current assets and current liabilities were also not directly provided, which meant some liquidity figures were back-solved from reported ratios. Energent presents these limitations as part of the evidence trail. The result is a clearer distinction between verified calculations, methodology differences, and unresolved inputs rather than an unsupported guarantee.
Teams can enter the product experience through the Energent application or request a demonstration. The provided information does not specify pricing details, so pricing should be confirmed directly with Energent. A demo can help clarify the appropriate workflow, source files, and output format for a specific use case. The platform also provides reusable workflows for repeating audit jobs. This means teams can discuss both an initial verification task and how recurring checks may be operationalized.
Verify the analysis before it reaches the decision-maker.
Start with an independent audit or discuss your profitability verification workflow with Energent.