A report you can inspect
The audit report makes the verdict reviewable rather than opaque. A reviewer can follow a number or assertion back to the source file, row, field, calculation, or deliverable location wherever that evidence is available.
Independent verification for AI-generated work
AI Agent Output Audit Solution for High-Stakes Teams Without Manual QC
Energent Audit independently recomputes numbers, traces claims to their source, and flags unsupported or inconsistent work before an AI-generated deliverable reaches the user.
150+
supported file types
3×
fewer hallucinations claimed
94.4%
published leaderboard accuracy
100k+
clients worldwide
Trusted by 100k+ companies across the globe.
What Is Energent Audit? A Quick Definition
Energent Audit is an independent AI auditor: a second agent, separate from the agent that performed the work, that checks deliverables against original source documents. It recomputes figures, verifies supporting evidence, identifies missing or inconsistent records, corrects errors where possible, and returns pass, partial, or fail findings with an evidence trail. For teams working with spreadsheets, PDFs, CAD, scans, G-code, and complex documents, the goal is to replace blanket manual checking with focused review of what the audit flags. This is the foundation of source-grounded audit trails that can be reviewed and reproduced.
A Live Audit, Start to Verdict
The same verification pattern applies across finance, operations, procurement, research, analytics, engineering, and other high-stakes workflows.
Evidence across complex files
Energent supports more than 150 file types, including CAD, scans, G-code, InDesign, BOMs, PDFs, XLSX, and DOCX. This allows the audit to examine the source material and the generated deliverable together instead of treating the final answer as a standalone text response.
Financial and operational verification
For financial work, the auditor checks denominators, periods, reconciliations, and derived claims. Teams can pair this with financial audit verification when outputs include budgets, vendor spend, revenue diagnostics, or ratio analysis.
Failures are part of the result
A fail is useful when it explains what went wrong. In one RTL Excel dashboard audit, two checks passed and two failed because the index column was misplaced and charts omitted the final data row.
What You Get
Stop reviewing everything. Review the rows, claims, and deliverables that are flagged instead of acting as your AI’s permanent quality-control layer.
Surface errors the same day. Move from discovering errors a month or two later to seeing findings during the delivery workflow.
Trace every important number. Follow figures to source files, rows, fields, and references where the evidence supports that level of detail.
Separate facts from assumptions. Identify unsupported growth rates, missing fields, mismatched periods, and causal claims that the data cannot establish.
Make corrections reusable. Turn recurring corrections into persistent workflows and audit rules over time.
Deliver reviewable outputs. Produce stakeholder-ready results with clear findings, evidence, and an overall verdict.
How the Audit Works
Step 1
Produce the deliverable
An AI agent creates a report, spreadsheet, analysis, or other deliverable from source material.
What you see: the original AI-generated output and its source files.
Step 2
Run an independent audit
A separate audit agent recomputes numbers, traces claims, and checks completeness, structure, and consistency.
What you see: evidence links, calculations, exceptions, and corrections.
Step 3
Review the verdict
The audit returns pass, partial, or fail findings with supported corrections and evidence attached.
What you see: a defensible report ready for focused human review.
Features, Grouped for Control
Core workflow features
Independent second-agent review
Number recomputation
Source and field tracing
Pass, partial, and fail findings
Correction where possible
Reliability & control
Evidence-backed claims
Completeness checks
Duplicate and malformed record detection
Denominator and period checks
Reusable audit rules
Integrations & export
150+ file types
Spreadsheets and PDFs
CAD and G-code
Scans and complex documents
Brandable stakeholder outputs
Audited Output Examples
These examples show the kinds of checks the audit can perform across finance, operations, analytics, and market research. The figures below are reported findings from the supplied audit outputs.
Revenue diagnostic
| Measure | July | August |
|---|---|---|
| Gross revenue | $83.3k | $84.8k |
| Conversion rate | 6.75% | 7.13% |
| Net revenue | $80.0k | $77.0k |
| Refunds | $3.2k | $7.8k |
The audit also found 11,801 sessions without source and campaign tags, identified 5,726 direct type-ins, and noted refunds for The Original Mr. Fuzzy rising from 42 units to 132 units.
Consulting savings verification
Pure elimination3.3 FTEs
Internal backfill4.9 FTEs
AI efficiency, 40% gain8.2 FTEs
6
checks
6
pass
0
fail
The audit confirmed baselines, assumptions, formulas, a 13-week workplan, and explicit flags for missing kickoff-deck, SOW, and savings-workbook inputs.
Vendor spend reconciliation
| Q1 spend | $1,284,500 |
| Source invoice rows | 412 |
| Q4 total | $1,147,000 |
| Correct Q1 increase | 12.0% |
| Top vendor | Acme Logistics |
The audit corrected an incorrectly stated 18% increase and marked a roughly 30% software-spend growth claim unsupported because no comparable prior-quarter software figure existed.
RTL Excel dashboard
2 pass2 failOverall: FAIL
The failed checks found the Continent index in Column B rather than Column A and charts referencing rows 3–6 even though the table extended through row 7.
More Use Cases for Verified Analysis
Budgets and cost centers
Verify adopted, estimated, and actual budgets, including denominator-sensitive variance claims. Use budget planning analysis when a generated output needs a second calculation pass.
Capital structure
Check margins, debt-to-equity, net debt, P/E, P/B, P/S, and limitations caused by missing assets or back-solved values. This supports balance-sheet analysis with explicit methodological disclosure.
Reconciliation workflows
Test whether line items align with totals, whether source rows reconcile, and whether unsupported claims are removed. These checks are useful for reconciliation workflows.
Operations and inventory
Review operational records for missing, duplicated, or inconsistent fields and distinguish data-quality issues from legitimate unattributed records. Teams can apply the same evidence discipline to inventory turnover analysis.
Market research
Verify that national proxy signals are not presented as direct evidence of local demand or adoption. For financial-market outputs, the audit can support market volatility monitoring with traceable sources.
Commodity and engineering evidence
Source-grounded checks can be applied to technical drawings, CAD files, BOMs, and analytical reports, including commodity fundamentals analysis where claims must remain tied to the supplied evidence.
Proof
- Energent reports 94.4% accuracy on a published HuggingFace leaderboard and a number-one placement on the cited leaderboard.
- The company reports 3× fewer hallucinations in public evaluations and 30% greater accuracy than the listed second-place alternative in its comparison.
- In the consulting savings example, six checks passed, with zero partial and zero fail findings.
- In the RTL dashboard example, the auditor caught two structural failures that content-only review could miss.
- The platform supports more than 150 file types and is positioned for analysts, finance, accounting, operations, procurement, engineering, research, and enterprise teams.
“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”
“Using Energent.ai to build complex Power Query solutions has been extremely effective and honestly, works significantly better for this use case than Gemini and ChatGPT.”
Comparison: Why Energent Audit vs Alternatives
| Decision factor | Energent Audit | Manual review | AI-only delivery |
|---|---|---|---|
| Independent second check | Yes, separate audit agent | Human-dependent | No separate auditor described |
| Number verification | Recomputes from source data | Reviewer performs checks | Original generation only |
| Evidence trail | Source file, row, field, and reference where available | Depends on documentation | Not inherent to the output |
| Verdict format | Pass, partial, or fail with findings | Varies by reviewer | Usually no audit verdict |
| Workflow learning | Corrections can become reusable audit rules | Often dependent on individual memory | No independent correction layer |
Credentials & Key Stats
100k+
clients worldwide, according to company information
150+
supported file types
94.4%
published HuggingFace leaderboard accuracy claim
3×
fewer hallucinations claimed in public evaluations
FAQs
Is Energent Audit suitable for high-stakes AI workflows?
Energent Audit is designed for teams that need rigorous, auditable results from generative AI and automation. It checks numbers, assertions, supporting evidence, completeness, and internal consistency before delivery. The supplied use cases include finance, procurement, operations, engineering, research, market analysis, and complex document workflows. It can audit another AI agent’s work rather than only checking Energent-generated outputs. Suitability still depends on the files, rules, and review requirements of the specific workflow.
How does the AI agent output audit work?
First, an AI agent produces a deliverable from source files. A separate audit agent then recomputes figures, traces claims to source files, rows, fields, and references where possible, and checks structural and logical requirements. Unsupported, incorrect, incomplete, duplicated, or inconsistent findings are flagged. Corrections can be provided where the evidence supports them. The final result is organized into pass, partial, or fail findings with an overall verdict and evidence trail.
What file types and integrations are supported?
Energent states that it supports more than 150 file types. Examples provided include CAD, scans, G-code, InDesign, BOMs, PDFs, XLSX, and DOCX. The audit is intended to compare source material with generated deliverables across high-volume enterprise workflows. The supplied information does not specify a complete connector catalog or a guaranteed integration for every external system. Teams should confirm the exact formats and workflow requirements for their implementation.
Can Energent Audit detect unsupported or misleading claims?
Yes, the audit principles explicitly require unsupported growth rates and causal claims to be removed or clearly labeled. It also checks whether derived percentages use the correct denominator and comparable periods. In the vendor-spend example, the audit corrected a 12.0% quarter-over-quarter increase that had been reported as 18%. In the Saudi market example, it separated national proxy signals from direct evidence of demand in a particular locality. The audit does not silently infer missing source fields.
How does Energent approach security and privacy?
Energent positions the platform as providing enterprise-grade privacy and security. Its target customers include enterprise teams handling high-stakes analysis and complex source documents. The available information does not list specific certifications, retention periods, hosting regions, or contractual controls. Those details should be confirmed directly through the company’s security materials or sales process. The audit’s evidence trail is intended to make outputs reviewable without treating the result as an unexplained black box.
How long does onboarding take?
The supplied information does not state a standard onboarding duration. Energent describes natural-language prompts, reusable workflows, and audit rules that can learn from repeating jobs over time. The practical setup effort will depend on the source formats, volume, verification rules, and required stakeholder output. A team can begin by defining one repeatable deliverable and its source evidence requirements. For a workflow-specific estimate, the available next step is to start in the product or book a demo.
Catch AI errors before they become deliverables.
Run an independent audit, trace the evidence, and review what actually needs your attention.