Independent verification for AI-generated financial work

AI-Powered Financial Audit Solutions for Finance Teams Without Hidden Errors

Energent Audit independently recomputes financial outputs, traces numbers to their source files, and issues a clear pass/fail verdict before the work reaches a stakeholder.

100,000+
Clients worldwide
94.4%
Published leaderboard accuracy claim
Fewer hallucinations in public evaluations
150+
Supported file types

Trusted by 100k+ companies across the globe.

Amazon
AWS
UC Berkeley
Experian
GE
PwC
Stanford

What Is Energent Audit?

Energent Audit is an independent AI auditor: a second agent that is separate from the AI that produced the original work. It checks financial deliverables by recomputing numbers, tracing each figure to the exact source file, row, and field, correcting what it can, and attaching evidence to a pass/fail verdict. It is designed for analysts, finance and accounting teams, operations, procurement, engineering, research, and enterprise workflows that need reproducible results rather than unreviewable answers.

The approach complements AI financial analysis with an explicit verification layer. Instead of asking the person who created the answer to validate every line, the audit separates production from checking and focuses human attention on what is flagged.

A Live Audit, Start to Verdict

The supplied audit example shows how a financial dashboard can be examined through scenarios, coverage thresholds, source context, and evidence instead of a single unexplained conclusion.

Energent Audit report showing a Project Cash Flow Dashboard with a pass or fail review

Project Cash Flow Dashboard

The report tests a French short-term rental project across baseline, rate-shock, and stagflation scenarios. Its focus is lender-style resilience: debt service coverage, occupancy needed to remain solvent, annual cash pressure, cumulative cash flow, and the 1.0x coverage threshold.

€620.0K
Entry value in Year 1
€28.8K
Baseline 10-year cash flow
8
Rate-shock years below 1.0x
73.6%
Peak break-even occupancy
Open the source dashboard

Scenario scorecard from the supplied audit data

ScenarioInterest rateMinimum DSCRYears below 1.0xPeak break-even occupancy10-year cumulative cash flow
Baseline5.74%1.02xNone64.3%€28.8K
Rate Shock (+200 bps)7.74%0.87x871.4%-€17.7K
Stagflation5.74%0.79x973.6%-€24.4K
Baseline DSCR1.02x
Rate shock DSCR0.87x
Stagflation DSCR0.79x

How the Audit Works

The workflow is built around separation, traceability, and a decision that can be reviewed later.

Step 1

Submit the deliverable

Provide the generated spreadsheet, PDF, scan, CAD file, or other supported source material and ask for the audit.

What you see: the work enters a separate review flow.

Step 2

Recompute and trace

The independent auditor checks assertions and numbers against the original source file, row, field, and reference.

What you see: evidence connecting each finding to its origin.

Step 3

Review the verdict

Receive a pass/fail result with attached evidence, including corrections when the auditor can resolve an issue.

What you see: a report ready for focused human review.

What You Get

Focus attention on exceptions: move from verifying everything to reviewing what is flagged, whether that means 8 rows or 500.

Catch errors the same day: identify problems before they surface a month or quarter later.

Trace every number: connect an output to the precise source file, field, and reference used for checking.

Create defensible reviews: preserve complete, cited, reproducible evidence for a review meeting.

Audit other AI systems: check work produced by another AI agent, not only Energent output.

Reuse audit rules: turn recurring corrections into persistent workflows and rules over time.

Features, Grouped for Financial Workflows

Core workflow features

• Independent second-agent verification

• Number recomputation

• Source-file, row, and field tracing

• Pass/fail deliverable verdicts

• Support for 150+ file types

Reliability and control

• Evidence attached to findings

• Corrections when the auditor can resolve them

• Reviewable audit trail

• Reusable workflows that learn audit rules

• Enterprise-grade privacy and security emphasis

Integrations and export

• Spreadsheets and XLSX files

• PDFs, DOCX files, and complex documents

• Scans and OCR-oriented documents

• CAD, G-code, BOMs, and InDesign files

• White-label and brandable stakeholder-ready outputs

See the Audit in Motion

The supplied video introduces the independent verification concept: retrace each figure, verify it against the source, and produce a report that can be defended.

Proof and Customer Experience

  • Energent.ai reports 94.4% accuracy on a published HuggingFace leaderboard and a number-one placement on the cited leaderboard.
  • The company supports more than 150 file types, including CAD, scans, G-code, InDesign, BOMs, PDFs, XLSX, and DOCX.
  • Public evaluations cited by the company report three times fewer hallucinations.
  • The platform is described as powering workflows for more than 100,000 clients worldwide.

“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”

Roberto C., Data Operations Specialist, Fortune 500 logistics

“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”

Alyse H., Digital Collection Curator

“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.”

Kay P., Power Query Analyst

“Energent.ai is a great platform... the interactive outputs add real value to my work.”

Amjad M., Telecommunications Engineer

Comparison: Why Energent.ai vs Generic Alternatives

The comparison below uses only capabilities described in the supplied information.

Energent.aiOriginal AI workflowManual review
Independent second agent checks the workThe producing agent has no separate verifier describedA person performs the checking
Recomputes numbers and traces them to source rows and fieldsOutputs may require separate validationTraceability depends on the reviewer’s process
Pass/fail verdict with evidence attachedA generated answer is the primary outputEvidence must be assembled manually
Can audit another AI’s workThe same system created the workReview is possible but human time is required
Reusable workflows can preserve audit rulesRules may need to be repeated in promptsCorrections depend on documented team practice

For teams formalizing repeatable controls, automated audit trails can complement the independent verification workflow.

Credentials and Key Stats

94.4%

Accuracy claim on a published HuggingFace leaderboard

Fewer hallucinations in public evaluations, according to the company

150+

Supported file types

100k+

Companies and clients cited by Energent.ai

Leaderboard placement cited by the company Enterprise-grade privacy and security emphasis

FAQs

What are AI-powered financial audit solutions?

AI-powered financial audit solutions use artificial intelligence to examine financial outputs, calculations, and supporting documents. Energent Audit adds an independent second agent that checks work produced by another AI or workflow. It recomputes numbers, traces them to source files, rows, and fields, and produces a pass/fail result with evidence. The purpose is to reduce the amount of manual quality control required before financial work is delivered. It is designed for high-stakes analysis where a reviewer needs a reproducible chain rather than an unsupported answer.

How does Energent Audit detect AI hallucinations in financial work?

Energent Audit operates as an independent auditor separate from the agent that performed the original task. It checks assertions and recomputes figures instead of simply accepting the first answer. It then traces each number back to the exact source file, row, and field used in the analysis. When it can resolve an issue, it fixes what it can and includes the evidence. The final pass/fail verdict helps surface quiet errors before they reach a stakeholder or review process.

Can Energent Audit verify work created by another AI system?

Yes, the supplied feature information specifically describes auditing another AI’s work as a shipped sample task. The audit is not limited to deliverables produced by Energent.ai. This separation allows the checking process to evaluate the output without relying on the original agent’s confidence or explanation. Teams can therefore use the auditor as a verification layer across AI-generated spreadsheets, reports, and other supported deliverables. The resulting evidence is intended to make the review more transparent and repeatable.

What files and financial documents can Energent.ai audit?

Energent.ai states that it supports more than 150 file types. The supplied examples include spreadsheets, PDFs, DOCX files, scans, CAD, G-code, InDesign, and bills of materials. This broad support is relevant when financial analysis combines structured tables with complex documents or scanned source material. The audit checks deliverables against the source material provided to the workflow. Exact handling can depend on the file and the task, so teams should use the product entry point or a demo to evaluate their own documents.

Is the audit trail suitable for finance reviews?

Energent Audit is designed to provide a reviewable evidence trail rather than a black-box answer. Each checked number can be traced to its source file, field, and reference according to the supplied feature description. The report also provides a pass/fail verdict and attaches evidence to the findings. That structure can help a reviewer understand how a result was built and what needs attention. It does not replace a company’s own accounting, legal, regulatory, or professional audit obligations.

How does Energent.ai support recurring financial audit workflows?

Energent.ai describes reusable workflows that learn audit rules over time. When a recurring correction is identified, the workflow can preserve that correction as a persistent audit rule rather than requiring the team to repeat the same instruction. This is intended to make repeated jobs more consistent. The company also describes white-label and brandable outputs for stakeholder-ready delivery. Teams can start from the product experience or request a demo to discuss their recurring workflow requirements.

Catch AI hallucinations before they cost you.

Give every financial deliverable an independent check, a source-grounded trail, and a verdict your team can review.