Independent AI audit for finance teams
Recompute valuations, trace every figure to its source, and deliver sensitivity analysis stakeholders can review without relying on unchecked AI output.
Trusted by 100k+ companies across the globe.
AI-powered corporate valuation uses artificial intelligence to analyze financial statements, operating assumptions, market inputs, and scenarios to produce valuation work. Energent Audit adds an independent verification layer: it recomputes the numbers, traces figures to source files, checks calculations, corrects issues where possible, and issues a pass/fail verdict with evidence. It is designed for analysts, finance and accounting teams, operations and procurement groups, engineering teams, researchers, and enterprise workflows that need reproducible results.
For broader AI financial analysis, teams can use the same source-grounded approach across spreadsheets, PDFs, scans, CAD files, and other complex documents.
Energent Audit checks the number, retraces the source, and shows how the result was built.
The following valuation example uses historical macro regimes from January 1, 2005, through May 1, 2026, across 257 monthly observations. It shows how discount-rate assumptions can change present value while keeping the audit trail visible.
| Regime | Risk-Free | Credit Spread | Term Spread | Fed Funds | Proxy WACC | PV |
|---|---|---|---|---|---|---|
| Base (Normal) | 2.84% | 1.23% | 0.65% | 1.41% | 4.08% | $1,478.6 |
| Bear (Tightening) | 4.10% | 1.01% | 0.10% | 3.94% | 5.10% | $1,376.2 |
| Bull (Easing) | 2.20% | 1.83% | 1.57% | 0.14% | 4.02% | $1,484.1 |
Source: discount-rate macro scenario dashboard. The complete analysis includes a PV tornado, grouped rate comparisons, inflation and policy-rate clustering, historical rate paths, and a monthly regime map.
A valuation is only as dependable as the statements, assumptions, and calculations behind it. Energent can turn financial files into reviewable dashboards and identify the specific years, balances, and ratios that deserve another look.
The analysis connects earnings to cash generation and separates the operating drivers that matter when assessing valuation quality.
It covers earnings-to-cash conversion, margin decomposition, CAPEX versus D&A, and the EBITDA-to-FCF bridge.
The latest review triggered three of four monitored signals, giving a diligence team a focused list of items to re-check.
| Year | Growth Gap | Watch Score |
|---|---|---|
| 2012 | +59.0 pp | 70/100 |
| 2018 | +13.9 pp | 66/100 |
| 2020 | -35.2 pp | 65/100 |
| 2024 | +11.2 pp | 55/100 |
| Fiscal Year | Growth Gap | Allowance Ratio | Accrued Swing | OCF / NI | OCF - NI | Watch Score |
|---|---|---|---|---|---|---|
| 2012 | +59.0 pp | 0.90% | +213.5% | 1.22x | $9.1B | 70/100 |
| 2018 | +13.9 pp | 0.00% | n/a | 1.30x | $17.9B | 66/100 |
| 2020 | -35.2 pp | 0.00% | n/a | 1.41x | $23.3B | 65/100 |
| 2022 | -0.5 pp | 0.00% | n/a | 1.22x | $22.3B | 64/100 |
| 2021 | +29.8 pp | 0.00% | n/a | 1.10x | $9.4B | 63/100 |
| 2024 | +11.2 pp | 0.00% | +3.4% | 1.26x | $24.5B | 55/100 |
Read the complete financial due diligence analysis with receivables, allowance coverage, accrued liabilities, and cash-flow drift visualizations.
Valuation work often needs more than a single forecast. Historical statements, capital allocation, balance-sheet structure, and peer ratios all provide context for scenario assumptions.
From FY2015 to FY2025, revenue increased from $233.7B to $416.2B, gross margin expanded from 40.1% to 46.9%, and R&D increased from $8.1B to $34.6B.
| Ratio | Apple | Microsoft | Leader |
|---|---|---|---|
| Current ratio | 0.89x | 1.35x | Microsoft |
| Debt to equity | 1.40x | 0.13x | Microsoft |
| Asset turnover | 1.16x | 0.46x | Apple |
| Gross margin | 46.9% | 68.8% | Microsoft |
| ROA | 31.2% | 16.5% | Apple |
| ROIC | 115.4% | 44.1% | Apple |
Microsoft led all three liquidity and all four leverage ratios; efficiency favored Apple on two of three measures, while profitability was split three-to-three.
Teams can also use financial ratio and margin analysis to compare liquidity, leverage, efficiency, and profitability without losing the underlying source context.
Check valuation outputs, financial calculations, and sensitivity assumptions independently.
Follow each figure back to the exact source file, row, field, and supporting reference.
Shift from checking every output manually to investigating exceptions and failed checks.
Attach evidence and a clear pass/fail verdict to a reproducible result.
Save valuation updates, stock-analysis routines, and recurring scenario analyses as named workflows.
Support workflows involving 150+ file types, including PDFs, XLSX, scans, CAD, G-code, BOMs, and DOCX.
An AI agent creates the valuation, financial analysis, or sensitivity model from the supplied files and instructions.
You see the initial output and its working files.Energent Audit recomputes numbers, verifies references, traces source fields, and fixes correctable issues.
You see sources, calculations, exceptions, and evidence.The system issues a pass/fail result and packages the reviewed work for stakeholders.
You see a cited, reviewable, stakeholder-ready deliverable.For recurring work, teams can combine interactive financial dashboards with reusable audit workflows and continue jobs from desktop, phone, or tablet.
“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”
“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”
“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.”
“Energent.ai is a great platform... the interactive outputs add real value to my work.”
| Energent | Generic Alternative A: unchecked AI output | Generic Alternative B: manual review |
|---|---|---|
| Independent second agent recomputes the work | May produce an answer without independent verification | Relies on a human to repeat or inspect calculations |
| Source trace to file, row, and field | Source context may be difficult to review | References must be assembled and checked manually |
| Pass/fail verdict with evidence | No stated independent verdict layer | Review outcome depends on individual documentation |
| Reusable valuation workflows | Repeating prompts can require repeated correction | Recurring reviews can consume the same manual effort |
| 150+ file types and stakeholder exports | File and output support varies | Teams assemble deliverables across separate tools |
clients worldwide
leaderboard accuracy claim
supported file types
monthly observations in scenario example
Energent also cites #1 placement on a HuggingFace leaderboard validated by a named contributor on its site.
Run an independent audit, trace the evidence, and deliver analysis you can stand behind.