Build scenario-ready financial models, dashboards, and diagnostics with source-grounded outputs that can be independently recomputed, traced, and reviewed.
Use a natural-language prompt and attach source files when ready.
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AI financial modeling uses artificial intelligence to organize financial source data, calculate relationships, test scenarios, and present the results in usable dashboards or reports. Energent.ai extends that workflow with Energent Audit, an independent AI auditor that checks another agent’s deliverable by recomputing numbers, tracing values to source files and fields, and issuing a pass or fail verdict with evidence. It is designed for analysts, finance and accounting teams, operations, procurement, research groups, and other users working with high-stakes spreadsheets, PDFs, scans, and complex documents. For teams exploring AI financial modeling, the central value is not only speed but a reviewable chain from source material to conclusion.
Energent-produced dashboards cover operating models, investments, macroeconomic relationships, property economics, and commercial performance. Each example below uses supplied model outputs and preserves the distinction between reported, modeled, inferred, and diagnostic measures.
The 10-year rental property cash-flow stress test starts with a €620.0K entry value and compares baseline, rate-shock, and stagflation cases. Baseline cumulative cash flow is €28.8K, while the rate-shock case reaches -€17.7K and the stagflation case reaches -€24.4K over 10 years.
View rental stress testThe software and payments dashboard reports $455.5M in 2025 revenue, a 43.5% gross margin, 0.74x gross-profit-to-opex coverage, and a -15.1% operating margin. The five-year checkpoint shows margin expansion in 2024 and 2025 while operating expenses remained above gross profit.
View operating leverage dashboardThe vendor-spend audit example places the report, source notes, and red or green audit findings in one reviewable interface. This is the practical role of financial audit verification: make exceptions visible before a deliverable reaches a stakeholder.
Explore audit verificationThe €40,000 ETF portfolio model allocates 65% to equities and 35% to bonds, with a modeled annual return of 15.3% and modeled volatility of 10.6%. A separate broker dashboard tracks IBKR’s 2024 disclosed mix at 35.0% commissions and 65.0% net interest share, while distinguishing Tiger’s thinner disclosure.
View ETF portfolio model| Model | Primary measure | Reported or modeled result | Decision signal |
|---|---|---|---|
| Rental property stress test | Minimum DSCR | 1.02x baseline; 0.87x rate shock; 0.79x stagflation | Coverage falls below 1.0x in adverse scenarios |
| Software and payments | 2025 operating margin | -15.1% | Improved from 2024 but remains below breakeven |
| ETF portfolio | Capital allocation | €40,000; 65% equity and 35% bonds | Equities drive 84.5% of modeled return contribution |
| Macro diagnostics | Realistic lagged-data R² | 18.6% | Timing alignment materially reduces apparent fit |
A financial modeling workflow that emphasizes usable outputs, explicit assumptions, and a clear route back to source evidence.
Follow a value back to its source file, extracted field, and reference used for checking.
Use an independent auditor to recalculate figures rather than relying only on the original agent.
Find failed checks, under-coverage periods, margin pressure, or timing problems before delivery.
Reuse audit rules over time so corrections can become persistent checks for recurring jobs.
Support is described for more than 150 file types, including CAD, scans, G-code, PDFs, XLSX, and DOCX.
Create dashboards and branded, reviewable reports with evidence attached to the result.
The workflow moves from source material to an inspectable financial conclusion.
Upload or reference spreadsheets, PDFs, scans, or other supported files and describe the financial question.
What you see: the files and requested analysis organized for processing.The workflow extracts information, recomputes relationships, compares scenarios, and produces dashboards or reports.
What you see: charts, tables, assumptions, and scenario-level findings.Energent Audit checks the deliverable independently and attaches the evidence behind pass, fail, or flagged results.
What you see: a traceable report you can review and stand behind.Energent Audit is designed to make quiet AI errors visible, especially when a financial deliverable needs to be defensible.
The audit checks each deliverable independently, retraces figures to their sources, fixes what it can, and issues a verdict with the evidence attached. That makes the review narrower and more practical: inspect what is flagged instead of treating every output as equally uncertain.
The supplied evidence combines published company claims, generated financial dashboards, and direct user reviews.
“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.”
This comparison uses only distinctions supported by the supplied information. “Generic alternative” refers to a conventional manual review or an AI workflow without the described independent audit layer.
| Decision dimension | Energent.ai | Manual review | Unaudited AI output |
|---|---|---|---|
| Verification approach | Independent second agent recomputes and checks the deliverable | Human reviewer checks the work | No independent audit layer described |
| Evidence trail | Number traced to source file, field, and reference | Depends on reviewer process and documentation | May provide an answer without the described traceable chain |
| Output status | Pass/fail verdict with evidence attached | Reviewer judgment and notes | Generated result without the described verdict |
| Recurring controls | Reusable workflows that retain audit rules over time | Rules must be maintained manually | No persistent audit-rule capability supplied |
| File coverage | 150+ file types described, including complex documents | Depends on the team and tools used | Depends on the particular AI workflow |
Metrics and benchmark statements are presented as supplied company claims or published dashboard results.
Answers for teams evaluating AI financial modeling and verification.
Start exploring AI financial modeling workflows or talk with Energent.ai about an audit-ready process.