Recompute financial numbers, trace every figure to its source, stress-test repayment capacity, and review a clear pass/fail evidence trail before analysis reaches decision-makers.
Corporate credit risk analysis evaluates whether a company, borrower, property, or portfolio can meet its financial obligations under expected and stressed conditions. Energent Audit adds an independent verification layer to that work by recomputing numbers, checking assertions against source documents, tracing figures to exact files and fields, and issuing a pass/fail verdict with supporting evidence. Credit, finance, operations, procurement, engineering, and research teams can use the workflow when analysis must be reviewable, reproducible, and defensible.
The workflow complements credit risk monitoring and detailed financial review without asking a human reviewer to verify every row manually.
Use the same audit logic across repayment capacity, operating leverage, market exposure, macroeconomic risk, and model reliability.
Energent Audit reviews work produced by other AI systems as well as Energent output. It recomputes calculations, checks references, fixes detected issues where possible, and separates the original analysis from the verification layer.
Every checked figure can be traced to its source file, extracted field, and reference. This creates an evidence trail for review meetings rather than leaving analysts with an unsupported summary.
Analyze spreadsheets, PDFs, scans, CAD, G-code, InDesign files, bills of materials, DOCX files, and other supported formats in high-volume workflows. The platform supports more than 150 file types.
Repeating jobs can become persistent workflows. Corrections become reusable audit rules, allowing teams to apply the same review logic as their process matures.
Reduce review burden by focusing attention on flagged rows instead of checking every calculation manually.
Detect calculation and source-reference issues on the same day rather than discovering them months later.
Trace figures from a deliverable back to the exact source file, row, and field.
Stress-test DSCR, occupancy, operating leverage, interest exposure, and macroeconomic assumptions.
Expose model failure caused by lookahead bias, non-stationarity, regime shifts, or common trends.
Present complete, cited, reproducible outputs suitable for credit committees and review meetings.
Provide the financial files, reports, spreadsheets, references, or existing AI deliverables that need review.
What you see: files and instructions ready for analysis.The independent auditor checks calculations, assertions, source references, and scenario logic against the original material.
What you see: evidence links, flagged issues, and corrected findings where possible.Receive a pass/fail result with an evidence trail that supports discussion, escalation, and repeatable review.
What you see: a defensible report ready for stakeholders.10-year rental property cash-flow scenario analysis.
| Scenario | Min DSCR | Years below 1.0x | 10-year cash flow |
|---|---|---|---|
| Baseline | 1.02x | None | €28.8K |
| Rate shock +200 bps | 0.87x | 8 | -€17.7K |
| Stagflation | 0.79x | 9 | -€24.4K |
Software and payments company financial checkpoints.
| Year | Revenue | Gross margin | Operating income |
|---|---|---|---|
| 2021 | $282.9M | 22.0% | -$53.9M |
| 2022 | $355.8M | 25.1% | -$58.0M |
| 2023 | $415.8M | 23.6% | -$59.7M |
| 2024 | $350.0M | 41.8% | -$79.1M |
| 2025 | $455.5M | 43.5% | -$68.8M |
Margins improved materially, but operating expenses still exceeded gross profit and operating income remained negative.
Explore operating leverage analysisIBKR and Tiger revenue indicators from 2018 through 2024.
Net interest income is the dominant component of IBKR’s disclosed revenue mix, making the revenue model sensitive to the rate cycle. Tiger’s dataset supports scale and growth analysis but does not disclose a comparable revenue split.
Review rate and spread analysisEvidence that stable-looking models can fail across regimes.
| Diagnostic | Value |
|---|---|
| Calm 2005–2007 RMSE | 0.32 pp |
| GFC / zero-lower-bound RMSE | 7.85 pp |
| Largest post-2020 miss | 30.532 pp |
| Levels regression R² | 98.1% |
| Realistic lagged-data R² | 18.6% |
Differencing reduced explanatory power from 98.1% to 19.0%, while removing lookahead reduced R² from 80.0% to 18.6%. Timing, regime shifts, non-stationarity, and lookahead bias therefore matter in credit models.
The auditor retraces figures to their sources, checks the calculations, verifies assertions against references, and produces a report that can be supported in a review meeting. This is useful when a credit analysis contains many rows, multiple source files, or assumptions that need to remain visible.
For broader financial review, teams can pair the workflow with balance sheet and capital structure analysis or budget and cost-center analysis.
Watch the audit process“The shift is from I have to verify everything to I only need to look at what's flagged. Check 8 rows, or check 500.”
“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”
— Alyse H., Digital Collection Curator, Fortune 500 Retail & E-commerce
“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
“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, Fortune 50 Financial Services
“Energent.ai is a great platform... the interactive outputs add real value to my work.”
— Amjad M., Telecommunications Engineer, Fortune 500 Telecommunications
| Decision dimension | Energent Audit | Manual review | Unverified AI output |
|---|---|---|---|
| Calculation checking | Recomputes numbers | Human checks required | Not independently verified |
| Source traceability | File, row, and field evidence | Depends on reviewer process | May be incomplete |
| Verdict | Pass/fail with supporting evidence | Reviewer conclusion | Generated answer or report |
| Repeatability | Reusable workflows and audit rules | Rework may be required | Prompt-dependent |
| High-volume files | 150+ supported file types | Limited by available reviewers | Depends on the system and inputs |
For portfolio-focused work, teams can also connect the review process to portfolio analysis and investment reporting.
Published HuggingFace leaderboard accuracy claim
Supported file types
Fewer hallucinations in public evaluations
Clients worldwide
Trusted by 100k+ companies across the globe.
Corporate credit risk analysis is the review of a borrower’s ability to meet financial obligations under baseline and stressed conditions. In this workflow, the review can include DSCR, cash flow, operating leverage, revenue composition, interest-rate exposure, portfolio metrics, macroeconomic indicators, and model diagnostics. Energent Audit independently recomputes numbers and checks assertions against source documents. It traces figures to the source file, row, and field where available. The result is a pass/fail verdict with evidence rather than an unsupported conclusion.
Yes, the provided workflow description says that Energent Audit reviews work produced by other AI systems as well as Energent’s own output. The auditor is independent from the agent that performed the original analysis. It recomputes financial numbers, retraces figures to their sources, and verifies calculations against references. It can fix detected issues where possible and identify findings that still require review. This separation is designed to reduce the risk of accepting an unverified AI deliverable.
Energent states that the platform supports more than 150 file types. The listed formats and document types include PDFs, XLSX, DOCX, scans, CAD, G-code, InDesign files, and bills of materials. Teams can use the workflow for financial spreadsheets, dashboards, source reports, and complex documents. The exact result depends on the quality and completeness of the submitted source material. The audit trail is intended to keep extracted figures and references visible for review.
Energent describes its platform as offering enterprise-grade privacy and security. The audit process adds control by keeping verification separate from the original analysis and showing supporting evidence for a verdict. A reviewer can focus on flagged rows or findings instead of treating every output as equally reliable. The available information does not specify a particular certification, retention period, or deployment architecture. Teams should use the security page and a direct company discussion to confirm requirements that are specific to their environment.
The supplied use-case data includes a 10-year rental property cash-flow stress test with baseline, rate-shock, and stagflation scenarios. The analysis compares minimum DSCR, years below the 1.0x threshold, break-even occupancy, and cumulative cash flow. In that example, the baseline minimum DSCR was 1.02x, while rate shock fell to 0.87x and stagflation fell to 0.79x. The workflow can therefore present scenario outputs that make coverage deterioration visible. It does not replace a credit team’s judgment about assumptions, borrower behavior, or approval policy.
Specific pricing figures are not provided in the supplied information. The available product entry point is the Energent application, and the company also provides a Book a Demo route. A demo is the appropriate way to discuss file volumes, workflow design, review requirements, and enterprise needs. The platform is described as supporting reusable workflows, but no fixed onboarding duration is stated. Teams should request current commercial and implementation details directly from Energent before making a purchasing decision.
Move from checking every number manually to reviewing the evidence and findings that matter.