AI hallucination detection
The auditor checks numbers and assertions instead of accepting an AI answer at face value. It recomputes calculations, compares them with source material, and marks unsupported claims for review.
AI hallucination detectionEnergent Audit independently recomputes, traces, and validates AI deliverables before they reach stakeholders, returning a reviewable pass/fail verdict with supporting evidence.
Trusted by 100k+ companies across the globe.
Enterprise AI audit software independently checks work produced by AI agents and automation systems. Energent Audit recomputes figures, traces claims to source files, rows, and fields, identifies unsupported assertions, and attaches evidence to a clear pass/fail or partial result. It is designed for analysts, finance and accounting teams, operations, procurement, engineering, research, and other groups responsible for defensible deliverables.
For teams evaluating enterprise AI audit, the central distinction is independence: the auditor is separate from the agent that performed the original work. That separation helps organizations move from checking every row manually to reviewing the specific items the audit flags.
The auditor checks numbers and assertions instead of accepting an AI answer at face value. It recomputes calculations, compares them with source material, and marks unsupported claims for review.
AI hallucination detectionEvery validated number can be connected to its source file, row, field, or reference. This creates a traceable chain for reviews, corrections, and stakeholder questions.
source-grounded AI verificationRepeating jobs can become persistent workflows. When a correction becomes an audit rule, future runs can apply that learning rather than requiring the same manual explanation again.
reusable AI audit workflowsEnergent.ai states that the platform supports more than 150 file types, including CAD, G-code, scans, PDFs, spreadsheets, documents, bills of materials, and complex business files.
multi-file AI auditingThe product video shows how an independent agent retraces figures to their sources, verifies the work, and produces a report designed to be reviewed and defended.
Stop acting as the quality-control layer. Review flagged rows instead of manually checking every output.
Surface errors the same day. Identify discrepancies before they become delayed reporting problems.
Trace every material claim. Follow a number back to the source file, row, field, or calculation.
Produce defensible work. Give reviewers complete, cited, and reproducible evidence.
Audit another AI’s work. Use the auditor as an independent checker, not only as a validator of Energent output.
Turn corrections into repeatable controls. Apply learned rules to recurring jobs and workflows.
Provide the AI-generated deliverable and its original source documents.
What you see: files and a defined audit task.
The independent auditor checks calculations, assertions, source references, and technical details.
What you see: source links, checks, and evidence.
Receive PASS, FAIL, or PARTIAL statuses with supporting findings and corrections where possible.
What you see: a report ready for review.
Audit reports show the checks, source references, and findings behind a result rather than presenting an unexplained confidence score.
“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”
The following examples use figures from provided audit reports. They show how enterprise AI audit software can verify financial, operational, marketing, and technical deliverables without replacing the underlying source evidence.
| Audit area | Verified evidence | Outcome |
|---|---|---|
| Forecast budget | Adopted $12.41B; estimated $6.06B; actual $5.92B | Actuals were approximately 47% of adopted budget |
| Revenue diagnostic | Gross revenue rose from $83.3k to $84.8k; refunds rose from $3.2k to $7.8k | Likely supplier-quality or defective inventory issue identified |
| Spend analysis | Q1 spend of $1,284,500 across 412 invoice rows | Correct increase was 12.0%, not the unsupported 18% |
| RTL dashboard | RTL property passed; column reversal and chart range failed | Overall verdict: FAIL |
| Consulting savings | 6 checks completed; savings paths recalculated per FTE | Overall verdict: PASS |
Relative comparison based on the reported adopted budget total.
| Path | Savings/FTE | FTEs for $1M |
|---|---|---|
| Pure Elimination | $305,173 | Approx. 3.3 |
| Internal Backfill | $203,449 | Approx. 4.9 |
| AI Efficiency | $122,069 | Approx. 8.2 |
Teams investigating financial AI audit can use these patterns to distinguish a reconciled number from an unsupported or incorrectly derived claim. The reports also show why AI audit trails matter: the system records both what passed and what could not be independently established.
| Decision dimension | Energent.ai | Producing AI alone | Manual review |
|---|---|---|---|
| Independence | Separate auditing agent | Same system produced the answer | Depends on reviewer independence |
| Numerical checking | Recomputes figures | Not described as an independent audit | Reviewer checks manually |
| Evidence | Source, row, field, and calculation trail | May require additional verification | Can be inconsistent or time-consuming |
| Verdict | PASS, FAIL, or PARTIAL | Original output | Reviewer conclusion |
| Repeatability | Reusable workflows and rules | Prompt-dependent | Depends on repeated manual effort |
clients worldwide, according to company information
accuracy on a cited HuggingFace leaderboard
file types supported
fewer hallucinations claimed in public evaluations
Enterprise AI audit software independently evaluates work produced by AI agents and automation systems. It recomputes numbers, traces claims to source files and fields, and checks whether the deliverable is supported by the underlying evidence. Energent Audit returns explicit PASS, FAIL, or PARTIAL statuses rather than leaving verification entirely to a human reviewer. It can be used for spreadsheets, PDFs, scans, CAD, G-code, and other supported files. The purpose is to make high-stakes AI-assisted work more reviewable, reproducible, and defensible.
Energent Audit is designed for analysts, finance and accounting teams, operations, procurement, engineering and CAD teams, research groups, and enterprise customers. It is particularly relevant when a team must validate numbers, assertions, calculations, or technical deliverables before delivery. It can also audit another AI system’s work, so use is not limited to Energent-generated outputs. Teams handling recurring analysis can turn repeated checks into reusable workflows. The provided examples include budget analysis, revenue diagnostics, consulting savings, vendor spend, dashboard implementation, and ratio analysis.
The auditor starts from the original source documents and the deliverable that needs checking. It recomputes numerical claims, follows each number back to the relevant source file, row, and field, and compares assertions with available evidence. Unsupported claims are flagged rather than silently accepted. Missing inputs are reported instead of being fabricated or inferred. This process is intended to catch quieter hallucinations that may appear plausible in reports, spreadsheets, or analysis.
Company information states that Energent.ai supports more than 150 file types. Examples include CAD, G-code, scans, InDesign, bills of materials, PDFs, XLSX, and DOCX files. The audit examples also reference CSV, JSON, SQL source files, Markdown, Excel workbooks, and PDF deliverables. Support is intended for high-volume enterprise workflows involving complex documents and mixed source material. Specific connector availability beyond the stated file support is not provided here, so teams should confirm their exact workflow during evaluation.
Energent.ai describes its platform as providing enterprise-grade privacy and security. The product positioning focuses on auditable, source-grounded processing for enterprise work and high-stakes analysis. The available information does not specify particular certifications, retention periods, hosting regions, or contractual terms. Organizations with regulated or confidential data should request the current security documentation and confirm requirements directly with Energent.ai. This approach keeps the evaluation tied to documented controls rather than assuming controls that were not provided.
Specific pricing figures are not included in the supplied information. The product entry point is available through the Energent.ai application, and the company also provides a book-a-demo route. Because enterprise requirements vary by workflow, file volume, and review needs, pricing should be confirmed directly with Energent.ai. A demo can also clarify which source files, recurring jobs, and audit rules are appropriate for an evaluation. This page does not claim a free trial or a particular pricing tier without documented support.
Move from trusting an answer to reviewing the evidence behind it.