Macro regime intelligence for high-stakes analysis
Cluster CPI inflation and Fed Funds observations into interpretable macro regimes, connect them to discount-rate scenarios, and verify every analytical output against its source.
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AI-powered inflation and policy-rate clustering analysis groups historical observations by the relationship between CPI year-over-year inflation and the Fed Funds rate. In this use case, the resulting Bull, Base, and Bear regimes connect macro conditions to risk-free rates, credit spreads, proxy WACC, and present value outcomes. Energent.ai helps finance, research, and operations teams work from complex source files while its independent AI auditor recomputes results, traces figures to their origins, and returns a reviewable pass/fail verdict.
The analysis can sit alongside AI financial analysis and macro regime analysis when a team needs more than a chart: it needs a reproducible chain from source data to decision-ready scenario output.
The dashboard covers 257 monthly observations from January 2005 through May 2026. It places CPI year-over-year inflation against Fed Funds and colors observations by assigned regime, then translates those regimes into discount-rate and valuation comparisons.
The clustering view makes the relationship between CPI inflation and Fed Funds visible across historical regimes. Each observation is assigned to Bull, Base, or Bear so analysts can inspect the macro context rather than rely on an unlabelled scatter plot.
The scenario layer compares risk-free rates, credit spreads, term spreads, Fed Funds, proxy WACC, and PV. This lets a team see how a tightening or easing assumption changes the valuation bridge.
An audit report can place a clear verdict beside the underlying report and flagged findings. The purpose is to focus review effort on exceptions rather than manually rechecking every row.
Structured metrics, insight panels, and tables make the result easier to discuss in an investment, finance, or research review. The same approach supports interactive financial dashboards built from source-grounded analysis.
The use case combines historical clustering, scenario valuation, and independent verification in one reviewable workflow.
Evaluate Base, Bear, and Bull conditions using the supplied rate and valuation assumptions.
Plot CPI year-over-year inflation against Fed Funds and inspect observations by assigned regime.
Relate risk-free rates, credit spreads, proxy WACC, and PV to the same scenario framework.
Use monthly regime maps and historical risk-free-rate and proxy-WACC paths to retain time-series context.
Recompute figures, compare them with source references, and identify failures before delivery.
Follow numbers to the exact source file, row, and field for a more defensible review.
The workflow moves from source material to regime interpretation and then to an independent audit verdict.
Provide the historical files and analytical materials containing inflation, policy-rate, market, or valuation inputs.
What you see: source-grounded inputs organized for analysis.
The workflow plots CPI against Fed Funds, assigns macro regimes, and compares their discount-rate and PV outputs.
What you see: regime charts, historical maps, and scenario tables.
Energent Audit independently recomputes numbers, checks references, and returns evidence with a pass or fail verdict.
What you see: flagged findings, traceable evidence, and a reviewable report.
The supplied scenario data shows how changes in rates and spreads flow through proxy WACC and present value. The figures below reproduce the provided dashboard inputs.
| Regime | Risk-Free Rate | 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 |
Scenario PV range: $1,376.2–$1,484.1. Baseline PV anchor: $1,460.6.
Total historical sample: 257 monthly observations from 2005-01-01 to 2026-05-01.
The linked dashboard contains the clustering plot, regime discount-rate build-up, historical rate path, monthly regime map, and PV tornado.
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Amjad M., Telecommunications Engineer, Fortune 500 Telecommunications
Energent Audit is separate from the agent that performs the analysis. It recomputes numbers, traces them to source material, checks references, fixes errors where possible, and provides evidence with a pass or fail verdict.
An independent AI agent double-checks figures, retraces each number to its source, verifies calculations, and shows how the result was built.
The result is designed to make failures visible before delivery and let reviewers focus on flagged rows. This supports auditable AI workflows for financial analysis and complex statistical modeling.
This comparison uses only distinctions documented in the supplied information. Generic alternatives describe common workflow patterns rather than named competitors.
| Decision dimension | Energent.ai | Generic Alternative A: manual review | Generic Alternative B: unverified AI output |
|---|---|---|---|
| Verification model | Independent AI auditor recomputes and checks the analysis | Human reviewer checks outputs manually | No independent verification is specified |
| Evidence trail | Traces figures to the exact source file, row, and field | Evidence depends on reviewer process | Source traceability is not specified |
| Macro scenario view | CPI and Fed Funds clustering with regime-linked PV and WACC | Requires separate spreadsheet or chart work | May return analysis without a reviewable regime trail |
| File coverage | 150+ file types, including scans, CAD, G-code, PDFs, XLSX, and DOCX | Depends on the tools used by the team | Depends on the model and ingestion method |
| Failure handling | Pass/fail verdict, attached evidence, and fixes where possible | Reviewer must identify and document exceptions | Errors can remain hidden without a separate audit step |
clients worldwide cited by the company
accuracy on a published HuggingFace leaderboard, company claim
supported file types
fewer hallucinations reported in evaluations
AI-powered inflation and policy-rate clustering analysis groups historical macro observations according to relationships between CPI year-over-year inflation and the Fed Funds rate. In this use case, the groups are interpreted as Bull or Easing, Base or Normal, and Bear or Tightening regimes. The analysis also connects each regime to risk-free rates, credit spreads, proxy WACC, and present value. It gives finance and research teams a structured way to compare macro conditions rather than reviewing isolated rate numbers. Energent Audit adds an independent verification layer so the calculations and source references can be checked before delivery.
The use case is relevant to analysts, finance and accounting teams, operations and procurement groups, engineering and CAD teams, research groups, and enterprise customers. It is particularly useful when a team must connect historical macro data to valuation or discount-rate scenarios. Users can work with natural-language prompts, which the company positions as accessible to non-experts. The workflow also supports complex documents and more than 150 file types. Teams that need a reviewable audit trail can use the independent auditor to inspect numbers and flagged rows.
A workflow begins with the source files and analytical materials containing the relevant macro and valuation inputs. The analysis then plots CPI year-over-year inflation against Fed Funds, assigns observations to regimes, and produces scenario comparisons. The provided dashboard covers 257 monthly observations from January 2005 through May 2026. Energent also describes reusable workflows that preserve corrections as audit rules over time. Teams can start through the product experience or discuss their process through a demo.
Energent.ai states that it supports more than 150 file types. The supplied examples include CAD, scans, G-code, InDesign, BOMs, PDFs, XLSX, and DOCX. This broad file coverage is intended for high-volume workflows that combine structured and complex documents. The company also describes white-label and brandable stakeholder-ready outputs. Exact integration behavior for a particular source system is not specified in the supplied information, so teams should confirm their required connection during evaluation.
Energent Audit operates as an independent AI auditor separate from the agent that performed the analysis. It recomputes analytical numbers, traces them to the exact source file, row, and field, and verifies figures against source references. It can fix errors where possible and issues a pass or fail verdict with attached evidence. This allows reviewers to focus on flagged rows instead of manually checking every result. The company reports up to 3× fewer hallucination errors in internal evaluations, while the supplied claim should be assessed in the context of the relevant evaluation.
Specific pricing figures are not provided in the supplied information, so this page does not state a price. Energent.ai provides a pricing page and a book-a-demo path for teams that need more information. The product entry point is available through the Energent app subdomain. Onboarding duration is also not specified, and it can depend on the files, workflow, and review requirements involved. Teams can use the product entry point or contact Energent to clarify pricing, access, and onboarding for their use case.
Explore AI-powered inflation and policy-rate clustering with an independent audit trail for every important number.