Methodology · Validation · Audit

Every prediction tracked.
Every outcome recorded.
ForVue tells you how accurate it is.

No other predictive maintenance platform closes the loop between prediction and outcome. ForVue does — automatically, continuously, and transparently. The current ±2-4% margin of error is a modeled tier — empirical precision and recall are published per portfolio in the Accuracy Validation report once 5+ outcome events accumulate.

The Numbers
±2-4%
Modeled Margin of Error
Per confidence tier: HIGH ±2%, MEDIUM ±3%, LOW ±4%. Empirical precision and recall published per portfolio in the Accuracy Validation report once 5+ outcome events accumulate.
12
Scoring Factors
Age, brand tier, climate zone (EDI), usage intensity, service history, Bayesian observations, insurance claims, portfolio correlation, and 4 more — feeding the composite failure probability for every component.
26
Cascade Rules
Upstream-downstream component relationships executed in two sequential passes. Upstream failures multiply downstream risk. Modeled from observed multifamily failure patterns.
169
Components Tracked
Across 30 residential appliance categories. Calibrated from ASHRAE 2019 Equipment Life Expectancy, HUD Useful Life Standards, NAHB Component Lifespan Study, BOMA, NEC, and EPA/CDC standards.
50
U.S. States EDI-Calibrated
Environmental Degradation Index (EDI) factors mapped by zip3 across every U.S. state. The same appliance ages faster in Phoenix than in Anchorage — ForVue accounts for it automatically.
The 4-Step Validation Loop

Predictions don't
evaporate. They get scored.

Every score generated by ForVue is paired with an outcome. When the outcome is recorded, the model learns. This is the difference between deterministic mathematics that improves and black-box AI that drifts.

1
Prediction Made
ForVue scores a component CRITICAL with a predicted failure window. The prediction is logged with timestamp, scoring inputs, and predicted timeline.
2
Outcome Logged
When the component is serviced, replaced, or fails, the result is logged in service history. Outcome ties back to the original prediction.
3
Model Updates
The Bayesian layer incorporates the outcome. Future predictions of similar components improve automatically. The model gets sharper as you use it.
4
Accuracy Reported
Your accuracy score updates in your dashboard — a living, auditable record. Banks, auditors, and underwriters can request an accuracy validation report at any time.
The Methodology

Weibull-Bayesian.
Patent pending. Deterministic.

The scoring engine combines two well-established mathematical frameworks: the Weibull distribution (a survival-analysis curve used in aerospace and reliability engineering since 1951) and Bayesian inference (a probabilistic update mechanism used in any system that learns from outcomes).

📐
Weibull Survival
Each appliance type has a calibrated Weibull failure curve (shape parameter k, scale parameter λ) derived from ASHRAE 2019 Equipment Life Expectancy data and validated against real Wise Capital failures. The curve tells you the probability of survival at any age.
🔄
Bayesian Update
Every logged outcome adjusts the posterior probability for similar components. After enough validation events, the model approaches the true failure distribution of your specific portfolio — not a generic industry average.
📋
Cited Sources
24 CFR Part 968. HUD Notice PIH 2011-7. ASHRAE 2019 Equipment Life Expectancy. NAHB Component Lifespan Study. BOMA. NEC inspection intervals. EPA/CDC indoor air quality. Every input traces to a citable standard.
⚖️
Deterministic Output
Same inputs always produce the same outputs. No randomness, no stochastic sampling, no hidden weights. Any auditor with access to the inputs can recreate any score by hand.
🔐
Patent Pending
USPTO Provisional Patent Application No. 64/032,704 — Composite Weibull-Bayesian Predictive Maintenance Scoring System. Filed April 8, 2026. Sole inventor: Christopher Wise, J.D.
🚫
Not Black-Box AI
The risk scores are pure mathematics. AI handles only the language layer — plain-English recommendations and photo parsing. The financial decisions stay with the math.
For Bank Examiners & Insurance Actuaries

Request an Accuracy Validation Report

Lenders underwriting multifamily loans and P&C carriers writing residential policies can request a portfolio-level accuracy validation report. The report includes the count of predictions made, count of outcomes logged, accuracy rate by component category, and validation_event log with audit-trail timestamps.

For court-admissible Weibull methodology documentation suitable for claim assessment or loan default review, contact us directly.

Request Validation Report
See It in Action

Math you can audit.
Numbers you can defend.

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