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New York City Fire Department

Mixed evidenceExceptions onlyQueue eliminationContinuous decisioning

FireCast risk-ranked inspection lists through RBIS

Fire companies can prioritize inspections by predicted risk instead of fixed cycles and local knowledge.

Engineering design · New York City, United States

Collections: Queue eliminated · Embodied work

An editorial scene for New York City Fire Department contrasts select and schedule inspectable buildings from policy requirements and local information. with scores and ranks buildings from fire history, characteristics, violations, complaints, and other risk factors. in the firecast risk-ranked inspection lists through rbis workflow.

Executive brief

The operating-model shift, in one view.

The measurable first-order result is better inspection yield, not proven fire prevention; leaders should separate queue quality from ultimate safety outcomes.

AI value · Building violations found after FireCast 2.0 deployment

Company-reported

Average violations increased 19% in the first 30 days and remained 10% higher after 60 days

Contemporary independent report quoting FDNY results; more violations found is a targeting metric, not proof that fires were prevented.

Before

Select and schedule inspectable buildings from policy requirements and local information. → Inspect assigned premises and record hazards.

After

Scores and ranks buildings from fire history, characteristics, violations, complaints, and other risk factors. → Receive prioritized lists, inspect buildings, and issue or follow up violations.

Human boundary

The model determines priority order; uniformed personnel decide findings and enforcement based on physical inspection.

Why it matters

Fire companies can prioritize inspections by predicted risk instead of fixed cycles and local knowledge.

How the work changed

Before

How the work ran before the change.

  1. Step 1 of 2

    Fire officers

    Select and schedule inspectable buildings from policy requirements and local information.

    ControlMandatory cycles and professional judgment

  2. Step 2 of 2

    Firefighters and inspectors

    Inspect assigned premises and record hazards.

    ControlFire code

What changed

Fire companies can prioritize inspections by predicted risk instead of fixed cycles and local knowledge.

Decision rightAI handles the default; humans own exceptions

After

How the same work runs now.

  1. Step 1 of 2

    FireCast

    Scores and ranks buildings from fire history, characteristics, violations, complaints, and other risk factors.

    ControlPredictive risk ranking

  2. Step 2 of 2

    Fire officers and field units

    Receive prioritized lists, inspect buildings, and issue or follow up violations.

    ControlHumans conduct inspections and enforcement

Process model built from the published workflow evidence for New York City Fire Department. Every step, actor, and control appears in full below.
Every step, actor, and control

Exception path

Mandatory inspections, public complaints, and officer judgment can trigger work outside the risk-ranked list.

Work removed

  • Manual aggregation of cross-agency building risk signals
  • Inspection capacity spent without citywide risk prioritization

Decision authority

The model determines priority order; uniformed personnel decide findings and enforcement based on physical inspection.

Before

  1. 01

    Fire officers

    Select and schedule inspectable buildings from policy requirements and local information.

    Control: Mandatory cycles and professional judgment

  2. 02

    Firefighters and inspectors

    Inspect assigned premises and record hazards.

    Control: Fire code

After

  1. 01

    FireCast

    Scores and ranks buildings from fire history, characteristics, violations, complaints, and other risk factors.

    Control: Predictive risk ranking

  2. 02

    Fire officers and field units

    Receive prioritized lists, inspect buildings, and issue or follow up violations.

    Control: Humans conduct inspections and enforcement

Work that left the path

  • Manual aggregation of cross-agency building risk signals
  • Inspection capacity spent without citywide risk prioritization

Human role before

Fire officers assembled inspection priorities without a citywide predictive ranking.

Human role after

Officers allocate field capacity from a risk-ranked list and retain inspection and enforcement judgment.

AI rolePredictive analytics that assigns building fire-risk scores and prioritizes inspection lists.

Outcomes

Building violations found after FireCast 2.0 deployment

Company-reported

Violation discovery rate before FireCast 2.0Average violations increased 19% in the first 30 days and remained 10% higher after 60 days

First 60 days after FireCast 2.0 deployment · New York City risk-based building inspections

Contemporary independent report quoting FDNY results; more violations found is a targeting metric, not proof that fires were prevented.

What leaders can reuse

Anti-pattern

Using risk rank as evidence of a code violation or claiming prevented fires from increased violation discovery.

Questions

  1. 01What is the right first-order metric for a risk-ranked queue?
  2. 02Which inspections must bypass model priority?

Portability conditions

  • Reliable entity matching across agencies
  • Field verification before enforcement
  • Mandatory and complaint-driven overrides

Reputation risk

medium

Evidence and authority

What the public record supports.

Current · updated

2 independent, 1 primary; publication outcomes are verified and reported.

Bundle 1.0.0 · reviewed 2026-08-23 · stable ID 4ab7ea0afa00d969

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Sources

Read the evidence, freshness, caveat, and version policy.