Technical Resource for Mitigation Programs

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-07-11 (day precision)Source: 2025-07-11T00:00:00

Withheld from public reporting

Not reported

Mission

Topic area

Emergency Management

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The FEMA Hazard Mitigation Assistance (HMA) AI solution addresses the challenge of managing complex grant processes that currently rely on manual review of thousands of applications, modifications, and closeout packages. Analysts must manually extract and reconcile data scattered across multiple nonstandardized systems (NEMIS, PARS, PDFs, spreadsheets), risking delays in obligation and closeout of grants. This fragmentation leads to inconsistent compliance determinations, increased audit risk, and inefficient use of limited staff resources. The initial document scan alone takes 45-60 human minutes per modification, with full reviews requiring 1-2 human days, significantly delaying the release of mitigation funds to communities in need.

Expected benefits

The AI solution will deliver significant benefits to both FEMA operations and disaster-affected communities. Operationally, it will reduce document review time by 40-70%, saving 15-20 analyst hours per week to prioritize higher-value activities requiring human judgement and stakeholder interaction. The system will enhance compliance through consistent regulatory interpretation, reducing errors and improving financial calculation accuracy. The AI will identify eligibility concerns in real-time, reducing the risk of funding grants that do not align with federal laws, regulations, and executive orders. For the public, the AI will accelerate application review, obligation and closeout of mitigation grants, enabling states, tribes, territories, and local communities to implement risk-reduction projects sooner. This faster release of funds directly enhances public safety and disaster resilience while providing a more consistent application experience across regions.

System outputs

The AI system produces both machine-readable and human-readable artifacts to support grant management throughout the lifecycle. These include structured findings reports that categorize issues by scope, schedule, and budget with source citations; anomaly/discrepancy KPIs highlighting timeline gaps, invoice pattern shifts, and budget-to-scope mismatches; and compliance checklists identifying missing or non-conforming items. For documentation support, the system generates auto-drafted Requests for Information (RFIs), lock-in letters, and closeout letters with precise regulatory citations in a professional tone. It also creates CSV exports listing flagged terms and financial variances with page references. Additionally, the system provides on-demand answers to regulatory questions and prioritized worklists showing grants needing immediate action, supporting knowledge democratization and workflow optimization.

Technology

AI classification

Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).

System name(s)

The HMA Mitigation Agent code executes within the Mosaic-authorized Databricks workspace and inherits all security controls documented in Mosaic’s ATO. No separate ATO has been issued for the AI component at this time.

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Developed in-houseSource: b) Developed in-house

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

Not reported

Training and evaluation data

The FEMA model is trained, fine-tuned, and evaluated using comprehensive datasets of historical grant management records, including subaward closeout documentation, financial reconciliation data, and management cost lock-in records from previous disaster declarations across HMGP, PDM, FMA, and BRIC programs.

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported