DOJ · DOJ-0279 · record R1704

NetDocuments

Department of Justice · Department of Justice / OPR

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
Start date: 2025-07 (month precision)Source: 07/2025
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Administrative Functions
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The AI functionality of NetDocuments consists of predictive suggestions for saving files, such as Word documents and Outlook emails. Through machine learning, NetDocuments predicts the OPR matter into which it thinks the end user should save a specific file. The AI feature is intended to make the process of saving files into the pertinent matter number more efficient and streamlined.

Expected benefits

Because NetDocuments is OPR's repository of records, it is important that OPR staff save all matter-related files into NetDocuments associated with the correct OPR matter number. The AI feature in NetDocuments is expected to make the process of saving files into NetDocuments more efficient and user-friendly. That will both encourage end users to save files into NetDocuments and assist in making sure that files are correctly associated with the proper OPR matter numbers.

System outputs

The AI output from NetDocuments consists of predictive recommendations for the specific OPR matter numbers to which OPR staff should associate files saved into NetDocuments.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Redacted for cybersecurity purposes.
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Purchased from vendorSource: a) Purchased from a vendor
Vendor (as reported): Inonde, NetDocuments
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: Yes
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: The case owner relied on DOJ AI governance practices to select and prepare data, as well as evaluate performance.
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported