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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
VA · VA-25-3275 · record R2228

Concept Clustering

Department of Veterans Affairs · OIT: Office of Information & Technology

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
High-impactSource: a) High-impact
Impact justification
Not reported
Start date
Not reported
Withheld from public reporting
Not reported

Mission

Topic area
Administrative Functions
Operational functions
FOIA and records management Derived by keyword rules; see methodology

Problem the AI is intended to solve

Electronic discovery (e-discovery) refers to discovery in legal proceedings such as litigation, government investigations, or Freedom of Information Act (FOIA) requests, where the information sought is in electronic format. As data volumes and the number of civil cases continue to rise, so does the need tools to help government agencies manage electronically stored information used for discovery for litigation, investigations, and FOIA requests. This AI model analyzes the content of documents to identify contextually similar clusters of text. It groups similar documents automatically to reveal themes, without predefined keywords. Runs once on workspace text and metadata to group documents by similarity. No external data or ongoing training.

Expected benefits

The expected benefits include reduced manual effort in organizing and identifying related documents, streamlined information retrieval, and improved efficiency for VA staff. Emphasis is placed on cost savings through reduction of labor-intensive processes, while also enhancing user experience by improving search and discovery.

System outputs

Identified Cluster groups

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)
Not reported
Custom-developed code
Yes
Public source code
Not reported

Sourcing

How it was built
Purchased from vendorSource: a) Purchased from a vendor
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
Not reported
Privacy Impact Assessment
Not reported
Authorization to Operate
Not reported
Demographic features
Not reported
Training and evaluation data
Not reported
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