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Da10 -- Greater Los Angeles Artificial Intelligence Threat Detection

One of 10,000+ tenders Taiyo tracks in California.

Source: SAM.gov

Tender
Sources Sought

The Department of Veterans Affairs (VA), Office of Security and Law Enforcement (OSLE), is a federal law enforcement organization with specialized responsibilities for reducing crime and protecting persons and assets within the jurisdictional boundaries of the Department of Veterans Affairs. OSLE is charged with safeguarding Veterans, their families, VA employees, volunteers, visitors, and contrac

Status
Prospective
Sector
Hospital, Residential, Commercial
Location
Los Angeles, California, United States of America
Updated
3 Nov 2026
Source
SAM.gov

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The tender “Da10 -- Greater Los Angeles Artificial Intelligence Threat Detection” is a tender record in Taiyo, linked to the Hospital sector. It is issued for works, goods, or services related to Los Angeles, California, United States of America.

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Not available for this record.

Status

Original status

sources sought

Taiyo status

Prospective

Taiyo last update

2026-11-03

Contact

Not available for this record.

Description

Description

The Department of Veterans Affairs (VA), Office of Security and Law Enforcement (OSLE), is a federal law enforcement organization with specialized responsibilities for reducing crime and protecting persons and assets within the jurisdictional boundaries of the Department of Veterans Affairs. OSLE is charged with safeguarding Veterans, their families, VA employees, volunteers, visitors, and contractors, as well as the physical infrastructure, medical assets, and sensitive information housed within VA facilities. In carrying out this mission, OSLE oversees VA Police operations, physical security programs, and the integration of security technologies that support the protection of VA persons and property. To ensure the safety and security of the Veterans, their families, VA staff, and VA facilities, OSLE has determined that the existing video surveillance system at the West Los Angeles VA Campus requires significant improvements, as it does not provide the coverage, reliability, or capability necessary to support modern security operations across a campus of this size and population density. Identified deficiencies including gaps in camera coverage, aging or degraded equipment, limited real-time monitoring capability, and the absence of intelligent analytics constrain OSLE's ability to detect, deter, and respond to security incidents in a timely manner and place an unsustainable burden on personnel who must manually monitor a large volume of camera feeds. Addressing these deficiencies is essential to protecting the growing resident population at NCWI and maintaining a secure environment across the West Los Angeles VA Campus. In support of enhanced security operations, the VA intends to incorporate Artificial Intelligence (AI) capabilities to assist in reviewing surveillance footage, enabling the rapid identification of weapons that are in plain view and not concealed. AI-based detection is intended to augment not replace, the vigilance of VA Police and security personnel by continuously analyzing video feeds, flagging visible weapons in real time, and accelerating the delivery of actionable alerts to responders. By pairing an upgraded, reliable video surveillance infrastructure with AI-driven weapons detection, OSLE seeks to reduce the time between the appearance of a visible threat and an effective security response. Early detection of brandished weapons can provide critical seconds for personnel to initiate protective actions, notify law enforcement, and safeguard the Veterans, families, and staff who live and work on the West Los Angeles VA Campus. SEE ATTACHED REQUEST FOR INFORMATION AND DRAFT PERFORMANCE WORK STATEMENT

Original sector

Professional, Scientific, and Technical Services

Location

Country

United States of America (USA)

State

California

City

Los Angeles

Source

Source

SAM.gov

URL

More Details

Record type

Tender

Sector

Hospital, Residential, Commercial

Article published date

2026-08-12

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