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Synthetic Data Generation and Modeling

Active

Federal

Response Deadline11/05/2026, 12:00:00 AM UTC

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

AI Contract Overview

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This subcontract for Synthetic Data Generation and Modeling supports prime contractors on projects for the Navy NSWC Port Hueneme under the Department of Defense. The primary objective is the provision of synthetic data generation services to simulate equipment degradation and the creation of anomalous condition datasets specifically designed for model testing and validation. The scope of work requires the delivery of validated synthetic datasets along with the tools used for their generation. This effort falls under NAICS code 541715, with a response deadline set for November 5, 2026.

General Info

Subcontract for Navy synthetic data generation and modeling services due November 5, 2026.

Agency

Department Of Defense → Commanding Officer

NAICS

541715 - Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)

Place of Performance

CA

Set-Aside

NONE

Documents

This scope was carved out of MATAC_27-03.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

CBM+ SOFTWARE AND ANALYTICS DEVELOPMENT (CBM+)

AI Contract Breakdown

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Timeline

PostedOct 22

subcontract

Response DeadlineNov 5

Submission deadline

Response Deadline

11/05/2026, 12:00:00 AM UTCin 13 days

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Organization & Contact Information

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AgencyDepartment Of Defense → Commanding Officer

ContactsNo contacts available

OfficeN/A

Organization / AgencyDepartment Of Defense → Commanding Officer

Office AddressN/A

ContactsNo contact information available

Full Description

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Supplies synthetic data generation services for prime contractors on Navy NSWC Port Hueneme projects. Generates synthetic data to simulate equipment degradation and creates anomalous condition datasets for model testing and validation. Delivers validated synthetic datasets and generation tools.

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