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This Subcontract opportunity from Government of Canada was posted on March 26, 2026. The submission period has ended. Browse the details below for market research, or find similar active opportunities.

AI/ML-Based Threat Classification and Tracking Software

Closed
International

Contract Overview

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

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NAICS: 541512
New
SLED
Cybersecurity Maturity Model Certification (CMMC) Program Services
Solicitation # 09112026
The University of Arkansas, Fayetteville (UAF) issued solicitation 09112026 for Cybersecurity Maturity Model Certification (CMMC) Program Services to secure a term contract for two years with two optional renewal years. The objective was to engage a qualified firm to provide cybersecurity advisory, architecture, engineering, and compliance services to mature the university's CMMC program. The scope focused on achieving compliance with CMMC Levels 1, 2, and 3 to protect Federal Contract Information (FCI) and Controlled Unclassified Information (CUI). Key deliverables included a compliance maturity roadmap, the design of a scalable security architecture, a continuous monitoring program, and assessment readiness support. The procurement process included several modifications, such as the addition of a 2 million dollar Professional Liability insurance requirement and extensions to the proposal deadline. Evaluation criteria were weighted toward the technical proposal (40 points), respondent qualifications (30 points), and cost (30 points). However, on October 19, 2026, the University of Arkansas, Fayetteville officially cancelled RFP 09112026 via Addendum 3. This cancellation was prompted by changes in federal guidance that necessitated a reassessment of the procurement scope, with the university intending to issue a new solicitation once requirements are clarified.
University of Arkansas Fayetteville (UAF)

POSTED

about 6 hours ago

DEADLINE

N/A

AI Contract Overview

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The contract involves the development of advanced machine learning models focused on identifying various types of drones, reducing erroneous detections (false positives), and enhancing capabilities for predictive tracking within a networked sensor system. This initiative aims to improve threat classification and real-time tracking accuracy by leveraging AI and ML technologies, ensuring effective monitoring and response against potential airborne threats. Issued by the Government of Canada's Department of Public Works and Government Services, this subcontract opportunity is categorized under the NAICS code 541512, which pertains to computer systems design services. Entities interested in participating must submit their proposals by June 24, 2026. The work will be conducted in Canada, with the overall goal of strengthening surveillance infrastructure through innovative software solutions tailored for drone threat detection and tracking.

General Info

Development of AI-driven drone identification and tracking models for Canadian government surveillance, proposals due June 2026.

Agency

Government of Canada → Department of Public Works and Government Services

Place of Performance

Canada, CAN

Set-Aside

NONE

Documents

This scope was carved out of W7714-268857.

The full solicitation package (12 documents), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Call for Proposals (CFP) Procurement and Operationalization of Innovation and New Technology (POINT)

AI Contract Breakdown

Uniform Contract Format

No documents to break down

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Timeline

PhaseClosed
Posted

subcontract

Response Deadline

Deadline has passed

Submission Closed

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Organization & Contact Information

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AgencyGovernment of Canada → Department of Public Works and Government Services
ContactsNo contacts available
OfficeN/A
Organization / Agency
Government of Canada → Department of Public Works and Government Services
Office AddressN/A
ContactsNo contact information available

Full Description

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Development of machine learning models to classify drone types, reduce false positives, and enable predictive tracking across a networked sensor array.

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