70RSAT24C00000026
Definitive Contract
Overview
Government Description
SBIR PHASE I TOPIC 2: DHS241-002 DATA LABELING AND CURATION AT SCALE (DLCS) FOR MACHINE LEARNING ALGORITHMS.
Awardee
Awarding Agency
Funding Agency
PSC
Place of Performance
Frisco, TX 75035 United States
Pricing
Fixed Price
Set Aside
Small Business Set Aside - Total (SBA)
Extent Competed
Full And Open Competition After Exclusion Of Sources
Related Opportunity
None
Avawatz Co was awarded
Definitive Contract 70RSAT24C00000026 (70RSAT-24-C-00000026)
for Sbir Phase I Topic 2: Dhs241-002 Data Labeling And Curation At Scale (DLCS) For Machine Learning Algorithms.
worth up to $174,477
by Science and Technology Directorate
in May 2024.
The contract
has a duration of 5 months and
was awarded
through SBIR Topic Data Labeling and Curation at Scale (DLCS) for Machine Learning Algorithms
with a Small Business Total set aside
with
NAICS 541715 and
PSC AJ11
via direct negotiation acquisition procedures with 1 bid received.
SBIR Details
Research Type
Small Business Innovation Research Program (SBIR) Phase I
Title
CLARIFIER - Data Labeling and Curation at Scale (DLCS) for Machine Learning Algorithms
Related Solicitation
Abstract
The Data Labeling and Curation at Scale (DLCS) project will create a system called CLARIFIER, which aims to revolutionize the way large volumes of complex data are processed and utilized for machine learning (ML) applications within the Department of Homeland Security (DHS). The primary purpose of this work is to develop an advanced system capable of ingesting, labeling, storing, and curating diverse data types, with a focus on enhancing the efficiency and accuracy of machine learning algorithm development. The DLCS system will leverage recent research done by the PIs, which employs advanced ML techniques for auto-labeling, supplemented by human verification to ensure high accuracy, and adapt it to handle specific DHS use cases such as millimeter-wave radar and x-ray imagery. This adaptation involves creating a robust data ingestion module capable of processing various file formats, including Hierarchical Data Formats (HDF) and Digital Imaging and Communications in Security (DICOS). Additionally, the system will integrate seamlessly into the existing DHS ecosystem, providing a streamlined workflow from data ingestion to storage. The anticipated outcome is a scalable, efficient, and accurate system for data labeling and curation. This system will significantly reduce the time and effort required for data processing, accelerating development of critical ML algorithms for security applications. In terms of commercial potential, the DLCS system has broad applicability beyond DHS. It can be adapted for various sectors requiring efficient handling of large-scale data, such as healthcare, aviation security, and defense, making it a valuable tool for both government and commercial entities.
Research Objective
The goal of phase I is to establish the technical merit, feasibility, and commercial potential of proposed R&D efforts and determine the quality of performance of the small business awardee organization.
Topic Code
DHS241-002
Agency Tracking Number
24.1 DHS241-002-0024-I
Solicitation Number
24.1
Contact
Rajini Anachi
Status
(Complete)
Last Modified 6/27/24
Period of Performance
5/7/24
Start Date
10/6/24
Current End Date
10/6/24
Potential End Date
Obligations
$174.5K
Total Obligated
$174.5K
Current Award
$174.5K
Potential Award
Award Hierarchy
Definitive Contract
70RSAT24C00000026
Subcontracts
Activity Timeline
Transaction History
Modifications to 70RSAT24C00000026
People
Suggested agency contacts for 70RSAT24C00000026
Competition
Number of Bidders
1
Solicitation Procedures
Negotiated Proposal/Quote
Evaluated Preference
None
Commercial Item Acquisition
Commercial Item Procedures Not Used
Simplified Procedures for Commercial Items
No
Other Categorizations
Subcontracting Plan
Plan Not Required
Cost Accounting Standards
Exempt
Business Size Determination
Small Business
Awardee UEI
S446QKCZNCC8
Awardee CAGE
85W10
Agency Detail
Awarding Office
70RSAT SCI TECH ACQ DIV
Funding Office
70YSA1
Created By
7001bickleya
Last Modified By
7001bickleya
Approved By
7001bickleya
Legislative
Legislative Mandates
None Applicable
Performance District
TX-04
Senators
John Cornyn
Ted Cruz
Ted Cruz
Representative
Patrick Fallon
Modified: 6/27/24