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KCORE ANALYTICS LLC

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Company Details

Name: KCORE ANALYTICS LLC
Jurisdiction: New York
Legal type: DOMESTIC LIMITED LIABILITY COMPANY
Status: Active
Date of registration: 04 May 2018 (7 years ago)
Entity Number: 5336086
ZIP code: 10031
County: New York
Place of Formation: New York
Address: PO BOX 189, NEW YORK, NY, United States, 10031

DOS Process Agent

Name Role Address
HERNÁN A. MAKSE DOS Process Agent PO BOX 189, NEW YORK, NY, United States, 10031

U.S. Small Business Administration Profile

The U.S. Small Business Administration (SBA) helps Americans start, grow, and build resilient businesses.

Note: SBA was created in 1953 as an independent agency of the federal government to aid, counsel, assist and protect the interests of small business concerns; preserve free competitive enterprise; and maintain and strengthen the overall economy of our nation. SBA reviews Congressional and testifies on behalf of small businesses. It assesses the impact of regulatory burden on small businesses.

Phone Number:
E-mail Address:
Contact Person:
HERNAN MAKSE
Ownership and Self-Certifications:
Self-Certified Small Disadvantaged Business
User ID:
P2411857

Unique Entity ID

A UEI is a government-provided number, like a tax ID number, that’s used to identify businesses eligible for federal grants, awards and contracts.

Note: In April 2022, the federal government replaced its old identifier of choice, the Data Universal Numbering System (DUNS) number, with a government-issued UEI. Now all the federal government’s Integrated Award Environment systems use UEI numbers instead of DUNS numbers. So any entity doing business with the federal government must register for a UEI.

Unique Entity ID:
DS26CZMZ69A4
CAGE Code:
8EYV1
UEI Expiration Date:
2026-05-12

Business Information

Activation Date:
2025-05-14
Initial Registration Date:
2019-10-18

Filings

Filing Number Date Filed Type Effective Date
180730000004 2018-07-30 CERTIFICATE OF PUBLICATION 2018-07-30
180504010539 2018-05-04 ARTICLES OF ORGANIZATION 2018-05-04

USAspending Awards / Financial Assistance

Date:
2023-09-20
Awarding Agency Name:
National Science Foundation
Transaction Description:
SBIR PHASE I: ARTIFICIAL INTELLIGENCE AND NETWORK THEORY FOR ELECTIONS -THE BROADER/COMMERCIAL IMPACT OF THIS SMALL BUSINESS INNOVATION RESEARCH (SBIR) PHASE I PROJECT PROMOTES AND ENHANCES TRANSPARENCY IN THE DEMOCRATIC PROCESS. IT ACCOMPLISHES THIS BY DEVELOPING A SOCIAL AWARENESS SYSTEM THAT CAN DETECT, UNDERSTAND, AND PREDICT OPINION TRENDS WITHIN A DEMOCRATIC SOCIETY. THROUGH THE DEVELOPMENT OF CUTTING-EDGE ARTIFICIAL INTELLIGENCE (AI) TECHNIQUES, THE PROJECT CONTRIBUTES TO SCIENTIFIC AND TECHNOLOGICAL KNOWLEDGE BY IMPROVING THE PREDICTION OF ELECTION RESULTS AND SOCIETAL OPINION TRENDS WITH HIGH ACCURACY. BY EMPLOYING MACHINE LEARNING, THE PROJECT AIMS TO SURPASS THE LIMITATIONS OF TRADITIONAL POLLING METHODS AND PROVIDE A REAL-TIME PREDICTOR OF ELECTION OUTCOMES WORLDWIDE. THE PROJECT WILL ADDRESS THE CREDIBILITY OF NEWS ON SOCIAL MEDIA SERVING TO STRENGTHEN THE RESILIENCE OF THE POPULATION AGAINST MISINFORMATION. IN ADDITION, THE PROJECT DEMONSTRATES A COMMITMENT TO INCLUSIVITY BY ACTIVELY SEEKING THE PARTICIPATION OF UNDERREPRESENTED MINORITIES. THIS SMALL BUSINESS INNOVATION RESEARCH (SBIR) PHASE I PROJECT AIMS TO PREDICT GLOBAL ELECTIONS IN REAL-TIME THROUGH THE INTEGRATION OF ARTIFICIAL INTELLIGENCE, NETWORK THEORY, AND BIG DATA SCIENCE. BY HARNESSING THE POWER OF ADVANCED MACHINE LEARNING MODELS AND ANALYZING VAST AMOUNTS OF PUBLICLY EXPRESSED OPINIONS ON SOCIAL MEDIA, THE TEAM OFFERS ACCURATE FORECASTS OF ELECTION OUTCOMES. THIS APPROACH HAS THE POTENTIAL TO DISRUPT THE CONVENTIONAL POLLING INDUSTRY, WHICH FACES GROWING UNCERTAINTIES AND CHALLENGES SUCH AS DECLINING RESPONSE RATES AND INHERENT BIASES IN SAMPLING. THE RESEARCH OBJECTIVES ENTAIL TACKLING CRITICAL RESEARCH AND DEVELOPMENT CHALLENGES, INCLUDING PREDICTING VOTER TURNOUT, EFFECTIVELY SAMPLING RURAL AREAS WITH LIMITED ONLINE COVERAGE, FILTERING OUT BOTS AND FAKE NEWS SOURCES, INFERRING THE PREFERENCES OF UNDECIDED VOTERS, ADJUSTING SAMPLE WEIGHTS ON A STATE-BY-STATE BASIS, ADDRESSING THE OPINIONS OF INDIVIDUALS NOT ACTIVE ON SOCIAL MEDIA, AND MITIGATING SOCIAL DESIRABILITY BIAS (WHERE RESPONDENTS CONCEAL THEIR INTENTION TO VOTE FOR CONTROVERSIAL CANDIDATES). THE ANTICIPATED TECHNICAL RESULTS INVOLVE THE DEVELOPMENT OF A TRANSFORMATIVE MACHINE LEARNING ARCHITECTURE BUILT UPON GRAPH NEURAL NETWORKS. THE FRAMEWORK ENABLES OPTIMIZED RESOURCE ALLOCATION AND SIGNIFICANTLY IMPROVES THE PRECISION OF PREDICTIONS. ULTIMATELY, THE RESULTS WILL EMPOWER DECISION-MAKERS WITH RELIABLE REAL-TIME INFORMATION, FACILITATING INFORMED CHOICES, AND ENHANCING THE RESILIENCE OF THE DEMOCRATIC PROCESS. THIS AWARD REFLECTS NSF'S STATUTORY MISSION AND HAS BEEN DEEMED WORTHY OF SUPPORT THROUGH EVALUATION USING THE FOUNDATION'S INTELLECTUAL MERIT AND BROADER IMPACTS REVIEW CRITERIA.- SUBAWARDS ARE NOT PLANNED FOR THIS AWARD.
Obligated Amount:
295000.00
Face Value Of Loan:
0.00
Total Face Value Of Loan:
0.00

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Date of last update: 23 Mar 2025

Sources: New York Secretary of State