UNITED STATES
SECURITIES AND EXCHANGE COMMISSION
Washington D.C. 20549
Form 8-K
CURRENT REPORT
Pursuant to Section 13 or 15(d) of the Securities Exchange Act of 1934
Date of Report (Date of earliest event reported): September 20, 2024
HALLMARK VENTURE GROUP, INC.
(Exact name of registrant as specified in its charter)
Commission file number 000-56477
| florida | | 34-2001531 |
| (State or other jurisdiction of | | (I.R.S. Employer |
| incorporation or organization) | | Identification No.) |
5112 West Taft Road, Suite M, Liverpool, NY | | 13088 |
| (Address of principal executive offices) | | (Zip Code) |
877-646-4833
(Registrant’s telephone number, including area code)
Check the appropriate box below if the Form 8-K filing is intended to simultaneously satisfy the filing obligation of the registrant under any of the following provisions:
| ☐ | Written communications pursuant to Rule 425 under the Securities Act (17 CFR 230.425) |
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| ☐ | Soliciting material pursuant to Rule 14a-12 under the Exchange Act (17 CFR 240.14a-12) |
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| ☐ | Pre-commencement communications pursuant to Rule 14d-2(b) under the Exchange Act (17 CFR 240.14d-2(b)) |
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| ☐ | Pre-commencement communications pursuant to Rule 13e-4(c) under the Exchange Act (17 CFR 240.13e-4(c)) |
Securities registered pursuant to Section 12(b) of the Act:
| Title of each Class | | Trading Symbol(s) | | Name of each exchange on which registered |
| Common Shares | | HLLK | | OTC Markets |
Indicate by check mark whether the registrant is an emerging growth company as defined in Rule 405 of the Securities Act of 1933 (§230.405 of this chapter) or Rule 12b-2 of the Securities Exchange Act of 1934 (§240.12b-2 of this chapter).
Emerging growth company ☐
If an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act. ☐
ITEM 1.01 ENTRY INTO MATERIAL DEFINITIVE AGREEMENT
On September 20, 2024, Hallmark Venture Group, Inc. entered into a Debt Cancellation Agreement with Archer & Greiner, P.C., and a total of $243,000 of legacy legal debts were canceled.
On September 26, 2024, Hallmark Venture Group, Inc. (the “Company”) and its Board of Directors of the Company approved the following; i) Agreement and Plan of Reorganization; ii) Change of Control Agreement; iii) Escrow Agreement, iv) Anti-Dilution Agreement; v) Cancellation of the 10/06/2022 Selkirk Global Holdings, LLC Note; vi) Cancellation of the 04/06/2023 Selkirk Global Holdings, LLC Note, vii) Cancellation of the 12/12/2023 Strickland Convertible Exchange Note; viii); ix); xi) the Company authorized its Secretary to open a bank account in the name of the Company.
A copy of the aforementioned documents which are filed as Exhibits hereto and incorporated by reference in this Current Report on Form 8-K.
ITEM 2.01 COMPLETION OF ACQUISITION.
On September 26, 2024, the Company and Jubilee Intel, LLC (“Jubilee”) entered into that certain Agreement and Plan of Reorganization (the “Merger”) whereby the Company acquired 100% membership interests in and to Jubilee in exchange for 100,000 shares of Series A Preferred Stock. As a result of the Merger, Jubilee has become a wholly owned and operating subsidiary of the Company.
Prior to the Merger, we ceased being an operating company and became a “shell company”. Pursuant to the Merger, we acquired the business of Jubilee to engage in the business of search engine marketing (“SEM”).
Form 10 Information
About Jubilee
Executive Summary
Our company’s proprietary SEM platform automates the creation, optimization, and scaling of digital advertising campaigns across key platforms like Facebook, GDN, and Taboola. By leveraging a direct feed from Yahoo’s partner network, we access real-time data that significantly enhances campaign performance. Through the integration of machine learning and AI, our platform optimizes ad spend, scales profitable campaigns, and pauses or restructures underperforming ones in real time.
In addition to our technological advancements, we are pursuing a strategic reverse merger into a fully reporting public shell company. This move will enable us to expand rapidly, gain access to capital markets, and position ourselves for a future acquisition. Our reverse merger, combined with a potential acquisition, provides a pathway for sustained growth and increased market presence.
SEM Automation and Machine Learning
Our platform integrates machine learning and AI to automate the entire SEM process, from keyword research to campaign generation and optimization. This system eliminates the need for manual intervention, drastically reducing the time and resources required for effective SEM management.
| ● | Keyword Research: Our platform analyzes massive datasets of search queries, user behaviors, and historical performance metrics to identify high-intent keywords that are most likely to convert. Unlike traditional methods of keyword selection, our machine learning algorithms continuously refine keyword targeting by learning from the performance of active campaigns, adjusting in real time. This enables us to stay ahead of changing trends and user behaviors, ensuring that our campaigns are always aligned with current search demand. |
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| ● | Campaign Generation: The platform generates thousands of ad variations tailored to specific audience segments. Using advanced data analytics, our system determines the best combination of ad copy, visuals, and keywords to maximize engagement. We analyze historical performance across similar campaigns and identify which variables contribute most to conversions, allowing us to create highly personalized and optimized ads at scale. |
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| ● | Optimization Algorithms: Our AI-driven optimization algorithms constantly analyze live data to adjust bids, placements, and budgets in real time. This ensures that ad spend is directed toward the most profitable opportunities while minimizing waste. Through predictive modeling, the platform anticipates shifts in user behavior patterns, adjusting the campaign strategy preemptively to maintain performance. |
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| ● | Scalability: One of the key strengths of our platform is its ability to automatically scale profitable campaigns. Once the system identifies a campaign that is delivering strong results, it rapidly increases the budget and bid amounts to capitalize on the momentum. Conversely, underperforming campaigns are paused or restructured, ensuring that ad spend is allocated efficiently. |
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| ● | Yahoo Partner Network Feed – Our access to a direct feed from Yahoo’s partner network gives us a competitive edge by providing access to first-party data, allowing us to target high-quality traffic that many other advertisers cannot reach. The integration enables us to refine our targeting strategies and optimize campaigns in real time, resulting in higher engagement and conversion rates. Additionally, this data allows us to build detailed audience profiles that help improve the precision of our keyword targeting, leading to better overall campaign performance. |
Technical Overview
Predictive Keyword Research Using Random Forest Regressor
We utilize a random forest regressor to process and analyze vast amounts of keyword data, often encompassing hundreds of thousands of data points. A random forest regressor is a machine learning algorithm used for predicting continuous values. It builds multiple decision trees during training, each using different subsets of the data, and averages the results to make predictions. This approach improves accuracy and reduces overfitting by leveraging the collective decision-making of several trees, which helps handle the complexity of large datasets.
The random forest model is particularly effective for our use case because it excels at handling large, complex datasets and can account for non-linear relationships between variables. Our model factors in key metrics such as search volume, bid cost, competition level, and historical performance to make accurate keyword predictions.
| ● | Feature Engineering: Our model utilizes a wide array of features, including search intent, seasonality, and location-specific factors, to improve the precision of its predictions. By incorporating dynamic features that adapt to user behavior in real time, our system is able to consistently identify the highest-performing keywords for each campaign. This continuous learning process ensures that our campaigns are always aligned with the most current trends and opportunities. |
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| ● | Scalability: The random forest model can handle extremely large datasets, making it ideal for processing the vast amounts of data generated by SEM campaigns. This scalability allows us to quickly adapt to new data inputs, ensuring that our keyword targeting remains sharp even as market conditions change. |
Timeseries Prediction Using Support Vector Regressor (SVR)
For predicting profitability based on time-series data, we utilize the Support Vector Regressor (SVR). SVR is a powerful machine learning model that excels at capturing non-linear relationships in the data. By using a kernel trick, SVR can map input data into higher-dimensional spaces, allowing it to capture complex patterns and trends in campaign profitability over time.
| ● | Time-of-Day Optimization: By using SVR to identify non-linear trends in profitability, we can predict the optimal time windows for launching campaigns, ensuring that ad spend is allocated during periods with the highest likelihood of conversions. This model helps us capture profitability dynamics more accurately than linear models, which may miss crucial non-linear relationships. |
Traffic Quality Improvement Using Support Vector Machine (SVM) Classifier
To improve traffic quality and block underperforming sites, we use a Support Vector Machine (SVM) Classifier. SVM is a supervised learning model that finds the optimal boundary between different classes of data points. It is particularly effective in high dimensional spaces and works well when there’s a clear margin of separation between classes. We use SVM to classify traffic sources as “good” or “bad” based on various engagement and performance metrics.
| ● | Traffic Quality (TQ) Scores: The SVM classifier evaluates traffic sources based on factors such as engagement rates, conversion potential, and historical performance. It assigns a quality score to each site, allowing us to block low-quality traffic while directing ad spend to high-quality sources. This classification process ensures that our advertisers receive the best possible traffic for their campaigns. |
Real-Time Centralized Reporting
All our reporting is centralized into a unified dashboard that provides real-time insights into campaign performance, keyword effectiveness, and traffic quality.
| ● | Unified Dashboard: Clients have full access to the dashboard, which allows them to monitor the progress of their campaigns, view performance metrics, and make data-driven decisions. This level of transparency fosters trust and allows for quick adjustments based on live data. |
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| ● | Insights at Scale: Our platform is capable of processing and displaying insights from vast datasets, providing a comprehensive view of campaign performance that helps clients optimize their strategies in real time. |
Competitive Advantage
Our platform stands apart because it seamlessly integrates real-time data, machine learning models, and direct feeds from top-tier search engines. Unlike many platforms that require manual adjustments, our system automates every aspect of campaign creation, optimization, and scaling, resulting in significant efficiency gains and performance improvements for our clients.
| ● | Unique Data Integration: Our direct feed from partner search engine networks allows us access to exclusive, high-quality data streams that significantly enhance our ability to target audiences with precision. |
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| ● | AI-Driven Insights: Our machine learning models are continuously learning and optimizing, which means that campaigns are consistently running at peak performance. |
Conclusion
Our proprietary SEM platform offers a cutting-edge solution for managing and scaling SEM campaigns through automation, AI, and machine learning. With access to a direct feed from Yahoo’s partner network and the ability to seamlessly integrate across platforms like Facebook, GDN, and Taboola, our system is optimized for profitability. Our partnership with a fully integrated credit firm, along with our strategic reverse merger, presents a significant growth and acquisition opportunity, underscoring the value of our platform.
Contact Information
More information on our SEM automation platform can be found at: https://jubileeintel.com
ITEM 5.01 CHANGES IN CONTROL OF REGISTRANT.
Pursuant to the Change of Control Agreement referenced in Item 1.01, Evan Bloomberg was assigned 100,000 shares of Series A Preferred Stock. By virtue of this stock assignment, Mr. Bloomberg assumed full voting control of the Company.
ITEM 5.02 DEPARTURE OF DIRECTORS OR PRINCIPAL OFFICERS; ELECTION OF DIRECTORS; APPOINTMENT OF PRINCIPAL OFFICERS.
On September 26, 2024, John D. Murphy, Jr resigned as Director and CEO of the Company.
On September 26, 2024, Evan Bloomberg was nominated as Director of the Company and appointed CEO of the Company.
Evan Bloomberg (33) has over 13 years of experience in the SEM industry and has been a driving force behind integrating machine learning and AI into marketing strategies. One of the early innovators in this space, Evan’s technical vision has reshaped how the company approaches automation and data-driven decision-making. A Georgetown University graduate with a degree in Computer Science, Evan combined his technical knowledge with strategic marketing to push the boundaries of what’s possible in SEM. His leadership has been a cornerstone of the company’s growth, driving both technological advancements and market expansion.
Outside of work, Evan’s competitive nature shines through his background as a Division 1 track athlete and his dedication to Mixed Martial Arts, where he holds an undefeated amateur record. His focus, discipline, and technical expertise have been key to the company’s continued innovation and success in the fast-evolving SEM and AI landscape.
ITEM 5.06 CHANGE IN SHELL COMPANY STATUS.
Prior to the Merger, we were a “shell company” (as such term is defined in Rule 12b-2 under the Exchange Act). As a result of the Merger, we have ceased to be a shell company. The information contained in this Current Report constitutes the current “Form 10 Information” necessary to satisfy the conditions contained in Rule 144(i)(2) of the Securities Act.
The information included in Item 1.01, Item 2.01, Item 5.01, Item 5.02 of this Current Report on Form 8-K is also incorporated by reference into this Item 2.03 of this Current Report on Form 8-K
ITEM 9.01 FINANCIAL STATEMENTS AND EXHIBITS
EXHIBIT INDEX
SIGNATURES
Pursuant to the requirements of the Securities Exchange Act of 1934, the Registrant has duly caused this Report to be signed on its behalf by the undersigned hereunto duly authorized.
Dated: October 1, 2024
| Hallmark Venture Group, Inc. | |
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| By: | /s/ Evan Bloomberg | |
| Name: | Evan Bloomberg | |
| Title: | Chief Executive Officer | |
