Core Viewpoint - NetraMark Holdings Inc. has engaged LodeRock Advisors Inc. for strategic investor relations and capital markets communications to enhance its outreach and investor engagement efforts in the pharmaceutical industry [1][2]. Group 1: Investor Relations Strategy - The investor relations program will span 12 months and include a marketing schedule, investor targeting, analysis, outreach, and collaboration with investment dealers for non-deal roadshows [2]. - NetraMark will compensate LodeRock with a monthly fee of CAD $11,000 for these ongoing strategic communication services, starting from January 22, 2026 [3]. Group 2: Company Overview - NetraMark is focused on developing Generative Artificial Intelligence (Gen AI) and Machine Learning (ML) solutions specifically for the pharmaceutical sector, utilizing a unique topology-based algorithm to analyze patient data [6]. - The company's technology allows for the segmentation of diseases and classification of patients based on their sensitivity to drugs and treatment efficacy, enabling the use of smaller datasets for more accurate results [6].

Core Insights - NetraMark Holdings Inc. successfully held its Annual General Meeting on June 9, 2025, where all matters presented were approved by shareholders [1] - The company focuses on developing Generative Artificial Intelligence (Gen AI) and Machine Learning (ML) solutions specifically for the pharmaceutical industry [3] Group 1: Company Developments - Shareholders elected PJ Haley, Dr. Joseph Geraci, George Achilleos, and Andrew Parks as directors, with Andrew Parks appointed as Chairman following the meeting [2] - The company utilizes a novel topology-based algorithm to parse patient data sets into related subsets, enhancing the ability to segment diseases and classify patients for drug sensitivity and treatment efficacy [3] Group 2: Industry Focus - NetraMark aims to transform clinical trial design in the pharmaceutical industry by leveraging its advanced AI and ML capabilities [1][4] - The company's approach allows for effective analysis of smaller datasets, improving the accuracy of disease classification and patient treatment assessments [3]