Grants and Contributions

This project aims to create an AI Asissted software environment for creating economic models using Limestone's proprietary unified cost-benefit analysis (UCBA) paradigm and automatically calculate the results and export the model and results in different formats (e.g., Excel, Word, PDF) for client submission.

Ratings IQ represents a transformative evolution in biomass supply chain assessment, combining advanced machine learning, predictive analytics, and natural language processing to dramatically enhance and expand the efficiency, market value and catalogue of IBRA’s rating services, and solidify Ecostrat’s position as the undisputed global leader for feedstock ratings in the cleantech sector.

The purpose of this project is to develop automated, deep learning-based imaging biomarkers that predict outcomes of breast cancer patients with solid tumors based on various modalities of medical imaging.
By enabling an artificial neural network to “learn” what visual features in thousands of imaging studies are associated with respective patients’ actual future outcomes, the resulting deep learning algorithm will be able to predict survival for a new patient’s imaging study.

During the project, Clinisys intends to integrate and test mature Generative AI tools such as scribe via API integration to AI-enable the firm's flagship product – Clinisys EMR. The firm will develop AI scribe specific interfaces that would allow relevant voice data through this interface to be transcribed using AI into text. The intended users will be physicians and clinicians using the system that would allow recording of patient interviews and population of relevant patient encounter fields from within Clinisys EMR interface. This will shorten the time of encounter where currently either the physicians must type the notes or use pre-configured patient encounter templates.

Winboxer is an innovative personalized and secure email visualization and management tool designed to transform email experience using Artificial Intelligence (AI) and Machine Learning (ML) to reduce spam and known malware while increasing user’s productivity.

Establish key metrics, data pipelines and models to be used to provide accurate financial well stress indicator that can be integrated trough various SAAS modules.

The Project aims to transform structure-property-performance relationships between a battery cathode and electrolyte using autonomous experimentation, Artificial Intelligence and Machine Learning (AI/ML) algorithms, and real-time Electrochemical Impedance Spectroscopy (EIS) to enhance the cathode-electrolyte interface, verified by solid-state 7Li NMR (ssNMR).

This project will support the evaluation of AI-assisted tools to improve efficiency in marketing, finance, quality assurance, and operations. The project will focus on identifying and assessing proven, off the shelf AI solutions with the potential to enhance operational activities.

The Project will support pilot testing of AI strategies, determine target architecture and quantitative technical objectives for implementation of an AI solution.

The Project will support the development of an innovation plan to explore integrating AI into the Firm’s core business.