Grants and Contributions

Our goal is to develop the tools and automation that will enable us to double our polyurethane foam product capabilities, maintain versatility on the production line for our growing product offering, and be able to fulfil orders and meet the growing global demand for our products.

This project will support a scoping initiative to validate the market opportunity, define target customers, analyze competitors, conduct technical AI research, and develop a structured roadmap for a Minimum Viable Product (MVP).

This project will support CETAC’s annual small business retreat, the Entrepreneur to CEO workshop.

Develop an advanced AI system that analyzes financial plans.

Improve the firm’s expertise by developing the integration of adaptive moulds for the North American market.

Introduction to the various aspects of intellectual property for innovative SMEs, advice and development of an intellectual property (IP) strategy.

Develop a standalone application to upload, store, and present component data for manufacturing equipment. The application will display critical information, including schematics, component details, and guided work instructions, enabling maintenance personnel to perform tasks with greater efficiency and accuracy.

SheBoot Corporation is a national non-profit organization headquartered in Ottawa, Canada, dedicated to increasing the investment readiness of women-identifying tech founders.

This project leverages the combined vaccine development platform technologies of Pegasus Biotech, ConserV Bio and 4BaseBio to evaluate and enhance the manufacturing capability of a novel Canadian plasmid DNA technology platform (BPE) in the production of a new class of human influenza vaccines based on ConserV Bio's FLU-v technology.

We propose a new approach to automated test scoring that combines recent advances in large language models (LLMs) with traditional clear-box ML approaches. We will use an LLM-as-a-judge framework in association with a rubric of scoring elements (developed in consultation with subject matter experts) to classify responses on the basis of these scoring elements. The LLM will also provide its reasoning for these decisions. We will then construct clear-box ML models that combine these scoring elements extracted from the responses into a single numeric score. This method will allow us to not only score a response, but also to identify precisely how individual elements of the rubric contributed to the score and why each element of the rubric was.