Grants and Contributions

This inventory will be used to train a machine learning model capable of automatically detecting different types of anomalies (e.g., cracks, voids, delamination, and corrosion signatures).

This project aims to create an AI-assisted trigger reporting for dairy farmers, comprehensive app AI integration, machine learning-driven outcome predictions (e.g. breeding outcomes), low code data access, and multi-site advisor dashboard.

The company is planning to grow through developing digital monitoring of EPCM automated machines in order to improve predictive maintenance , and improving the durability and useful life of the electroplating electrode design.

As Armilla scales to a larger number of platform users, a productivity-enhancing feature is to use LLMs as risk copilots to assist in AI risk reporting, recommending testing controls, and keeping abreast with AI regulatory changes.

Development of a real-time personalized learning and content enhancement MVP, utilizing machine learning coupled with NLP tools.

The Firm's proposal outlines the development of an AI-driven Financial Services Broker Platform that will transform broker-client interactions, data analysis, and value delivery. Using AI, machine learning, and natural language processing, the platform provides brokers with real-time insights, streamlines operations, and enhances client satisfaction.

Software development project for an AI-powered, real-time stock market scanner to enhance trading insights and user experience. The tool will use machine learning to analyze historical data, news sentiment, and market conditions, delivering predictive trade signals and intelligent alerts.

This project aims to develop a novel in-service concrete pipe wire break detection AI system that features an advanced deep learning models and an intuitive Graphical User Interface.

The goal of this project is to develop a fully-automated product for use in the quality inspection of turbine blades using the X-ray Laue technique. For some time, the missing link in bringing such a product to market has been the intervention of an operator when determining the orientations of the grain(s) being imaged. This project would build upon proof-of-concept examples in the literature, whereby a feed-forward neural network is trained to extract the orientations of one or more crystal grains from a synchrotron Laue image, and apply this approach to the analysis of laboratory Laue images (which are qualitatively lower-resolution).

The Project goals are to optimize the design of the McGill University innovation, nanodigital inline holography microscopy (AI-Nano-DIHM), such that it can be portable and deliver accurate results in real-time, remote settings of nano and microplastic detection, and four-dimensional physicochemical capability that is necessary to enable more precise identification of pollution hotspots and enhance the remediation and recycling of nano- and microplastic pollution, both in Canada and globally. Existing commercial technologies must support in situ, real-time detection while also providing physicochemical information. Despite the availability of numerous research technologies, there remains an unmet need for real-time and in-situ, four-dimensional physicochemical analysis of nano/microplastic particles. The team will conduct tests and comparisons with input from several independent stakeholders and develop data needed for modelling and sound policy. This Project will enable the further development and commercialization of the AI-Nano-DIHM technology, addressing critical environmental challenges.