Grants and Contributions

This project will develop a self-onboarding system for the Firm's AI-enabled document intelligence platform that lays the foundation for scaleable purchase order processing and broader market adoption.

The purpose of this CanExport Innovation agreement is to support Canadian organizations to carry out activities related to pursuing opportunities that would lead to establishing new collaborative Research and Development (R&D) partnerships between the Recipient and foreign organizations in order to support R&D on the Recipient’s technology and advance its commercialization.

The primary goal of this initiative is to minimize risks associated with individual chip chemistries, ensuring their accuracy and reliability in measuring urine albumin and creatinine and serum cystatin-c and creatinine. By validating the performance of each biomarker against a well-established predicate device, we aim to confirm their suitability for clinical use.

Development of an autonomous acoustic data acquisition, detection and tracking suite for passive acoustic listening from uncrewed maritime systems to commercial product readiness. The developped suite will become an integral value addition to STL's thin-line towed array (TLTA) line COMPASS, and can also be combined with other systems.

Predict calibration parameters using AI for a semiconductor equipment with the goal of increasing yield, throughput and uptime

This project focuses on designing and building the front-end experience for a digital school meal ordering system. It aims to make it easier for parents to place orders and for foodservice providers to manage and fulfill them. By replacing manual processes like paper forms and fragmented communication, the project will reduce errors and save time for school staff. This is part of a larger effort to support future rollouts across school districts, with a user-friendly, scalable solution built for daily use.

The project will pilot test AI strategies, determine target architecture and quantitative technical objectives for implementation of an AI solution.

The operation of a robotic cleaner will be optimized to reduce collisions with various obstacles on the navigation path of the robot. Different packages for the software will be developed. The software will primarily deal with control system, sensor data, navigation and data communication.

The development of an innovative system required for next-generation BVLOS data management and processing, integrating key features for very large scale operations improving turnaround times and terrain model data quality. The project will also integrate AI and AR enhanced functionality and automation into aerial inspection workflows and post-inspection management systems, boosting productivity and the introduction of an innovative client management platform to custom tailor reports and bring automation to inspections requiring detailed methane detection, reducing reporting turnaround times.

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