Total Power
AI-Powered Intelligent Field Service Scheduling Platform
Total Power Limited is currently listed as a 51–200 employee organization, headquartered in Mississauga. Its operations span multiple Canadian regions and locations.
Total Power is one of Canada's established critical-power specialists, supporting generator systems and critical infrastructure for customers across industries including data centres, healthcare, government, commercial facilities and industrial environments. Its operations include a geographically distributed field-service organization supporting customers across Canada.
Cylix developed and deployed an intelligent field service scheduling platform designed to enhance Total Power's existing dispatch operations with data-driven scheduling, technician allocation and route optimization.
Rather than replacing the organization's existing operational systems, the platform was architected as an intelligence layer alongside Microsoft Dynamics 365, using operational data to generate optimized scheduling recommendations while maintaining human oversight of field-service decisions. The solution progressed from prototype validation through real-world data testing, shadow-mode operations, controlled D365 integration and production deployment within Cylix's Canadian-hosted infrastructure.
Solution Implemented
Problem Statement
Field-service scheduling requires dispatch teams to continuously balance technician availability, job requirements, geography, travel time, operational constraints and changing service priorities. For an organization operating across multiple Canadian regions, improving scheduling efficiency requires more than simply calculating the shortest route. The scheduling platform needed to understand real operational data and constraints while integrating safely with Total Power's existing Microsoft Dynamics 365 environment. A critical requirement was therefore to introduce intelligent scheduling without disrupting established dispatch operations or immediately allowing an automated system to make uncontrolled production changes. The project also required validation of data quality — including job duration, technician information, location accuracy and regional mapping — before deeper automation could safely be introduced.
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