Tel: (613) 868-7843
Chief Strategy Officer
Tel: (613) 868-7843
80 Aberdeen Street, Suite 100
Ottawa, ON, K1S 5R5
Contextere is an ‘Industrial Internet of Things’ (IIOT) software company empowering the 21st-century industrial workforce. We are developing an intelligent personal agent delivering curated guidance and actionable insights to the industrial worker.Today’s employee is overwhelmed by data yet lacks the relevant information to execute, as a result spending over 60% of their time on non-productive activities. Using human-centric machine learning and intelligent context generation Contextere answers the simple question – Now What? A mobile personal agent delivering automated actionable insights at the point of service will make every worker skilled, reduce human error and savlives.
Contextere is addressing the challenge of ’The Last Tactical Mile’ – global industries with remote or mobile assets are losing money through workforce- related inefficiency where warm hands touch cold steel. Existing enterprise software and equipment automation solutions do not effectively engage, leverage or address the field workforce component. We are targeting the business need to increase productivity, reduce human error and increase the safety rate of field-based maintenance workers or operators. Currently, as much as 60% of a field based maintenance worker’s time is unproductive – time spent searching, waiting, or revisiting a site location. At the same time, even when the right information is available, the task is performed incorrectly 25% of the time. The result is lost human and equipment productivity, the potential for catastrophic failure and risks to employee safety.
The Contextere Platform is an enterprise software environment that integrates a mobile/wearable notification and data collection capability with an enterprise/cloud-based context curation engine. There are two elements to a customer solution – (1) on-premises/cloud back-end data integration with a machine-learning based curation platform, and (2) mobile end-user work guidance and data collection that functions on phones, tablets, and wrist or head-worn wearables. Information is automatically extracted from the mobile platform to understand the user’s context. Using this context, the enterprise curation engine determines the appropriate information to deliver to the user to tell them what to do next.
By understanding the end-user’s context, contextere will curate and assemble the right information to be delivered at the right time on the right device in the field, enabling the end user to know at all times what their next step should be and what other activities of relevance are happening around them. By incorporating machine learning as part of the curation engine, contextere ensures that the work guidance continuously evolves and maintains its relevance as the individual’s performance and operational environment changes.
The combination of the Industrial Internet of Things (IIOT), big data, mobility, augmented reality and wearables has created an opportunity for massive disruption in ‘Big Iron’ verticals .The IIOT presents the promise of increased equipment efficiency and significantly reduced unplanned downtime through
a combination of sensor enabled machines, edge- based analytics and centralized analytics. Edge-based analytics are designed to process sensor data on device and adjust machine performance in real-time. Centralized ‘big-data’ analytics are processed and interpreted by data scientists to support long-term operations, maintenance planning, and CAPEX investment decisions. Automated analytics and intelligent machines represent an idealized future scenario. However, the foreseeable future requires hands-on management, operation, and maintenance of remote mobile disconnected or semi-intelligent machines by a workforce supported by smart digital tools and evolutionary analytics.
Across the world, industrial organizations struggle to hire, train, and retain appropriately skilled workers. Lost knowledge and expertise through accelerating workforce retirement, characterized as ‘grey-out’, further exacerbates an already urgent problem. In a 2012 global labor market study, McKinsey Global Institute estimated that the unmet demand for skilled workers would exceed 95M individuals by 2020. It is widely recognized that existing and forecasted training capacity in educational institutions and private organizations will not be able to address this gap in the workforce.This problem is in sharp contrast to the estimated 75M unemployed (or underemployed) youth unable to earn a livelihood worldwide – a ready and able resource if enabled with the appropriate guidance and infrastructure.