Digiqub
Digiqub provides AI-powered predictive maintenance and condition monitoring solutions for industrial facilities, helping them reduce unplanned downtime and optimize maintenance costs. The company combines IoT sensors, machine learning, and cloud analytics to predict equipment failures with up to 99% accuracy, enabling proactive maintenance decisions. Their platform includes wireless plug-and-play sensors, real-time monitoring dashboards, and mobile applications for remote access.
- Artificial Intelligence
- Data & Analytics
- Energy
- Hardware
- Industrial Automation
- Internet of Things
- Manufacturing / Industry 4.0
- Software Only
Funding
Founders
Product
Problem
Industrial facilities face significant financial losses from unplanned equipment failures and downtime, often detecting issues only after they have escalated into costly breakdowns. Traditional maintenance approaches rely on reactive strategies or fixed schedules that fail to optimize equipment lifespan or predict failures with sufficient accuracy, leaving maintenance teams without the actionable insights needed to prevent production losses.
Solution
Digiqub provides an integrated predictive maintenance platform that combines IoT sensors, artificial intelligence, and cloud analytics to monitor industrial equipment health in real time. The company's wireless, plug-and-play sensor systems collect vibration and operational data from rotating equipment and transformers, which is then analyzed by machine learning algorithms to predict remaining useful life with up to 99% accuracy. Maintenance teams access user-friendly dashboards and mobile applications that deliver alerts, notifications, and actionable insights, allowing them to perform maintenance at the optimal time and minimize costs. Digiqub's end-to-end solution covers the entire digital transformation journey, from sensor installation to automated analysis, with support from experienced technical teams and maintenance solution partners.
Target Audience
Primary customers are maintenance departments and operations teams at industrial facilities, including manufacturing plants, processing facilities, and energy infrastructure operators seeking to reduce downtime and optimize maintenance resources.
Features
- Wireless, plug-and-play sensor systems that can be installed without specialized technical support
- AI and IoT integration achieving up to 99% accuracy in predicting remaining useful life of equipment
- Real-time condition monitoring platforms accessible from anywhere via web portal and mobile applications
- Automated analysis capabilities that extend beyond simple data collection to connect with other digital transformation components
- SMS alerts and push notifications for immediate awareness of equipment anomalies
- Scalable coverage for rotating equipment and transformers with seamless network and power options
- Predictive maintenance for components such as bearings, with demonstrated success in conveyor systems and multi-stage gearboxes