Predictive maintenance

Prevent failures and reduce operational and maintenance costs using data science and machine learning.

Our clients

Manufacturing
Finance
Hi-tech
Retail
Vehicles
  • Collect near real-time telematic data from mining trucks and machinery, warehouse forklifts, and on-road vehicles
  • Optimize maintenance schedules and maintenance packages offered to vehicle owners
  • Accelerate responses to requests for roadside assistance with diagnostic and location data from connected vehicles
  • Optimize usage and charging patterns for EV vehicles

Industrial robotics
  • Collect sensor data from assembly lines, heavy machinery, and robots
  • Receive health reports that measure servo stress over time
  • Identify early signs of mechanical failure and avoid major breakdowns 
  • Detect issues in early stages and prevent failure propagation
  • Estimate robot service life and optimize maintenance schedules and costs 
Telecommunication networks
  • Collect bitrate data and that from temperature sensors, radio network controllers (RNCs), and other sources
  • Reduce unplanned emergency work and perform maintenance only when necessary
  • Increase network utilization and availability
  • Reduce operational expenses by increasing the operations team’s productivity
  • Reduce customer churn by improving the customer experience
Smart buildings
  • Collect signals from gas, CO2, air pressure, noise, and vibration sensors  
  • Optimize regular inspections of equipment
  • Identify, repair, and replace any defective equipment parts before a major breakdown
  • Adjust controls for optimal performance and energy efficiency
  • Identify moving parts that need to be lubricated to reduce wear-and-tear

We provide flexible engagement options to help you build predictive maintenance solutions faster. Contact us today to start with a workshop, discovery, or proof of concept (POC)

Workshops

We offer free half-day workshops with our top experts in data science and predictive maintenance to discuss your processes, analytics tools and technologies, and opportunities for improvement.

Proof of concept

If you have already identified a specific use case for anomaly detection, we can usually start with a 4‒8 week proof-of-concept project to deliver improvements and tangible results.

Discovery

If you are in the requirements analysis and strategy development stage, we can start with a 2‒3 week discovery phase to identify the right use cases for predictive maintenance and anomaly detection, design your solution or product using industry best practices, and build a roadmap.