Save costs, prevent downtime, and predict failures using AI-powered visual search and deep training.

Transform predictive maintenance using AI.

Predictive maintenance is designed to help determine the condition of equipment in order to estimate when maintenance should be performed. This approach promises cost savings, because tasks are performed only when warranted. According to data from McKinsey, predictive maintenance tools can reduce manufacturing machine downtime by 30 to 50% and increase machine life by 20 to 40%.

With AI-powered visual search and Clarifai’s deep learning algorithm monitor the performance and condition of equipment to reduce the likelihood of failures, lengthen inspection times, and increase the life of capital assets.

• Equipment monitoring

  Manufacturers can implement safeguards that notify the right people when a piece of equipment needs to be inspected. Build AI models to anticipate the likelihood of a potential breakdown before it occurs. Identify which equipment is at greatest risk of failing, allowing maintenance teams to respond accordingly.

• Maintenance scheduling

  Supply Chain Operators can build models to inform the team how long an asset, system, or component could be offline, allowing them to plan accordingly.

• Asset planning

  OEMs can collect data from customers’ equipment and build models to provide their customers with insights and equipment-specific maintenance schedules.

   

• Asset efficiency

  Public sector agencies can use computer vision and machine learning to monitor operations more efficiently, and better manage supply chain operations. See when new parts and overhauls will be required, keeping expensive assets, like helicopters, aircraft, and weapons systems in use longer. Address time-consuming FMECAs by running models that can predict patterns based on different asset environments.

Capture AI-based risk predictions through continuous visibility into asset health and delay capital expenditures, physical inspections, and downtime.

• Get early warning notifications ahead of potential problems.

  Stop machines from failing and avoid unplanned downtime.

  Reduce unscheduled repairs during off hours and in remote locations.

• Get a better handle of asset replacement planning and delay CapEx.

  Lengthen time between physical inspections.

  Contribute to employee safety and business continuity.

Best-in-class deep learning platform and advanced visual search models to gain insights into asset health.

• Pre-trained models

  Take advantage of pre-built models to get started. Explore our ready-to-use image recognition models to suit specific needs.

• Deep learning AI lifecycle platform

  Grow into one platform that integrates search, training, and prediction capabilities for multiple use cases.

• Easy and flexible MLOps

  Explore multiple deployment options via Cloud API integration, secure on-Premise service, or as embedded Edge SDKs.

• Automated MLOps

  Speed product indexing and data labeling using AI automated annotation and generate deeper metadata and training data.