When productivity and performance are on the line, high-value, mission-critical equipment failure is not an option.

Prevent Costly Failure, Maintain Productivity
Reduce the likelihood of unexpected operational failure and downtime costs and extend the life of high-value equipment assets with an AI-based early warning system that detects early indicators of asset failure.

In a Fraction of the Time, at a Fraction of the Cost
Boon Logic’s Amber—an AI-based predictive maintenance and condition monitoring solution—can be trained and deployed in hours instead of months or years. That’s because Amber doesn’t require highly skilled, hard-to-find talent resources like traditional AI development methods.

Why is that? Powered by Boon Nano, Boon Logic’s next-gen clustering algorithm, Amber, builds a reliable “normal operation” model for each asset using a proprietary unsupervised machine learning approach. In addition to building unsupervised machine learning models faster, this approach produces fewer false alarms and automatically generates insights as to what is causing a failure condition to take place.

As the most cost-effective solution of its kind, organizations can take advantage of this AI-based solution to proactively reduce operational failure that’s often tied directly to service level agreements or site-level uptime objectives.

Amber is flexible in its ability to train on historical data or streaming data, in the cloud or on-premise. Plus, it works with most user interfaces via a REST API for easy integration.

In a pilot test, Amber detected an anomaly in an oil and gas pump and provided a warning 90 days in advance of failure that would have cost the company $75k had it failed. With AMBER hedging the risk of failure, the company can avoid capital equipment redundancy costs of inventorying gas and oil pumps ($50,000-$80,000) to protect against the $20,000/day cost of downtime, which typically lasts 3-4 days.

If you’re looking for a way to guard against unexpected downtime, costly repairs and productivity loss, let’s start a conversation about a proof-of-concept program.