Bicycle Transit Systems Takes a Ride with Loom Systems to Predict and Prevent Problems in its Digital Business

SAN FRANCISCO, May 24, 2017 -- Loom Systems today announced that Bicycle Transit Systems is using Loom’s SaaS log analytics tool to monitor key performance criteria to manage the data and operations of more than one million bicycle rides across four cities. Loom provides Bicycle Transit Systems with an automated, machine-assisted log analysis tool that correlates cross-application events and gives the root cause of system errors and system inefficiencies, in real time.

Bicycle Transit Systems provides hands-on operation of bike share to make it a safe, easy, accessible and enjoyable way to get around. Bicycle Transit Systems’ mission is to create the perfect interface between bike equipment providers and municipal, business and institutional clients. With a distributed workforce across multiple office locations, Bicycle Transit Systems hosts all its corporate data in the cloud – with no physical IT footprint.

With Loom’s machine learning and predictive analytics features, the IT team can predict events and issues before they escalate, to reduce their impact on the day to day business and to make applications run flawlessly.

“The efficiency of the machine learning in Loom detects issues across environments and applications to give us full visibility of our digital environment, and provide us with the root-cause of problems in the most simple and comprehensive way,” said Tim McGraw, IT Director, Bicycle Transit Systems. “With Loom we are now more confident, that as we grow, we can detect and resolve issues, in real-time, while keeping our business unaffected.”

Accelerating the detection and resolution of IT problems in real-time, Loom helps reduce the cost and complexity of working with operational analytics and the need for manual pattern detection. Loom easily generates insights from raw data and with zero configuration or maintenance of the IT stack. Loom can detect data types and choose the most appropriate display form, such as a gauge for temperature or a histogram for comparative values. It then determines whether a signal has shifted, as well as the type of shift that has occurred. The signal types are distinguished, and anomaly detection algorithms are tailored to fit them.

“When we look at the major challenges and pain-points derived from transforming into a digital business, we see that it is almost impossible to be able to collect, analyze and prevent problems in a complex multi-layered environment, with vast amounts of data,” said Sabo Taylor-Diab, Vice President, Marketing, Loom Systems. “We’ve built a platform to understand, reason and learn about constantly evolving digital environments and operational complexity. By operating at this intelligence layer, we can show Bicycle Transit Systems what’s changed about its situation and then identify the root cause – with unprecedented speed and scale.”

About Loom Systems

Founded in 2015, Loom Systems delivers an advanced AI solution to predict and prevent problems in the digital business. Loom stands alone in the industry as an AI analysis platform requiring no prior math knowledge from operators, leveraging the existing staff to succeed in the digital era.  With offices in San Francisco and Tel Aviv, Loom Systems works with customers across industries. Connect with Loom Systems on Twitter and LinkedIn.

Media Contact:

Kim Pegnato
A3 Communications
781-620-0016