<img src="//bat.bing.com/action/0?ti=5739181&amp;Ver=2" height="0" width="0" style="display:none; visibility: hidden;">
News & Press / Loom Systems Announces New Advances to its AI Powered Log Analysis Platform

Loom Systems Announces New Advances to its AI Powered Log Analysis Platform

AIOps Software Now Monitors OpenStack, Provides a Holistic View of the IT Stack and Traces Entities


San Francisco, August 3, 2017Loom Systems, a leading provider of AI-powered log analysis software platform, today announced a series of significant platform releases to its AIOps monitoring software. Loom's AIOps platform automates the skill-intensive tasks associated with preparing data for analysis while simplifying implementation and speeding time to value.

Loom’s AIOps platform provides real-time analysis of the entire IT production environment, detecting and addressing issues simultaneously. Utilizing different algorithms, the Loom platform analyzes data, performance and activity, at once, and gives IT operations continuous insight.

Loom notifies IT and DevOps teams of meaningful issues in their environment with the full context needed for troubleshooting, including the actual root-cause of the issue and recommended resolutions from loom's proprietary knowledge base (TriKB™).

“IT operations data can be noisy and confusing, even normal behavior can look really anomalous,” said Gabby Menachem, founder and CEO, Loom Systems. “When an IT organization looks at metrics, they are viewed in different time spans, from a periodic view so you know that signal, through anomalous, would be something a human would find interesting. That’s a statistical way to do that. By mathematically modeling how humans analyze digital information, Loom combines analytical skills with computational speed to simulate and enhance the entire data analysis cycle. We help our customers become four times more proactive about their problems.”

OpenStack Monitoring
While OpenStack allows organizations to instantly deploy virtual machines (VMs) and other resources in the management of their cloud environment, it is still a complex and evolving system that continuously generates vast amounts of metrics and log data. Digital businesses have struggled with accessing all of their data, making monitoring both a major obstacle and a critical piece of their OpenStack setup.

Loom automates OpenStack log monitoring – using AI to analyze logs in real-time to detect and correlate anomalies across the entire OpenStack environment. Loom finds issues before they affect the business, and radically simplifies the root-cause analysis process.

Business Dashboard
The Loom data feed is suited for analyzing the incidents and alerts located and provides a holistic view of the IT stack.

The dashboard presents the general health score of applications, based on the number and the severity of the incidents in services, data flow and other metrics, and gives a summarized view of open incidents, error rate and the general flow of data into the Loom platform. This view helps build a more comprehensive picture of all activity within the application and helps IT teams focus on what’s most interesting and critical.

Distributed Entity Tracing
In many cases, when faced with an alert from one service or application, a human will be analyzing the incident by tracing the behavior of a specific entity throughout the chain of dependent applications. By doing so, he or she can identify the exact request and/or moment where things started to go wrong, focus attention on this particular part of the application and expose the root cause of the incident.

Loom automates this process using Machine Learning. Once an incident has been created, Loom will apply its algorithms to extract the meaningful entities in the incident. After doing that, Loom will search across the stack for those “prime suspects” (entities such as a User, a Path, an IP or a Host) that were identified as part of the incident and can be found in different services or applications.

New Correlation View
A correlation is an incident composed of multiple alerts from different services and/or applications across the IT stack that Loom is able to deem related. Loom uses the metadata of the alerts to correlate incidents based on their mutual entities and presents findings. The new correlation view clearly presents the entities Loom used to establish the correlation, helping companies understand the nature of the incident while signaling out cross-applicative incidents.

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.


US Media Contact:
Kim Pegnato
A3 Communications

See What Loom Can Do for You

Get Started