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Announcing Sophie 2.4: ServiceNow Integration, Improved Patterning Algorithm, and Histogram Metrics

A new year has come, and here at Loom Systems, we can’t think of a better way to start it than announcing that Sophie 2.4 is now available for all of our customers!

While deployment has started for many of our customers, we wanted to share with you some of our new features and key changes.


ServiceNow Integration


With an ever-increasing number of tools and solutions, users often find themselves forced to change their workflow and adapt to the whims and quirks of new tools. Although adaption and open-mindedness are positive characteristics of an organization, we believe that tools should serve the user and not the other way around.

From day one Loom Systems has been working on removing the tedium of manual work, empowering our users to focus their time on their goals and create an even bigger impact on their organizations.

As ServiceNow, a leader in the world of ITSM is being used by many of our enterprise customers, we are thrilled to integrate Sophie’s alerts directly to your ServiceNow environment!

Sophie-ServiceNow integration supports two different usage methods, allowing the users to interact with the platform however they prefer:

  • Once Sophie has found a new alert, users can open a ticket in ServiceNow directly from by clicking on the dedicated “Now”button in the interface:



During the creation of the ticket, Sophie enables the user to directly fill in all the essential information and assign it to the relevant individuals.





  • Users can also consume Sophie’s Custom Alerts within ServiceNow’s UI by using ServiceNow’s API.

The integration is bi-directional, thus if the user closes the ticket in ServiceNow Sophie will be notified and change the corresponding incident’s internal status to “close” and removing it from the feed automatically.




Loved it by now? There’s more!


Improved Patterning Algorithm


  • In order to extract real information from log analysis and make meaningful recommendations based on that information, one must be able to parse the logs correctly, understand their meaning, and create clusters of the logs that point the same direction – we call this process “Patterning”. Easier said than done, as there are numerous different patterns of logs over a massive amount of data.
  • Loom-Systems solves this problem by applying machine-learning based algorithms, that breaks down each log to its components, tag its variables, and create rules to identify similar patterns in the future. Each time a new log is being digested our algorithm automatically determines whether it handled comparable patterns before or if it’s a new instance, in which case it learns it and adds it to its known patterns. This solution lets us tackle any kind of logs, whether they are generated by a proprietary app or a 3rd party from the get-go.
  • As we constantly improve our solution, Sophie’s version 2.4 will include a new version of the Patterning algorithm, adding another layer to our AI, increasing its precision so it could cluster different log patterns quicker. This will result in a faster detection of anomalies for our customers while further increasing the noise level and false-positive alerts.



New Data Metric Type - Histograms


Traditionally, log management tools help users monitor their IT stack by manually adjusting baselines and thresholds for events they would like to be alerted on. Although it’s better than having no alerts all, this approach is an outdated and repetitive, resulting in IT professionals spending more time on discovering anomalies than solving them. Even worse, users of these legacy tools are being reactive, setting alerts for anomalies that happened trying to make sure they can prevent them from happening again.

Remember the “tools should serve the user” mantra I’ve been babbling about? That’s exactly where it is implemented. Sophie doesn’t wait for you define and create alerts on your own (although you can totally do that if you want.) but does that automatically. By creating predictive models based on your logs, Sophie created alerts on any anomaly within your stack, both removing the time required to set the alerts and alerting on anomalies you weren’t aware of at all.

Sounds familiar? It is, because we have been doing that the whole time. So, what is new?

The granularity. Sophie uses a lot of different metrics in order to identify anomalies; From gauges measuring changes in specific values listed in your machine data, to meters counting the number of occurrences for specific events.

For example, let’s assume that there is a 20% increase in the number of logs resulting from failed transactions all over the world. Sophie would easily discover it and compare it to your baselines, look at past data in order to make sure it’s not due to seasonality, and alert.

But we wanted to look even deeper and to see how a certain value behaves when compared to other values in the same field – we call that “Histogram metrics”. How would that manifest in the example above?

We had a 20% increase in failed transactions worldwide, but what if 40% of them originated in the US? looking at the different values of the aforementioned logs, Sophie will understand that the transactions derive from different countries around the world, learn what is normal for each location and show their corresponding metrics, and alert accordingly.

Our new year’s resolution was to constantly improve our product and keep releasing cool features, and we our next big release is all about that. We hope that all of you have started the year off right, we sure did!





Loom Systems delivers an AIOps-powered log analytics solution, Sophie, to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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