A little more than four years ago, Anodot started applying advanced AI/ML and unsupervised learning technologies to simplify monitoring challenges for DevOps teams.

Today our company has customers from a variety of verticals and departments harnessing our unique platform to monitor business health, user behavior, product usage, IT ops, machine learning processes and even IoT. The quality we introduced to the anomaly detection market beats any competition in terms of time to detection and false positive ratios. In press coverage,  Anodot is referred to as the “anomaly detection company”. Over the past few months, tech giants such as Google and AWS, among others, have confirmed the growing market demand for anomaly detection products. Anodot is a success. And yet we’ve decided to rebrand. Why?

The short answer is because now we know better.

As a young, technology-driven company, we were committed to accomplishing our concept anomaly detection powered by AI and ML. We weren’t able to anticipate the extent to which clients would utilize this technology. And, yes, we can appreciate the irony that a predictive analytics company couldn’t predict its own future. But, that’s often how it is when you’re doing something without precedent. There are many more roles, from DevOps and product, to support, marketing and engineering teams, struggling to monitor metrics at scale, and they’re turning to Anodot to directly resolve that pain.

Anodot is doing in real time what had previously taken data scientists and analysts weeks and months to do, if they even noticed the issue. On the surface, the best use of Anodot’s technology is for analytics, however it’s important to specify how. Rather than replace analysts, it frees up their time for the kind of analytics where their attention and creativity would be most valuable.


As we know, there are two types of analytics:

The first is Creative Analytics or Human Analytics. The kind of analytics where companies ask themselves how to optimize a conversion funnel or how to reduce friction for end users. They dive into the numbers and identify churn buckets, so they can develop creative solutions and improve the bottom line. Creative Analytics requires ad-hoc analysis, research, deep creative thinking and enhanced solutions.

And then there’s Monitoring or Machine Analytics. It helps companies understand whether a product works as expected. The concept is simple and the execution can be very tedious. Once you establish a baseline for metrics, it’s simply about ensuring everything works properly. Monitoring requires no creativity as such, but it’s complex in that there are endless combinations of factors to monitor, such as conversions per device, OS type, country, marketing campaign, segment and so on. You need a good set of eyes, always open, able to monitor every metric and compare behavior day after day, week after week, year after year.

People shouldn’t have to monitor metrics this way any longer. Over the past few years we’ve seen teams use Anodot to refocus their time on the creative endeavors. They’re utilizing Anodot to monitor all their data without unnecessary alerts and dashboards, and to shorten the time to detection and resolution. We are helping our customers manage their business more effectively.

From this standpoint, we came to understand that what we were actually alleviating the biggest headache for those performing analytics and monitoring volume by making the process autonomous.

We learned from our customers that Anodot is truly an Autonomous Analytics platform.

With that in mind, we’ve decided to rebrand for two main reasons:

  1. In order to convey how Anodot is used and not simply the problem we solve.
  2. And to set the stage for where the AI Analytics market is going. We’ll continue developing our messaging as we expand to the next phase forecasts


What’s next?

During the first few years of the company, we carefully built, trained and tested our anomaly detection capabilities. Over the past 18 months, we focused on time to value, introducing more than 30 different integrations with partners such as AWS, Google Cloud, mParticle, Slack, and PagerDuty, among many others. As our anomaly detection platform matures, and the ecosystem expands and the clients line up, we are now gearing up to introduce the third element of the autonomous analytics vision: Autonomous Forecast.

Today, Anodot Autonomous Forecast is moving to a closed public beta stage. We invite enterprising analytics teams to join some of our clients already using this capability to plan ahead of time.

As we introduce forecasting and expanded capabilities, there’s clearly a need to update our tagline and the rest of our brand. We are no longer simply “the anomaly detection company”. We are spearheading an entirely new suite of products that’s using machine learning to push business intelligence further, towards Autonomous Analytics. You can read more and check out our new look and feel on our new site.

We’re excited for the next leg of our journey. Thank you to all our clients for the ongoing support – we can’t wait to show you what’s next.


Wrestling with your data? Finding it hard to monitor manually? Contact us for a quick demo to see how Autonomous Analytics can help.

Written by Amit Levi

As Anodot's VP Product and Marketing, Amit Levi brings vast experience in planning, developing and shipping large-scale data and analytics products to top mobile and web companies. A product and data expert, Amit has a unique ability to explain complex requirements in simple words. His product leadership has led to major revenue growth at both Yokee Music and Cooladata.

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