Intelligent IT Infrastructure Modeling

Grok’s plug and play machine learning model automatically learns and builds representation of infrastructure without CMDB or machine learning expertise.

Taking Complexity out of AI and Machine Learning

As organizations struggle with the rapid growth of additional data and events, many have kicked off IT initiatives around AI and machine learning to address these challenges. Grok can be an integral part of your strategy. Grok takes the complexity out of AI and machine learning, while enabling you to reap the operational benefits of both.


Plug and Play Machine Learning to Quickly and Easily Model Your Environment

Grok’s machine learning model is plug and play, meaning that we quickly learn and build a sophisticated multi-dimensional representation of your infrastructure. Known information from existing event streams are used to build the relationships in the model without the need of external CMDBs or topology maps. Unlike other AIOps solutions, Grok does not utilize offline training or rely on months of historical event data. This approach is ineffective especially in today’s modern IT environments. Grok learns in real-time from live event streams. Grok can adapt and continuously optimize infrastructure models as changes occur. This approach allows organizations to leverage the power of machine learning to continue to learn and adapt.

Delivering Real-Time Results and Quick Time to Value

We understand the importance of showing results and realizing the value of your investment. Grok understands the patterns of behavior within any telemetry data stream, making your IT operations act proactively against events that could lead to downtime. With Grok, we can be delivering real and measurable operational value in days, not weeks or months like some of the other solutions. Event clustering, classification, anomaly detection can all start providing results right from the start and, of course, the model becomes more efficient as it continues to learn over time. Grok incrementally updates the representational memory model as it learns. No more static models or configuring complex algorithms just to get started.