The Industry's First Complete AIOPS Framework For Service Assurance

Finally a solution to the Same Ol’, Same Ol’ Problem

Over the past 20 years IT Operations have invested millions of dollars in an effort to improve reliability and efficiency of the infrastructure and continue to struggle with too much noise, long troubleshooting times and increasing OPEX costs. It’s time for a new approach and GROK has the solution.

The Solution

GROK’s innovative AIOps platform allows organizations to ingest events, logs and performance metrics and apply our proprietary Machine Learning algorithms for event/log clustering and anomaly detection. This creates Machine Learning signatures that feed our proprietary Incident Prediction and Remediation Classification Algorithms resulting in auto-created incidents and intelligent automation.

What does all this Machine Learning tell me about my business operations?

If you haven’t asked yourself this question you probably haven’t gone too far down the path of leveraging Machine Learning in IT Service Assurance. At GROK we know that one of the biggest challenges in applying Machine Learning to IT Operations is providing Context to the Machine Learning output. That is answering the question, What does this output mean to my business. At GROK we solve the Machine Learning context problem by combining unsupervised Machine Learning algorithms with supervised classification algorithms that provide the critical context needed to identify incidents before they happen.

GROK's
3 Key
Algorithms

Anomaly Detection

Through our partnership with Numenta GROK leverages the HTM algorithm for anomaly detection. HTM has consistently been the best performing anomaly detection algorithm in the industry resulting in less noise, less false positives and more accurate detection. Algorithms that provide the critical context needed to identify incidents before they happen.

Event and Log Clustering

GROK leverages various machine learning techniques for Event and Log clustering including pattern recognition and dynamic time warping which provides industry leading noise reduction in IT
Operations.

Incident Prediction

GROK has developed proprietary machine learning techniques to ingest the output from both the anomaly detection and clustering algorithms to accurately predict IT incidents with significant advanced notification over current Service Assurance methods.

Industry Leading Machine Learning Capability

GROK deeply understands the patterns of behavior within any telemetry data stream, making your IT operations act proactively against events that could lead to downtime. Each individual metric has a corresponding data model, which is used to generate a likelihood score for data points received by the platform.

Behavior-Based Alerting

Traditional operations and alerting platforms produce too much noise and force your team to chase down rabbit holes. GROK only alerts your team when it is worth their attention, using machine intelligence to assess the health of all metrics, events and logs on every app or service.

Works With Any IT Operations Data

GROK can accept data from any on-premise or cloud resources with its out of the box connectors and REST API. GROK has ingested data from the network infrastructure, applications, servers, IoT connected sensors, solar arrays, and customer experience metrics

Works With Tools You Trust

GROK can use its insights to trigger actions to tools you trust, conducting complex automations with intelligent triggers that fire the moment there’s a potential issue. For example, you can use GROK to restart a cloud service when its CPU Utilization shows anomalous activity.

Features For Companies at Every Scale

  • Customizable Dashboards: organize monitored data sources by service, region or pipeline.
  • Script-based Automation: Trigger Python-based scripts to execute upon a detected event within a given data source or service.
  • Continuous learning on streaming data from all IT infrastructure and services.
  • Microservices architecture that scales to support large complex IT infrastructures.