Imagine dramatically accelerating the analytics process by eliminating nearly all of the time spent on tedious tasks and by automating analysis. This alone would allow your data scientists to build models, get recommendations and actionable insights faster than your competitors.

As a result, every one of your data scientists can perform like an entire department. That frees up the team to focus on improving the business rather than manual, non-revenue producing tasks.

The starting point is data from anywhere – customer transactions, internal or external databases, social media, CRM entries and more. A financial predictor might, for example, incorporate customer transactions from an SQL database, clickstream and market data from text files, and customer profile data from a CRM system.

Because the technology behind Context Relevant is based on machine learning, each additional dataset increases the system’s value as the automated algorithms gain knowledge.

Context Relevant also enables you to leverage tools you have in place and offers the flexibility to deploy on-premises, on a cloud like Amazon Web Services, or hosted in our data center.

Download CloudPulse Strategies Analysis:
Context Relevant Seeks to Automate Machine Learning’s “Last Mile”

Here is an overview of how the solution works:


Work smarter, not harder.

At its core, the Context Relevant solution automates feature engineering to produce a set of features that are used to train and optimize predictive models. The software automatically assembles algorithms to identify patterns across data stores and enrich the data store with new columns of data based on the patterns discovered. The enhanced data enables dramatic compression, greatly accelerating analytics.


Forget the forklift. No rip and replace needed.

Rather than replacing the tools scientists rely on (i.e. SAS, SQL, Excel, Hadoop, Tableau and dozens of others), Context Relevant allows analysts to export any existing topology into the data center. For example, a learned model can be deployed in a high performance REST interface or it can be exported to SQL, HQL, Java, C, Python, or R.

Predictions can be readily incorporated into online applications without coding the models. Incremental model updates occur quickly, allowing the system to continuously respond to changes in context. This makes it possible for data scientists to analyze far more experiments in less time.


See why content may be king, but context is pure gold.

Context Relevant’s pre-built applications were created to address specific business opportunities and implement automated and sophisticated data science to give content contextual meaning. These applications specify preferred strategies, or model types, for solving the problems in a particular domain.

Built on a distributed, scale-out computing architecture, designed expressly for computationally intensive machine learning tasks, the platform can scale to accommodate any volume of data, from an analyst’s laptop to an entire datacenter.

Download CloudPulse Strategies Analysis:
Context Relevant Seeks to Automate Machine Learning’s “Last Mile”