Confident and actionable predictions

Explainable machine learning

Understanding predictions

Understanding why a machine learning model delivers a specific prediction is fundamental to being confident about the decision based on that information. 

That's why explainable machine learning is so important. So employees understand the reasoning and factors behind the predictions - at all times.


Discover smarter machine learning

Traditional machine learning platforms deliver predictions and scoring. What they don't provide is the why. Our easy-to-use workbench and explainable outputs allow analytics and business teams to apply smart machine learning quickly and efficiently.

Transparency that mitigates risks

Recent legislation is asking for greater transparency into AI-powered applications. That demands more understanding of the why from the organizations, and that's where explainable machine learning comes into play.

A different customer experience

Studies have shown that customer-focused and disruptive tech strategies outperform their competitors by 300 - 400%. With the help of predictive segmentation, that differentiated customer experience is made possible.

More than just descriptive statistics

Receive predictions based on an action, outcome, or another central business objective. Explainable machine learning is more than just descriptive statistics.

Forecast and adapt

Forecast with decisive precision for best fit, next offer, optimal recommendations, and quickly adapt without the need for code. Keeping track of all data across every channel is no longer a possibility. The amount, cadence, platforms, and speed it's being transferred with are beyond human comprehension. Explainable machine learning is a new instrument in your toolbox to operate and navigate these challenges through transparency and insights - at an enterprise scale.

Don't let bias
harm you

Harmful bias often tends to sneak its way into models, which should raise warning flags for everyone - not just those in charge of risk and ethics. Bias may not be universally harmful. However, detecting how harmful bias impacts your business is becoming essential. 

Built-in tools for detecting bias in machine learning platforms do not only reveal instances; it quantifies and mitigates potential threats.

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Know your customer's needs - before they do

Forecast the needs and wants to improve the customer experience significantly using real-time data powering recommendations.

Keeping up with compliance

Create auditable records of decisions with underlying factors fueled by algorithms to stay compliant with current or pending regulations.

Deeper customer insights

Dive deeper than just industry or type and get to know your customer. Cluster and analyze actual conversation and comment content.

Improve supply chains and customer lifetime value

Analyze supply and predict future demand with incomparable depth to improve planning and sales results.

Guided, no-code machine learning models

Optimize model creation and deployment with AutoML.

Build. Validate. Deploy.

Build, validate, and deploy workflows with an intuitive graphical user interface to empower teams to build and deploy models fast.


Security is paramount; our platform is SOC 2 and HIPAA compliant.

Identify patterns

Preventing fraud - with machine-like accuracy

Cybercriminals find new ways to deploy fraud techniques all the time. 

There are ways to counter hazardous fraud patterns from taking hold. One is understanding fraud cases and their defining characteristics to enable further investigations and identify new emerging patterns. 

Explainable machine learning collects insights and can determine if it's a new emerging pattern while allowing employees to understand why it's identified as such. Combining machine learning predictions with a decision platform creates a foundation for dynamic business logic to monitor and prevent new patterns.

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