More than artificial intelligence.

Actionable insights.

If you can’t understand why a machine learning model delivers a certain prediction, how can you be completely confident about the decisions you make using that information?

With explainable machine learning from InRule Technology, understand the factors behind a prediction.

Every prediction.

Every time.

XAI WB Os2

We open the black box of traditional machine learning to deliver smarter… well, anything.

Unlike traditional ML platforms which only deliver a prediction and confidence score, our predictions with the why® make it easy for analytics and business teams to apply machine learning quickly and effectively with an easy-to-use workbench and explainable outputs – all without code, and with detailed factors behind every single prediction.

Capture the customer moment

Companies with disruptive, customer-focused tech strategies outperform their industries by 300 to 400%. Our dynamic predictive segmentation helps deliver differentiated customer experiences.

Segment based on intent and action

Our predictive technology clusters and segments based on an action, outcome or other meaningful business objective – not just descriptive statistics.

Decrease risk and improve transparency

Existing and emerging legislation is pointing toward greater transparency in AI-enabled applications. Predictions with the why explain every prediction, every time. We understand that security is paramount; our platform is SOC 2 and HIPAA compliant. Learn more

Deliver contextual relevance in every interaction

Precisely forecast the best fit, next offer, ideal recommendation or identify ripples in micro-markets and quickly adapt – without sifting through code.

The amount, frequency and velocity of customer data across channels, platforms and devices has surpassed the capacity of human-driven, statistical-based analytics. Explainable ML from InRule Technology enhances any application in your enterprise with the transparency and insight your team needs – at massive scale.

Don’t let bias harm your brand, your business, or your customers.

As you work to employ AI across all aspects of your digital transformation strategy, the sneaky ways harmful bias can creep into models should be enough to worry anyone – not just those in charge of risk and ethics.

True, not all bias is harmful. But if you’re not using technology that has built in tools to help you detect bias, then how do you know what impact harmful bias may have on your business?

Explainable ML from InRule Technology has built-in bias detection features designed to help enterprises quantify and mitigate potential hazards.

Create contextual customer experiences

Anticipate what a customer will want, prefer, or need, dramatically improving the customer experience with real-time data fueling sub-second recommendations.

Ensure compliance with evolving regulations

Create an auditable record of predictive algorithm-enhanced decisions and their underlying factors to comply with current or pending legal requirements.

Know what your customers really think

Go beyond customer type and sentiment to understand your customers with much greater depth by clustering and analyzing actual conversation and comment content.

Enhance supply chains and increase customer lifetime value

Improve planning and sales results by analyzing supply and demand forecast factors and predicting future demand curves with unparalleled depth and detail.

Build, validate and deploy machine learning models in hours

Easy-to-manage user workflow and an intuitive graphical user interface enable teams to build and deploy models quickly.

Fraud prevention: know what the machine knows.

New fraud techniques emerge every day from increasingly adaptive cybercriminals.

Fraud and risk managers need machine learning applications that provide full explainability for each prediction, to understand fraud causes and their defining factors, enable investigation and review, detect new emerging patterns and constantly monitor changes and differences in fraud methods.

With our explainable machine learning technology, understand why a transaction is being predicted as fraudulent, gather insights to determine if it represents an emerging trend, and when combined with our decision automation platform, easily adjust business logic to prevent new patterns from taking hold.

100% of firms agree: not adopting DPS is risky.

Dynamic predictive segmentation (DPS) is an approach that segments customers based on propensities to take a specific action grouped by the most important shared characteristics revealed for each segment.

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