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Trustworthy insights from your marketing data

Quickly see your most likely converters, and reveal the most effective touchpoints, campaigns, channels.

XMI: Explainable Marketing Intelligence Engine

Connect your marketing data, get a dashboard of predictions & attributions, powered by Explainable AI

Connect your marketing event-sequence data

Connect your data to the XMI engine directly using our stand-alone app, or via integrations on CDPs (Customer Data Platforms) or ABM (Account Based Marketing) platforms.

Predict your top leads

The XMI neural event-sequence model learns from hundreds to millions of historical customer journeys to predict each lead's conversion likelihood, at any point in time.

Reveal the most impactful factors

XMI's Explainability engine quantifies how much each touchpoint, campaign or channel is contributing to a lead's prediction.

Super-charge your marketing efficiency

View all results in a rich, granular dashboard so you can focus your sales and marketing efforts, and boost your campaign ROI.


For those who want a more general purpose BI tool where they can work with SQL queries, we have an Explainable BI (XBI) platform.

Why XaiPient?

Event-sequence expertise

Event-sequences are ubiquitous in marketing and other business-analytics. XMI leverages our expertise in event-sequence prediction and attribution models. 

Trustworthy, actionable 

Besides lead scoring, XMI's event-sequence attribution engine quantifies the contribution of every point in the customer journey. These help you validate and trust the predictions, and allocate your resources to improve ROI.

Beyond recency and frequency (RF)

Predictive models based on hand-crafted features like Recency and Frequency can be misleading.  XMI's neural event-sequence models sniff our patterns from hundreds to millions of sequences, without relying on hand-crafted features.

Data to dashboards, no code

Get informative dashboards in minutes to an hour, instead of waiting days or weeks for a Data Science team to build and deploy a predictive model.

Use-Cases

Focus your marketing resources where they make a difference. 
Boost your ROI. Leapfrog your competition.

Ready to be a super-analyst?

Faster, actionable insights.
Powered by Automated, Explainable ML

If you're a business analyst familiar with SQL, try our general-purpose XBI platform.

If you're a brand that wants insights from marketing touch-point sequences, try XMI.

Decades of deep industry and research experience in Machine Learning

 World-class team of ML engineers, UI/UX/Product designers, full-stack developers.

Prasad Chalasani 

CoFounder, CEO

PhD/ML (CMU)
ex Los Alamos, Goldman Sachs, Yahoo, MediaMath

Somesh Jha

CoFounder, Chief Scientist

PhD/CS (CMU)
Chaired Prof (CS) UW-Madison,
Center for Trustworthy ML

We're proud to be accepted into the Creative Destruction Lab Acclerator program 2020-2021

Papers and Articles

Mar 2021: XBI app announced

Supercharge your business intelligence with automated, explainable ML

Mar 2021: Borealis AI interview 

Borealis AI interview with CEO Prasad Chalasani on Explainable ML research and products aimed at business analysts

Nov 2020: API announcement

Introducing XaiPient’s API for Model-Explanations

July 2020: (ICML)  Explainability and adversarial learning

Concise Explanations of Neural Networks using Adversarial Training

July 2020: (ICML) Causality from Event Sequences

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

July 2020: (ICML) Differential Privacy in Graphical Models

Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models 

2019: Intro to Explainability in Deep Neural Networks

Explainability in Deep Neural Networks, Parts 1-4

Dec 2019: Robustness and Explainability

Robust Attribution Regularization (NeurIPS 2019)

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