Full Stack Enterprise Causal AI/ML

CML Insight's mission is to level-up an organization's AI capabilities through consulting and software.

We have over thirty years of experience bringing predictive and causal analytics to Healthcare, Defense, Education, and Fintech.

Cause and Effect

What is Causal AI/ML?

Causal AI/ML identifies the underlying web of causes of a behavior or event and furnishes critical insights that correlational models cannot do. 

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Human Capital

How do we find & recruit the best employees? What Learning & Development resources are paying off? What investments help us retain employees? What actions help us with workplace safety?



What supports and interventions drive student success outcomes? How do we quantify the impact? What edtech products really work and demonstrate evidence of efficacy. How do we improve the cycle of student from lead to application to enrollment to graduation?



How do we know which activities lead to higher CLTV and client satisfaction? How do we predict churn, when should we intervene and how? What strategies help our clients achieve the financial outcomes they desire in a timely way?



What treatments, nudges, and interventions drive better patient results? What combination of treatments help patients struggling with chronic disease. How do we optimize our portfolio of limited resources to drive the greatest return on patient outcomes?


Customer Success

How do we predict customer churn? What activities and actions improve our chances to increase renewals and reduce attrition? How do we measure successful strategies and build an evidence-based catalog of actions to help train future CS and Sales professionals?


Create impact with real data

CML Insight is a full cycle advanced data science consulting and software company. We kickstart and augment your AI analytics teams' work.

We built CML to help level-up an organization's AI/ML strategy through custom predictive and causal modeling to measure impact and efficacy of initiatives and products. 

We are the go-to company for CEOs, COOs, CHROs, CLOs, and Chief Analytics Officers who want to generate critical insights and continuously improve on their critical KPIs. 

Causation Triangle



Scale & Automate AI/ML

Launch continuous production models in a matter of weeks for decision making with rigorous validation and accuracy.


Build an Intervention Catalog

Build a quantifiable evidence-based database that can supercharge corporate Knowledge Management systems.


Initiate Predictive Analytics

Generate key actionable insights based on what is likely to happen with customers, agents, students, patients, or staff.


Discover Causal Analytics

Receive advanced prescriptive (causal) insights into what actions are leading to successful outcomes and how impact potential they have.


Optimize Your Resources

Create the ability to understand and visualize which actions and investments drive ROI for better portfolio optimization.


Generate Lifecycle Insights

Visualize key momentum points, interventions, actions that lead to success or LTV for students, customers, or patients.


Get our free ebook on the Causal AI/ML revolution in Education

Get our free ebook on how Causal AI/ML will improve the education sector


Shallow Steps Methodology

We work with each company to ease their way into advanced AI/ML analytics through a "shallow steps" strategy. This begins with descriptive to predictive and eventually causal and experimental analytics. 


Step 1 - KPI Inventory

First, we help you get started on your AI/ML journey by cataloging your critical KPIs and the integrated customer touch-points (treatments) so we can begin to deliver key insights to improve the organization.


Step 2 - Getting Your MVD

Second, our engineering and ML teams work with your analytics staff to develop the Minimum Viable and Most Valuable Data (MVD) needed to deliver automated predictive, prescriptive, and causal insights.


Step 3 - Causal Insights

At this stage, we deliver our initial causal insights. We share with you, among many things, activities and investments that are causally related to KPIs. And we deliver evidence-based recommendations so you don’t have to guess what action to take.


Step 4 - Experimentation

And lastly, once we have implemented a framework to deliver automated insights, we or your team can begin to optimize your portfolio of investments and further experiment with treatment combinations over time to improve KPIs as business conditions change.


Meet Our Team

CEO / Founder

David Kil

Dave has over thirty years of experience leading data science teams and pushing the frontiers of predictive and causal analytics. A former Chief Data Scientist at Humana and Civitas Learning, Dave is a thought leader in Healthcare, Education, and the Financial sector.

CTO / Co-founder

Daya Wimalasuriya

Daya has worked both in academia and the software industry including stints as a college professor and a principal data scientist. Daya obtained his Ph.D. from the Department of Computer and Information Science at the University of Oregon.

Chief Growth Officer / Co-founder

Rupal Shah

Rupal is a high growth start-up operator with over twenty-five years of experience as a consultant and sales leader. He has held senior positions at start-ups backed by Andreessen Horowitz, Emergence Capital, Goldman Sachs and many impact investors.




ASU: Catalyzing a Culture of Care and Innovation Through Prescriptive and Impact Analytics To Create Full-Cycle Learning


Stanford Social Innovation Review: The Case for Causal AI


Educause: Preventing a Winter of Disillusionment: Artificial Intelligence and Human Intelligence in Student Success


Educause: Why Data Matters for Student Success in a Post-Pandemic World