AI Platform for Evidence-Based Intelligence
Transform Data Into Causal Intelligence
Leverage real-world evidence with causal AI to drive business results, empower your teams, and accelerate learning across your enterprise.
Trusted by 18+ enterprises across education, fintech, and utilities
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Enterprise Clients
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Industry Verticals
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Data Compression
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Retention Improvement
Trusted by Organizations
How It Works
From Data to Action in Four Steps
A systematic approach to evidence-based intelligence
Step 1 of 4
Integrate Your Data
Connect enterprise data sources, real-world evidence, and domain knowledge into a unified platform.
Automated data pipelines from multiple sources
Real-time ingestion with schema validation
Domain-specific knowledge graph integration
Secure, compliant data handling
Outcome
Unified data foundation ready for analysis
Capabilities
Enterprise AI That Delivers Results
Four core capabilities powering evidence-based intelligence across your organization
Causal Learning for Evidence-Based Actions
Our causal learning algorithms integrate RWE with enterprise evidence (e.g., well logs, maintenance logs) to recommend precise, evidence-based actions. Identify root causes of production bottlenecks or equipment failures with dynamic causal tracking.
10-15% higher success ratesAI-Powered Time-Series Data Compression
Our state-of-the-art AI models (e.g., CNNs, autoencoders) compress data by 100:1-1,000:1, reducing petabytes to gigabytes while preserving critical insights.
100:1 to 1,000:1 compression ratioContinuous Process Improvement
Our MLOps pipelines enable continuous process improvement through dynamic causal tracking. Dynamic updates to AI models ensure insights remain relevant, optimizing workflows and ROI.
20-30% efficiency gainsAgentic LLM Frontend App
Our agentic LLM app provides a natural language interface, enabling employees to query insights (e.g., "What caused the recent prediction drop?"), access predictive/prescriptive recommendations, and engage in upskilling modules for continuous learning.
30% reduction in training timeWhy CML Insight
What Sets Us Apart
The difference between insight and intelligence
Causal, Not Just Correlational
While others find patterns, we uncover causes. Our causal AI goes beyond correlation to identify the real drivers behind your outcomes.
Real-World Evidence
Built on actual outcomes, not synthetic benchmarks. Every model is grounded in real-world data from the domains we serve.
Enterprise-Ready From Day One
Production-grade MLOps, scalable pipelines, and continuous monitoring — not a prototype that needs years to productionize.
Platform
Building Your AI Infrastructure to Empower Your Teams
Scalable MLOps
Enterprise-grade pipelines with continuous model monitoring and updates.
Trustworthy AI
Bias detection, explainability, and equity-aware modeling built in.
Real-Time Insights
From data ingestion to actionable recommendations in minutes, not months.
Industries
Proven Impact Across Sectors
Delivering measurable results in education, fintech, and utilities
What Our Partners Say
Trusted by Industry Leaders
Organizations across sectors rely on CML Insight for evidence-based intelligence
“CML Insight's causal AI models helped us identify the real drivers of student retention — not just correlations, but actionable causes we could address immediately.”
VP of Academic Affairs
Major National University
Education“The data compression alone was transformational. We went from drowning in data to having clear, evidence-based recommendations our teams could act on.”
Chief Data Officer
Leading Fintech Company
Fintech“Within months, we saw a measurable improvement in conservation outcomes. Their platform gave us the causal insight we'd been missing for years.”
Director of Operations
Regional Water Utility
UtilitiesWhat's Happening
Latest Insights & Research
Stay up to date with our latest thinking on causal AI and evidence-based analytics
Empowering Water Utilities with Data Science for Sustainable Resource Management
At CML Insight, our mission is to empower organizations with advanced AI and machine learning solutions that drive measurable results. We specialize in partnering with businesses and government organizations, including water utilities, to elevate their data science capabilities and unlock actionable insights. Our expertise in delivering custom predictive and causal models is particularly valuable in addressing the pressing challenges faced by water utilities in today's era of increasing water scarcity and environmental concerns.
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Change is Hard
These are turbulent times for higher education. There are many accelerators and disruptors that are driving change, especially transformative change. These disruptors include the use of technology; overcoming educational, economic, and social inequities; new ecosystems for work; large-scale change efforts that impact the entire organization; financial distress and declining public support; climate change; and pandemics. While each of these serves as a catalyst for change, taken together, they provide major challenges for institutional leaders navigating complex organizational transformations.
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Preparing for a New Intelligence
As we move forward in the rapid development of artificial intelligence (AI), especially large language models across industries and opportunities, we are being asked to recognize a change in the way knowledge is created. A new collaborative intelligence (CI) will enable humans and machines to work together to solve complex problems and create innovative solutions, but only if we determine and apply the parameters that each brings to the table. This collaborative intelligence combines human creativity, critical thinking, and problem-solving abilities with AI's ability to rapidly apply algorithms, data, and computational power to recognize patterns, suggest best-case solutions, and identify relationships not readily apparent to human processing.
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The Causal AI/ML Revolution in Education
Artificial Intelligence (AI) and Machine Learning (ML) have become ubiquitous in our everyday lives from consumer applications to enterprise systems. While predictive analytics has matured in education, concerns remain around black-box algorithms, trust in prediction scores, and using past data to model an ever-changing future. Today, we are witnessing a new development that has quietly emerged as a proven analytics approach: Causal AI/ML. Unlike predictive analytics which imparts a sense of finality, Causal AI/ML helps students and educators improve outcomes through interventions and feedback that produce high returns on investment. This revolutionary approach offers a major advance in understanding the cause and effect of student success initiatives and the efficacy of edtech investments at scale.
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Trustworthy ML/AI in Higher Education
CML Insight's mission is to help students do better by democratizing causal machine learning, which focuses on understanding causal relationships between treatment and its impact on student success. This conversation with Dave Kil explores how to ensure ethical, non-biased AI results in higher education while preserving student privacy and data security. Too frequently, ML/AI has been traditionally associated with risk prediction using sensitive student data, especially demographic and other non-malleable data, which can potentially exacerbate equity gaps. This discussion reveals innovative ways of using integrated analytics to lower equity gaps, going beyond just predictions using black-box models to deliver actionable insights in a safe, ethical manner.
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How to lower student equity gaps through analytics
Machine learning (ML) has become popular in many industries as a way to improve business outcomes. As machine learning is becoming commoditized, it is important to understand potential downside risks of improperly using machine learning and to proactively design ML/AI systems to improve equity and effectiveness in the real world of heterogeneities. ML in its native form is only as good as the data it learns from, and can suffer from equity gaps when the underlying data is sampled inadequately to represent population heterogeneity. This article explores innovative approaches to lower equity gaps through proper feature engineering, crowdsourcing analytics, and maximizing human-AI synergy in educational settings.
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For too long...
For too long, risk predictive modeling has represented the core of machine learning analytics, leaving real-world evidence (RWE) of treatment effectiveness untouched. As many have found out, predictions alone do not lead to student success outcomes, often being used to discourage students. Further, their opaque and nonlinear nature can lead to human suspicions and more often an exercise of explaining scores instead of taking actions. Randomized controlled trials (RCTs) are slow, expensive, and sometimes unethical. Furthermore, population heterogeneities can make such RCT results difficult to replicate. CML Insight was founded to help institutions go beyond predictive analytics and to democratize causal machine learning (ML) to discover causal insights in heterogeneous populations.
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Ready to transform your business with evidence-based AI?
Let our team show you how causal intelligence can drive real results for your organization.
Or reach out at info@cmlinsight.com