Trusted by Organizations
Leverage Real-World Evidence with Causal AI using next-gen Hybrid SaaS - Conversation Agent Applications to accelerate learning, empower your teams and improve KPIs.
Our proven pipeline transforms your data into a production Causal AI application
We analyze your data using LLM-powered AI agents and set up a data lake that functions as a Single Source of Truth (SST) for the application. Our agents are designed to identify key components of data based on the value of data necessary to develop a Causal AI application to improve organizational outcomes. Our AI Engineers and Data Engineers supervise this process.
We analyze your data using LLM-powered AI agents and set up a data lake that functions as a Single Source of Truth (SST) for the application. Our agents are designed to identify key components of data based on the value of data necessary to develop a Causal AI application to improve organizational outcomes. Our AI Engineers and Data Engineers supervise this process.
Using our CML Insight Causal Insights software, we analyze the data in the SST to uncover causal relationships in your data. Where available, we also evaluate the efficacy of past interventions you have executed on KPIs that matter to your business. All these findings are made available to the application being built and to LLM RAG with vector embeddings to equip agents with causal reasoning capabilities.
Using our CML Insight Curator software, we extract high-quality, peer-reviewed research findings on outcome improvement for your domain. We merge these findings with any organizational findings you have to further strengthen the knowledge base used by our hybrid SaaS-LLM application.
Once all the key pieces are in place, we develop a prototype application to review with your team. This is done with the help of AI agents familiar with our key design principles.
The next step is to build the production backend infrastructure required for the application. We do this in either your cloud computing environment or ours using the platform agnostic, highly-scalable Kubernetes architecture. We implement MLOps, LLMOps, and AIOps systems needed for the application in Kubernetes.
Together with setting up the backend infrastructure, we develop the frontend of the application consisting of the conversation agents and familiar software UI starting from the prototype application. These AI agents are continuously improved using the ADLC framework based on user feedback and impact findings.
After thorough testing with your team, we deploy the application so that you can start using Causal AI to improve your organizational outcomes.
The technology powering evidence-based intelligence across your organization
Our causal learning algorithms integrate real-world evidence with your enterprise data to recommend precise, evidence-based actions. Identify root causes behind outcome changes with dynamic causal tracking.
Our state-of-the-art AI models (e.g., CNNs, autoencoders) compress time-series data dramatically, reducing petabytes to gigabytes while preserving critical insights for analysis.
Our MLOps pipelines enable continuous process improvement through dynamic causal tracking. Dynamic updates to AI models ensure insights remain relevant, optimizing workflows and ROI.
Our agentic LLM app provides a natural language interface, enabling your team to query insights (e.g., "What caused the recent outcome change?"), access predictive/prescriptive recommendations, and engage in upskilling modules for continuous learning.
The difference between insight and intelligence
While others find patterns, we uncover causes. Our causal AI goes beyond correlation to identify the real drivers behind your outcomes.
Built on actual outcomes, not synthetic benchmarks. Every model is grounded in real-world data from the domains we serve.
Production-grade MLOps, scalable pipelines, and continuous monitoring — not a prototype that needs years to productionize.
Enterprise-grade pipelines with continuous model monitoring and updates.
Bias detection, explainability, and equity-aware modeling built in.
From data ingestion to actionable recommendations in minutes, not months.
Let our team show you how causal intelligence can drive real results for your organization.
Or reach out at info@cmlinsight.com
Trusted by Organizations
Delivering measurable results in education, fintech, and utilities
Stay up to date with our latest thinking on causal AI and evidence-based analytics
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.
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.
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.
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.
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.
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.
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.