Demo Hub
View our platform demos, interactive use-case examples, and in-depth product features in one place.
Platform Demos
3-Minute Platform Overview
An overview of discovering cause-effect in data and driving better decisions in the enterprise.15-Minute Platform Demo
A deeper dive into our platform for Data Scientists, focusing on the Causal AI modeling pipeline.Interactive Use Case Examples
View these interactive application examples built with our platform.
Root Cause Analysis for Manufacturing
Empowering Manufacturing Leaders to discover the root causes of low Overall Equipment Effectiveness (OEE).
View demoCustomer Retention Strategies
Empowering Revenue Leaders to increase retention and profit margins by focusing on the right set of customers.
View demoPricing and Promotion Strategies
Empowering Revenue Leaders to gain competitive advantage by balancing profit margins & market share.
View demoSupply Chain Optimization
Empowering Supply Chain leaders to understand root causes of order delays & preventing them in advance.
View demoProfit Margin Optimizer
Empowering Business Leaders to optimize profit margins by exploring powerful ‘what-if’ scenarios.
View demoCredit Risk Mitigation
Empowering financial institutions to understand the root causes of loan defaults and optimal actions to mitigate risk.
View demoPlatform Deep Dives
Deep dive into product and feature overviews
Human-Guided Causal Discovery
Combine domain expertise with algorithmic causal discovery to discover cause-effect relationships.
Watch VideoCausalNet
Our proprietary structural causal model is inherently explainable, predicts robustly and unlocks the accurate estimation of interventions & counterfactuals.
Watch VideoDecisionOps
Don’t simply track predictive accuracy. Measure the impact of your decisions and compare them to counterfactual scenarios.
Watch VideoRoot Cause Analysis
Rapidly discover the root causes of faults & inefficiencies of your internal processes, helping enterprises to act in a timely manner.
Watch VideoGenAI Features
Ground LLMs in causality to avoid hallucinations + add LLMs’ knowledge in causal graphs
Watch Video