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Demo Hub

View our platform demos, interactive use-case examples, and in-depth product features in one place.

decisionOS – accelerating your data science teams

Demo #1: CAIA (Causal Agent)

In this example, we show our Causal Agent, CAIA, accelerate the data science workflow for manufacturing root cause analysis.

Demo #2: Multi-Agent Framework

In this example, we show our multi-agent workflow in practice, to accelerate the data science workflow for customer churn analysis.

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).

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Customer Retention Strategies

Empowering Revenue Leaders to increase retention and profit margins by focusing on the right set of customers.

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Pricing and Promotion Strategies

Empowering Revenue Leaders to gain competitive advantage by balancing profit margins & market share.

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Supply Chain Optimization

Empowering Supply Chain leaders to understand root causes of order delays & preventing them in advance.

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Profit Margin Optimizer

Empowering Business Leaders to optimize profit margins by exploring powerful ‘what-if’ scenarios.

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Credit Risk Mitigation

Empowering financial institutions to understand the root causes of loan defaults and optimal actions to mitigate risk.

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Platform 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.

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CausalNet

Our proprietary structural causal model is inherently explainable, predicts robustly and unlocks the accurate estimation of interventions & counterfactuals.

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DecisionOps

Don’t simply
track predictive accuracy. Measure the impact of your decisions and compare them to counterfactual scenarios.

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Root Cause Analysis

Rapidly discover the root causes of faults & inefficiencies of your internal processes, helping enterprises to act in a timely manner.

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GenAI Features

Ground LLMs in causality to avoid hallucinations + add LLMs’ knowledge in causal graphs

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Decision Optimization Engines

Use causal models to produce actionable recommendations

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