Anticipate the effects of decisions before implementation
Optimize strategies in complex and uncertain environments
Use Cases
Use case in health to anticipate and optimize clinical trials: Causal simulation of protocols and patients
Traditional approaches analyze trials once they are launched. ScalTwin simulates patient trajectories and protocol scenarios to anticipate results before implementation.
- Test "what-if" scenarios on inclusion criteria, dosages, or visits
- Identify the most responsive patient subgroups
- Reduce failure risks and optimize trial design
Use case in chemistry to accelerate molecular R&D: Causal simulation of reactions and processes
Traditional approaches rely on costly and iterative experimental trials. ScalTwin simulates chemical reactions and experimental conditions to anticipate the performance of molecules and processes.
- Virtually explore experimental conditions and formulations
- Identify the key factors influencing yield, stability, and quality
- Accelerate development and reduce experimentation costs
The Problem
Teams that model complex systems face structural limits:
- Static models that cannot test alternative decisions
- Correlation-based forecasts that break under new conditions
- Costly and slow real-world experimentation
- No way to explore counterfactual "what-if" scenarios
- Uncertainty that is hard to quantify
Result:
- Decisions made without testing their consequences
- High experimentation costs
- Missed risks and opportunities
The Solution
ScalTwin introduces a causal approach to simulation.
The Projector™ platform enables you to:
- Build a causal digital twin of your system
- Simulate the impact of decisions before you act
- Test counterfactual and stress scenarios
- Quantify the uncertainty around each outcome
- Combine observed data with business expertise
Each simulation is:
- Explainable
- Reproducible
- Grounded in cause and effect
What You Can Do
With ScalTwin, you can:
- Anticipate the effects of a decision before implementation
- Optimize protocols, formulations, or operations
- Identify the levers with the greatest impact
- Reduce trial-and-error and experimentation costs
- Plan for rare and extreme scenarios
Results
The models developed enable:
- Faster, lower-risk decision cycles
- Better generalization to unseen conditions
- A measurable reduction in experimentation costs
Above all:
- You test decisions before you commit to them
- You understand why a scenario unfolds as it does
- You accelerate innovation with confidence
Unlike Traditional Approaches
- You don’t just forecast → you simulate causes
- You don’t experiment blindly → you explore virtually
- You don’t react to outcomes → you anticipate them
Frequently Asked Questions
Causal simulation of complex systems
ScalTwin builds a causal digital twin of your system, modeling the cause-and-effect relationships between variables so you can test decisions and scenarios before acting in the real world.
Counterfactual "what-if" scenarios
Explore alternative decisions, conditions, and stress scenarios virtually. ScalTwin quantifies how each change propagates through the system, revealing impacts that static or correlation-based models miss.
Lower experimentation costs and risk
By simulating outcomes before implementation, ScalTwin reduces costly trial-and-error across clinical trials, molecular R&D, and industrial operations — accelerating development while controlling risk.
