Causal structure
Model relationships that help explain how and why outcomes move.
Simulacra
Building a more useful form of intelligence.
Gerard is Co-Founder and CMO of Simulacra, a causal AI and synthetic-data company focused on high-fidelity scenario modeling and predictive research.
Causal pathwaysThe premise
Preserve the structure of reality.
Research becomes more valuable when teams can explore why outcomes change—not simply predict what might happen. Simulacra combines causal structure with synthetic populations so organizations can pressure-test scenarios before committing in the real world.
The focus is executable insight: evidence designed to change a decision.
High-fidelity synthetic data
Extend research while preserving the relationships and heterogeneity that make real populations useful.
Counterfactual simulation
Explore interventions, tradeoffs and possible outcomes before committing resources.
Decision support
Turn complex analysis into clearer choices for the people accountable for results.