Simulate individuals, teams, or systems that behave in believable, context-sensitive ways.

Generate rich, dynamic settings (markets, organizations, and networks) that respond to agent actions and evolve over time.

Achieve realism without using real data. Synthetic simulations replicate complexity and nuance while remaining compliant and risk-free.

Tracks your position and actions within the simulation, offering proactive support and suggestions based on what's unfolding.

Transforms complex agent interactions and system behaviors into digestible summaries, so you understand not just what happened, but why.

Allows you to ask "what if?" questions and receive dynamic feedback, enabling structured exploration of edge cases, counterfactuals, or unintended consequences.

Use information-theoretic models to identify and construct scenarios where model predictions are least certain or most disruptive.

Navigate edge cases with tools that highlight unexpected behaviors and counterfactual possibilities, especially where human oversight is critical.

Generate rich, privacy-safe synthetic data to explore cases that are rare, risky, or underrepresented in real-world datasets.

Simulate realistic multi-table datasets where agent behaviors create consistent, observable patterns (e.g., transactions linked to clients, proposals tied to projects, surveys mapped to respondent histories).

Build evaluation sets for financial workflows, compliance reporting, risk monitoring and more, mirroring real-world complexity without exposing real data.

Ensure data consistency across forms, documents, and agent actions so that observed behaviors make sense within the larger synthetic world, enabling behavioral analysis and edge case discovery.