AI-powered behavioral research for nonprofits and academia
Simulate, orchestrate, and analyze human behavioral experiments at scale. Built for researchers who need rigorous insights without the overhead.
Explore the DemoHow It Works
From research question to insights in five automated steps.
Submit Study
Define your research question, target population, and behavioral parameters in minutes.
Agents Orchestrate
AI agents coordinate to design experiment protocols, assign roles, and prepare the simulation environment.
Personas Simulate
Synthetic personas with distinct behavioral profiles participate in the study, generating rich response data.
Results Analyze
Statistical models process simulation outputs, surfacing patterns, significance, and behavioral insights.
Report Generated
A structured research report with visualizations, methodology, and exportable findings is delivered instantly.
Submit Study
Define your research question, target population, and behavioral parameters in minutes.
Agents Orchestrate
AI agents coordinate to design experiment protocols, assign roles, and prepare the simulation environment.
Personas Simulate
Synthetic personas with distinct behavioral profiles participate in the study, generating rich response data.
Results Analyze
Statistical models process simulation outputs, surfacing patterns, significance, and behavioral insights.
Report Generated
A structured research report with visualizations, methodology, and exportable findings is delivered instantly.
Platform Capabilities
Every component of the research pipeline, automated and integrated.
Agent Orchestration
Specialized AI agents coordinate end-to-end, from protocol design to result synthesis. Each agent handles a discrete research role, passing structured data through a directed graph. No manual handoffs, no bottlenecks.
See it in action →Synthetic Persona Engine
Generate hundreds of behaviorally-distinct synthetic participants in seconds. Each persona carries demographic profiles, psychological traits, and decision heuristics grounded in social science literature. Reproducible and bias-auditable by design.
See it in action →Dr. Aisha M.
Age 34 · Risk-averse
Marcus T.
Age 28 · Impulsive
Elena R.
Age 52 · Analytical
Experiment Submission
Define your study in plain language: research question, target population, conditions, and sample size. The platform translates your specification into a structured experimental protocol automatically. Launch in minutes, not months.
See it in action →Research Question
How does framing affect donation intent?
Sample Size
n = 500
Conditions
2 arms
Simulation Results
Real-time aggregation of synthetic participant responses with built-in statistical analysis. Effect sizes, confidence intervals, and significance testing surface automatically. Export-ready data for downstream analysis.
See it in action →Donation Intent by Condition
p < 0.01 · Cohen's d = 0.42
Academic Report Renderer
Simulation outputs are compiled into publication-ready research reports with methodology, findings, and citations formatted to academic standards. Inline references, statistical tables, and visualizations included. Peer-review-ready from day one.
See it in action →Framing Effects on Prosocial Behavior: A Synthetic Cohort Study
J. Chen et al. · Theory of Change Platform · 2025
Results indicate that loss-framed appeals significantly increased donation intent compared to gain-framed conditions (β = 0.31, p< .01)[1]. These findings replicate prior work on prospect theory in charitable contexts[2].
[1] Kahneman & Tversky (1979). Prospect Theory. Econometrica.
[2] Cialdini et al. (1987). Empathy-based helping. JPSP.
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