Observed Chennai context · synthetic policy simulation
Make policy trade-offs easier to see.
PolicyForge turns a policy question into a transparent synthetic experiment, anchored in clearly labelled Chennai context and designed for review—not false certainty.
OBSERVED DATA — sourced contextSYNTHETIC AGENTS — generatedSIMULATION RESULTS — model output
10,000synthetic agents per experiment
9reported simulation metrics
7supported policy mechanisms
3evidence types clearly labelled
Start here
Move from question to a reviewable result.
Start with an AI policy question, an editable simulation, or a previous result. Every experiment is seeded, reviewable, and reproducible.
How to read results
Compare patterns, not predictions.
PolicyForge shows baseline-versus-policy changes under stated assumptions. Use the paired seeded ranges to identify robust model patterns, then test the decision with domain experts and additional evidence.
A simple workflow
1Frame the questionChoose the policy concern and what matters most.
2Review the experimentInspect the policy settings, assumptions and targeted wards.
3Compare the resultRead baseline changes and seeded model ranges.