CAUSALITY is a research-informed scenario and hypothesis exploration tool for examining how assumptions, evidence and causal relationships may influence possible outcomes. Instead of presenting a single prediction, it helps users structure a question, define relevant factors, explore alternative scenarios and understand the uncertainty behind them. Evidence and sources remain traceable, making it easier to distinguish assumptions from supported information and to compare different interpretations of the same problem.
Technically, CAUSALITY is built with Next.js 16 and TypeScript, using Supabase for authentication, persistent data and database-level access control through Row Level Security. AI-powered analysis and research workflows run server-side through OpenRouter, while the application is deployed on Vercel. The project was developed with an agent-assisted workflow using explicit requirements, iterative verification, automated testing and security checks, with particular attention to source provenance, user-data isolation and keeping sensitive configuration out of the client.
