Full-stack systems, ML pipelines and Web3 protocols.

Problem

Every submission in the election education vertical built a chatbot. They shared a broken premise: citizens feel disconnected from elections because they lack information. The actual gap is that most people have never been made responsible for anyone else's democracy — not even in simulation. Information does not create that feeling. Responsibility does.

Approach

CIVITAS makes you the Returning Officer of a simulated micro-election for your actual neighbourhood. You do not read about constituency design, ballot rules, or dispute resolution — you do them, using real Google tools that produce real artefacts directly in your Google account. The simulation is structured as three sequential gated acts. Progress is gated by task completion, not time.

The design is built on three behavioral mechanisms: role-taking theory (occupying the official's role builds faster empathy for system constraints than any explainer), the ownership effect (rules you created are retained far longer than rules you were told), and perspective inversion (running an election from the inside produces richer understanding than observing it from outside).

Architecture

Next.js 14 + TypeScript + Zustand (localStorage-persisted state). Gemini 1.5 Pro with Search Grounding acts as Chief Election Commissioner — fetching real jurisdiction-specific electoral law, reviewing ballot designs, and providing legal advisories during dispute resolution. Eleven Google APIs integrated as functional infrastructure: Maps for live constituency drawing and booth coverage validation, Sheets and Calendar for real artefacts in the user's own Google account, Forms for ballot generation, Slides for the official results declaration deck, Firebase Firestore for real-time vote streaming, Cloud Translation for minority language obligations.

System Flow