A self-initiated systems design project to explore async coordination, contributor visibility, and AI-powered operations in DAOs.

Purpose of this Case Study

This project was created as part of my personal portfolio to demonstrate how operational systems and AI agents can be applied to real-world coordination problems in Web3 environments.

Important Note: This is not an official engagement with Gitcoin DAO. All observations and designs are based on publicly available data and hypothetical scenarios — used here to explore, learn, and showcase what’s possible.

Overview

Company Studied: Gitcoin DAO (open ecosystem research)

Domain: Web3 / DAO Coordination

Goal: Design a contributor operating system that improves visibility, async collaboration, and alignment with DAO-wide goals — enhanced by a reasoning-capable AI agent.

Problem Hypothesis

Through open analysis of Gitcoin DAO’s ecosystem structure, I identified recurring challenges that many DAOs face at scale:

• Workstreams operating in silos (e.g. MMM, dGrants, Moonshot)
• Low visibility into contributor workloads and output
• High drop-off and unclear expectations
• Strategic misalignment between DAO goals and on-the-ground execution
• No reasoning layer to support async stewardship

My Approach