PowerLegal

Inspiration

Legal professionals spend 40% of their time on administrative tasks instead of practicing law. We saw an opportunity to revolutionize legal case management by creating AI agents that could work together like a legal team. Instead of building another single-purpose legal tool, we wanted to create a collaborative multi-agent system where specialized AI agents could coordinate, delegate tasks, and work in parallel to handle complex legal workflows.

What it does

PowerLegal is a sophisticated multi-agent legal case management system that coordinates 3 specialized AI agents:

Records Wrangler: Automatically collects missing evidence and medical records
Client Communication Guru: Drafts empathetic, professional client messages
Legal Researcher: Finds relevant case law, precedents, and legal strategies

The system processes complex legal cases in parallel, generating comprehensive case management deliverables in just seconds with high accuracy. It integrates with law firm data, handles email parsing, document management, and provides a complete case management dashboard.

How we built it

Frontend: Next.js 16 with React 19, TypeScript, and Tailwind CSS for a modern, responsive interface

Backend: Google ADK multi-agent system with Python agents that coordinate using:

Sequential, parallel, and hierarchical workflow patterns
Real-time agent communication and task delegation
Shared session state management Database: Supabase for real-time data synchronization and case management

Challenges we ran into

Agent Coordination: Getting 4 different AI agents to work together seamlessly was our biggest challenge. We had to implement session management and communication protocols to ensure agents could delegate tasks and share context effectively.

Database Integration: Connecting the agent system to the databases required careful handling of legal data and ensuring real-time synchronization without conflicts.

Performance Optimization: Achieving sub-second processing times while maintaining accuracy required heavy optimization of our agent workflows and database queries.

Email Parsing Accuracy: Matching emails to specific legal cases required developing custom algorithms that could handle variations in case names, numbers, and legal terminology.