The Problem
Finding the right lawyer is often a daunting, biased, and opaque process. Most platforms rely on "pay-to-rank" models where lawyers pay to appear first, regardless of whether their specialization aligns with the client's actual legal needs. This results in poor client experiences and inefficient case management for law firms.
The Solution
I built Law Connect to fundamentally change how cases are routed. Instead of relying on manual sorting or paid rankings, I implemented a Machine Learning classification model that automatically routes incoming cases to the most suitable lawyers based on three core pillars: Case Type, Urgency, and Lawyer Specialization.
- ML-Driven Case Routing: Developed a classification pipeline using scikit-learn that eliminates pay-to-rank bias.
- Digital Verification System: Built a robust admin panel to handle lawyer verification, ensuring clients only interact with credentialed professionals.
- End-to-End Tracking: Implemented a secure case tracking dashboard for both clients and lawyers.
The Architecture
The platform required a robust backend to handle sensitive user data, machine learning inference, and real-time updates. The core architecture relies on: