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 LawPrix 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.
Key Features
ML-Driven Case Routing
Developed a classification pipeline using scikit-learn that completely eliminates pay-to-rank bias in finding legal representation.
LLM-Powered Analysis
Integrated Large Language Models via OpenRouter API to provide automated, intelligent case analysis and summarization for lawyers.
Digital Verification System
Built a robust admin portal to meticulously handle lawyer verification, ensuring clients only interact with credentialed legal professionals.
End-to-End Tracking
Implemented a highly secure, real-time case tracking dashboard that keeps both clients and lawyers completely in sync.
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: