Sanjay Azhagan Logo

AI

Exploring the intersection of LLMs, agentic workflows, and real-world utility.

Philosophy

I prioritize building AI systems that augment human capabilities safely and transparently.

Engineering Logs

A chronological record of milestones, learnings, and builds.

2026-06-01Project Build

Launched AI Copilot

Deployed the first version of the AI IDE extension.

2025-09-10competition

A challenge for helping woman development

Empowering Women Through AI: Building a Legal & Financial Advisor

Access to clear, actionable legal advice is still a major hurdle for a lot of people. Legal documents, statutory frameworks, and rights-related literature are often written in heavy jargon that is nearly impossible to parse if you don't have a background in law. For women navigating questions around workplace rights, property ownership, domestic safety, or financial independence, this barrier can feel even higher.

To help bridge this gap, I built Legal Advisor Chatbotβ€”a targeted conversational platform designed to give women direct, easy-to-understand insights into their legal rights and key financial concepts.


The Problem

When someone faces a legal or financial issue, getting reliable answers quickly usually comes with two main challenges:

  1. High Cost & Friction: Professional consultation isn't always immediately accessible or affordable.
  2. Dense Terminology: Raw legal statutes and financial terms are rarely written for everyday comprehension.

Standard chatbots can give general summaries, but they frequently miss domain-specific grounding. The main goal of this project was to take complex legal framework information and break it down into clean, empathetic, and easily digestable advice.


Tech Stack & Architecture

The project relies on a full-stack architecture built to keep response times fast while managing conversational state smoothly.

  • Frontend: Built with React. It handles the interactive chat flow, message state management, and real-time streaming updates.
  • Backend: Powered by a lightweight API service to bridge client interactions with the intelligence layer.
  • AI & Knowledge Pipeline: Uses retrieval workflows (via LangChain / RAG patterns) grounded on legal documentation and financial resources to keep answers focused and accurate.