⚖️ Aurelius AI

Explainable Legal Intelligence Platform

Aurelius AI is a research-oriented legal NLP and explainable AI platform designed for transformer-inspired rhetorical role analysis, abstractive summarization, explainability visualization, and dynamic legal intelligence simulation.

🌐 Live Platform
https://aurelius-legal-ai.vercel.app/

📄 IEEE Research Publication
https://ieeexplore.ieee.org/document/11411272

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Overview

Aurelius AI simulates modern AI-assisted legal reasoning workflows through a dynamic and context-aware legal analysis pipeline.

The platform processes uploaded legal documents and generates:

• Context-aware rhetorical role labeling
• Dynamic legal summaries
• Explainability visualizations
• SHAP-style reasoning outputs
• LIME-style feature attribution
• Legal entity extraction
• Statute and precedent references
• Attention-based token importance simulation
• Confidence scoring

Unlike static showcase interfaces, Aurelius AI adapts its outputs dynamically based on uploaded legal document content using semantic heuristics, lightweight analysis pipelines, and intelligent state generation.

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Core Features

📄 Dynamic Legal Document Analysis

• PDF-aware document parsing
• Legal keyword extraction
• Domain-aware classification
• Context-sensitive inference simulation
• Dynamic legal analysis pipeline

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🧠 Rhetorical Role Labeling

The system identifies legal reasoning structures including:

• Facts
• Arguments
• Precedents
• Statutory References
• Conclusions

Outputs dynamically vary based on uploaded legal content.

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✨ Explainable AI Simulation

Interactive explainability layer inspired by:

• SHAP
• LIME
• Transformer attention mechanisms
• Token attribution systems

The platform simulates believable inference-aware reasoning behavior without requiring heavy transformer infrastructure.

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⚡ Legal Intelligence Engine

Context-sensitive logic includes:

• Legal entity extraction
• Statute identification
• Precedent mapping
• Semantic keyword analysis
• Domain heuristics
• Confidence estimation

Supported domains include:

• Constitutional Law
• Criminal Law
• Civil Litigation
• Corporate Law
• Taxation
• Tribunal Matters

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Frontend Architecture

Built using a scalable modern frontend stack:

• React
• TypeScript
• Vite
• Tailwind CSS
• Framer Motion
• shadcn/ui
• TanStack Router

The platform architecture is optimized for:

• Vercel deployment
• Netlify deployment
• GitHub portfolio presentation
• Scalable frontend engineering workflows
• Production-ready UI systems

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Tech Stack

Category	Technologies
Frontend	React, TypeScript, Vite
Styling	Tailwind CSS, shadcn/ui
Animation	Framer Motion
Routing	TanStack Router
AI Simulation	Semantic Heuristics, Dynamic State Generation
Document Processing	PDF Parsing, Text Extraction
Explainability	SHAP-style + LIME-style Simulation

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Project Structure

Aurelius-AI/
│
├── public/
├── src/
│   ├── components/
│   ├── hooks/
│   ├── lib/
│   ├── routes/
│   ├── styles.css
│   ├── main.tsx
│   └── router.tsx
│
├── package.json
├── vite.config.ts
├── tsconfig.json
└── index.html

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Local Development

Clone Repository

git clone https://github.com/n-ikitasingh/Aurelius-AI.git

cd Aurelius-AI

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Install Dependencies

npm install

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Run Development Server

npm run dev

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Production Build

npm run build

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Deployment

Aurelius AI is deployable on:

• Vercel
• Netlify
• GitHub Pages

Live Deployment

https://aurelius-legal-ai.vercel.app/

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Engineering Highlights

Production-Oriented Frontend Migration

The project architecture was refactored into a standard Vite + React deployment pipeline to remove framework lock-in and improve maintainability.

Migration Improvements

• Removed proprietary runtime dependencies
• Standardized SPA architecture
• Added deployment-ready Vite configuration
• Improved routing structure
• Optimized repository organization
• Enhanced frontend maintainability

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Research Foundation

This platform is inspired by research in:

• Legal NLP
• Explainable AI (XAI)
• Transformer-based summarization
• Rhetorical role labeling
• AI-assisted legal analytics

Associated IEEE Publication

Legal Document Analysis and Summarization Using Deep NLP Techniques

Published in IEEE Xplore:

https://ieeexplore.ieee.org/document/11411272

Research areas explored:

• Legal document intelligence
• Semantic legal reasoning
• Transformer-based summarization
• Explainable legal AI
• Dynamic legal text analysis

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Future Enhancements

Planned upgrades include:

• Real transformer inference integration
• Vector embeddings
• Retrieval-Augmented Generation (RAG)
• Multi-document legal comparison
• Citation graph analysis
• Case recommendation systems
• AI-powered legal assistant
• Knowledge graph integration