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Nayana Shaji  • Peerlist

ATHENIS is a web platform built during HackOdisha 5.0 to simplify complex legal documents and surface risks in plain language. The goal was to make contracts, policies, and legal text more accessible to individuals and small businesses without legal expertise. The system processes long legal documents in real time and streams simplified output back to the user as analysis progresses. It is deployed on decentralized GPU infrastructure to keep inference fast and cost efficient. What the system does: - Accepts legal documents in PDF, DOCX, or text formats - Simplifies clauses into plain language while preserving meaning - Flags potentially risky clauses using AI assisted analysis - Streams results live to the UI for long documents - Supports multilingual input and long form text processing My contributions: - Contributed to backend services handling document ingestion and AI inference workflows - Designed and built the frontend interface for document upload, live analysis view, and result visualization - Integrated frontend and backend using real time Server Sent Events for streaming output - Containerized services using Docker and prepared the application for decentralized deployment - Managed deployment on the Akash Network with GPU enabled inference - Set up CI and CD pipelines for automated builds and testing Technical focus: A key challenge was handling long legal documents without blocking the UI. This was solved using streaming responses and careful backend orchestration, allowing users to see partial results while processing continues. Tech stack: Python, Flask, PyTorch, Transformers HTML, CSS, JavaScript, Tailwind CSS Docker, Akash Network, CI CD pipelines Server Sent Events for real time updates Outcome: Top 6 finalist at HackOdisha 5.0 in the Akash Network track, selected from a large national level participant pool. GitHub: https://github.com/gabsgj/athenis Frontend Deployment: https://athenis.gabrieljames.me/

ATHENIS – Legal Document Simplifier

Proud to share a project I contributed to during HackOdisha 5.0, focusing on democratizing access to legal information. ATHENIS is a decentralized AI-powered web app that simplifies, summarizes, and analyzes legal documents in real time. 🚨 Challenge: Legal documents—contracts, NDAs, policies—are often dense and full of jargon, making them inaccessible for individuals, small businesses, and non-native speakers. Misunderstandings can lead to financial or legal risks. 💡 Solution: ATHENIS leverages AI to convert complex legal text into plain language, extract key points, and identify potential risks. The app supports multiple languages, handles long documents efficiently, and streams results in real time. It’s deployed on the Akash Network, enabling decentralized, GPU-accelerated inference for fast, scalable processing. 🔧 Key Features: Upload and analyze PDFs, DOCX, or TXT AI-powered simplification, summarization, and risk detection Real-time streaming results via Server-Sent Events (SSE) Automatic language detection with 15+ language support Contextual embeddings for accurate analysis of long documents Exportable JSON reports and print-friendly summaries 🛠️ Tech Stack & Tools: Backend: Python, Flask, Go microservice for document parsing Frontend: HTML, CSS, JavaScript Infrastructure: Docker, CI/CD pipelines, Akash Network GPU deployment Performance: Redis caching, SSE for live updates 🏁 My Contributions: Designed UI/UX for a responsive, user-friendly interface Implemented Docker containerization and service orchestration Managed Akash Network deployment and GPU integration Developed CI/CD pipelines for automated testing and deployment Ensured real-time streaming of AI results and export functionality 📂 GitHub: github.com/gabsgj/athenis 👨💻 Team Arete: Gabriel James · Harinarayanan R · Jany Sabarinath · Nayana Shaji #AkashNetwork #AI #Flask #Python #DecentralizedAI #DocumentSimplification #SSE #Docker #CI_CD #TeamArete

🏛️ ATHENIS – Legal Document Simplifier ⚖️

Excited to share a project I helped build during Hack Hazards '25: an AI-powered web application that detects potential stampede risks from live or uploaded videos in real time. 🚨 The Problem: Crowd stampedes claim hundreds of lives each year. Whether it’s concerts, festivals, or transit hubs, detecting and acting on crowd surges early is critical. 💡 Our Solution: Stampede Predictor uses computer vision and streaming data to detect and visualize high-risk crowd zones, enabling real-time alerts for organizers and public safety teams. 🔧 Key Features: ✅ Real-time camera + video analysis ✅ YOLOv8-powered person detection ✅ Crowd density heatmap grid (Normal → Critical) ✅ Live SSE risk updates on UI ✅ Audio alerts for CRITICAL risk ✅ Fluvio message streaming pipeline ✅ Downloadable processed video with overlays 🛠️ Stack & Tools: • YOLOv8 Nano (Ultralytics) • Flask + OpenCV (Python) • HTML/CSS/JavaScript (Frontend) • Fluvio (event streaming) • SSE (live UI updates) • Threading + Queues (live status sharing) 🏁 My Contributions: I worked on: • Flask backend architecture • YOLO-based detection + risk grid logic • Fluvio integration for real-time streaming • Server-Sent Events (SSE) for live UI updates • Audio alert trigger system • Deployment readiness and modular structure 🏆 Top 15 — Fluvio Track, Hack Hazards '25 This project placed among the Top 15 in the Fluvio Track (from 17,000+ hackers across 25+ countries). Huge thanks to my teammates and The NAMESPACE Community for the opportunity! 📂 GitHub: github.com/gabsgj/Stampede-Predictor 🎥 Demo: youtu.be/KKmF_QUh2yI 📽️ Devfolio: devfolio.co/projects/stampede-predictor 👨💻 Team Arete: Gabriel James · Nayana Shaji · Jany Sabarinath · Vrindha P #HackHazards25 #Fluvio #YOLOv8 #ComputerVision #CrowdSafety #OpenCV #StreamingAI #HackathonProject #AIForGood #Python #RealtimeAI #GECTThrissur #TeamArete

🧠 Real-Time AI for Public Safety — Stampede 

Stampede Predictor is a real-time computer vision system built during Hackhazards ’25 to identify dangerous crowd density and movement patterns from live or recorded video. The goal was to detect potential stampede risks early enough for organizers and public safety teams to take action. What the system does: - Processes live camera feeds or uploaded videos in real time - Detects people using YOLOv8 and maps them onto spatial grid regions - Computes crowd density and classifies risk levels from normal to critical - Streams live risk updates to the UI using Server Sent Events - Triggers audio alerts when critical risk thresholds are crossed - Produces downloadable output videos with visual overlays My contributions: - Designed the Flask backend architecture for real time video processing - Implemented YOLO based detection and spatial risk grid logic - Integrated Fluvio for event streaming and real time data propagation - Implemented Server Sent Events for live UI updates - Built the audio alert trigger system for critical risk scenarios - Structured the system for deployment readiness and modular extension Technical focus: A key challenge was maintaining low latency while processing video frames, computing risk metrics, and updating the UI in real time. This was addressed through efficient frame handling, threading and queue based pipelines, and decoupled streaming using Fluvio. Tech stack: Python, Flask, OpenCV YOLOv8 Nano (Ultralytics) Fluvio - Event streaming HTML, CSS, JavaScript Server Sent Events, threading, queues Outcome: Top 15 project in the Fluvio Track at Hackhazards ’25, selected from over 17,000 participants across 25+ countries. GitHub: https://github.com/gabsgj/Stampede-Predictor Demo: https://youtu.be/KKmF_QUh2yI Devfolio: https://devfolio.co/projects/stampede-predictor Team Arete: Gabriel James, Nayana Shaji, Jany Sabarinath, Vrindha P #ComputerVision #RealTimeSystems #Flask #YOLOv8 #CrowdSafety #EventStreaming #Python #SoftwareEngineering