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EndGame RAGHub is a scalable multimodal Retrieval-Augmented Generation platform supporting document, image, audio, and video understanding using FastAPI, Qdrant, LangGraph, and modern open-source AI infrastructure.

StudentAI - AI-powered RAG chatbot for document analysis and question answering using FastAPI, React, Ollama and Retrieval-Augmented Generation (RAG).

A Streamlit-based research paper assistant that uses AI (RAG + PyMuPDF4LLM) for document summarization, Q&A, and multimedia analysis with local-first inference via Ollama.

AI-powered legal document analyzer for India. Upload any contract or agreement — LegalLens finds hidden clauses, checks against 73 Indian laws, and explains everything in plain language.

MedScribe — Offline-first prescription & lab report digitizer. Converts photos of handwritten/printed medical records into structured, queryable data using local OCR (Tesseract) and a local SLM (Ollama, qwen2.5:1.5b) — no cloud APIs, fully CPU-only, works air-gapped. Built for the CPU-First Hackathon ("Local AI").

An offline-first, CPU-optimized suite of classroom helper tools for teachers and students in low-connectivity areas. Powered by local OCR (PaddleOCR) and local Small Language Models (Qwen 2.5 via Ollama) to automate grading and flashcard generation entirely on a standard laptop

Smart Cab Finder is an AI-powered ride comparison platform that helps users compare fares, estimated arrival times, and ride options across multiple cab providers. The application includes an intelligent AI assistant for ride recommendations, supports Bring Your Own Key (BYOK) integrations, and is designed to support local AI inference through Ollama. To improve accessibility, the platform provides multilingual support in English, Hindi, and Telugu. Built using Python and Flask, the project follows secure development practices with automated testing, CI/CD pipelines, security scanning, and compliance-driven documentation.

MediScann — offline, CPU-first CLI that reads prescriptions and medical reports and uses a local LLM (ollama) to summarize the problem, list medications, suggest informational options, and flag warnings. No cloud, no telemetry.

Memento AI – A CPU-first, offline AI assistant that transforms documents, images, audio, video, and text into structured knowledge using local LLMs, SQLite, and Retrieval-Augmented Generation (RAG), with

Offline-first, CPU-optimized AI platform that transforms unstructured documents into structured, searchable data using local OCR, embeddings, llama.cpp, FAISS, and SQLite—no cloud inference or external APIs required.

PaperLens Offline is an offline-first, CPU-optimized AI research paper analyzer that extracts structured knowledge from academic PDFs using local LLMs (Ollama), stores the results in SQLite, and enables fast local search without any cloud APIs.

AI Medical Record Extractor is an offline-first web application that converts unstructured medical documents into structured JSON data using CPU-only inference. It supports PDF, image, and text files while ensuring complete privacy by processing all data locally without cloud services. Built with FastAPI, React, and SQLite, the application provides a user-friendly interface for extracting patient information, diagnoses, medications, and other medical details. The project is designed to be lightweight, secure, open source, and accessible in low-connectivity environments.

DriverOS is an AI-powered driver assistance and safety platform designed for commercial drivers, truck drivers, delivery partners, and fleet operators across India. The platform focuses on driver safety, health monitoring, emergency response, trip management, document storage, and real-time support services.

Offline Medical Report Structurer

Project Description

Offline Medical Report Structurer is an AI-powered healthcare application designed to transform unstructured medical reports into organized, structured, and easy-to-understand data without requiring an internet connection. The system processes medical documents such as laboratory reports, prescriptions, discharge summaries, and diagnostic reports, automatically extracting key information including patient details, diagnoses, medications, test results, and clinical observations.

By leveraging Natural Language Processing (NLP) and document parsing techniques, the application converts complex medical text into a standardized format that improves accessibility, readability, and data management. Since the solution operates entirely offline, it ensures patient privacy, data security, and usability in low-connectivity environments such as rural clinics, hospitals, and healthcare camps.

Key Features

Offline processing with no internet dependency Automated extraction of medical information Structured JSON/database output for easy integration Support for multiple medical report formats Secure and privacy-focused data handling Quick search and retrieval of patient information User-friendly interface for healthcare professionals

NyayaBloom is an AI-powered Judicial Intelligence Platform designed to democratize legal knowledge and improve judicial transparency across India. The platform enables citizens, lawyers, law students, researchers, journalists, and judicial professionals to access legal information, understand judicial processes, explore case precedents, and improve legal literacy through a multilingual AI-powered ecosystem. Instead of replacing judges, courts, or legal professionals, NyayaBloom serves as an intelligent legal knowledge assistant that simplifies legal information and promotes informed civic participation.

MedStruct AI is an offline AI-powered medical document structuring system that extracts structured information from prescriptions using OCR and local LLMs.

Smriti is an AI-powered heritage exploration platform that brings Telangana's monuments, history, culture, and tourist destinations to life through intelligent storytelling and immersive digital experiences. The platform preserves cultural heritage by combining artificial intelligence, interactive experiences, and historical insights to help users discover the stories behind every monument and place.

MedRepo is an offline, CPU-first AI application designed to convert medical Complete Blood Count (CBC) reports from PDFs or images into clean, structured JSON. The project prioritizes patient privacy by processing all data locally without requiring an internet connection or cloud services. It combines OCR and lightweight local language models to accurately extract and organize medical information for further analysis or integration with healthcare applications. Built with a modular architecture, MedRepo is easy to set up, extend, and maintain. The project aims to make medical report digitization faster, more secure, and accessible on low-resource devices while supporting future enhancements and broader diagnostic report formats.

Offline-first, CPU-first receipt and invoice OCR, local Qwen categorization, structured expense reporting, and JSON/CSV export.

F2P Solver extracts mathematical formulas from PDF research papers and converts them into interactive Python simulations — entirely offline, on CPU. Upload a paper, and for every formula you get a callable Python function plus a live chart with sliders for each variable: drag a slider and the curve updates instantly, with no further AI calls. Built with Streamlit, pymupdf, ollama (Qwen2.5-7B), numpy, and Plotly. Runs on any Linux/macOS machine with a single command: ./run.sh.