O

ollama

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").

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.

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

MyDocVault is a secure, offline-first AI platform designed to help users store, organize, understand, and manage government documents in a privacy-preserving environment. The application enables users to upload documents such as Aadhaar, PAN, Passport, Driving Licence, Voter ID, educational certificates, and other official records, while ensuring that all processing happens locally without relying on external servers or internet connectivity.

Powered by an intelligent document-aware assistant, MyDocVault extracts key information, summarizes documents in simple language, answers user queries based solely on locally stored documents, and provides guidance on the documents required for various government services. The system never fabricates information, clearly identifies missing details, and prioritizes user privacy by keeping all data offline.

Key Features 🔒 Offline-first and privacy-focused document management 📄 Secure storage and organization of government documents 🤖 AI-powered document understanding and summarization 🔍 Automatic extraction of important document details 📋 Government service document requirement guidance 📂 Intelligent document search and retrieval ⚠️ Identification of missing or incomplete documents 🛡️ Local processing with no external API or internet dependency 💬 Simple, conversational AI assistant for document-related queries 📊 Structured JSON extraction for document metadata

Tech Stack: React.js, Node.js, Express.js, SQLite, OCR, Local AI Models, HTML, CSS, JavaScript

Goal: To provide citizens with a secure, intelligent, and completely offline digital document vault that simplifies document management while ensuring maximum privacy and accessibility.

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.

Dream2Play AI is an AI-powered platform that transforms natural language prompts into playable games, stories, characters, missions, and worlds with support for cloud AI, local AI inference ( Ollama ), and BYOK.

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.

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.

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.

CPU-powered, offline-first tax invoice parser and analytics dashboard

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.