PrivyDoc: Building a Zero-Data-Leak AI with Foundry Local & Microsoft's Agent Framework
Tired of choosing between powerful AI insights and sacrificing your data's privacy? PrivyDoc offers a groundbreaking solution. In this article, Microsoft MVP in AI, Shivam Goyal, introduces his innovative project that brings robust AI document analysis directly to your local machine, ensuring zero data ever leaves your device. Discover how PrivyDoc leverages two cutting-edge Microsoft technologies: Foundry Local: The secret sauce for 100% on-device AI processing, allowing advanced models to run securely without cloud dependency. Microsoft Agent Framework: The intelligent orchestrator that builds a sophisticated multi-agent pipeline, handling everything from text extraction and entity recognition to summarization and sentiment analysis. Learn about PrivyDoc's intuitive web UI, its multi-format support, and crucial features that make it perfect for sensitive industries like legal, healthcare, and finance. Say goodbye to privacy concerns and hello to AI-powered document intelligence without compromise.Edge AI for Beginners : Getting Started with Foundry Local
In Module 08 of the EdgeAI for Beginners course, Microsoft introduces Foundry Local a toolkit that helps you deploy and test Small Language Models (SLMs) completely offline. In this blog, I’ll share how I installed Foundry Local, ran the Phi-3.5-mini model on my windows laptop, and what I learned through the process. What Is Foundry Local? Foundry Local allows developers to run AI models locally on their own hardware. It supports text generation, summarization, and code completion — all without sending data to the cloud. Unlike cloud-based systems, everything happens on your computer, so your data never leaves your device. Prerequisites Before starting, make sure you have: Windows 10 or 11 Python 3.10 or newer Git Internet connection (for the first-time model download) Foundry Local installed Step 1 — Verify Installation After installing Foundry Local, open Command Prompt and type: foundry --version If you see a version number, Foundry Local is installed correctly. Step 2 — Start the Service Start the Foundry Local service using: foundry service start You should see a confirmation message that the service is running. Step 3 — List Available Models To view the models supported by your system, run: foundry model list You’ll get a list of locally available SLMs. Here’s what I saw on my machine: Note: Model availability depends on your device’s hardware. For most laptops, phi-3.5-mini works smoothly on CPU. Step 4 — Run the Phi-3.5 Model Now let’s start chatting with the model: foundry model run phi-3.5-mini-instruct-generic-cpu:1 Once it loads, you’ll enter an interactive chat mode. Try a simple prompt: Hello! What can you do? The model replies instantly — right from your laptop, no cloud needed. To exit, type: /exit How It Works Foundry Local loads the model weights from your device and performs inference locally.This means text generation happens using your CPU (or GPU, if available). The result: complete privacy, no internet dependency, and instant responses. Benefits for Students For students beginning their journey in AI, Foundry Local offers several key advantages: No need for high-end GPUs or expensive cloud subscriptions. Easy setup for experimenting with multiple models. Perfect for class assignments, AI workshops, and offline learning sessions. Promotes a deeper understanding of model behavior by allowing step-by-step local interaction. These factors make Foundry Local a practical choice for learning environments, especially in universities and research institutions where accessibility and affordability are important. Why Use Foundry Local Running models locally offers several practical benefits compared to using AI Foundry in the cloud. With Foundry Local, you do not need an internet connection, and all computations happen on your personal machine. This makes it faster for small models and more private since your data never leaves your device. In contrast, AI Foundry runs entirely on the cloud, requiring internet access and charging based on usage. For students and developers, Foundry Local is ideal for quick experiments, offline testing, and understanding how models behave in real-time. On the other hand, AI Foundry is better suited for large-scale or production-level scenarios where models need to be deployed at scale. In summary, Foundry Local provides a flexible and affordable environment for hands-on learning, especially when working with smaller models such as Phi-3, Qwen2.5, or TinyLlama. It allows you to experiment freely, learn efficiently, and better understand the fundamentals of Edge AI development. Optional: Restart Later Next time you open your laptop, you don’t have to reinstall anything. Just run these two commands again: foundry service start foundry model run phi-3.5-mini-instruct-generic-cpu:1 What I Learned Following the EdgeAI for Beginners Study Guide helped me understand: How edge AI applications work How small models like Phi 3.5 can run on a local machine How to test prompts and build chat apps with zero cloud usage Conclusion Running the Phi-3.5-mini model locally with Foundry Localgave me hands-on insight into edge AI. It’s an easy, private, and cost-free way to explore generative AI development. If you’re new to Edge AI, start with the EdgeAI for Beginners course and follow its Study Guide to get comfortable with local inference and small language models. Resources: EdgeAI for Beginners GitHub Repo Foundry Local Official Site Phi Model LinkInstall and run Azure Foundry Local LLM server & Open WebUI on Windows Server 2025
Foundry Local is an on-device AI inference solution offering performance, privacy, customization, and cost advantages. It integrates seamlessly into your existing workflows and applications through an intuitive CLI, SDK, and REST API. Foundry Local has the following benefits: On-Device Inference: Run models locally on your own hardware, reducing your costs while keeping all your data on your device. Model Customization: Select from preset models or use your own to meet specific requirements and use cases. Cost Efficiency: Eliminate recurring cloud service costs by using your existing hardware, making AI more accessible. Seamless Integration: Connect with your applications through an SDK, API endpoints, or the CLI, with easy scaling to Azure AI Foundry as your needs grow. Foundry Local is ideal for scenarios where: You want to keep sensitive data on your device. You need to operate in environments with limited or no internet connectivity. You want to reduce cloud inference costs. You need low-latency AI responses for real-time applications. You want to experiment with AI models before deploying to a cloud environment. You can install Foundry Local by running the following command: winget install Microsoft.FoundryLocal Once Foundry Local is installed, you download and interact with a model from the command line by using a command like: foundry model run phi-4 This will download the phi-4 model and provide a text based chat interface. If you want to interact with Foundry Local through a web chat interface, you can use the open source Open WebUI project. You can install Open WebUI on Windows Server by performing the following steps: Download OpenWebUIInstaller.exe from https://github.com/BrainDriveAI/OpenWebUI_CondaInstaller/releases. You'll get warning messages from Windows Defender SmartScreen. Copy OpenWebUIInstaller.exe into C:\Temp. In an elevated command prompt, run the following commands winget install -e --id Anaconda.Miniconda3 --scope machine $env:Path = 'C:\ProgramData\miniconda3;' + $env:Path $env:Path = 'C:\ProgramData\miniconda3\Scripts;' + $env:Path $env:Path = 'C:\ProgramData\miniconda3\Library\bin;' + $env:Path conda.exe tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main conda.exe tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r conda.exe tos accept --override-channels --channel https://repo.anaconda.com/pkgs/msys2 C:\Temp\OpenWebUIInstaller.exe Then from the dialog choose to install and run Open WebUI. You then need to take several extra steps to configure Open WebUI to connect to the Foundry Local endpoint. Enable Direct Connections in Open WebUI Select Settings and Admin Settings in the profile menu. Select Connections in the navigation menu. Enable Direct Connections by turning on the toggle. This allows users to connect to their own OpenAI compatible API endpoints. Connect Open WebUI to Foundry Local: Select Settings in the profile menu. Select Connections in the navigation menu. Select + by Manage Direct Connections. For the URL, enter http://localhost:PORT/v1 where PORT is the Foundry Local endpoint port (use the CLI command foundry service status to find it). Note that Foundry Local dynamically assigns a port, so it isn't always the same. For the Auth, select None. Select Save ➡️ What is Foundry Local https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-local/what-is-foundry-local ➡️ Edge AI for Beginners https://aka.ms/edgeai-for-beginners ➡️ Open WebUI: https://docs.openwebui.com/Edge AI for Student Developers: Learn to Run AI Locally
AI isn’t just for the cloud anymore. With the rise of Small Language Models (SLMs) and powerful local inference tools, developers can now run intelligent applications directly on laptops, phones, and edge devices—no internet required. If you're a student developer curious about building AI that works offline, privately, and fast, Microsoft’s Edge AI for Beginners course is your perfect starting point. What Is Edge AI? Edge AI refers to running AI models directly on local hardware—like your laptop, mobile device, or embedded system—without relying on cloud servers. This approach offers: ⚡ Real-time performance 🔒 Enhanced privacy (no data leaves your device) 🌐 Offline functionality 💸 Reduced cloud costs Whether you're building a chatbot that works without Wi-Fi or optimizing AI for low-power devices, Edge AI is the future of intelligent, responsive apps. About the Course Edge AI for Beginners is a free, open-source curriculum designed to help you: Understand the fundamentals of Edge AI and local inference Explore Small Language Models like Phi-2, Mistral-7B, and Gemma Deploy models using tools like Llama.cpp, Olive, MLX, and OpenVINO Build cross-platform apps that run AI locally on Windows, macOS, Linux, and mobile The course is hosted on GitHub and includes hands-on labs, quizzes, and real-world examples. You can fork it, remix it, and contribute to the community. What You’ll Learn Module Focus 01. Introduction What is Edge AI and why it matters 02. SLMs Overview of small language models 03. Deployment Running models locally with various tools 04. Optimization Speeding up inference and reducing memory 05. Applications Building real-world Edge AI apps Each module is beginner-friendly and includes practical exercises to help you build and deploy your own local AI solutions. Who Should Join? Student developers curious about AI beyond the cloud Hackathon participants looking to build offline-capable apps Makers and builders interested in privacy-first AI Anyone who wants to explore the future of on-device intelligence No prior AI experience required just a willingness to learn and experiment. Why It Matters Edge AI is a game-changer for developers. It enables smarter, faster, and more private applications that work anywhere. By learning how to deploy AI locally, you’ll gain skills that are increasingly in demand across industries—from healthcare to robotics to consumer tech. Plus, the course is: 💯 Free and open-source 🧠 Backed by Microsoft’s best practices 🧪 Hands-on and project-based 🌐 Continuously updated Ready to Start? Head to aka.ms/edgeai-for-beginners and dive into the modules. Whether you're coding in your dorm room or presenting at your next hackathon, this course will help you build smarter AI apps that run right where you need them on the edge.
