Azure Logic App AI-Powered Monitoring Solution: Automate, Analyze, and Act on Your Azure Data
Introduction In today’s cloud-driven world, monitoring and analyzing application health is critical for business continuity and operational excellence. However, the sheer volume of monitoring data can make it challenging to extract actionable insights quickly. Enter the Azure Logic App AI-Powered Monitoring Solution—an intelligent, serverless pipeline that leverages Azure Logic Apps and Azure OpenAI to automate monitoring, analyze data, and deliver comprehensive reports right to your inbox. This solution is ideal for organizations seeking to modernize their monitoring workflows, reduce manual analysis, and empower teams with AI-driven insights for faster decision-making. What Does This Solution Accomplish? The Azure Logic App AI-Powered Monitoring Solution creates an automated pipeline that: Extracts monitoring data from Azure Log Analytics using KQL queries. Analyzes data with AI using the Azure OpenAI GPT-4o model. Generates intelligent reports and sends them via email. Runs automatically on a daily schedule. Uses managed identity for secure authentication across Azure services. Business Case Solved Automated Monitoring: No more manual log reviews—let AI do the heavy lifting. Actionable Insights: Receive daily, AI-generated summaries highlighting system health, key metrics, potential issues, and recommendations. Operational Efficiency: Reduce time-to-insight and empower teams to act faster on critical events. Secure and Scalable: Built on Azure’s serverless and identity-driven architecture. Key Features Serverless Architecture: Built on Azure Logic Apps Standard for scalability and cost efficiency. AI-Powered Insights: Uses Azure OpenAI for advanced data analysis and summarization. Infrastructure as Code: Deployable via Bicep templates for reproducibility and automation. Secure by Design: Managed identity and Azure RBAC ensure secure access. Cost Effective: Pay-per-execution model with optimized resource usage. Customizable: Easily modify KQL queries and AI prompts to fit your monitoring needs. Solution Architecture Technologies Involved Azure Logic Apps Standard: Orchestrates the workflow. Azure OpenAI Service (GPT-4o): Performs AI-powered data analysis and summarization. Azure Log Analytics: Source for monitoring data, queried via KQL. Application Insights: Monitors workflow execution and telemetry. Azure Storage Account: Stores Logic App runtime data. Managed Identity: Secures authentication across Azure services. Infrastructure as Code (Bicep): Enables automated, repeatable deployments. Office 365 Connector: Sends email notifications. Support Documentation: https://docs.microsoft.com/en-us/azure/logic-apps/ Issues: https://github.com/vinod-soni-microsoft/logicapp-ai-summarize/issues Star this repository if you find it helpful!Building AI-Powered Clinical Knowledge Stores with Azure AI Search
👀 Missed Session 01? Don’t worry—you can still catch up. But first, here’s what AI HLS Ignited is all about: What is AI HLS Ignited? AI HLS Ignited is a Microsoft-led technical series for healthcare innovators, solution architects, and AI engineers. Each session brings to life real-world AI solutions that are reshaping the Healthcare and Life Sciences (HLS) industry. Through live demos, architectural deep dives, and GitHub-hosted code, we equip you with the tools and knowledge to build with confidence. Session 01 Recap: In our first session, we introduced the accelerator MedIndexer - which is an indexing framework designed for the automated creation of structured knowledge bases from unstructured clinical sources. Whether you're dealing with X-rays, clinical notes, or scanned documents, MedIndexer converts these inputs into a schema-driven format optimized for Azure AI Search. This will allow your applications to leverage state-of-the-art retrieval methodologies, including vector search and re-ranking. Moreover, by applying a well-defined schema and vectorizing the data into high-dimensional representations, MedIndexer empowers AI applications to retrieve more precise and context-aware information... The result? AI systems that surface more relevant, accurate, and context-aware insights—faster. 🔍 Turning Your Unstructured Data into Value "About 80% of medical data remains unstructured and untapped after it is created (e.g., text, image, signal, etc.)" — Healthcare Informatics Research, Chungnam National University In the era of AI, the rise of AI copilots and assistants has led to a shift in how we access knowledge. But retrieving clinical data that lives in disparate formats is no trivial task. Building retrieval systems takes effort—and how you structure your knowledge store matters. It’s a cyclic, iterative, and constantly evolving process. That’s why we believe in leveraging enterprise-ready retrieval platforms like Azure AI Search—designed to power intelligent search experiences across structured and unstructured data. It serves as the foundation for building advanced retrieval systems in healthcare. However, implementing Azure AI Search alone is not enough. Mastering its capabilities and applying well-defined patterns can significantly enhance your ability to address repetitive tasks and complex retrieval scenarios. This project aims to accelerate your ability to transform raw clinical data into high-fidelity, high-value knowledge structures that can power your next-generation AI healthcare applications. 🚀 How to Get Started with MedIndexer New to Azure AI Search? Begin with our guided labs to build a strong foundation and get hands-on with the core capabilities. Already familiar with the tech? Jump ahead to the real-world use cases—learn how to build Coded Policy Knowledge Stores and X-ray Knowledge Stores. 🧪 Labs 🧪 Building Your Azure AI Search Index: 🧾 Notebook - Building your first Index Learn how to create and configure an Azure AI Search index to enable intelligent search capabilities for your applications. 🧪 Indexing Data into Azure AI Search: 🧾 Notebook - Ingest and Index Clinical Data Understand how to ingest, preprocess, and index clinical data into Azure AI Search using schema-first principles. 🧪 Retrieval Methods for Azure AI Search: 🧾 Notebook - Exploring Vector Search and Hybrid Retrieval Dive into retrieval techniques such as vector search, hybrid retrieval, and reranking to enhance the accuracy and relevance of search results. 🧪 Evaluation Methods for Azure AI Search: 🧾 Notebook - Evaluating Search Quality and Relevance Learn how to evaluate the performance of your search index using relevance metrics and ground truth datasets to ensure high-quality search results. 🏥 Use Cases 📝 Creating Coded Policy Knowledge Stores In many healthcare systems, policy documents such as pre-authorization guidelines are still trapped in static, scanned PDFs. These documents are critical—they contain ICD codes, drug name coverage, and payer-specific logic—but are rarely structured or accessible in real-time. To solve this, we built a pipeline that transforms these documents into intelligent, searchable knowledge stores. This diagram shows how pre-auth policy PDFs are ingested via blob storage, passed through an OCR and embedding skillset, and then indexed into Azure AI Search. The result: fast access to coded policy data for AI apps. 🧾 Notebook - Creating Coded Policies Knowledge Stores Transform payer policies into machine-readable formats. This use case includes: Preprocessing and cleaning PDF documents Building custom OCR skills Leveraging out-of-the-box Indexer capabilities and embedding skills Enabling real-time AI-assisted querying for ICDs, payer names, drug names, and policy logic Why it matters: This streamlines prior authorization and coding workflows for providers and payors, reducing manual effort and increasing transparency. 🩻 Creating X-ray Knowledge Stores In radiology workflows, X-ray reports and image metadata contain valuable clinical insights—but these are often underutilized. Traditionally, they’re stored as static entries in PACS systems or loosely connected databases. The goal of this use case is to turn those X-ray reports into a searchable, intelligent asset that clinicians can explore and interact with in meaningful ways. This diagram illustrates a full retrieval pipeline where radiology reports are uploaded, enriched through foundational models, embedded, and indexed. The output powers an AI-driven web app for similarity search and decision support. 🧾 Notebook - Creating X-rays Knowledge Stores Turn imaging reports and metadata into a searchable knowledge base. This includes: Leveraging push APIs with custom event-driven indexing pipeline triggered on new X-ray uploads Generating embeddings using Microsoft Healthcare foundation models Providing an AI-powered front-end for X-ray similarity search Why it matters: Supports clinical decision-making by retrieving similar past cases, aiding diagnosis and treatment planning with contextual relevance. 📣 Join Us for the Next Session Help shape the future of healthcare by sharing AI HLS Ignited with your network—and don’t miss what’s coming next! 📅 Register for the upcoming session → AI HLS Ignited Event Page 💻 Explore the code, demos, and architecture → AI HLS Ignited GitHub RepositoryAzure OpenAI GPT model to review Pull Requests for Azure DevOps
In recent months, the use of Generative Pre-trained Transformer (GPT) models for natural language processing (NLP) has gained significant traction. GPT models, which are based on the Transformer architecture, can generate text from arbitrary sources of input data and can be trained to identify errors and detect anomalies in text. As such, GPT models are increasingly being used for a variety of applications, ranging from natural language understanding to text summarization and question-answering. In the software development world, developers use pull requests to submit proposed changes to a codebase. However, reviews by other developers can sometimes take a long time and not accurate, and in some cases, these reviews can introduce new bugs and issues. In order to reduce this risk, During my research I found the integration of GPT models is possible and we can add Azure OpenAI service as pull request reviewers for Azure Pipelines service. The GPT models are trained on developer codebases and are able to detect potential coding issues such as typos, syntax errors, style inconsistencies and code smells. In addition, they can also assess code structure and suggest improvements to the overall code quality. Once the GPT models have been trained, they can be integrated into the Azure Pipelines service so that they can automatically review pull requests and provide feedback. This helps to reduce the time taken for code reviews, as well as reduce the likelihood of introducing bugs and issues.Restricting Azure Cognitive Search & Azure OpenAI Output with Azure Entra Security Groups
Azure Cognitive Search & OpenAI Output can be effectively restricted with the help of Azure Entra Security Groups. With Azure Entra Security Groups, organizations can limit access to an Azure search instance or an OpenAI Output instance based on group membership of the user. This ensures that users only have access to the data within the scope of their job responsibilities. Azure Entra Security Groups also provide advanced authentication and authorization services for Azure services, offering additional layers of security for organizations to protect their data.Healio - Healthcare Chatbot using C# Semantic Kernel, planner and Azure OpenAI
In this article we will go through a business use case of developing Healio - Healthcare Chatbot: Access information from different data sources and create its own plan using C# Semantic Kernel, planner and Azure OpenAI. You might be familiar with the fact that Retrieval Augmented Generation (RAG) pattern is an easy and efficient way to allow Azure OpenAI to "talk to your data". This pattern lets you query your data with Azure Cognitive Search (a search engine), find pertinent pieces of information from your data, then include that extra information to your prompts to the Azure OpenAI service to reply in a natural language.Unleashing variations on a theme through hackathons
Hackathons can bring together individuals with a shared passion for technology, encouraging them to tackle real-world problems. In this blog, we will delve into some key principles that can help participants make the most of their hackathon experience and achieve remarkable results.Azure OpenAI Assistance for Power Virtual Agents
Recently Greg Beaumont posted to the HLS Tech Community blog Azure OpenAI ChatGPT Solves Cryptic Error Codes from Power BI, SQL Server, Power Apps, and more!. He highlighted in the post (and video) some advantageous ways to leverage Azure OpenAI’s Chat GPT playground to respond to odd error codes. He asked at the end of the post about enabling this through a Power App, bot, or something else. Challenge accepted!Unlocking the Power of Open AI – Azure DevOps Backlogs from Images/PDFs
In today's digital world, the need to convert images and PDFs to text is becoming increasingly important. However, the process of manually transcribing images and PDFs can be time-consuming and error-prone. Fortunately, there is a better way. With the Azure Open AI service, you can easily and quickly convert images and PDFs to text.
