The Future of AI: Creating a Web Application with Vibe Coding
Discover how vibe coding with GPT-5 in Azure AI Foundry transforms web development. This post walks through building a Translator API-powered web app using natural language instructions in Visual Studio Code. Learn how adaptive translation, tone and gender customization, and Copilot agent collaboration redefine the developer experience.The Future of AI: Vibe Code with Adaptive Custom Translation
This blog explores how vibe coding—a conversational, flow-based development approach—was used to build the AdaptCT playground in Azure AI Foundry. It walks through setting up a productive coding environment with GitHub Copilot in Visual Studio Code, configuring the Copilot agent, and building a translation playground using Adaptive Custom Translation (AdaptCT). The post includes real-world code examples, architectural insights, and advanced UI patterns. It also highlights how AdaptCT fine-tunes LLM outputs using domain-specific reference sentence pairs, enabling more accurate and context-aware translations. The blog concludes with best practices for vibe coding teams and a forward-looking view of AI-augmented development paradigms.
Explore Azure AI Services: Curated list of prebuilt models and demos
Unlock the potential of AI with Azure's comprehensive suite of prebuilt models and demos. Whether you're looking to enhance speech recognition, analyze text, or process images and documents, Azure AI services offer ready-to-use solutions that make implementation effortless. Explore the diverse range of use cases and discover how these powerful tools can seamlessly integrate into your projects. Dive into the full catalogue of demos and start building smarter, AI-driven applications today.Announcing a new Azure AI Translator API (Public Preview)
Microsoft has launched the Azure AI Translator API (Public Preview), offering flexible translation options using either neural machine translation (NMT) or generative AI models like GPT-4o. The API supports tone, gender, and adaptive custom translation, allowing enterprises to tailor output for real-time or human-reviewed workflows. Customers can mix models in a single request and authenticate via resource key or Entra ID. LLM features require deployment in Azure AI Foundry. Pricing is based on characters (NMT) or tokens (LLMs).Azure AI Translation Preprocessing Workflow
In today's global enterprise landscape, translation accuracy and efficiency are critical to delivering seamless multilingual experiences. Yet, many organizations struggle with inconsistent results, API errors, and high costs when using machine translation services. That's where our custom-built Azure AI Translation Preprocessing Workflow comes in. 🌐 Solution Introduction The Azure AI Translation Preprocessing Workflow is a purpose-built solution designed to bridge the gap between raw document inputs and high-quality machine translation outcomes. Developed in response to real-world enterprise challenges, this service automates the preprocessing of multilingual documents, ensuring they meet Azure AI Translate’s strict formatting and content standards. By intelligently analyzing, validating, and optimizing files before translation, it empowers organizations to achieve greater accuracy, lower costs, and eliminate common API failures. Whether you're dealing with inconsistent document formats, embedded content, or language detection issues, this workflow provides a scalable and intelligent foundation for enterprise-grade translation pipelines. It’s not just a tool—it’s a strategic enabler for global communication. 🎯 Why This Service? This solution was designed to address real-world challenges faced by enterprise teams using Azure AI Translation Services: 🚀 Azure AI Translate Ready: Fully optimized for Azure's translation service standards. 📊 92% Translation Quality Improvement: Pre-validates and optimizes content for better results. 💰 30% Cost Reduction: Intelligent content filtering reduces unnecessary translation costs. ⚡ Zero API Errors: Format validation prevents common Azure AI service failures. 🔍 Intelligent Analysis: Advanced content segmentation and language detection. ✨ Key Features 🎯 Azure AI Translate Optimization Readiness Scoring (0-100%): Quantifies document preparation for Azure AI Translate. Language Detection: Automatic identification of document languages. Content Segmentation: Optimizes text segments for Azure's 5,000 character limit. Format Compliance: Ensures Strict Open XML Document standards. Translation Quality Enhancement: Intelligent content filtering and preparation. 🛠️ Core Capabilities Multi-format Support: Converts DOCX, DOC, RTF, ODT, TXT → DOCX. Comprehensive Validation: Advanced DOCX format verification. Content Intelligence: Detects and analyzes translatable text. REST API: 9 endpoints with interactive Swagger UI documentation. Background Processing: Async file processing capabilities. Detailed Analytics: Word counting, language hints, content type analysis. 🔧 Technical Excellence FastAPI Framework: High-performance async web framework. LibreOffice Integration: Professional document conversion. Comprehensive Logging: Detailed operation tracking and metadata. Error Handling: Robust validation and error recovery. Docker Ready: Containerization support for easy deployment. 🚀 Real-World Impact This solution was born out of a critical support case where translation failures were impacting production workflows. By automating preprocessing and validation, we not only resolved the immediate issue but also architected a scalable, long-term solution that enhances translation reliability and reduces operational overhead. 📢 Get Involved Explore the GitHub repository, contribute, or adapt the solution to your enterprise needs: 👉 https://github.com/vinod-soni-microsoft/azure-ai-translation-preprocessing-workflow Let’s build smarter, faster, and more reliable translation workflows together.Enter new era of enterprise communication with Microsoft Translator Pro & document image translation
Microsoft Translator Pro: standalone, native mobile experience We are thrilled to unveil the gated public preview of Microsoft Translator Pro, our robust solution designed for enterprises seeking to dismantle language barriers in the workplace. Available on iOS, Microsoft Translator Pro offers a standalone, native experience, enabling speech-to-speech translated conversations among coworkers, users, or clients within your enterprise ecosystem. Watch how Microsoft Translator Pro transforms a hotel check-in experience by breaking down language barriers. In this video, a hotel receptionist speaks in English, and the app translates and plays the message aloud in Chinese for the traveler. The traveler responds in Chinese, and the app translates and plays the message aloud in English for the receptionist. Key features of the public preview Our enterprise version of the app is packed with features tailored to meet the stringent demands of enterprises: Core feature - speech-to-speech translation: Break language barriers: Real-time speech-to-speech translation allows you to have seamless communication with individuals speaking different languages. Unified experience: View or hear both transcription and translation simultaneously on a single device, ensuring smooth and efficient conversations. On-device translation: Harness the app's speech-to-speech translation capability without an internet connection in limited languages, ensuring your productivity remains unhampered. Full administrator control: Enterprise IT Administrators wield extensive control over the app's deployment and usage within your organization. They can fine-tune settings to manage conversation history, audit, and diagnostic logs, with the ability to disable history or configure automatic exportation of the history to cloud storage. Uncompromised privacy and security: Microsoft Translator Pro provides enterprises with a high level of translation quality and robust security. We know that Privacy and security are top priorities for you. Once granted access by your organization's admin, you can sign in the app with your organizational credentials. Your conversational data remains strictly yours, safeguarded within your Azure tenant. Neither Microsoft nor any external entities have access to your data. Join the Preview To embark on this journey with us, please complete the gating form . Upon meeting the criteria, we will grant your organization access to the paid version of the Microsoft Translator Pro app, which is now available in the US. Learn more and get started: Microsoft Translator Pro documentation. Document translation translates text embedded in images Our commitment to advancing cross-language communication takes a major step forward with a new enhancement in Azure AI Translator’s Document Translation (DT) feature. Previously, Document Translation supported fully digital documents and scanned PDFs. Starting January 2025, with this latest update, the service can also process mixed-content documents, translating both digital text and text embedded within images. Sample document translated from English to Spanish: (Frames in order: Source document, translated output document (image not translated), translated output document with image translation) How It Works To enable this feature, the Document Translation service now leverages Microsoft Azure AI Vision API to detect, extract, and translate text from images within documents. This capability is especially useful for scenarios where documents contain a mix of digital text and image-based text, ensuring complete translations without manual intervention. Getting Started To take advantage of this feature, customers can use the new optional parameter when setting up a translation request: Request A new parameter under "options" called "translateTextWithinImage" has been introduced. This parameter is of type Boolean, accepting "true" or "false." The default value is "false," so you’ll need to set it to "true" to activate the image text translation capability. Response: When this feature is enabled, the response will include additional details for transparency on image processing: totalImageScansSucceeded: The count of successfully translated image scans. totalImageScansFailed: The count of image scans that encountered processing issues. Usage and cost For this feature, customers will need to use the Azure AI Services resource, as this new feature leverages Azure AI Vision services along with Azure AI Translator. The OCR service incurs additional charges based on usage. Pricing details for the OCR service can be found here: Pricing details Learn more and get started (starting January 2025): Translator Documentation These new advancements reflect our dedication to pushing boundaries in Document Translation, empowering enterprises to connect and collaborate more effectively, regardless of language. Stay tuned for more innovations as we continue to expand the reach and capabilities of Microsoft Azure AI Translator.How Anker soundcore Uses Azure AI Speech for Seamless Multilingual Communication
“We’re excited to be part of Microsoft Build and to demonstrate what’s possible when AI meets every day tech. Built on deep technical integration and shared innovation goals, we’re able to deliver smarter, more intuitive, and responsive audio products for users around the world.” — Dongping Zhao, President of Anker Innovations Imagine talking to anyone, no matter the language. soundcore, Anker Innovations' audio brand, has incorporated Microsoft Azure AI Speech services into its new devices to eliminate language barriers. These wireless earbuds now offer real-time speech translation and voice interactions, showcasing how cloud-based AI speech technologies can create immersive, multilingual experiences on consumer devices. Anker’s Mission and Challenges Anker Innovations is a global smart hardware technology company known for its breakthroughs in charging, portable power, and consumer electronics. Its product portfolio encompasses premium audio equipment, mobile accessories, and smart home solutions. soundcore, established in 2014 as Anker's dedicated audio brand, has rapidly ascended to become one of the top three audio brands globally in terms of wireless headphone shipment volume. Today, soundcore counts over 52 million users worldwide who enjoy its headphones, earbuds, and speakers. With cross-cultural and multilingual communication becoming increasingly prevalent, whether during international travel or business meetings, users encounter growing challenges in bridging language gaps. Today's users are looking for intelligent, multi-functional tools capable of adapting to diverse scenarios. People seek more than just audio; they desire utility, versatility, and smart interactions. To meet these needs, Anker partners with Microsoft Azure AI. By integrating voice AI technology directly into Anker’s soundcore earbuds, Anker succeeds in delivering a more natural, intelligent, and efficient multilingual experience. Speech-to-Speech Translation in the soundcore Aerofit 2 Earbuds The soundcore Aerofit 2 wireless earbuds, originally launched last year, added AI Speech Translation capabilities in March 2025. These earbuds come with built-in speech translation features driven by Azure AI Speech, allowing users to communicate across languages in real time. Take face-to-face translation as an example: Two people can carry on a conversation while each wears an earbud and speaks their native language. The connected smartphone app uses Azure AI Speech's translation feature to translate each person’s speech, then voices the translation through the other person’s earbud using Azure AI Speech's text-to-speech capability. This happens in near real-time, enabling a natural back-and-forth conversation. Early user feedback has been very positive – the experience is like having a human translator whispering in one’s ear, except it’s all done by AI. Given the strong response, soundcore anticipates a surge in demand for the Aerofit 2, highlighting the value users see in breaking language barriers. Innovative Solution At the core of this innovation is Azure AI Speech Translation, a cloud service that enables real-time, multilingual translation of speech. This service can listen to a speaker and automatically identify the spoken language – eliminating the need to manually select an input language. It supports over 100 languages and dialects, providing broad global coverage. Even if speakers switch languages during a conversation, Azure AI Speech adapts on the fly to keep the dialogue flowing. Translations are delivered almost instantly – allowing two people to converse naturally with minimal lag. The end result is a seamless, face-to-face style conversation across languages – powered entirely by AI in the cloud. Leveraging the capabilities of real-time voice conversations and live speech translation, these advancements are all aimed at achieving one singular goal: enabling interactions with technology to be as seamless and natural as speaking with a friend. At //Build 2025: Embrace the Future of Voice Agents Anker is exploring deeper integration of Azure AI to enable conversational voice assistants in future soundcore devices. This vision revolves around Azure’s new Voice Live API, just announced at //Build 2025, which can be used to simplify creating voice agents with fluent and natural speech to speech conversational experiences. In the future, soundcore's users will not only get translation, but also the ability to engage in natural spoken conversations with an AI assistant, all through the earbuds. Imagine asking a voice assistant, powered by Azure AI, to summarize the latest emails, schedule a meeting, or even have a casual Q&A, and hear thoughtful spoken responses in return – all through the earbuds! Technically, the Voice Live API orchestrates multiple Azure AI components in one workflow: it uses speech recognition to understand end user request; then a natively supported foundation model acts on the request with specialized tools; finally, Azure’s text-to-speech converts the result into a natural voice response. All of this happens in real time via the cloud. The audio experience is enhanced with features like echo cancellation, noise suppression, interruption handling, and end-of-turn detection for more natural conversations. soundcore's upcoming earbuds, featuring Azure's conversational AI capabilities, aim to let people interact with AI anywhere. In the future, a customer could ask their earbuds for weather updates or translation help during a jog and get a seamless response without lifting a finger.Announcing Azure AI Language new features to accelerate your agent development
In today’s fast-moving AI landscape, businesses are racing to embed conversational intelligence and automation into every customer touchpoint. However, building a reliable and scalable agent from scratch remains complex and time-consuming. Developers tell us they need a streamlined way to map diverse user intents, craft accurate responses, and support global audiences without wrestling with ad-hoc integrations. At the same time, rising expectations around data privacy and compliance introduce yet another layer of overhead. To meet these challenges, today, we’re excited to announce a suite of powerful new tools and templates designed to help developers build intelligent agents faster than ever with our Azure AI Language service. Working together with Azure AI Agent Service, whether you’re triaging user intents, serving up precise answers, or translating content on the fly, our latest releases have you covered. Our latest releases include three ready-to-use agent templates and MCP server, enhanced Conversational Language Understanding (CLU) and Custom Question Answering (CQA) with an all-new authoring experience in Azure AI Foundry portal, updated conversational agent accelerator project, and strengthened privacy controls in Personally Identifiable Information (PII) detection service. New Agent Templates We are releasing three agent templates in Azure AI Agent Service catalog to bootstrap developers to address complex conversational scenarios efficiently: intent routing agent, exact question-answering agent and text translation agent. Each of these templates includes sample code available on GitHub to set up agents powered by core capabilities in Azure AI Language and Translator. Figure 1: New agent templates available in agent catalog Intent routing agent Leverage the combined power of our Custom Language Understanding (CLU) and Custom Question Answering (CQA) products. This template creates an agent that automatically detects which pre-defined business intent a user query maps to or returns the exact answer verbatim via CQA. It gives you fully predictable and controllable intent routing with no custom model training required. You can further extend the capabilities of this agent based on your needs. For example, add additional Knowledge to the agent to handle non-critical and unpredictable user questions through RAG, or connect with other agents to route the user query based on identified intents. Check out the GitHub repo for more info about intent routing agent. Exact question-answering agent Focused solely on delivering verbatim answers from your curated knowledge base in CQA, this template is perfect for creating agent for FAQ bots, support portals, and any scenario where precision matters above all else. Similar to intent routing agent, you can enhance the exact question-answering agent with additional Knowledge to handle a wider range of user questions through RAG, improving traffic coverage and customer satisfaction. Check out the GitHub repo for more info about exact question-answering agent. Text translation agent Integrate text translation seamlessly into your agent’s workflow with Azure AI Translator. This template facilitates multilingual support through straightforward agent setup, enabling your agent to communicate with customers in their preferred language and manage translation requests across various languages with high accuracy. Check out the GitHub repo for more info about text translation agent. MCP Server with PII and Translator Tools In addition to the agent templates, we are also announcing our new language MCP server with built-in core Language service capabilities as tools. This first release includes PII detection and translation tooling, allowing developers to easily integrate it with any agents. Check out its source code and more details in the GitHub repo. CLU and CQA Enhancements To empower the new agent templates, we continued enhancing our CLU and CQA capabilities and experience: LLM-based intent detection in CLU Conversational Language Understanding (CLU) service powers intent detection and entity extraction that can be customized for various business. In addition to the traditional model training experience optimized for extreme high accuracy and low latency needs, CLU now also provides a new option that utilizes Azure OpenAI models to detect user intents. No additional training steps, datasets, or fine-tuning required. Simply define your intents and quick deploy. Figure 2: Two CLU deployment options available in Azure AI Foundry portal New query reference settings in CQA Conversational Question Answering (CQA) service delivers highly precise responses from your pre-defined question-answer pairs, ensuring users receive the exact information your business requires. To further improve the question understanding and configurability, we are introducing “queryPreferences” property in the CQA API, with the support of new query matching policy, semantic ranker and also the classic ranker used in QnA Maker to support the needs from our QnA Maker customers to migrate to CQA. All these new features will be available at the end of this week. New authoring experience in Azure AI Foundry portal We are introducing CLU and CQA authoring experience in Azure AI Foundry portal. Regardless of whether you are using Azure AI Foundry resource, the AI Hub resource or Azure AI Language resource, you can now create Custom Question Answering (CQA) task in your Foundry projects to manage your question-answer pairs, and enjoy all the above new capabilities too! By the end of this week, you will also be able to create Conversational Language Understanding (CLU) task in Foundry projects to manage your intents and entities. No longer need to switch back and forth between the Foundry portal and Language Studio to manage different projects. Figure 3: New CQA authoring experience in Azure AI Foundry portal Updated Accelerator Project for Conversational Agent We know getting started has never been simpler. To empower developers to make the best use of those new agent templates, an updated accelerator project for “Build your conversational agent” template will be available by the end of this week. The refreshed accelerator project will demonstrate how you can use the intent routing agent in an end-to-end solution from front-end to backend with sample data for testing. To access the project for the update at the end of this week, you can visit the Templates page in your Foundry project or check out the source code directly in the GitHub repo. Figure 4: "Build your conversational agent" template in the Foundry portal Enhanced Privacy Controls with Text PII Detection When building agents with large language models, safeguarding user data is paramount. With today’s announcement, we are introducing several new capabilities to our Text PII Detection service to meet our customers’ needs with more customizability and entity/language coverage: Support for PII redaction in scanned PDF documents. The document support in PII redaction allows you to provide a document file and get the redacted file in return. In addition to .docx, .txt and text PDF file, you can now also provide a scanned file in .pdf for redaction. For more information how to use the native document support, see Detect and redact Personally Identifying Information in native documents (preview). Support for custom synonyms of PII detection entities. Now you can use “synonyms” property in the API call to define your own synonyms for a PII entity to achieve better detection rate. Support for exclusion of specific entity values from the detection. Use “ValueExclusionPolicy” property to specify words and terms that you want to exclude from the PII detection. Extended context window span limit for rule-based entities detection. The context window span is the length of the continuous data interval (or “chunks”) within your input text that the service internally takes at once for detecting the entities. We’ve extended it for rule-based entities to 500 characters to match the span used by our model-based detectors, ensuring the consistent detection behavior across all entities. For other service limits, including the maximum characters of input text, see Data limits for Language service features. Support new entity type, Date of Birth. For all supported entities, see Supported Personally Identifiable Information (PII) entity categories. Enhanced capabilities in Text PII container: Support custom regex in Text PII container. Only available in our container offering, now you can define your own regular expressions directly within the Text PII container to catch any patterns you care about. By the end of this month, we’ll also support more new languages, Chinese, Japanese, Korean and Thai, in PII container to keep the parity on the language support between the cloud service and the container. We can’t wait to see the innovative agents you’ll build with these new capabilities. Let us know what you create, share your feedback, and stay tuned for even more enhancements coming soon! Resources: Azure AI Language Azure AI Translator Azure AI Agent Service Intent routing agent Exact question-answering agent Text translation agent MCP server with PII detection and translator Conversational Language Understanding (CLU) Custom Question Answering (CQA) “Build your conversational agent” accelerator project Personally Identifiable Information (PII) detectionFrom Foundry to Fine-Tuning: Topics you Need to Know in Azure AI Services
With so many new features from Azure and newer ways of development, especially in generative AI, you must be wondering what all the different things you need to know are and where to start in Azure AI. Whether you're a developer or IT professional, this guide will help you understand the key features, use cases, and documentation links for each service. Let's explore how Azure AI can transform your projects and drive innovation in your organization. Stay tuned for more details! Term Description Use Case Azure Resource Azure AI Foundry A comprehensive platform for building, deploying, and managing AI-driven applications. Customizing, hosting, running, and managing AI applications. Azure AI Foundry AI Agent Within Azure AI Foundry, an AI Agent acts as a "smart" microservice that can be used to answer questions (RAG), perform actions, or completely automate workflows. can be used in a variety of applications to automate tasks, improve efficiency, and enhance user experiences. Link AutoGen An open-source framework designed for building and managing AI agents, supporting workflows with multiple agents. Developing complex AI applications with multiple agents. Autogen Multi-Agent AI Systems where multiple AI agents collaborate to solve complex tasks. Managing energy in smart grids, coordinating drones. Link Model as a Platform A business model leveraging digital infrastructure to facilitate interactions between user groups. Social media channels, online marketplaces, crowdsourcing websites. Link Azure OpenAI Service Provides access to OpenAI’s powerful language models integrated into the Azure platform. Text generation, summarization, translation, conversational AI. Azure OpenAI Service Azure AI Services A suite of APIs and services designed to add AI capabilities like image analysis, speech-to-text, and language understanding to applications. Image analysis, speech-to-text, language understanding. Link Azure Machine Learning (Azure ML) A cloud-based service for building, training, and deploying machine learning models. Creating models to predict sales, detect fraud. Azure Machine Learning Azure AI Search An AI-powered search service that enhances information to facilitate exploration. Enterprise search, e-commerce search, knowledge mining. Azure AI Search Azure Bot Service A platform for developing intelligent, enterprise-grade bots. Creating chatbots for customer service, virtual assistants. Azure Bot Service Deep Learning A subset of ML using neural networks with many layers to analyze complex data. Image and speech recognition, natural language processing. Link Multimodal AI AI that integrates and processes multiple types of data, such as text and images(including input & output). Describing images, answering questions about pictures. Azure OpenAI Service, Azure AI Services Unimodal AI AI that processes a single type of data, such as text or images (including input & output). Writing text, recognizing objects in photos. Azure OpenAI Service, Azure AI Services Fine-Tuning Models Adapting pre-trained models to specific tasks or datasets for improved performance. Customizing models for specific industries like healthcare. Azure Foundry Model Catalog A repository of pre-trained models available for use in AI projects. Discovering, evaluating, fine-tuning, and deploying models. Model Catalog Capacity & Quotas Limits and quotas for using Azure AI services, ensuring optimal resource allocation. Managing resource usage and scaling AI applications. Link Tokens Units of text processed by language models, affecting cost and performance. Managing and optimizing text processing tasks. Link TPM (Tokens per Minute) A measure of the rate at which tokens are processed, impacting throughput and performance. Allocating and managing processing capacity for AI models. Link PTU(provisioned throughput) provisioned throughput capability allows you to specify the amount of throughput you require in a deployment. Ensuring predictable performance for AI applications. Link
