Now in Foundry: Cohere Transcribe, Nanbeige 4.1-3B, and Octen Embedding
This week's Model Mondays edition spans three distinct layers of the AI application stack: Cohere's cohere-transcribe, a 2B Automatic Speech Recognition (ASR) model that ranks first on the Open ASR Leaderboard across 14 languages; Nanbeige's Nanbeige4.1-3B, a compact 3B reasoning model that outperforms models ten times its size on coding, math, and deep-search benchmarks; and Octen's Octen-Embedding-0.6B, a lightweight text embedding model that achieves strong retrieval scores across 100+ languages and industry-specific domains. Together, these three models illustrate how developers can build full AI pipelines—from audio ingestion to language reasoning to semantic retrieval—entirely with open-source models deployed through Microsoft Foundry. Each operates in a different modality and fills a distinct architectural role, making this week's selection especially well-suited for teams assembling production-grade systems across speech, text, and search. Models of the week Cohere's cohere-transcribe-03-2026 Model Specs Parameters / size: 2B Primary task: Automatic Speech Recognition (audio-to-text) Why it's interesting Top-ranked on the Open ASR Leaderboard: cohere-transcribe-03-2026 achieves a 5.42% average Word Error Rate (WER) across 8 English benchmark datasets as of March 26, 2026—placing it first among open models. It reaches 1.25% WER on LibriSpeech Clean and 8.15% on AMI (meeting transcription), demonstrating consistent accuracy across both clean speech and real-world, multi-speaker environments. Benchmarks: Open ASR Leaderboard. 14 languages with a dedicated encoder-decoder architecture: The model uses a large Conformer encoder for acoustic representation extraction paired with a lightweight Transformer decoder for token generation, trained from scratch on 14 languages covering European, East Asian (Chinese Mandarin, Japanese, Korean, Vietnamese), and Arabic. Unlike general-purpose models adapted for ASR, this dedicated architecture makes it efficient without sacrificing accuracy. Long-form audio with automatic chunking: Audio longer than 35 seconds is automatically split into overlapping chunks and reassembled into a coherent transcript—no manual preprocessing required. Batched inference, punctuation control, and per-language configuration are all supported through the standard API. Try it Click on the window above, upload an audio file, and watch how quickly the model transcribes it for you. Or click the link to experiment with the Cohere Transcribe Space and record audio directly from your device. Use Case Prompt Pattern Meeting transcription Submit recorded audio with language tag; retrieve timestamped transcript per speaker turn Call center quality review Batch-process customer call recordings, extract transcript, pass to classification model Medical documentation Transcribe clinical encounters; feed transcript into summarization or structured note pipeline Multilingual content indexing Process podcasts or video audio in any of 14 supported languages; store as searchable text Sample prompt for a legal services deployment: You are building a contract negotiation assistant. A client submits a recorded audio of a 45-minute supplier negotiation call. Using the cohere-transcribe-03-2026 endpoint deployed in Microsoft Foundry, transcribe the call with punctuation enabled for the English audio. Once the transcript is available, pass it to a downstream language model with the following instruction: "Identify all pricing commitments, delivery deadlines, and liability clauses mentioned in this negotiation transcript. For each, note the speaker's position (client or supplier) and flag any terms that appear ambiguous or require legal review." Nanbeige's Nanbeige4.1-3B Model Specs Parameters / size: 3B Context length: 131,072 tokens Primary task: Text generation (reasoning, coding, tool use, deep search) Why it's interesting Reasoning performance that exceeds its size class: Nanbeige4.1-3B scores 76.9 on LiveCodeBench-V6, these results suggest that targeted post-training using Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) on a focused dataset can yield improvements that scale-based approaches cannot replicate at equivalent parameter counts. Read the technical report: https://huggingface.co/papers/2602.13367. Strong preference alignment at the 3B scale: On Arena-Hard-v2, Nanbeige4.1-3B scores 73.2, compared to 56.0 for Qwen3-32B and 60.2 for Qwen3-30B-A3B—both significantly larger models. This indicates that the model's outputs consistently match human preference for response quality and helpfulness, not just accuracy on structured tasks. Deep-search capability previously absent from small general models: On xBench-DeepSearch-2505, Nanbeige4.1-3B scores 75—matching search-specialized small agents. The model can sustain complex agentic tasks involving more than 500 sequential tool invocations, a capability gap that previously required either specialized search agents or significantly larger models. Native tool-use support: The model's chat template and generation pipeline natively support tool call formatting, making it straightforward to connect to external APIs and build multi-step agentic workflows without additional scaffolding. Try it Use Case Prompt Pattern Code review and fix Provide failing test + stack trace; ask model to diagnose root cause and write corrected implementation Competition-style math Submit problem as structured prompt; use temperature 0.6, top-p 0.95 for consistent reasoning steps Agentic task execution Provide tool definitions as JSON + goal; let model plan and execute tool calls sequentially Long-document Q&A Pass full document (up to 131K tokens) with targeted factual questions; extract structured answers Sample prompt for a software engineering deployment: You are automating pull request review for a backend engineering team. Using the Nanbeige4.1-3B endpoint deployed in Microsoft Foundry, provide the model with a unified diff of a proposed code change and the following system instruction: "You are a senior software engineer reviewing a pull request. For each modified function: (1) summarize what the change does, (2) identify any edge cases that are not handled, (3) flag any security or performance regressions relative to the original, and (4) suggest a specific improvement if one is warranted. Format your output as a structured list per function." Octen's Octen-Embedding-0.6B Model Specs Parameters / size: 0.6B Context length: 32,768 tokens Primary task: Text embeddings (semantic search, retrieval, similarity) Why it's interesting Retrieval performance above larger proprietary models at 0.6B: On the RTEB (Retrieval Text Embedding Benchmark) public leaderboard, Octen-Embedding-0.6B achieves a mean task score of 0.7241—above voyage-3.5 (0.7139), Cohere-embed-v4.0 (0.6534), and text-embedding-3-large (0.6110), despite being a fraction of their parameter count. The model is fine-tuned from Qwen3-Embedding-0.6B via Low-Rank Adaptation (LoRA), demonstrating that targeted fine-tuning on retrieval-specific data can close the gap with larger embedding models. Vertical domain coverage across legal, finance, healthcare, and code: Octen-Embedding-0.6B was trained with explicit coverage of domain-specific retrieval scenarios—legal document matching, financial report Q&A, clinical dialogue retrieval, and code search including SQL. This makes it suitable for regulated-industry applications where generic embedding models tend to underperform on specialized terminology. 32,768-token context for long-document retrieval: The extended context window supports encoding entire legal contracts, earnings reports, or clinical case notes as single embeddings—removing the need to chunk long documents and re-aggregate scores at query time, which can introduce ranking errors. 100+ language support with cross-lingual retrieval: The model handles multilingual and cross-lingual retrieval natively, with strong coverage across languages including English, Chinese, and other major languages via its Qwen3-based architecture—practical for global enterprise applications that span multiple languages. Use Case Prompt Pattern Semantic search Encode user query and document corpus; rank documents by cosine similarity to query embedding Legal precedent retrieval Embed case briefs and query with legal question; retrieve most semantically relevant precedents Cross-lingual document search Encode multilingual document set; submit query in any supported language for cross-lingual retrieval Financial Q&A pipeline Embed earnings reports or filings; retrieve relevant passages to ground downstream language model responses Sample prompt for a global enterprise knowledge base deployment: You are building a clinical decision support tool. Using the Octen-Embedding-0.6B endpoint deployed in Microsoft Foundry, embed a corpus of 10,000 clinical case notes at ingestion time and store the resulting 1024-dimensional vectors in a vector database. At query time, encode an incoming patient presentation summary and retrieve the 5 most semantically similar historical cases. Pass the retrieved cases and the current presentation to a language model with the following instruction: "Based on these five similar cases and their documented outcomes, summarize the most common treatment approaches and flag any cases where the outcome differed significantly from the initial prognosis." Getting started You can deploy open-source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation: Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryNow in Foundry: Qwen3-Coder-Next, Qwen3-ASR-1.7B, Z-Image
This week's spotlight features three models from that demonstrate enterprise-grade AI across the full scope of modalities. From low latency coding agents to state-of-the-art multilingual speech recognition and foundation-quality image generation, these models showcase the breadth of innovation happening in open-source AI. Each model balances performance with practical deployment considerations, making them viable for production systems while pushing the boundaries of what's possible in their respective domains. This week's Model Mondays edition highlights Qwen3-Coder-Next, an 80B MoE model that activates only 3B parameters while delivering coding agent capabilities with 256k context; Qwen3-ASR-1.7B, which achieves state-of-the-art accuracy across 52 languages and dialects; and Z-Image from Tongyi-MAI, an undistilled text-to-image foundation model with full Classifier-Free Guidance support for professional creative workflows. Models of the week Qwen: Qwen3-Coder-Next Model Specs Parameters / size: 80B total (3B activated) Context length: 262,144 tokens Primary task: Text generation (coding agents, tool use) Why it's interesting Extreme efficiency: Activates only 3B of 80B parameters while delivering performance comparable to models with 10-20x more active parameters, making advanced coding agents viable for local deployment on consumer hardware Built for agentic workflows: Excels at long-horizon reasoning, complex tool usage, and recovering from execution failures, a critical capability for autonomous development that go beyond simple code completion Benchmarks: Competitive performance with significantly larger models on SWE-bench and coding benchmarks (Technical Report) Try it Use Case Prompt Pattern Code generation with tool use Provide task context, available tools, and execution environment details Long-context refactoring Include full codebase context within 256k window with specific refactoring goals Autonomous debugging Present error logs, stack traces, and relevant code with failure recovery instructions Multi-file code synthesis Describe architecture requirements and file structure expectations Financial services sample prompt: You are a coding agent for a fintech platform. Implement a transaction reconciliation service that processes batches of transactions, detects discrepancies between internal records and bank statements, and generates audit reports. Use the provided database connection tool, logging utility, and alert system. Handle edge cases including partial matches, timing differences, and duplicate transactions. Include unit tests with 90%+ coverage. Qwen: Qwen3-ASR-1.7B Model Specs Parameters / size: 1.7B Context length: 256 tokens (default), configurable up to 4096 Primary task: Automatic speech recognition (multilingual) Why it's interesting All-in-one multilingual capability: Single 1.7B model handles language identification plus speech recognition for 30 languages, 22 Chinese dialects, and English accents from multiple regions—eliminating the need to manage separate models per language Specialized audio versatility: Transcribes not just clean speech but singing voice, songs with background music, and extended audio files, expanding use cases beyond traditional ASR to entertainment and media workflows State-of-the-art accuracy: Outperforms GPT-4o, Gemini-2.5, and Whisper-large-v3 across multiple benchmarks. English: Tedlium 4.50 WER vs 7.69/6.15/6.84; Chinese: WenetSpeech 4.97/5.88 WER vs 15.30/14.43/9.86 (Technical Paper) Language ID included: 97.9% average accuracy across benchmark datasets for automatic language identification, eliminating the need for separate language detection pipelines Try it Use Case Prompt Pattern Multilingual transcription Send audio files via API with automatic language detection Call center analytics Process customer service recordings to extract transcripts and identify languages Content moderation Transcribe user-generated audio content across multiple languages Meeting transcription Convert multilingual meeting recordings to text for documentation Customer support sample prompt: Deploy Qwen3-ASR-1.7B to a Microsoft Foundry endpoint and transcribe multilingual customer service calls. Send audio files via API to automatically detect the language (from 52 supported options including 30 languages and 22 Chinese dialects) and generate accurate transcripts. Process calls from customers speaking English, Spanish, Mandarin, Cantonese, Arabic, French, and other languages without managing separate models per language. Use transcripts for quality assurance, compliance monitoring, and customer sentiment analysis. Tongyi-MAI: Z-Image Model Specs Parameters / size: 6B Context length: N/A (text-to-image) Primary task: Text-to-image generation Why it's interesting Undistilled foundation model: Full-capacity base without distillation preserves complete training signal with Classifier-Free Guidance support (a technique that improves prompt adherence and output quality), enabling complex prompt engineering and negative prompting that distilled models cannot achieve High output diversity: Generates distinct character identities in multi-person scenes with varied compositions, facial features, and lighting, critical for creative applications requiring visual variety rather than consistency Aesthetic versatility: Handles diverse visual styles from hyper-realistic photography to anime and stylized illustrations within a single model, supporting resolutions from 512×512 to 2048×2048 at any aspect ratio with 28-50 inference steps (Technical Paper) Try it Use Case Prompt Pattern Multilingual transcription Send audio files via API with automatic language detection Call center analytics Process customer service recordings to extract transcripts and identify languages Content moderation Transcribe user-generated audio content across multiple languages Meeting transcription Convert multilingual meeting recordings to text for documentation E-commerce sample prompt: Professional product photography of a modern ergonomic office chair in a bright Scandinavian-style home office. Natural window lighting from left, clean white desk with laptop and succulent plant, light oak hardwood floor. Chair positioned at 45-degree angle showing design details. Photorealistic, commercial photography, sharp focus, 85mm lens, f/2.8, soft shadows. Getting started You can deploy open‑source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft FoundryWhat is trending in Hugging Face on Microsoft Foundry? Feb, 2, 2026
Open‑source AI is moving fast, with important breakthroughs in reasoning, agentic systems, multimodality, and efficiency emerging every day. Hugging Face has been a leading platform where researchers, startups, and developers share and discover new models. Microsoft Foundry brings these trending Hugging Face models into a production‑ready experience, where developers can explore, evaluate, and deploy them within their Azure environment. Our weekly Model Monday’s series highlights Hugging Face models available in Foundry, focusing on what matters most to developers: why a model is interesting, where it fits, and how to put it to work quickly. This week’s Model Mondays edition highlights three Hugging Face models, including a powerful Mixture-of-Experts model from Z. AI designed for lightweight deployment, Meta’s unified foundation model for image and video segmentation, and MiniMax’s latest open-source agentic model optimized for complex workflows. Models of the week Z.AI’s GLM-4.7-flash Model Basics Model name: zai-org/GLM-4.7-Flash Parameters / size: 30B total -3B active Default settings: 131,072 max new tokens Primary task: Agentic, Reasoning and Coding Why this model matters Why it’s interesting: It utilizes a Mixture-of-Experts (MoE) architecture (30B total parameters and 3B active parameters) to offer a new option for lightweight deployment. It demonstrates strong performance on logic and reasoning benchmarks, outperforming similar sized models like gpt-oss-20b on AIME 25 and GPQA benchmarks. It supports advanced inference features like "Preserved Thinking" mode for multi-turn agentic tasks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: From the Foundry catalog, users can deploy on a A100 instance or unsloth/GLM-4.7-Flash-GGUF on a CPU. ource SOTA scores among models of comparable size. Additionally, compared to similarly sized models, GLM-4.7-Flash demonstrates superior frontend and backend development capabilities. Click to see more: https://docs.z.ai Try it Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑context agent workflows (local or low‑cost autonomous agents) Call out long‑horizon consistency and context preservation. Instruct the model to retain earlier assumptions and decisions across turns. Now that you know GLM‑4.7‑Flash works best when you give it a clear goal and let it reason through a bounded task, here’s an example prompt that a product or engineering team might use to identify risks and propose mitigations: You are a software reliability analyst for a mid‑scale SaaS platform. Review recent incident reports, production logs, and customer issues to uncover edge‑case failures outside normal usage (e.g., rare inputs, boundary conditions, timing/concurrency issues, config drift, or unexpected feature interactions). Prioritize low‑frequency, high‑impact risks that standard testing misses. Recommend minimal, low‑cost fixes (validation, guardrails, fallback logic, or documentation). Deliver a concise executive summary with sections: Observed Edge Cases, Root Causes, User Impact, Recommended Lightweight Fixes, and Validation Steps. Meta's Segment Anything 3 (SAM3) Model Basics Model name: facebook/sam3 Parameters / size: 0.9B Primary task: Mask Generation, Promptable Concept Segmentation (PCS) Why this model matters Why it’s interesting: It handles a vastly larger set of open-vocabulary prompts than SAM 2, and unifies image and video segmentation capabilities. It includes a "SAM 3 Tracker" mode that acts as a drop-in replacement for SAM 2 workflows with improved performance. Best‑fit use cases: Open-vocabulary object detection, video object tracking, and automatic mask generation What’s notable: Introduces Promptable Concept Segmentation (PCS), allowing users to find all matching objects (e.g., "dial") via text prompt rather than just single instances. Try it This model enables users to identify specific objects within video footage and isolate them over extended periods. With just one line of code, it is possible to detect multiple similar objects simultaneously. The accompanying GIF demonstrates how SAM3 efficiently highlights players wearing white on the field as they appear and disappear from view. Additional examples are available at the following repository: https://github.com/facebookresearch/sam3/blob/main/assets/player.gif Use case Best‑practice prompt pattern Agentic coding (multi‑step repo work, debugging, refactoring) Treat SAM 3 as a concept detector, not an interactive click tool. Use short, concrete noun‑phrase concept prompts instead of describing the scene or asking questions. Example prompt: “yellow school bus” or “shipping containers”. Avoid verbs or full sentences. Video segmentation + object tracking Specify the same concept prompt once, then apply it across the video sequence. Do not restate the prompt per frame. Let the model maintain identity continuity. Example: “person wearing a red jersey”. Hard‑to‑name or visually subtle objects Use exemplar‑based prompts (image region or box) when text alone is ambiguous. Optionally combine positive and negative exemplars to refine the concept. Avoid over‑constraining with long descriptions. Using the GIF above as a leading example, here is a prompt that shows how SAM 3 turns raw sports footage into structured, reusable data. By identifying and tracking players based on visual concepts like jersey color so that sports leagues can turn tracked data into interactive experiences where automated player identification can relay stats, fun facts, etc when built into a larger application. Here is a prompt that will allow you to start identifying specific players across video: Act as a sports analytics operator analyzing football match footage. Segment and track all football players wearing blue jerseys across the video. Generate pixel‑accurate segmentation masks for each player and assign persistent instance IDs that remain stable during camera movement, zoom, and player occlusion. Exclude referees, opposing team jerseys, sidelines, and crowd. Output frame‑level masks and tracking metadata suitable for overlays, player statistics, and downstream analytics pipelines. MiniMax AI's MiniMax-M2.1 Model Basics Model name: MiniMaxAI/MiniMax-M2.1 Parameters / size: 229B-10B Active Default settings: 200,000 max new tokens Primary task: Agentic and Coding Why this model matters Why it’s interesting: It is optimized for robustness in coding, tool use, and long-horizon planning, outperforming Claude Sonnet 4.5 in multilingual scenarios. It excels in full-stack application development, capable of architecting apps "from zero to one”. Previous coding models focused on Python optimization, M2.1 brings enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript, and other languages. The model delivers exceptional stability across various coding agent frameworks. Best‑fit use cases: Lightweight local deployment, multi-turn agentic tasks, and logical reasoning applications. What’s notable: The release of open-source weights for M2.1 delivers a massive leap over M2 on software engineering leaderboards. https://www.minimax.io/ Try it Use case Best‑practice prompt pattern End‑to‑end agentic coding (multi‑file edits, run‑fix loops) Treat the model as an autonomous coding agent, not a snippet generator. Explicitly require task decomposition and step‑by‑step execution, then a single consolidated result. Long‑horizon tool‑using agents (shell, browser, Python) Explicitly request stepwise planning and sequential tool use. M2.1’s interleaved thinking and improved instruction‑constraint handling are designed for complex, multi‑step analytical tasks that require evidence tracking and coherent synthesis, not conversational back‑and‑forth. Long‑context reasoning & analysis (large documents / logs) Declare the scope and desired output structure up front. MiniMax‑M2.1 performs best when the objective and final artifact are clear, allowing it to manage long context and maintain coherence. Because MiniMax‑M2.1 is designed to act as a long‑horizon analytical agent, it shines when you give it a clear end goal and let it work through large volumes of information—here’s a prompt a risk or compliance team could use in practice: You are a financial risk analysis agent. Analyze the following transaction logs and compliance policy documents to identify potential regulatory violations and systemic risk patterns. Plan your approach before executing. Work through the data step by step, referencing evidence where relevant. Deliver a final report with the following sections: Key Risk Patterns Identified, Supporting Evidence, Potential Regulatory Impact, Recommended Mitigations. Your response should be a complete, executive-ready report, not a conversational draft. Getting started You can deploy open‑source Hugging Face models directly in Microsoft Foundry by browsing the Hugging Face collection in the Foundry model catalog and deploying to managed endpoints in just a few clicks. You can also start from the Hugging Face Hub. First, select any supported model and then choose "Deploy on Microsoft Foundry", which brings you straight into Azure with secure, scalable inference already configured. Learn how to discover models and deploy them using Microsoft Foundry documentation. Follow along the Model Mondays series and access the GitHub to stay up to date on the latest Read Hugging Face on Azure docs Learn about one-click deployments from the Hugging Face Hub on Microsoft Foundry Explore models in Microsoft Foundry
OptiMind: A small language model with optimization expertise
Turning a real world decision problem into a solver ready optimization model can take days—sometimes weeks—even for experienced teams. The hardest part is often not solving the problem; it’s translating business intent into precise mathematical objectives, constraints, and variables. OptiMind is designed to try and remove that bottleneck. This optimization‑aware language model translates natural‑language problem descriptions into solver‑ready mathematical formulations, can help organizations move from ideas to decisions faster. Now available through public preview as an experimental model through Microsoft Foundry, OptiMind targets one of the more expertise‑intensive steps in modern optimization workflows. Addressing the Optimization Bottleneck Mathematical optimization underpins many enterprise‑critical decisions—from designing supply chains and scheduling workforces to structuring financial portfolios and deploying networks. While today’s solvers can handle enormous and complex problem instances, formulating those problems remains a major obstacle. Defining objectives, constraints, and decision variables is an expertise‑driven process that often takes days or weeks, even when the underlying business problem is well understood. OptiMind tries to address this gap by automating and accelerating formulation. Developed by Microsoft Research, OptiMind transforms what was once a slow, error‑prone modeling task into a streamlined, repeatable step—freeing teams to focus on decision quality rather than syntax. What makes OptiMind different? OptiMind is not just as a language model, but as a specialized system built for real-world optimization tasks. Unlike general-purpose large language models adapted for optimization through prompting, OptiMind is purpose-built for mixed integer linear programming (MILP), and its design reflects this singular focus. At inference time, OptiMind follows a multi‑stage process: Problem classification (e.g., scheduling, routing, network design) Hint retrieval tailored to the identified problem class Solution generation in solver‑compatible formats such as GurobiPy Optional self‑correction, where multiple candidate formulations are generated and validated This design can improve reliability without relying on agentic orchestration or multiple large models. In internal evaluations on cleaned public benchmarks—including IndustryOR, Mamo‑Complex, and OptMATH—OptiMind demonstrated higher formulation accuracy than similarly sized open models and competitive performance relative to significantly larger systems. OptiMind improved accuracy by approximately 10 percent over the base model. In comparison to open-source models under 32 billion parameters, OptiMind was also found to match or exceed performance benchmarks. For more information on the model, please read the official research blog or the technical paper for OptiMind. Practical use cases: Unlocking efficiency across domains OptiMind is especially valuable where modeling effort—not solver capability—is the primary bottleneck. Typical use cases include: Supply Chain Network Design: Faster formulation of multi‑period network models and logistics flows Manufacturing and Workforce Scheduling: Easier capacity planning under complex operational constraints Logistics and Routing Optimization: Rapid modeling that captures real‑world constraints and variability Financial Portfolio Optimization: More efficient exploration of portfolios under regulatory and market constraints By reducing the time and expertise required to move from problem description to validated model, OptiMind helps teams reach actionable decisions faster and with greater confidence. Getting started OptiMind is available today as an experimental model, and Microsoft Research welcomes feedback from practitioners and enterprise teams. Next steps: Explore the research details: Read more about the model on Foundry Labs and the technical paper on arXiv Try the model: Access OptiMind through Microsoft Foundry Test sample code: Available in the OptiMind GitHub repository Take the next step in optimization innovation with OptiMind—empowering faster, more accurate, and cost-effective problem solving for the future of decision intelligence.The Future of AI: From Noise to Insight - An AI Agent for Customer Feedback
This post explores how Microsoft’s AI Futures team built a multi-agent system to transform scattered customer feedback into actionable insights. The solution aggregates feedback from multiple channels, uses advanced language models to cluster themes, summarize content, and identify sentiment, and delivers prioritized insights directly in Microsoft Teams. With human-in-the-loop safeguards, the system accelerates triage, prioritization, and follow-ups while maintaining compliance and traceability. Future enhancements include richer automation, trend visualization, and expanded feedback sources.The Future of AI: Structured Vibe Coding - An Improved Approach to AI Software Development
In this post from The Future of AI series, the author introduces structured vibe coding, a method for managing AI agents like a software team using specs, GitHub issues, and pull requests. By applying this approach with GitHub Copilot, they automated a repetitive task—answering Microsoft Excel-based questionnaires—while demonstrating how AI can enhance developer workflows without replacing human oversight. The result is a scalable, collaborative model for AI-assisted software development.The Future of AI: Harnessing AI agents for Customer Engagements
Discover how AI-powered agents are revolutionizing customer engagement—enhancing real-time support, automating workflows, and empowering human professionals with intelligent orchestration. Explore the future of AI-driven service, including Customer Assist created with Azure AI Foundry.
Announcing the Text PII August preview model release in Azure AI language
Azure AI Language is excited to announce a new preview model release for the PII (Personally Identifiable Information) redaction service, which includes support for more entities and languages, addressing customer-sourced scenarios and international use cases. What’s New | Updated Model 2025-08-01-preview Tier 1 language support for DateOfBirth entity: expanding upon the original English-only support earlier this year, we’ve added support for all Tier 1 languages: French, German, Italian, Spanish, Portuguese, Brazilian Portuguese, and Dutch New entity support: SortCode - a financial code used in the UK and Ireland to identify the specific bank and branch where an account is held. Currently we support this in only English. LicensePlateNumber - the standard alphanumeric code for vehicle identification. Note that our current scope does not support a license plate that contains only letters. Currently we support this in only English. AI quality improvements for financial entities, reducing false positives/negatives These updates respond directly to customer feedback and address gaps in entity coverage and language support. The broader language support enables global deployments and the new entity types allow for more comprehensive data extraction for our customers. This ensures an improved service quality for financial, criminal justice, and many other regulatory use cases, enabling more accurate and reliable service for our customers. Get started A more detailed tutorial and overview of the service feature can be found in our public docs. Learn more about these releases and several others enhancing our Azure AI Language offerings on our What’s new page. Explore Azure AI Language and its various capabilities Access full pricing details on the Language Pricing page Find the list of sensitive PII entities supported Try out Azure AI Foundry for a code-free experience We are looking forward to continuously improving our product offerings and features to meet customer needs and are keen to hear any comments and feedback.Project Maria: Bringing Speech and Avatars Together for Next-Generation Customer Experiences
In an age where digital transformation influences nearly every aspect of business, companies are actively seeking innovative ways to differentiate their customer interactions. Traditional text-based chatbots, while helpful, often leave users wanting a more natural, personalized, and efficient experience. Imagine hosting a virtual brand ambassador—a digital twin of yourself or your organization’s spokesperson—capable of answering customer queries in real time with a lifelike voice and expressive 2D or 3D face. This is where Project Maria comes in. Project Maria is an internal Microsoft initiative that integrates cutting-edge speech-to-text (STT), text-to-speech (TTS), large language model and avatar technologies. Using Azure AI speech and custom neural voice models, it seeks to create immersive, personalized interactions for customers—reducing friction, increasing brand loyalty, and opening new business opportunities in areas such as customer support, product briefings, digital twins, live marketing events, safety briefings, and beyond. In this blog post, we will dive into: The Problem and Rationale for evolving beyond basic text-based solutions. Speech-to-Text (STT), Text-to-Speech (TTS) Pipelines, Azure OpenAI GPT-4o Real-Time API that power natural conversations. Avatar Models in Azure, including off-the-shelf 2D avatars and fully customized custom avatar Neural Voice Model Creation, from data gathering to training and deployment on Azure. Security and Compliance considerations for handling sensitive voice assets and data. Use Cases from customer support to digital brand ambassadors and safety briefings. Real-World Debut of Project Maria, showcased at the AI Leaders’ Summit in Seattle. Future Outlook on how custom avatar will reshape business interactions, scale presence, and streamline time-consuming tasks. If you’re developing or considering a neural (custom) voice + avatar models for your product or enterprise, this post will guide you through both conceptual and technical details to help you get started—and highlight where the field is heading next. 1. The Problem: Limitations of Text-Based Chatbots 1.1 Boredom and Fatigue in Text Interactions Text-based chatbots have come a long way, especially with the advent of powerful Large Language Models (LLMs) and Small Large Models (SLMs). Despite these innovations, interactions can still become tedious—often requiring users to spend significant personal time crafting the right questions. Many of us have experienced chatbots that respond with excessively verbose or repetitive messages, leading to boredom or even frustration. In industries that demand immediacy—like healthcare, finance, or real-time consumer support—purely text-based exchanges can feel slow and cumbersome. Moreover, text chat requires a user’s full attention to read and type, whether in a busy contact center environment or an internal knowledge base where employees juggle multiple tasks. 1.2 Desire for More Engaging and Efficient Modalities Today’s users expect something closer to human conversation. Devices ranging from smartphones to smart speakers and in-car infotainment systems have normalized voice-based interfaces. Adding an avatar—whether a 2D or 3D representation—deepens engagement by combining speech with a friendly visual persona. This can elevate brand identity: an avatar that looks, talks, and gestures like your company’s brand ambassador or a well-known subject-matter expert. 1.3 The Need for Scalability In a busy customer support environment, human representatives simply can’t handle an infinite volume of conversations or offer 24/7 coverage across multiple channels. Automation is essential, yet providing high-quality automated interactions remains challenging. While a text-based chatbot might handle routine queries, a voice-based, avatar-enabled agent can manage more complex requests with greater dynamism and personality. By giving your digital support assistant both a “face” and a voice aligned with your brand, you can foster deeper emotional connections and provide a more genuine, empathetic experience. This blend of automation and personalization scales your support operations, ensuring higher customer satisfaction while freeing human agents to focus on critical or specialized tasks. 2. The Vision: Project Maria’s Approach Project Maria addresses these challenges by creating a unified pipeline that supports: Speech-to-Text (STT) for recognizing user queries quickly and accurately. Natural Language Understanding (NLU) layers (potentially leveraging Azure OpenAI or other large language models) for comprehensive query interpretation. Text-to-Speech (TTS) that returns highly natural-sounding responses, possibly in multiple languages, with customized prosody and style. Avatar Rendering, which can be a 2D animated avatar or a more advanced 3D digital twin, bringing personality and facial expressions to the conversation. By using Azure AI Services—particularly the Speech and Custom Neural Voice offerings—can deliver brand-specific voices. This ensures that each brand or individual user’s avatar can match (or approximate) a signature voice, turning a run-of-the-mill voice assistant into a truly personal digital replicas 3. Technical Foundations 3.1 Speech-to-Text (STT) At the heart of the system is Azure AI Services for Speech, which provides: Real-time transcription capabilities with a variety of languages and dialects. Noise suppression, ensuring robust performance in busy environments. Streaming APIs, critical for real-time or near-real-time interactions. When a user speaks, audio data is captured (for example, via a web microphone feed or a phone line) and streamed to the Azure service. The recognized text is returned in segments, which the NLU or conversation manager can interpret. 3.1.1 Audio Pipeline Capture: The user’s microphone audio is captured by a front-end (e.g., a web app, mobile app, or IoT device). Pre-processing: Noise reduction or volume normalization might be applied locally or in the cloud, ensuring consistent input. Azure STT Ingestion: Data is sent to the Speech service endpoint, authenticated via subscription keys or tokens (more on security later). Result Handling: The recognized text arrives in partial hypotheses (partial transcripts) and final recognized segments. Project Maria (Custom Avatar) processes these results to understand user intent 3.2 Text-to-Speech (TTS) Once an intent is identified and a response is formulated, the system needs to deliver speech output. Standard Neural Voices: Microsoft provides a wide range of prebuilt voices in multiple languages. Custom Neural Voice: For an even more personalized experience, you can train a voice model that matches a brand spokesperson or a distinct voice identity. This is done using your custom datasets, ensuring the final system speaks exactly like the recorded persona. 3.2.1 Voice Font Selection and Configuration In a typical architecture: The conversation manager (which could be an orchestrator or a custom microservice) provides the text output to the TTS service. The TTS service uses a configured voice font—like en-US-JennyNeural or a custom neural voice ID (like Maria Neural Voice) if you have a specialized voice model. The synthesized audio is returned as an audio stream (e.g., PCM or MP3). You can play this in a webpage directly or in a native app environment. Azure OpenAI GPT-4o Real-Time API integrates with Azure's Speech Services to enable seamless interactions. First, your speech is transcribed in near real time. GPT-4o then processes this text to generate context-aware responses, which are converted to natural-sounding audio via Azure TTS. This audio is synchronized with avatar models to create a lifelike, engaging interface 3.3 Real-Time Conversational Loop Maria is designed for real-time or text to speech conversations. The user’s speech is continuously streamed to Azure STT. The recognized text triggers a real-time inference step for the next best action or response. The response is generated by Azure OpenAI model (like GPT-4o) or other LLM/SLM The text is then synthesized to speech, which the user hears with minimal latency. 3.4 Avatars: 2D and Beyond 3.4.1 Prebuilt Azure 2D Avatars Azure AI Speech Services includes an Avatar capability that can be activated to display a talking head or a 2D animated character. Developers can: Choose from prebuilt characters or import basic custom animations. Synchronize lip movements to the TTS output. Overlay brand-specific backgrounds or adopt transparency for embedding in various UIs. 3.4.2 Fully Custom Avatars (Customer Support Agent Like Maria) For organizations wanting a customer support agent, subject-matter expert, or brand ambassador: Capture: Record high-fidelity audio and video of the person you want to replicate. The more data, the better the outcome (though privacy and licensing must be considered). Modeling: Use advanced 3D or specialized 2D animation software (or partner with Microsoft’s custom avatar creation solutions) to generate a rigged model that matches the real person’s facial geometry and expressions. Integration: Once the model is rigged, it can be integrated with the TTS engine. As text is converted to speech, the avatar automatically animates lip shapes and facial expressions in near real time. 3.5 Latency and Bandwidth Considerations When building an interactive system, keep an eye on: Network latency: Real-time STT and TTS require stable, fast connections. Compute resources: If hosting advanced ML or high concurrency, scaling containers (e.g., via Docker and Kubernetes) is critical. Avatars: Real-time animation might require sending frames or instructions to a client’s browser or device. 4. Building the Model: Neural Voice Model Creation 4.1 Data Gathering To train a custom neural voice, you typically need: High-quality audio clips: Ideally recorded in a professional studio to minimize background noise, with the same microphone setup throughout. Matching transcripts for each clip. Minimum data duration: Microsoft recommends a certain threshold (e.g., 300+ utterances, typically around 30 minutes to a few hours of recorded speech, depending on the complexity of the final voice needed). 4.2 Training Process Data Upload: Use the Azure Speech portal or APIs to upload your curated dataset. Model Training: Azure runs training jobs that often require a few hours (or more). This step includes: Acoustic feature extraction (spectrogram analysis). Language or phoneme modeling for the relevant language and accent. Prosody tuning, ensuring the voice can handle various styles (cheerful, empathetic, urgent, etc.). Quality Checks: After training, you receive an initial voice model. You can generate test phrases to assess clarity, intonation, and overall quality. Iteration: If the voice quality is not satisfactory, you gather more data or refine the existing data (removing noisy segments or inaccurate transcripts). 4.3 Deployment Once satisfied with the custom neural voice: Deploy the model to an Azure endpoint within your subscription. Configure your TTS engine to use the custom endpoint ID instead of a standard voice. 5. Securing Avatar and Voice Models Security is paramount when personal data, brand identity, or intellectual property is on the line. 5.1 API Keys and Endpoints Azure AI Services requires an API key or an OAuth token to access STT/TTS features. Store keys in Azure Key Vault or as secure environment variables. Avoid hard-coding them in the front-end or source control. 5.2 Access Control Role-Based Access Control (RBAC) at both Azure subscription level and container (e.g., Docker or Kubernetes) level ensures only authorized personnel can deploy or manage the containers running these services. Network Security: Use private endpoints if you want to limit exposure to the public internet. 5.3 Intellectual Property Concerns Avatar and Voice Imitation: A avatar model and custom neural voice that mimics a specific individual must be authorized by that individual. Azure has a verification process in place to ensure consent. Data Storage: The training audio data and transcripts must be securely stored, often with encryption at rest and in transit. 6. Use Cases: Bringing It All Together 6.1 Customer Support A digital avatar that greets users on a website or mobile app can handle first-level queries: “Where can I find my billing information?” “What is your return policy?” By speaking these answers aloud with a friendly face and voice, the experience is more memorable and can reduce queue times for human agents. If the question is too complex, the avatar can seamlessly hand off to a live agent. Meanwhile, transcripts of the entire conversation are stored (e.g., in Azure Cosmos DB), enabling data analytics and further improvements to the system. 6.2 Safety Briefings and Public Announcements Industries like manufacturing, aviation, or construction must repeatedly deliver consistent safety messages. A personal avatar can recite crucial safety protocols in multiple languages, ensuring nothing is lost in translation. Because the TTS voice is consistent, workers become accustomed to the avatar’s instructions. Over time, you could even create a brand or site-specific “Safety Officer” avatar that fosters familiarity. 6.3 Digital Twins at Live Events Suppose you want your company’s spokesperson to simultaneously appear at multiple events across the globe. With a digital twin: The spokesperson’s avatar and voice “present” in real time, responding to local audience questions. This can be done in multiple languages, bridging communication barriers instantaneously. Attendees get a sense of personal interaction, while the real spokesperson can focus on core tasks, or appear physically at another event entirely. 6.4 AI Training and Education In e-learning platforms, a digital tutor can guide students through lessons, answer questions in real time, and adapt the tone of voice based on the difficulty of the topic or the student’s performance. By offering a face and voice, the tutor becomes more engaging than a text-only system. 7. Debut: Maria at the AI Leaders Summit in Seattle Project Maria had its first major showcase at the AI Leaders Summit in Seattle last week. We set up a live demonstration: Live Conversations: Attendees approached a large screen that displayed Maria’s 2D avatar. On-the-Fly: Maria recognized queries with STT, generated text responses from an internal knowledge base (powered by GPT-4o or domain-specific models), then spoke them back with a custom Azure neural voice. Interactive: The avatar lip-synced to the output speech, included animated gestures for emphasis, and even displayed text-based subtitles for clarity. The response was overwhelmingly positive. Customers praised the fluid voice quality and the lifelike nature of Maria’s avatar. Many commented that they felt they were interacting with a real brand ambassador, especially because the chosen custom neural voice had just the right inflections and emotional range. 8. Technical Implementation Details Below is a high-level architecture of how Project Maria might be deployed using containers and Azure resources. Front-End Web App: Built with a modern JavaScript framework (React, Vue, Angular, etc.). Captures user audio through the browser’s WebRTC or MediaStream APIs. Connects via WebSockets or RESTful endpoints for STT requests. Renders the avatar in a <canvas> element or using a specialized avatar library. Backend: Containerized with Docker. Exposes endpoints for STT streaming (optionally passing data directly to Azure for transcription). Integrates with the TTS service, retrieving synthesized audio buffers. Returns the audio back to the front-end in a continuous stream for immediate playback. Avatar Integration: The back-end or a specialized service handles lip-sync generation (e.g., via phoneme mapping from the TTS output). The front-end renders the 2D or 3D avatar in sync with the audio playback. This can be done by streaming timing markers that indicate which phoneme is currently active. Data and Conversation Storage: Use an Azure Cosmos DB or a similar NoSQL solution to store transcripts, user IDs, timestamps, and optional metadata (e.g., conversation sentiment). This data can later be used to improve the conversation model, evaluate performance, or train advanced analytics solutions. Security: All sensitive environment variables (like Azure API keys) are loaded securely, either through Azure Key Vault or container orchestration secrets. The system enforces user authentication if needed. For instance, an internal HR system might restrict the avatar-based service to employees only. Scaling: Deploy containers in Azure Kubernetes Service (AKS), setting up auto-scaling to handle peak loads. Monitor CPU/memory usage, as well as TTS quota usage. For STT, ensure the service tier can handle simultaneous requests from multiple users. 9. Securing Avatar Models and Voice Data 9.1 Identity Management Each avatar or custom neural voice is tied to a specific subscription. Using Azure Active Directory (Azure AD), you can give fine-grained permissions so that only authorized DevOps or AI specialists can alter or redeploy the voice. 9.2 API Gateways and Firewalls For enterprise contexts, you might place an API Gateway in front of your containerized services. This central gateway can: Inspect requests for anomalies, Enforce rate-limits, Log traffic to meet compliance or auditing requirements. 9.3 Key Rotation and Secrets Management Frequently rotates keys to minimize the risk of compromised credentials. Tools like Azure Key Vault or GitHub’s secret storage features can automate the rotation process, ensuring minimal downtime. 10. The Path Forward: Scaling Custom Avatar 10.1 Extended Personalization While Project Maria currently focuses on voice and basic facial expressions, future expansions include: Emotion Synthesis: Beyond standard TTS expressions (friendly, sad, excited), we can integrate emotional AI to dynamically adjust the avatar’s tone based on user sentiment. Gesture Libraries: 2D or 3D avatars can incorporate hand gestures, posture changes, or background movements to mimic a real person in conversation. This reduces the “uncanny valley” effect. 10.2 Multilingual, Multimodal As businesses operate globally, multilingual interactions become paramount. We have seen many use cases to: Auto-detect language from a user’s speech and respond in kind. Offer real-time translation, bridging non-English speakers to brand content. 10.3 Agent Autonomy Systems like Maria won’t just respond to direct questions; they can initiate proactivity: Send voice-based notifications or warnings when critical events happen. Manage long-running tasks such as scheduling or triaging user requests, akin to an “executive assistant” for multiple users simultaneously. 10.4 Ethical and Social Considerations With near-perfect replicas of voices, there is a growing concern about identity theft, misinformation, and deepfakes. Companies implementing digital twins must: Secure explicit consent from individuals. Implement watermarking or authentication for voice data. Educate customers and employees on usage boundaries and disclaimers 11. Conclusion Project Maria represents a significant leap in how businesses and organizations can scale their presence, offering a humanized, voice-enabled digital experience. By merging speech-to-text, text-to-speech, and avatar technologies, you can: Boost Engagement: A friendly face and familiar voice can reduce user fatigue and build emotional resonance. Extend Brand Reach: Appear in many locations at once via digital twins, creating personalized interactions at scale. Streamline Operations: Automate repetitive queries while maintaining a human touch, freeing up valuable employee time. Ensure Security and Compliance: By using Azure’s robust ecosystem of services and best practices for voice data. As demonstrated at the AI Leaders Summit in Seattle, Maria is already reshaping how businesses think about communication. The synergy of avatars, neural voices, and secure, cloud-based AI is paving the way for the next frontier in customer interaction. Looking ahead, we anticipate that digital twins—like Maria—will become ubiquitous, automating not just chat responses but a wide range of tasks that once demanded human presence. From personalized marketing to advanced training scenarios, the possibilities are vast. In short, the fusion of STT, TTS, and avatar technologies is more than a novel gimmick; it is an evolution in human-computer interaction. By investing in robust pipelines, custom neural voice training, and carefully orchestrated containerized deployments, businesses can unlock extraordinary potential. Project Maria is our blueprint for how to do it right—secure, customizable, and scalable—helping organizations around the world transform user experiences in ways that are both convenient and captivating. If you’re looking to scale your brand, innovate in human-machine dialogues, or harness the power of digital twins, we encourage you to explore Azure AI Services’ STT, TTS, and Avatar solutions. Together, these advancements promise a future where your digital self (or brand persona) can meaningfully interact with users anytime, anywhere. Detailed Technical Implementation:- https://learn.microsoft.com/en-us/azure/ai-services/speech-service/text-to-speech-avatar/what-is-custom-text-to-speech-avatar Text to Speech with Multi-Agent Orchestration Framework:- https://github.com/ganachan/Project_Maria_Accelerator_tts Contoso_Maria_Greetings.mp4
