Recently, Cohere launched two new models—Command A and Embed 4—on Microsoft Azure AI Foundry, significantly enhancing enterprise-grade RAG (Retrieval Augmented Generation) and agent AI workflows. These production-ready and developer-friendly models are widely applicable to intelligent document Q&A, enterprise Copilots, and scalable search applications.
Command A: A High-Efficiency Engine for Agent AI
Command A is a large language model (LLM) designed by Cohere specifically for agent AI workflows. It seamlessly integrates into complex enterprise applications. Supported by Azure AI Foundry, it offers superior semantic reasoning and task execution capabilities, particularly suitable for scenarios requiring multi-step logic processing and real-time decision-making. For example, businesses can use Command A to build intelligent document Q&A systems or develop Copilot assistants that interact with business systems, thereby improving operational efficiency.
Thanks to Azure's managed services, Command A supports rapid deployment and scaling, freeing developers from the burden of managing underlying infrastructure. Furthermore, Command A's deep integration with Azure AI Foundry's toolchain allows developers to build production-level AI workflows with minimal code. This "out-of-the-box" capability makes it an ideal choice for businesses seeking rapid AI innovation.
Embed 4: A Multimodal Embedding Model Empowering RAG
Embed 4 is Cohere's high-performance embedding model, optimized for RAG and semantic search, with the following core features:
Multilingual Support: Supports text embeddings in over 100 languages, ensuring global businesses can build multilingual search and Q&A systems.
Multimodal Capabilities: Embed 4 includes an image encoder that generates image embeddings. Developers can leverage Azure AI Foundry's ImageEmbeddingsClient to establish semantic links between images and text. For instance, businesses can search for relevant text documents based on image content, significantly expanding RAG's application scenarios.
Matryoshka Embeddings: Using scalable Matryoshka Representation Learning technology, Embed 4 allows for embedding vector truncation to smaller sizes while maintaining high accuracy, thus reducing storage requirements and computational costs.
Efficient Quantization: Supports int8 quantization and binary embedding output, further improving search speed and reducing storage usage, making it suitable for large-scale enterprise deployments.
These features make Embed 4 the preferred tool for building fast, scalable, and multilingual RAG pipelines, widely applicable to enterprise workloads across industries such as finance, healthcare, government, and manufacturing.
Azure AI Foundry: A One-Stop AI Empowerment Platform
The launch of Cohere's new models relies on the robust ecosystem support of Azure AI Foundry. As Microsoft's comprehensive AI development platform, Azure AI Foundry not only provides a model catalog of over 1800 models from providers including Cohere, OpenAI, and Meta, but also simplifies the entire process from experimentation to production deployment through its secure, compliant, and scalable cloud services. Developers can quickly deploy Command A and Embed 4 using Azure AI Foundry's SDK and model catalog, and seamlessly integrate them using the platform's toolchain.
Additionally, Azure AI Foundry ensures model output quality and security through built-in AI content safety filters and automated evaluation tools. Enterprise users can integrate Cohere's advanced AI capabilities into their actual businesses in the shortest time possible, meeting service level agreements (SLAs) and compliance requirements.
