Cohere Launches Embed 4 for Advanced Multimodal Enterprise Search
Enterprise AI startup Cohere has released Embed 4, a powerful new embedding model designed to help organisations search and make sense of large volumes of unstructured data, including documents combining text with visuals. Tailored for enterprise use, Embed 4 supports applications like AI assistants and agents that need a deeper understanding of a company’s internal knowledge. It can process documents up to 1,28,000 tokens in length, roughly 200 pages, making it ideal for handling long-form content such as annual reports, legal agreements or technical documentation. The model supports more than 100 languages, including widely used business languages like Arabic, French, Japanese and Korean. This makes it easier for global teams to find and access the information they need, regardless of language. Security and deployment flexibility are central to Embed 4’s design. It can be run within virtual private clouds or on premises, helping organisations regulate sectors such as finance, healthcare, or manufacturing meet strict data privacy and compliance requirements. One of Embed 4’s standout strengths is its ability to deal with real-world document imperfections. Whether formatting issues, typos or scanned images are involved, the model is built to handle messy data often found in business documents such as invoices, charts or handwritten notes. It also excels at representing multimodal content—documents that mix text with images, tables, graphs and even code—in a single embedding. This improves the accuracy of enterprise search tools and cuts down on the need for heavy pre-processing when analysing complex files. Embed 4 is a key component in retrieval-augmented generation (RAG) pipelines, where generative models like Cohere’s Command R rely on high-quality data retrieval before producing responses. As the engine behind this retrieval, Embed 4 helps ensure that generated content is more reliable, grounded in the right context and less prone to hallucination. Embed 4, along with Cohere’s Command A language model, is now available via the Azure AI Foundry model catalogue.
Security and deployment flexibility are central to Embed 4’s design.
Security and deployment flexibility are central to Embed 4’s design.
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["AI assistants", "startup", "cloud_platforms:Azure", "AI", "Modal", "R", "programming_languages:R", "RAG", "GAN", "Azure", "Cohere"]
["AI", "RAG", "AI assistants", "Azure", "R", "GAN", "startup", "Modal", "cloud_platforms:Azure", "programming_languages:R"]
I hold a degree in political science, and am interested in how AI and online culture intersect. I am at aditi.suresh@analyticsindiamag.com &amp; x.com/aditisuresh12