Engineering Program Manager Intern

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Enterprise retrieval augmented generation (RAG) remains integral to the current agentic AI craze. Taking advantage of the continued interest in agents, Cohere released the latest version of its embeddings model with longer context windows and more multimodality. Cohere’s Embed 4 builds on the multimodal updates of Embed 3 and adds more capabilities around unstructured data. Thanks to a 128,000 token context window, organizations can generate embeddings for documents with around 200 pages. “Existing embedding models fail to natively understand complex multimodal business materials,‬‭ leading companies to develop cumbersome data pre-processing pipelines that only slightly‬‭ improve accuracy,” Cohere said in a blog post. “Embed 4 solves this problem, allowing enterprises and their employees to‬‭ efficiently surface insights that are hidden within mountains of unsearchable information.‬”Enterprises can deploy Embed 4 on virtual private clouds or on-premise technology stacks for added data security. Companies can generate embeddings to transform their documents or other data into numerical representations for RAG use cases

Ottawa is investing $240 million in Cohere's $725 million project to acquire AI compute resources at a new Canadian data center set to open this year.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Canadian AI startup Cohere — cofounded by one of the authors of the original transformer paper that kickstarted the large language model (LLM) revolution back in 2017 — today unveiled Command A, its latest generative AI model designed for enterprise applications.As the successor to Command-R, which debuted in March 2024, and Command R+ following it, Command A builds on Cohere’s focus on retrieval-augmented generation (RAG), external tool use and enterprise AI efficiency — especially with regards to compute and the speed at which it serves up answers.That’s going to make it an attractive option for enterprises looking to gain an AI advantage without breaking the bank, and for applications where prompt responses are needed — such as finance, health, medicine, science and law.With faster speeds, lower hardware requirements and expanded multilingual capabilities, Command A positions itself as a strong alternative to models such as GPT-4o and DeepSeek-V3 — classic LLMs, not the new reasoning models that have taken the AI industry by storm lately.Unlike its predecessor, which supported a context length of 128,000 tokens (referencing the amount of information the LLM can handle in one input/output exchange, about equivalent to a 300-page novel), Command A doubles the context length to 256,000 tokens (equivalent to 600 pages of text) while improving overall efficiency and enterprise readiness.It also comes on the heels Cohere for AI — the non-profit subsidiary of the company — releasing an open-source (for research only) multilingual vision model called Aya Vision earlier this month.A step up from Command-RWhen Command-R launched in early 2024, it introduced key innovations like optimized RAG performance, better knowledge retrieval and lower-cost AI deployments.It gained traction with enterprises, integrating into business solutions from companies like Oracle, Notion, Scale AI, Accenture and McKinsey, though a November 2024 report from Menlo Ventures surveying enterprise adoption put Cohere’s market share among enterprises at a slim 3%, far below OpenAI (34%), Anthropic (24%), and even small startups like Mistral (5%).Now, in a bid to become a bigger enterprise draw, Command A pushes these capabilities even further. According to Cohere, it:. Matches or outperforms OpenAI’s GPT-4o and DeepSeek-V3 in business, STEM and coding tasksOperates on just two GPUs (A100 or H100), a major efficiency improvement compared to models that require up to 32 GPUsAchieves faster token generation, producing 156 tokens per second — 1.75x faster than GPT-4o and 2.4x faster than DeepSeek-V3Reduces latency, with a 6,500ms time-to-first-token, compared to 7,460ms for GPT-4o and 14,740ms for DeepSeek-V3Strengthens multilingual AI capabilities, with improved Arabic dialect matching and expanded support for 23 global languages