Cohere Releases Embed 4: Multimodal Embedding Engine for Enterprise AI

Created on April 16|Last edited on April 16
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Cohere has announced the release of Embed 4, its latest multimodal embedding model designed for enterprise-grade search and retrieval. This model pushes the boundaries of what’s possible in AI-powered document understanding by offering seamless handling of complex documents that include text, images, code, tables, and diagrams. Embed 4 supports documents up to 128,000 tokens in length — which is roughly the equivalent of 200 pages — and works across more than 100 languages, including Arabic, Korean, Japanese, and French.
Handling Complex Business DataOne of the most critical improvements in Embed 4 is its native ability to process multifaceted, noisy business documents. These might include poorly scanned PDFs, presentation decks, or data-heavy documents like legal filings and medical records. Unlike previous embedding models that struggled with such inputs, Embed 4 was trained to understand and search them directly, eliminating the need for elaborate preprocessing or format conversions.
Applications Across Regulated IndustriesEmbed 4 is particularly suited for industries with sensitive or structured information, such as finance, healthcare, and manufacturing. The model has been optimized to recognize and extract relevant data from highly specialized documents, including financial reports, clinical trial summaries, and equipment repair manuals. This means companies in regulated sectors can implement powerful AI search tools without compromising data integrity or security, thanks to deployment options in VPC or on-premise environments.
Multilingual and Cross-lingual SupportWith support for over 100 languages and the ability to search across languages, Embed 4 removes language barriers that typically hinder multinational organizations. Whether an employee is querying in English but the relevant documents are in French or Arabic, Embed 4 can locate and surface accurate results. This cross-lingual functionality is measured using standard benchmarks like NDCG@10 and shows consistent high performance.
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Real-World Use Cases and Industry FeedbackEarly users like Hunt Club and Agora have reported substantial performance improvements with Embed 4. Hunt Club cited a 47 percent improvement in search accuracy over Embed 3 for complex talent search applications. Agora, an e-commerce platform, praised the model’s ability to handle product listings that combine rich imagery and nuanced descriptions, improving both search speed and quality.
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Support for Enterprise AI AgentsEmbed 4 is designed to work as the backbone for Retrieval-Augmented Generation (RAG) systems. These systems use search engines to retrieve contextually relevant information before an AI assistant responds, improving accuracy and reducing hallucinations. Paired with Cohere’s Command A model, Embed 4 allows businesses to build intelligent, context-aware agents. Additionally, the model supports compressed embeddings, enabling companies to reduce storage costs by up to 83 percent without sacrificing performance.
Platform Integration and AvailabilityEmbed 4 is now available on Cohere’s own platform as well as Microsoft Azure AI Foundry and Amazon SageMaker. It also supports private deployments within virtual private clouds or on-premise environments. The model integrates tightly with Cohere’s enterprise suite, including North and Compass, giving companies a secure and scalable way to leverage AI across their internal workflows.
ConclusionEmbed 4 marks a significant step forward in enterprise AI infrastructure. With advanced multimodal understanding, robust multilingual support, and native handling of messy business data, the model is built to meet the needs of modern organizations. Whether for internal assistants, advanced knowledge management systems, or domain-specific agents, Embed 4 provides a strong foundation for future AI applications.
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