In the ever-expanding landscape of unstructured enterprise data—PDFs, presentations, diagrams, multilingual reports—retrieval is everything. And with the launch of Cohere’s Embed 4, enterprise AI teams now have a sharper semantic lens to find what matters.

Embed 4 isn’t just an upgrade. It’s a new class of embedding model: multimodal, multilingual, and ready for production-scale deployment across the full diversity of enterprise content. Designed to support retrieval-augmented generation (RAG), intelligent search, classification, clustering, and more—it brings long-context reasoning, visual search, and scalable dimensionality to the vector backbone of enterprise AI.

Why does Embed 4 matter for enterprise AI?

Multimodal indexing, faster vector search, 128k-token inputs, and cross-lingual performance. These aren’t buzzwords—they’re real breakthroughs powering use cases like legal document mining, global customer support, and technician assistants that "see" both text and diagrams.

With native support for int8 quantization, Matryoshka representations, and binary embeddings, Embed 4 makes it possible to compress and scale massive vector databases without losing fidelity. And early benchmarks show it surpasses previous models (including OpenAI’s Ada-002) on search relevance in complex domains like finance and healthcare.

This is the infrastructure layer that AI agents will rely on to reason accurately.

What makes Embed 4 different—and better—for enterprise AI?

Multimodal vectors: Text and images can now be embedded into a single unified vector, enabling true multimodal search. That means users can search by screenshot and find relevant documents—or embed an entire report, tables and all, into one retrievable unit.
Longer context, less chunking: With a 128,000-token limit, Embed 4 allows you to embed entire documents—manuals, filings, clinical trial reports—without breaking them apart. The result? Smarter retrieval, fewer hallucinations in RAG workflows, and more context preserved.
Matryoshka embeddings: These flexible vectors support truncation to 256, 512, or 1024 dimensions with minimal accuracy loss. That’s essential for balancing speed, cost, and accuracy in high-throughput environments.
100+ languages, one vector space: Cross-lingual support means a question in Arabic can retrieve a policy written in English, or vice versa. This makes it possible to index a global knowledge base once and unlock it for multilingual users everywhere.
Deployment-ready efficiency: Embed 4 supports enterprise-scale deployment via Azure AI Foundry and AWS SageMaker, and is optimized for real-time, high-volume scenarios. For secure environments, it runs in private cloud or on-prem without sending data to a third party.

Real-world use cases: Where Embed 4 unlocks enterprise value

Retrieval-augmented generation (RAG) assistants
Combine Embed 4 with a long-context LLM like Cohere’s Command A to create enterprise agents that answer complex questions using indexed company knowledge. For example, a financial analyst can retrieve key insights from hundreds of pages of earnings transcripts—grounded in facts, not guesswork.
Cross-lingual customer support
Multinational organizations can serve global users more effectively with cross-language search. Embed 4 powers assistants that understand queries in one language and retrieve content in another, dramatically improving relevance and response time.
Semantic indexing of visual knowledge
From engineering diagrams to maintenance photos, Embed 4 allows teams to embed visual content into vector search pipelines. For manufacturing, healthcare, and legal teams, this capability transforms the way knowledge is accessed and reused.
High-volume classification & clustering
Need to organize millions of documents, contracts, or support tickets? Embed 4’s high-dimensional, domain-aware vectors allow for efficient semantic clustering—grouping by meaning, not just keywords.

The bigger picture: Embeddings as enterprise infrastructure

The launch of Embed 4 reflects a broader trend: enterprise AI is moving beyond generative models alone. Embeddings are now core infrastructure, enabling agentic systems that retrieve, reason, and act with context.

At Turing, we see this shift in every AGI transformation we help drive. Whether optimizing cross-functional RAG pipelines in BFSI or powering intelligent knowledge agents for tech enterprises, embeddings like Embed 4 form the connective tissue.

Whether you're building autonomous research agents, multimodal assistants, or scalable RAG pipelines, embeddings are just the beginning. The future of AI isn’t generative or search—it’s agentic.

Talk to an expert about how to architect your next AI capability—powered by frontier embeddings, post-training expertise, and adaptive workflows.