Cohere
Enterprise AI for embeddings, search and retrieval. Build RAG systems with best-in-class embeddings, reranking and generation that keep answers grounded in your data.
Cohere model landscape
Cohere offers purpose-built models for embeddings, reranking and generation that work together to power enterprise search and RAG systems.
Embed v3
State-of-the-art text embeddings for semantic search. Multilingual support across 100+ languages with compression-aware embeddings for efficient storage.
Rerank 3
Reorder search results by relevance. Improves retrieval accuracy by 20-40%. Critical for production RAG systems where precision matters.
Command R+
Most capable generation model. Multilingual across 10+ languages with 128k token context. Grounded generation with citations to reduce hallucination.
Why choose Cohere
Cohere specialises in embeddings, search and retrieval with enterprise-grade data privacy and deployment flexibility.
Embeddings and search expertise
Best-in-class embeddings with Embed v3. Rerank dramatically improves search accuracy. Purpose-built for semantic search and RAG with multilingual support across 100+ languages.
Enterprise-first approach
Private deployment options with data sovereignty guarantees. No training on customer data. SOC 2, ISO 27001 and GDPR compliant.
RAG-optimised stack
Embed for semantic search, Command for generation, Rerank for relevance. An integrated workflow for retrieval-augmented generation with grounded, cited outputs.
Cost-effective for search
Embeddings cheaper than competitors. Rerank more cost-effective than re-embedding. Efficient for high-volume search use cases.
Grounded generation
Generate answers with citations to source documents. Reduces hallucination and provides transparency so users see which documents informed the answer.
Multilingual capabilities
Embed v3 supports 100+ languages for embeddings. Command R+ handles 10+ languages for generation. Strong for global deployments.
Use cases for Cohere
Enterprise knowledge search
Semantic search across internal documents, wikis, SharePoint and Confluence. Embed for retrieval, Rerank for precision, Command for conversational answers with citations. 30-50% better search accuracy vs keyword search.
Customer support with RAG
Answer customer questions using help docs, manuals and past tickets. Retrieve relevant content, reorder by relevance, generate answers with sources. Grounded responses with citations reduce hallucination.
Legal document discovery
Search case law, contracts and legal documents by semantic meaning. Multilingual support for cross-border legal work. 60-80% reduction in manual document review.
Multilingual content search
Search content in 100+ languages with a single embedding space. Query in English, find documents in any language. Useful for global organisations.
Research and academic search
Semantic search across research papers, journals and academic databases. Embed papers, Rerank by relevance to query, summarise findings. 20-30% better retrieval than keyword search.
RAG pipeline deployment
End-to-end retrieval-augmented generation: index documents with Embed, retrieve candidates, rerank for precision, generate grounded answers with Command. Higher accuracy than embeddings alone.
Frequently Asked Questions
What makes Cohere different from OpenAI or Anthropic?
Cohere specialises in embeddings and retrieval. Embed v3 outperforms competitors on search benchmarks. Rerank is unique. Command models are capable but not leading edge like GPT-4 or Claude. Choose Cohere if search and RAG are primary, OpenAI or Claude if generation is primary.
Do we need Rerank if we have good embeddings?
Yes, for production systems. Embeddings retrieve candidates but are not perfect at ranking. Rerank improves accuracy by 20-40% in our testing. Essential if precision matters.
Can Cohere work with our existing vector database?
Yes. Cohere Embed works with all major vector databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector and Milvus. Generate embeddings with Cohere and store wherever you prefer.
What about data privacy and training?
Cohere guarantees no training on customer data. Private deployments are available on AWS, Azure, GCP and on-premise. Data stays in your environment. SOC 2, ISO 27001 and GDPR compliant.
How long to deploy a Cohere RAG system?
Simple search with Embed only takes three to four weeks. Full RAG with Embed, Rerank and Command takes six to eight weeks. Complex enterprise search with multiple sources and custom UI takes ten to fourteen weeks.
Build with Cohere
Whether you need enterprise search, embeddings or full RAG systems, we can help you build with Cohere.