As AI consultants based in Rosenheim, Germany, we recommend Cohere Command for enterprises with a strong focus on RAG and Enterprise Search. The native RAG capabilities and outstanding embedding model make Cohere the first choice for knowledge-based applications.
Enterprise Focus
Cohere is a Canadian AI company with a strong enterprise focus. Unlike OpenAI or Anthropic, Cohere does not focus on consumer products but exclusively on B2B applications.
Why Cohere for Enterprises?
- RAG-Native: Optimized from the ground up for Retrieval-Augmented Generation
- Embeddings: Leading embedding model (Embed v3)
- Reranking: Integrated rerank model for better search results
- Enterprise-Ready: SOC 2, HIPAA, GDPR-compliant (check contracts)
Key Strengths
RAG & Enterprise Search
Cohere is the specialist for knowledge-based AI:
- Grounded Generation: Answers based on your documents
- Citations: Automatic source references
- Embeddings: State-of-the-art vectorization
- Reranking: Precise relevance sorting
Aya - Multilingual Open-Source Model
With Aya Expanse, Cohere offers a strong multilingual model:
- 23 languages supported
- Open weights (CC-BY-NC)
- Self-hosting possible
- Particularly good for non-English languages
Tool Use & Agents
Command A is specifically optimized for agentic workflows:
- Multi-step tool use
- Function calling
- Structured outputs
- Autonomous task execution
Comparison to Other Models
| Feature | Cohere | OpenAI | Anthropic |
|---|---|---|---|
| RAG-Native | Yes | No | No |
| Embeddings | Leading | Good | None |
| Reranking | Yes | No | No |
Integration with CompanyGPT
Cohere Command is integrated in CompanyGPT and is particularly suited for enterprises that want to make internal knowledge searchable.
Our Recommendation
Cohere Command R+ is our top recommendation for RAG applications and Enterprise Search. If you want to make your enterprise documents intelligently searchable, Cohere is the best choice.
For general chat applications without RAG focus, we alternatively recommend OpenAI GPT or Anthropic Claude.