Cohere’s official site documents its current platform and deployment options.
What does Cohere provide?
Cohere provides generation models, embeddings, and reranking services. These components can work together in a retrieval system rather than forcing one model to do every job. A retrieval augmented generation pipeline can retrieve candidate passages, rerank them, then ask a generation model to answer from the strongest evidence.
Retrieval quality often matters more than adding a larger generation model.
When should a company consider Cohere?
Cohere is relevant when the application depends on private documents, multilingual search, or controlled deployment. Teams should test the complete retrieval and answer pipeline on representative business questions. Compare answer grounding, ranking quality, latency, data controls, and operating cost with other LLM providers. For sensitive knowledge systems, the provider decision also belongs in the wider enterprise RAG architecture.
How should Cohere be evaluated for enterprise search?
Build a test set from real employee questions, approved documents, and cases where the correct response is to say that evidence is missing. Measure retrieval relevance, grounded answer accuracy, citation quality, and access-control enforcement as separate outcomes. Multilingual organizations should include the languages and terminology used in each market. A strong generated answer is not useful if the retrieval layer selected the wrong or unauthorized source. Trace each response through RAG observability and compare the full pipeline with another embedding model or provider before standardizing.