Glossary · AI & Agents
Cohere
Overview
What Cohere is
Cohere is an AI company focused on enterprise use of large language models rather than consumer chatbots. It builds and hosts its own models and offers them through an API and private deployments, so companies can run them in their own cloud or on-premises. Its emphasis is data security, deployment flexibility, and strong support for retrieval and search workloads.
The model lineup
Cohere's main products are Command, its family of generation models for chat, writing, and tool use; Embed, which turns text into vectors for semantic search and RAG; and Rerank, which reorders search results by relevance to sharpen retrieval. Embed and Rerank are widely used to improve accuracy in RAG pipelines, often alongside other providers' generation models.
How a team uses Cohere
In practice, a full-service team uses Cohere's Embed and Rerank models to make a knowledge assistant return the right passages, then a generation model to write grounded answers. Because Cohere supports private deployment, it suits AI agents, chatbots, and RAG projects where data cannot leave a company's environment. It is often mixed with other models to balance cost, accuracy, and control.
Where we use it
Related Zen in Tech services
How our team puts Cohere to work in real projects.
FAQ
Cohere — common questions
What is Cohere used for?
Cohere is used to power enterprise AI features like semantic search, retrieval-augmented generation, chatbots, and agents. Its Embed and Rerank models improve search relevance, while its Command models generate text, all deployable in private or cloud environments.
Cohere vs OpenAI: what's the difference?
Both offer powerful models via API. Cohere targets enterprises with strong retrieval and reranking models, private and on-premises deployment, and a data-security focus, while OpenAI is broader and more consumer-known. Many RAG projects use Cohere's Embed and Rerank even with other generation models.
What is Cohere Rerank?
Cohere Rerank is a model that reorders a list of retrieved documents by how relevant they are to a query. Adding it to a RAG or search pipeline typically improves answer accuracy by pushing the best passages to the top.
Need Cohere done right?
Book a free consultation and we’ll map the fastest, most cost-effective path for your project.
Knowledge hub
From our knowledge hub
All articles →How to Reduce AI Voice Agent Latency
How to cut AI voice agent latency to sub-second, human-like turn-taking: where lag comes from (STT, LLM, TTS, network) and the fixes that actually work.
Read · 7 min →AI AutomationAutomating Lead Follow-Up and Onboarding for Coaches and Agencies
Follow-up automation for coaches and agencies: respond to leads in minutes, qualify prospects before calls, and automate onboarding so you focus on clients.
Read · 6 min →AI AutomationAI Automation for Enrollment Inquiries: Answer Every Family Fast
Slow replies lose enrollments. See how AI chatbots and automated follow-up answer every inquiry fast, day or night, and hand warm leads to your team.
Read · 6 min →