Cohere

API

Cohere is an enterprise AI platform offering large language models for text generation, embeddings, and reranking throug

cohere.com

Last updated: April 2026

Cohere is an enterprise AI platform offering large language models for text generation, embeddings, and reranking through an API and on-premises deployment.

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About

Cohere is an enterprise AI company specializing in natural language processing models and the infrastructure for deploying them at scale. Founded in 2019 by researchers from the Google Brain team, Cohere focuses on building large language models and associated capabilities specifically designed for enterprise use cases, with a strong emphasis on security, deployment flexibility, and integration with enterprise data sources.

Cohere Command is the flagship text generation model, optimized for enterprise tasks such as summarization, content generation, question answering, classification, and structured data extraction. Command models are trained with particular attention to instruction following, factual accuracy, and appropriate refusal of harmful requests, making them well-suited for customer-facing applications and internal productivity tools.

Cohere Embed is a family of embedding models that convert text into high-dimensional vector representations for use in semantic search, recommendation systems, classification, and clustering applications. Cohere Embed supports over 100 languages and multiple embedding types optimized for search (asymmetric) and classification/clustering (symmetric) use cases. The models are available in different sizes for different latency and throughput requirements.

Cohere Rerank is a cross-encoder reranking model that significantly improves the precision of search results by re-evaluating and re-ordering candidate documents based on their relevance to a query. When used in a two-stage retrieval system, a fast but less precise retrieval method identifies a candidate set of documents, and Rerank performs precise scoring of each candidate, dramatically improving the quality of final search results.

Cohere RAG (Retrieval Augmented Generation) provides a grounded, citation-producing text generation capability where the model is provided with source documents and produces responses based strictly on the provided content, with citations pointing to the source documents. This grounded generation approach dramatically reduces hallucination and makes AI responses auditable by humans.

One of Cohere's most significant differentiators is its deployment flexibility. Cohere models can be accessed through the Cohere cloud API, deployed to customer-managed VPCs on AWS, Azure, or Google Cloud through the Cohere Private Deployment offering, or deployed completely on-premises on the customer's own GPU hardware. This flexibility enables enterprises with strict data sovereignty, air-gap requirements, or regulated data handling to use Cohere models without routing data through third-party infrastructure.

The Cohere Toolkit is an open source application template that demonstrates how to build enterprise RAG applications using Cohere models, providing a starting point for teams building internal knowledge assistants, document Q and A systems, and customer support bots.

Positioning

Cohere is the enterprise AI platform that builds and deploys large language models specifically designed for business use cases rather than consumer chat. While OpenAI and Anthropic target the broadest possible audience, Cohere focuses on making LLMs production-ready for enterprises — with deployable models that run on any cloud or on-premises, fine-tuning capabilities, and retrieval-augmented generation (RAG) that grounds responses in your organization's actual data. Cohere's models power AI at companies like Oracle, McKinsey, and Fujitsu.

Co-founded by Aidan Gomrat, co-author of the foundational Transformer paper ("Attention Is All You Need"), Cohere brings deep research credentials to a platform focused on practical business outcomes. Their Command model family excels at enterprise tasks like summarization, content generation, and data extraction, while Embed produces best-in-class text embeddings for semantic search and RAG systems. Crucially, Cohere models can be deployed in customers' private cloud environments — a hard requirement for regulated industries.

What You Get

Command Models
Text generation models optimized for enterprise tasks including summarization, content creation, data extraction, and multi-step reasoning — available via API or private deployment.
Embed Models
Multilingual text embedding models supporting 100+ languages that power semantic search, RAG, and classification with state-of-the-art retrieval accuracy.
Rerank
Cross-encoder reranking model that dramatically improves search relevance by re-scoring results from any retrieval system — keyword, vector, or hybrid.
Fine-Tuning
Custom model fine-tuning on your organization's data for improved performance on domain-specific tasks, with the fine-tuned model remaining privately deployed.
RAG (Retrieval-Augmented Generation)
Built-in RAG capabilities with citation generation, grounding on enterprise data sources, and connectors for databases, documents, and knowledge bases.
Private Deployment
Deploy Cohere models on AWS, GCP, Azure, Oracle Cloud, or on-premises — keeping data within your security perimeter while using production-grade AI.

Core Areas

Enterprise Search & RAG

Semantic search and retrieval-augmented generation using Embed and Rerank models, turning enterprise knowledge bases into accurate, citable AI-powered information systems.

Content Intelligence

Automated summarization, extraction, and classification of business documents at scale — processing contracts, reports, support tickets, and communications.

Multilingual AI

Native multilingual support across 100+ languages in both generation and embedding models, enabling global enterprises to deploy AI without per-language model management.

Why It Matters

Most enterprises can't send proprietary data to third-party AI APIs, and they need models that excel at their specific domain rather than general chat. Cohere addresses both constraints with deployable models that run in private environments and fine-tuning that adapts those models to specialized business contexts. This makes enterprise AI adoption practical rather than theoretical.

Cohere's focus on embedding and retrieval quality is particularly significant because RAG has emerged as the primary pattern for enterprise AI applications. The combination of Embed for semantic search and Rerank for precision creates an information retrieval pipeline that's measurably superior to keyword search or single-model approaches — the foundation on which reliable enterprise AI is built.

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