Cohere is a generative AI platform designed for enterprise and developer use, focusing on natural language processing (NLP) applications such as semantic search, summarization, text classification, and RAG (retrieval-augmented generation). Built for scalability, data privacy, and enterprise deployment, it provides a foundation model ecosystem that supports private cloud, on-premise, and API-based usage.

Target users include AI and ML teams, data scientists, product teams, and software engineers at mid-size to large enterprises. Cohere is particularly well-suited to industries dealing with large-scale unstructured text data, such as financial services, legal, healthcare, and technology.

Key features include access to custom-tuned large language models (LLMs), enterprise-grade RAG capabilities, and multilingual embeddings. Standout benefits are the platform’s emphasis on model transparency and its flexible deployment options, which help organizations maintain control over sensitive data and infrastructure.

What are the main features of Cohere?

Customizable foundation models for enterprise NLP

Cohere provides access to a family of generative models—Command R+, Embed, and others—designed to serve specific NLP functions.

• Command R+ is a general-purpose generative model optimized for RAG tasks, with competitive performance on industry benchmarks like MTEB and HELM.

• Models can be fine-tuned or adapted with domain-specific data to improve performance on internal applications.

• Multilingual support enables text understanding across 100+ languages.

These models are available via API or can be deployed privately, giving organizations flexible infrastructure control.

High-performance retrieval-augmented generation (RAG)

Cohere specializes in RAG systems, where external knowledge is retrieved and synthesized into natural language output.

• Compatible with open-source retrieval frameworks like LangChain and LlamaIndex.

• Embedding models (such as Embed v3) support dense retrieval with state-of-the-art semantic search accuracy.

• Reduces hallucination and improves factual accuracy in generated content.

RAG is particularly useful for enterprise use cases like knowledge base Q&A, internal document summarization, and support automation.

Enterprise-ready deployment and privacy options

Cohere allows enterprises to deploy models in a way that aligns with their infrastructure and compliance needs.

• Available via public API, virtual private cloud (VPC), or fully on-premises.

• No data is used for model training unless explicitly permitted.

• Enables organizations to meet privacy, residency, and governance requirements.

This flexibility is a differentiator for sectors with strict data compliance mandates.

Multilingual and domain-specific embeddings

Cohere offers embedding models that excel in multilingual semantic understanding, powering use cases like search, recommendation, and clustering.

• Embed v3 outperforms most open-source and commercial offerings on the MTEB benchmark.

• Optimized for diverse tasks: classification, reranking, question answering, and retrieval.

• Supports more than 100 languages for global enterprise coverage.

These embeddings are essential for companies operating across multiple regions or languages.

Developer tools and ecosystem integrations

Cohere supports rapid integration into existing workflows with comprehensive tools and SDKs.

• Offers Python SDK, OpenAPI support, and documentation for quick prototyping.

• Integration-ready with common frameworks such as Hugging Face Transformers, LangChain, and Haystack.

• Continuous model updates and support for evaluation and monitoring.

This facilitates experimentation and scaling within existing data pipelines.

Why choose Cohere?

• Flexible deployment models: Cohere supports full model deployment in private or hybrid environments, addressing data privacy and control requirements not met by API-only platforms.

• Specialization in RAG and enterprise NLP: Its model architecture and tooling are built with enterprise knowledge retrieval and language understanding in mind, not just general-purpose generation.

• Strong multilingual capabilities: Cohere’s embeddings perform reliably across languages, making it suitable for global operations without compromising accuracy.

• Transparent and ethical model design: Cohere provides insight into model training, data usage, and benchmarking, giving enterprises confidence in model behavior and limitations.

• Developer-friendly ecosystem: Robust APIs, SDKs, and integration support lower the barrier to incorporating generative AI into existing products and services.