Cohere is a company headquartered in Toronto, Canada, founded in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst. Gomez had been a co-author of "Attention Is All You Need", the 2017 paper that introduced the Transformer (architecture) architecture underlying nearly all modern Large language model systems, giving Cohere an early credibility claim distinct from labs founded by outsiders to that research lineage. Frosst, like Gomez, had worked under Geoffrey Hinton before co-founding the company.
Enterprise focus
Unlike consumer-facing rivals such as OpenAI with ChatGPT or Anthropic with Claude (AI model family), Cohere positioned itself from early on primarily as a business-to-business provider, selling API access to its Command family of models for tasks such as summarization, retrieval, and enterprise search, and emphasizing data privacy and on-premises or virtual private cloud deployment options attractive to regulated industries such as finance and government. This positioning let Cohere avoid direct feature-for-feature competition with better-funded consumer assistants while targeting large enterprise contracts.
Aya and multilingual research
Cohere's nonprofit research arm, Cohere For AI, launched the Aya initiative, an open research project aimed at improving Large language model performance and evaluation across languages underserved by AI research, which has historically concentrated heavily on English. Aya released both open datasets built with contributions from a global volunteer research community and open-weights multilingual models, positioning Cohere's research output as complementary to fully closed labs and to fully open collectives such as EleutherAI and Allen Institute for AI.
Funding and market position
Cohere raised significant funding from investors including Nvidia, Salesforce, and Oracle, reaching a valuation reported in the billions of dollars by 2024, though consistently below the valuations of OpenAI and Anthropic. Analysts and journalists have periodically questioned whether Cohere's enterprise-first strategy can generate revenue at a pace that justifies continued large-scale Pretraining investment against better-capitalized rivals, a competitive pressure common to smaller labs competing at the Frontier model tier. Cohere has responded by emphasizing efficiency and total cost of deployment for enterprise customers rather than competing purely on raw AI benchmark leaderboard position, and by expanding into agentic tooling for enterprise workflows as AI agent adoption grew industry-wide in 2025 and 2026.