Cohere Doubles Revenue to $100 Million with Enterprise-Focused AI Strategy

AI firm Cohere doubles annualized revenue to $100 million on enterprise focus

NEW YORK, May 15 (Reuters) - AI startup Cohere has doubled its annualized revenue since the beginning of the year, driven by growing demand for secure, customized AI tools among enterprise clients in regulated sectors, the company told Reuters. The company has crossed an annualized revenue of $100 million as of May 2025, according to one person familiar with the matter. A Cohere spokesperson declined to comment on the financials. The growth followed a strategic shift in the third quarter of 2024 with a focus on private deployments tailored for customers in sectors such as finance, healthcare and government. Chief Executive Aidan Gomez outlined the new direction in a year-end memo, emphasizing a move toward building tailored models for enterprise users over larger foundation models. Most of its revenue stems from long-term contracts, the company said. Roughly 85% of Cohere's business now comes from such private deployments, the company said, with margins reaching 80%. As part of the strategy, Cohere in January launched a ChatGPT-style application called North, aimed at helping knowledge workers with tasks such as summarizing documents. The product is currently being tested with a limited group of customers, including Royal Bank of Canada and LG. Founded in 2019, Cohere has raised more than $900 million from investors including Nvidia, Cisco and Inovia Capital. Its customers also include Fujitsu, Oracle and Notion. The company was last valued at $5.5 billion. Cohere's shift toward smaller, specialized models reflects a broader trend in the AI sector, as companies prioritize domain-specific tools over large, generalized systems. The move comes as AI labs report diminishing returns from scaling up model size -- a strategy that once drove major breakthroughs. Reporting by Echo Wang in New York; Editing by Daniel Wallis Our Standards: The Thomson Reuters Trust Principles., opens new tab Suggested Topics:Artificial Intelligence Echo Wang Thomson Reuters Echo Wang is a correspondent at Reuters covering U.S. equity capital markets, and the intersection of Chinese business in the U.S, breaking news from U.S. crackdown on TikTok and Grindr, to restrictions Chinese companies face in listing in New York. She was the Reuters' Reporter of the Year in 2020.

AI firm Cohere doubles annualized revenue to $100 million on enterprise focus

NEW YORK (Reuters) -AI startup Cohere has doubled its annualized revenue since the beginning of the year, driven by growing demand for secure, customized AI tools among enterprise clients in regulated sectors, the company told Reuters. The company has crossed an annualized revenue of $100 million as of May 2025, according to one person familiar with the matter. A Cohere spokesperson declined to comment on the financials. The growth followed a strategic shift in the third quarter of 2024 with a focus on private deployments tailored for customers in sectors such as finance, healthcare and government. Chief Executive Aidan Gomez outlined the new direction in a year-end memo, emphasizing a move toward building tailored models for enterprise users over larger foundation models. Most of its revenue stems from long-term contracts, the company said. Roughly 85% of Cohere's business now comes from such private deployments, the company said, with margins reaching 80%. As part of the strategy, Cohere in January launched a ChatGPT-style application called North, aimed at helping knowledge workers with tasks such as summarizing documents. The product is currently being tested with a limited group of customers, including Royal Bank of Canada and LG. Founded in 2019, Cohere has raised more than $900 million from investors including Nvidia, Cisco and Inovia Capital. Its customers also include Fujitsu, Oracle and Notion. The company was last valued at $5.5 billion. Cohere's shift toward smaller, specialized models reflects a broader trend in the AI sector, as companies prioritize domain-specific tools over large, generalized systems. The move comes as AI labs report diminishing returns from scaling up model size -- a strategy that once drove major breakthroughs. (Reporting by Echo Wang in New York; Editing by Daniel Wallis)