The company raised more than $200 million from investors that include Citigroup, Capital One Ventures, Databricks and Sequoia, valuing the firm at $2.2 billion, according to a Tuesday (Feb. 27) blog post.
“Our investors join us in our belief that Glean is the right solution, built by the right team, at exactly the right time,” the post said.
Glean offers an AI-powered work assistant that uses search and retrieval-augmented generation (RAG) technology to uncover relevant, up-to-date information for large language models to use. The company said in the post that its goal is to become the platform companies turn to when they need an AI work assistant, while also offering tools to “build custom generative AI experiences grounded in company knowledge.”
Glean’s funding follows a report from last fall by venture firm Accel which found that generative AI companies are the main driver of unicorn valuations — or startups valued at $1 billion or more — with 60% of new unicorns falling into that category.
Accel partner Philippe Botteri said the research shows a “new normal” settling in, with valuations, investment levels and unicorn creation returning to 2019 levels.
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“That said, today is a very different environment,” wrote Botteri. “Companies have moved away from growth at all costs to focus on profitability, and GenAI is redefining the potential of software, bringing the opportunities for enterprise automation and productivity improvement to a new level.”
Research shows companies that use AI tend to be more productive than those that don’t, an argument that Glean CEO Arvind Jain made in the blog post, noting that most chief information officers see the potential for AI to improve productivity.
“However, many are hesitating due to the uncertainties posed by security risks and shadow IT. Ad hoc adoption of generative AI comes with both heavy risks and costs, two of the greatest concerns for IT leaders and CIOs,” wrote Jain in the post. “So, although AI has been identified as mission critical for businesses over the coming years, implementation is a considerable challenge with serious implications down the line if not performed properly.”
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