The contest for dominance in enterprise artificial intelligence is entering a decisive phase, with Microsoft embedding Copilot into Office, Google rolling out Gemini across Workspace, and firms such as OpenAI and Anthropic marketing advanced models directly to corporate clients. As software providers race to own the user interface through AI assistants, Glean is charting a quieter path, positioning itself as the intelligence layer that operates beneath those visible tools.
Founded seven years ago, Glean initially sought to become the Google of the enterprise, indexing and searching across workplace platforms including Slack, Jira, Google Drive, and Salesforce. Its strategy has since evolved beyond building a smarter chatbot to creating connective infrastructure that links powerful language models with a company’s internal knowledge and workflows.
Chief executive Arvind Jain said the company’s early focus on search required a deep understanding of how employees work, what they prioritize, and how information flows inside organizations. That foundation, he argued, is now central to building high quality AI agents. While large language models can generate fluent responses, they lack awareness of a specific company’s structure, products, and personnel, making contextual integration essential.
Glean’s assistant often serves as the entry point for clients, offering a chat interface powered by a blend of proprietary and open source models, including ChatGPT, Gemini, and Claude. However, Jain contends that the real value lies underneath the interface. Glean acts as an abstraction layer that allows enterprises to switch between models or combine them as capabilities evolve, rather than locking into a single provider. In this framing, major AI labs are not rivals but partners whose innovations strengthen Glean’s platform.
Beyond model flexibility, the company emphasizes deep system connectors and governance. By integrating with enterprise tools, Glean maps how information moves across platforms and enables agents to operate within them. Crucially, it builds a permissions aware governance and retrieval layer that filters responses based on a user’s access rights, verifies outputs against source documents, and provides line by line citations to limit hallucinations.
The strategic question is whether such a middle layer can endure as platform giants extend their reach. With Microsoft and Google already embedded in daily enterprise workflows, some observers wonder whether standalone intelligence layers will remain relevant. Jain argues that large organizations prefer neutral infrastructure that avoids dependence on a single productivity suite or model provider.
Investors appear to share that view. In June 2025, Glean secured a 150 million dollar Series F funding round that nearly doubled its valuation to 7.2 billion dollars. Unlike frontier AI laboratories that require vast computing resources, Glean’s model centers on integration and orchestration rather than raw model training, a distinction Jain says underpins a fast growing and financially healthy business.