Competition in enterprise AI is intensifying. Microsoft integrates Copilot into Office, while Google embeds Gemini into Workspace. OpenAI and Anthropic market directly to businesses, and nearly every SaaS provider now includes an AI assistant. Amid this rush to dominate user interfaces, Glean is focusing on a less conspicuous role: serving as the foundational intelligence layer underneath.

Originally launched seven years ago as an enterprise search tool—akin to a Google for company data—Glean indexed and searched across SaaS platforms like Slack, Jira, Google Drive, and Salesforce. Its strategy has since evolved from refining an enterprise chatbot to acting as the crucial link between AI models and corporate systems. According to the company, this groundwork is now essential for developing effective AI agents.

Large language models, though powerful, lack specific business context. They don’t understand organizational roles, workflows, or products. Glean bridges that gap by connecting the generative capabilities of models with a company’s internal knowledge. Its platform maps this context, positioning itself between AI models and enterprise data.

For many clients, the Glean Assistant serves as the initial touchpoint—a chat interface powered by a blend of leading proprietary models (such as ChatGPT, Gemini, and Claude) and open-source alternatives, all anchored in the organization’s data. However, the real retention driver lies in the underlying infrastructure.

First, Glean provides flexible model access. Instead of locking companies into one LLM provider, it acts as an abstraction layer, allowing businesses to switch or combine models as technology advances. This approach frames other AI developers not as rivals, but as collaborators whose innovations enhance Glean’s offerings.

Second, deep integrations with tools like Slack, Jira, Salesforce, and Google Drive enable Glean to trace information flows and allow AI agents to operate within those systems. Third, and most critically, governance ensures secure, permissions-aware data retrieval. Responses are filtered based on user access rights, a necessity for large-scale AI deployment in enterprises.

Glean also prioritizes accuracy, verifying model outputs against source documents, providing citations, and preventing hallucinations. This focus on reliability and security addresses a key barrier to widespread enterprise adoption.

A looming question is whether such a middleware layer can endure as giants like Microsoft and Google expand their control over enterprise workflows. If their assistants, such as Copilot or Gemini, can natively access internal systems with appropriate permissions, is a separate intelligence platform still relevant? Glean contends that businesses prefer a neutral infrastructure layer over being tied to a single vendor’s ecosystem, valuing flexibility and avoiding lock-in.

Investors have supported this vision. In June 2025, Glean secured $150 million in a Series F round, nearly doubling its valuation to $7.2 billion. Unlike AI labs with enormous computational budgets, Glean emphasizes its sustainable, rapidly growing business model, built on integrating and securing enterprise AI rather than training foundational models.