The battle for enterprise AI is heating up. Microsoft is bundling Copilot into Office. Google is pushing Gemini into Workspace. OpenAI and Anthropic are selling directly to enterprises. Every SaaS vendor now ships an AI assistant. In this scramble for the user interface, Glean is betting on something less visible: becoming the intelligence layer beneath it all.
Seven years ago, Glean set out to be the Google for enterprise. It was an AI-powered search tool designed to index and search across a company’s entire SaaS tool library, from Slack to Jira and Google Drive to Salesforce. Today, the company’s strategy has shifted from building a better enterprise chatbot to becoming the connective tissue between AI models and enterprise systems.
The company’s founder, Arvind Jain, explains that the layer they built initially—a capable search product—required them to deeply understand people, how they work, and what their preferences are. All of that knowledge is now becoming foundational for building high-quality AI agents.
He says that while large language models are powerful, they are also generic. The AI models themselves do not really understand anything about your business. They do not know who the different people are, what kind of work you do, or what products you build. You have to connect the reasoning and generative power of the models with the specific context inside your company.
Glean’s pitch is that it already maps that context and can sit between the model and the enterprise data. The Glean Assistant is often the entry point for customers. It is a familiar chat interface powered by a mix of leading proprietary and open-source models, all grounded in the company’s internal data. But what retains customers, Jain argues, is everything operating underneath that interface.
First is model access. Rather than forcing companies to commit to a single LLM provider, Glean acts as an abstraction layer, allowing enterprises to switch between or combine models as capabilities evolve. This is why Jain sees companies like OpenAI, Anthropic, and Google not as competition, but as partners. Their product improves by leveraging the ongoing innovation in the market.
Second are the connectors. Glean integrates deeply with systems like Slack, Jira, Salesforce, and Google Drive to map how information flows across them. This enables AI agents to act meaningfully inside those tools.
Third, and perhaps most important, is governance. You need to build a permissions-aware governance and retrieval layer that can bring the right information to the right person. It must filter information based on individual access rights. In large organizations, this layer can be the difference between piloting AI solutions and deploying them safely at scale. Enterprises cannot simply load all their internal data into a model and create a wrapper to sort out permissions later.
Also critical is ensuring the models do not hallucinate. Glean’s system verifies model outputs against source documents, generates line-by-line citations, and ensures that all responses respect existing access rights.
The question is whether this middle layer survives as platform giants push deeper into the stack. Microsoft and Google already control much of the enterprise workflow surface area, and they are hungry for more. If Copilot or Gemini can access the same internal systems with the same permissions, does a standalone intelligence layer still matter?
Jain argues that enterprises do not want to be locked into a single model or productivity suite. They would rather opt for a neutral infrastructure layer than a vertically integrated assistant.
Investors have bought into that thesis. Glean raised a substantial Series F round in June 2025, nearly doubling its valuation. Unlike the frontier AI labs, Glean does not require massive compute budgets. The company maintains it has a very healthy, fast-growing business.