Enterprise AI is competing for land. Glean builds layers beneath the interface.

In the scramble for interfaces, Glean is betting on something less tangible: a layer of intelligence underneath.

Seven years ago, Glean set out to become the Google for the enterprise, an AI-powered search tool designed to index and search across a company’s library of SaaS tools, from Slack to Jira, Google Drive to Salesforce. The company’s strategy has now shifted from building better enterprise chatbots to becoming the connective tissue between models and enterprise systems.

“The first layer we built (a great search product) required a deep understanding of people and how they work and what their preferences are,” Jain told TechCrunch on last week’s episode of Equity recorded at Web Summit Qatar. “All of this is now becoming the cornerstone of building high-quality agents.”

While large-scale language models are powerful, they are also general-purpose, he says.

“The AI ​​model itself doesn’t really understand anything about the business,” Jain says. “They don’t know who the different people are. They don’t know what kind of work you do or what products you make. So they need to couple the inference and generative power of the model with the internal context of the company.”

Glean’s pitch is that it already maps that context and can sit between your model and your enterprise data.

Glean Assistant is often the entry point for your customers. A familiar chat interface that combines leading proprietary (ChatGPT, Gemini, Claude) and open source models based on your company’s internal data. But Jain maintains that it’s all the underlying things that keep customers going.

First is model access. Rather than forcing companies to commit to a single LLM provider, Glean acts as an abstraction layer that allows companies to switch and combine models as their capabilities evolve. That’s why Jain says he sees OpenAI, Anthropic and Google as partners rather than competitors.

“Our products will be better because we can leverage the innovations they are doing in the market,” Jain said.

The second is the connector. Glean deeply integrates with systems like Slack, Jira, Salesforce, and Google Drive to map how information flows between systems and empower agents to act within these tools.

And third, and perhaps most importantly, governance.

“You need to build a privilege-aware governance and acquisition layer that can provide the right information, but you also need to know who is asking the questions and be able to filter the information based on their access rights,” Jain said.

For large organizations, this layer can be the difference between piloting an AI solution and deploying it at scale. Companies can’t just load all their internal data into a model and later create a wrapper to categorize the solution, Jain says.

It is also important to ensure that the model does not hallucinate. Jain says its system validates the model’s output against the source document, generates line-by-line citations, and ensures that responses respect existing access rights.

The question is whether the middle layer can survive as the platform giants move deeper into the stack. Microsoft and Google already control much of the surface area of ​​enterprise workflows, and they want more. Even if Copilot or Gemini can access the same internal systems with the same privileges, is a standalone intelligence layer still important?

Jain argues that businesses don’t want to be tied to a single model or productivity suite, and would rather choose a neutral infrastructure layer rather than a vertically integrated assistant.

Investors support that theory. Green raised a $150 million Series F in June 2025, nearly doubling its valuation to $7.2 billion. Unlike Frontier AI Labs, Glean doesn’t require huge computing budgets.

“Our business is very healthy and growing rapidly,” Jain said.