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In the scramble for the interface, Glean is betting on something less visible: becoming the intelligence layer beneath it. 

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

“The layer we built initially – a good search product – required us to deeply understand people and how they work and what their preferences are,” Jain told TechCrunch on last week’s episode of Equity, which we recorded at Web Summit Qatar. “All of that is now becoming foundational in terms of building high quality agents.”

He says that while large language models are powerful, they’re also generic. 

“The AI models themselves don’t really understand anything about your business,” Jain said. “They don’t know who the different people are, they don’t know what kind of work you do, what kind of products you build. So you have to connect the reasoning and generative power of the models with the 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 — a familiar chat interface powered by a mix of leading proprietary (ie, ChatGPT, Gemini, Claude) and open-source models, grounded in the company’s internal data. But what keeps customers, Jain argues, is everything underneath it. 

First is model access. Rather than forcing companies to commit to a single LLM provider, Glean acts as the abstraction layer, allowing enterprises to switch between or combine models as capabilities evolve. That’s why Jain says he doesn’t see OpenAI, Anthropic, or Google as competition, but rather as partners. 

“Our product gets better because we’re able to leverage the innovation that they are making in the market,” Jain said. 

Second are the connectors. Glean integrates deeply with systems like Slack, Jira, Salesforce, and Google Drive to map how information flows across them and enable agents to act inside those tools. 

And third, and perhaps most important, is governance. 

“You need to build a permissions-aware governance layer and retrieval layer that is able to bring the right information, but knowing who’s asking that question so that it filters the information based on their access rights,” Jain said. 

In large organizations, that layer can be the difference between piloting AI solutions and deploying them at scale. Enterprises can’t simply load all their internal data into a model and create a wrapper to sort out the solutions later, says Jain. 

Also critical is ensuring the models don’t hallucinate. Jain says its system verifies model outputs against source documents, generates line-by-line citations, and ensures that responses respect existing access rights. 

The question is whether that 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’re 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 enterprises don’t want to be locked into a single model or productivity suite and would rather opt for a neutral infrastructure layer rather than a vertically integrated assistant.

Investors have bought into that thesis. Glean raised a $150 million Series F in June 2025, nearly doubling its valuation to $7.2 billion. Unlike the frontier AI labs, Glean doesn’t need massive compute budgets.

“We have a very healthy, fast-growing business,” Jain said.