The enterprise AI land grab is on. Glean is building the layer beneath the interface.

In the struggle over the façade, Glenn is betting on something less obvious: becoming the layer of intelligence beneath it.

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

“The layer we initially built — a good search product — required us to deeply understand people, 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 has now become key in terms of building high-quality dealerships.”

He says that although large language models are powerful, they are also general.

“The AI ​​models themselves don’t really understand anything about your business,” Jin said. “They don’t know who different people are, they don’t know what kind of work you do, what kind of products you make. So you have to connect the logic and generative power of models with the context within your company.”

Glenn’s pitch is that it actually maps to that context and can sit between the model and the enterprise data.

Glean Assistant is often the entry point for customers – a familiar chat interface powered by a combination of leading proprietary (such as ChatGPT, Gemini, Cloude) and open source models, grounded in a company’s internal data. But what keeps customers, Jain says, is everything underneath.

The first is access to the form. Rather than forcing companies to stick to a single LLM provider, Glean acts as an abstraction layer, allowing organizations to switch between or combine models as capabilities evolve. That’s why Jain says he doesn’t see OpenAI, Anthropic, or Google as competitors, but as partners.

“Our product is getting better because we are able to leverage the innovation they are bringing to the market,” Jain said.

The second is connectors. Glean integrates deeply with systems like Slack, Jira, Salesforce, and Google Drive to map how information flows through them and empower agents to act within those tools.

The third, and perhaps most important, is governance.

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

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

It is also important to ensure that the models are not hallucinating. Jain says its system checks form output against source documents, generates line-by-line citations, and ensures responses respect existing access rights.

The question is whether that middle layer will survive as the platform giants push deeper into the stack. Microsoft and Google already control a large portion of the enterprise workflow space, and they’re hungry for more. If Copilot or Gemini can access the same internal systems with the same permissions, is the independent intelligence layer still important?

Jain says companies don’t want to be locked into a single model or production stack and prefer to choose a neutral infrastructure layer rather than a vertically integrated assistant.

Investors have bought into this thesis. Glenn raised a $150 million Series F in June 2025, nearly doubling its valuation to $7.2 billion. Unlike leading AI labs, Glean doesn’t need huge computing budgets.

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