Enterprise artificial intelligence is gaining ground. Glean creates a layer below the interface. | TechCrunch

In the battle for the interface, Glean is betting on something less visible: becoming the intelligence layer underneath.

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

“The layer that we originally built—a good search product—required us to have a deep understanding of people and how they work and what their preferences are,” Jain told TechCrunch in last week’s Equity episode we recorded at Web Summit Qatar. “All of this now becomes the basis for building high-quality agents.”

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

“AI models alone 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 make. So you have to connect the reasoning and the generative power of the models to the context inside your company.”

The thing about Glean is that it already maps that context and can sit between the model and the business data.

Glean Assistant is often the entry point for customers – a familiar chat interface based on a combination of leading proprietary (ie ChatGPT, Gemini, Claude) and open-source models that are based on internal company data. But what keeps customers going, says Jain, is everything underneath.

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

“Our product is getting better because we’re able to take advantage of the innovations they’re making in 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 between them and enable agents to act within those tools.

And the third, and perhaps most important, is governance.

“You have to create a management and search layer that’s permission-aware, that’s able to bring in the right information, but know who’s asking that question so they can filter 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. Enterprises can’t simply load all their internal data into a model and create a wrapper to solve the problem later, Jain says.

It is also important to ensure that the models are not hallucinating. Jain says his system verifies model outputs with source documents, generates line citations, and ensures that responses respect existing access rights.

The question is whether this mid-tier will survive as the platform giants push deeper into the stack. Microsoft and Google already control much of the enterprise workflow, and they want more. If Copilot or Gemini can access the same internal systems with the same permissions, does a separate intelligence layer matter?

Jain says businesses don’t want to be locked into a single productivity model or suite, and would rather opt for a neutral infrastructure layer than a vertically integrated assistant.

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

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