AI Land Grab: Glean's Foundational Enterprise Play

The competition in enterprise AI is intensifying rapidly. Microsoft is integrating Copilot into its Office suite, while Google is embedding Gemini throughout Workspace. OpenAI and Anthropic are directly engaging with businesses, and nearly every SaaS vendor now offers an AI assistant.

Amidst this intense competition for the user interface, Glean is pursuing a less conspicuous yet fundamental strategy: establishing itself as the core intelligence layer operating beneath these visible surfaces.

Seven years ago, Glean embarked on a mission to become the "Google for enterprise," developing an AI-powered search solution capable of indexing and searching across a company’s entire library of SaaS tools, from Slack and Jira to Google Drive and Salesforce. Today, the company's focus has evolved from merely creating a superior enterprise chatbot to becoming the crucial connective tissue linking AI models with complex enterprise systems.

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

He further elaborated that while large language models (LLMs) possess immense power, they are inherently generic.

"The AI models themselves don’t really understand anything about your business," Jain stated. "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 core proposition is that it already maps this vital enterprise context and can serve as the essential intermediary between AI models and a company's proprietary data.

The Glean Assistant frequently serves as the initial touchpoint for customers, offering a familiar chat interface powered by a blend of leading proprietary models (such as ChatGPT, Gemini, and Claude) and open-source alternatives, all grounded in the company's internal data. However, Jain contends that the true value, and what retains customers, lies in the sophisticated infrastructure beneath this interface.

The first key differentiator is model access. Instead of compelling organizations to commit to a single LLM provider, Glean functions as an abstraction layer, providing enterprises the flexibility to switch between or combine various models as their capabilities advance. This strategic positioning leads Jain to view OpenAI, Anthropic, and Google not as competitors, but as crucial partners.

Jain emphasized this collaborative advantage, saying, "Our product gets better because we’re able to leverage the innovation that they are making in the market."

Secondly, Glean boasts robust connectors. The platform integrates deeply with critical enterprise systems like Slack, Jira, Salesforce, and Google Drive, allowing it to map the flow of information across these tools and empower AI agents to execute actions directly within them.

And third, and arguably most critically, 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 explained.

For large organizations, this sophisticated governance layer is often the linchpin that transforms AI solution pilots into widespread, scaled deployments. Jain asserts that enterprises cannot simply ingest all their internal data into a model and then attempt to retroactively implement solutions with a basic wrapper.

Equally vital is Glean's commitment to preventing model hallucinations. Jain highlights that their system rigorously verifies model outputs against original source documents, generates line-by-line citations, and ensures that all responses strictly adhere to existing access rights.

A pertinent question arises regarding the long-term viability of such a middle layer as dominant platform giants increasingly expand their reach deeper into the tech stack. Microsoft and Google already command significant portions of the enterprise workflow landscape and are actively seeking further integration. If their respective offerings, Copilot or Gemini, can access the same internal systems with identical permissions, does a distinct intelligence layer retain its indispensable value?

Jain counters this by arguing that enterprises are wary of being locked into a singular model or productivity suite. They prefer a neutral, foundational infrastructure layer over a vertically integrated assistant controlled by a single vendor.

Investors have clearly embraced this thesis. Glean successfully secured a $150 million Series F funding round in June 2025, which nearly doubled its valuation to an impressive $7.2 billion. Notably, unlike many frontier AI laboratories, Glean operates without the need for massive, continuous compute budgets.

Jain concluded optimistically, stating, "We have a very healthy, fast-growing business."