Hajra Naz
The race to control enterprise AI is in full swing – and while everyone’s fighting for the visible interface, one startup is quietly trying to own what’s underneath.
Big players are scrambling: Microsoft has folded Copilot into its productivity suite, Google is pushing Gemini across Workspace, and newer labs like OpenAI and Anthropic are pitching directly to corporate customers. But Glean has a different read on the market.
Glean launched seven years ago with a simple promise: make a Google for the workplace—crawl a company’s tools (Slack, Jira, Drive, Salesforce) and surface the answer when someone asks. Over time, that mission has evolved. Instead of trying to be the flashiest chatbot, Glean is positioning itself as the connective tissue that sits between large language models and a company’s internal systems.
“As we built a strong search product, we learned a lot about how people actually work and what they prefer,” CEO Niko Jain said on a recent episode of Equity. “That knowledge turns out to be exactly what you need to build reliable, high-quality agents.”
The logic is straightforward: the big language models are powerful but generic. They don’t automatically know an organization’s org chart, product lines, or who’s allowed to see what. That’s where Glean says it adds value – by mapping that context and feeding it into models so answers are relevant and compliant.
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Glean’s offering has three pillars. First, it gives customers flexible access to models, acting as an abstraction layer so companies can mix or switch providers rather than being locked into one. Second, it plugs deeply into enterprise systems, tracing how information moves across tools so automated agents can act inside them. Third – and perhaps most crucial – is governance: a permissions-aware retrieval layer that returns the right information only to the right people.
That governance piece matters for more than policy. In large organizations, it’s often the difference between a pilot and a company-wide rollout. You can’t just dump all of an enterprise’s data into a model and hope a wrapper will sort permissions later.
Glean also leans on verification: checking model outputs against source documents, producing line-by-line citations, and enforcing access rules so answers are both accurate and secure.
Still, the question looms: can an independent middle layer hold its ground as the platform giants push deeper into the stack? If the major productivity suites can reach the same internal systems with equivalent permissions, what role remains for a neutral intelligence layer?
Jain’s counterpoint is that most enterprises don’t want to be tied to a single model or a single vendor’s productivity suite. They prefer an infrastructure piece that stays neutral, one that lets them choose tools and swap models as the market evolves.
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Investors seem to agree. Glean closed a $150 million Series F in June 2025, pushing its valuation to about $7.2 billion – a sign that there’s appetite for a behind-the-scenes play that doesn’t require the massive compute budgets of frontier labs. As Jain put it, the company is focused on steady product growth rather than trying to outspend anyone on raw model training.
Whether the market ends up favoring vertically integrated assistants or neutral infrastructure is still up for grabs. For now, Glean is betting that knowing how people work inside an organization – and being able to connect that knowledge to whatever models companies choose to use – will keep it relevant no matter who wins the interface.
