Glean Positions Itself as Critical Intelligence Layer in Enterprise AI Infrastructure Race

The enterprise artificial intelligence market is experiencing intense competition as major technology vendors vie for dominance. Microsoft is integrating Copilot across its Office suite, Google is embedding Gemini into Workspace, and frontier AI labs like OpenAI and Anthropic are pursuing direct enterprise sales strategies. Meanwhile, virtually every SaaS provider has launched an AI assistant offering.

Amid this scramble for user-facing interfaces, Glean is pursuing a differentiated strategy: becoming the foundational intelligence layer that sits beneath these interfaces, connecting AI models with enterprise data systems.

Evolution from Enterprise Search to Intelligence Infrastructure

Founded seven years ago, Glean initially positioned itself as an AI-powered enterprise search solution designed to index and query across an organization's entire SaaS ecosystem—spanning platforms like Slack, Jira, Google Drive, and Salesforce. The company's strategic focus has since evolved from building a superior enterprise chatbot to establishing itself as the connective infrastructure between large language 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," explained Jain. "All of that is now becoming foundational in terms of building high quality agents."

Addressing the Context Gap in Enterprise AI

While acknowledging the capabilities of large language models, Jain emphasizes their inherent limitations in enterprise contexts. "The AI models themselves don't really understand anything about your business," he noted. "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 value proposition centers on its existing capability to map organizational context and function as an intermediary layer between AI models and enterprise data repositories.

Three-Pillar Infrastructure Strategy

The Glean Assistant serves as the customer entry point—a conversational interface powered by multiple proprietary models (ChatGPT, Gemini, Claude) and open-source alternatives, grounded in the organization's internal data. However, Jain argues that the underlying infrastructure provides the sustainable competitive advantage:

1. Model Abstraction Layer
Rather than forcing vendor lock-in to a single LLM provider, Glean functions as an abstraction layer, enabling enterprises to switch between or orchestrate multiple models as capabilities evolve. This positioning allows Glean to view OpenAI, Anthropic, and Google as partners rather than competitors. "Our product gets better because we're able to leverage the innovation that they are making in the market," Jain stated.

2. Deep System Integration
Glean maintains extensive connectors with enterprise systems including Slack, Jira, Salesforce, and Google Drive, mapping information flows across these platforms and enabling agents to execute actions within these tools.

3. Permissions-Aware Governance
Perhaps most critically, Glean has built a comprehensive governance infrastructure. "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.

In large-scale enterprise deployments, this governance layer represents the difference between proof-of-concept pilots and production-grade implementations. Organizations cannot simply ingest all internal data into a model and retroactively implement access controls.

Mitigating Hallucination Risks

Glean's system implements verification mechanisms that validate model outputs against source documents, generates granular citations, and ensures responses respect existing access control policies—addressing one of the most significant concerns in enterprise AI deployment.

Market Position and Competitive Dynamics

The critical question facing Glean is whether an independent intelligence layer remains viable as platform giants expand their stack integration. Microsoft and Google already control substantial enterprise workflow surface area and are aggressively expanding their AI capabilities. If Copilot or Gemini can access identical internal systems with equivalent permissions, the value proposition of a standalone intelligence layer becomes less clear.

Jain's counterargument emphasizes enterprise preferences for vendor neutrality and infrastructure flexibility over vertically integrated solutions that create platform lock-in.

Financial Position and Growth Trajectory

Market validation of Glean's strategy is evident in its recent funding round. The company secured $150 million in Series F financing in June 2025, nearly doubling its valuation to $7.2 billion. Unlike frontier AI laboratories requiring massive computational infrastructure investments, Glean operates a capital-efficient model. "We have a very healthy, fast-growing business," Jain confirmed.

As the enterprise AI infrastructure landscape continues to consolidate, Glean's bet on becoming the neutral intelligence layer—rather than competing at the interface level—represents a strategic differentiation in an increasingly crowded market.