Glean Positions Itself as the Enterprise AI Middleware Layer

Glean, originally built as an AI‑powered search tool for enterprise SaaS data, is shifting its focus to become the connective intelligence layer between large language models and corporate systems. By abstracting model access, integrating deeply with tools like Slack, Jira, Salesforce, and Google Drive, and providing a permissions‑aware governance and retrieval framework, Glean aims to deliver reliable, context‑rich AI assistants without locking customers into a single model or productivity suite. The company highlights model‑output verification, citation generation, and strict access controls as differentiators that could enable large‑scale AI deployments across organizations.

From Search to Middleware: Glean’s Strategic Pivot

When Glean launched seven years ago, its ambition was to become the "Google for enterprise"—an AI‑driven search platform that indexed data across a company’s SaaS ecosystem, from Slack to Jira, Google Drive to Salesforce. Over time, the company has transitioned from building a better enterprise chatbot to constructing the underlying intelligence layer that links large language models (LLMs) with internal business data.

Why a Middle Layer Matters

According to Glean’s leadership, LLMs are powerful but generic; they lack knowledge of a specific organization’s people, products, and workflows. To make these models useful in a corporate setting, they must be combined with contextual information that only resides within the enterprise. Glean positions itself as that bridge, mapping internal data, user roles, and access rights, then feeding this enriched context to the models.

Three Core Capabilities

Model Access Flexibility. Glean acts as an abstraction layer, allowing enterprises to switch between or combine models from providers such as OpenAI, Google, and Anthropic. This flexibility lets companies leverage the latest innovations without committing to a single vendor.

Deep Connectors. The platform integrates tightly with widely used tools—including Slack, Jira, Salesforce, and Google Drive—to understand how information flows across them. These connectors enable AI agents to act directly within the tools that employees already use.

Governance and Permissions. Glean builds a permissions‑aware retrieval system that filters information based on the requester’s access rights. This governance layer is critical for scaling AI solutions in large organizations, where data security and compliance are paramount.

Ensuring Trustworthy Outputs

To prevent hallucinations, Glean verifies model responses against source documents, generates line‑by‑line citations, and enforces access controls. These safeguards aim to provide enterprises with reliable, auditable AI assistance.

Competitive Landscape

Tech giants Microsoft and Google already dominate much of the enterprise workflow surface and are extending their AI assistants—Copilot and Gemini—into the same toolsets. Glean’s argument is that enterprises prefer a neutral infrastructure that avoids lock‑in to a single model or productivity suite. By offering a platform‑agnostic middleware, Glean hopes to remain relevant even as platform owners push deeper into the stack.

Market Validation

Investors have backed Glean’s thesis, with a $150 million Series F round in June 2025 that lifted its valuation to $7.2 billion. The company emphasizes that, unlike frontier AI labs, it does not require massive compute budgets, focusing instead on fast‑growing, healthy business operations.

Outlook

Glean’s success will hinge on its ability to maintain seamless integrations, robust governance, and model‑agnostic flexibility while delivering trustworthy AI experiences. If enterprises continue to seek independent, secure AI layers, Glean’s middleware approach could become a foundational component of the corporate AI stack.