Glean

Glean is an enterprise Work AI platform built by Arvind Jain, a former Google Distinguished Engineer who co-founded Rubrik before starting Glean in 2019 with co-founders T.R. Vishwanath, Piyush Prahladka, and Tony Gentilcore. The founding insight was blunt: employees at fast-growing companies were losing hours every day because institutional knowledge was scattered across dozens of SaaS tools with no unified way to surface it. Glean was built to fix that with a permissions-aware knowledge graph that indexes everything, understands relationships between people and content, and returns results personalized to each employee's role and work history. By early 2026, the platform had crossed $250M+ ARR with 150%+ year-over-year growth, making it one of the fastest-scaling enterprise AI companies in the market.

The platform has three core pillars. Glean Search connects 100+ enterprise apps including Google Drive, Slack, Confluence, Jira, Salesforce, GitHub, ServiceNow, and Zendesk into a single permissions-aware search layer, returning results personalized by role and team. Glean Assistant is an AI assistant for Q&A, summarization, content drafting, and incident triage - the third-generation version launched September 2025 introduced an Enterprise Graph that combines a company-wide knowledge graph with a personal graph for each employee, learning writing tone and work patterns over time. Glean Apps and Glean Agents (general availability January 2026) let non-technical users build and deploy custom AI agents configured via natural language, with a no-code drag-and-drop Agent Builder and a library of pre-built Quickstart Agents. A Model Hub supports 15+ LLMs including Amazon Bedrock, Azure OpenAI, and Google Vertex, giving enterprises choice over their AI backbone.

What Glean actually does in May 2026

Glean's core mechanism is a knowledge graph that indexes not just documents but the relationships between them: who wrote what, who owns which repository, who the subject-matter experts are for a given topic, which documents reference each other, and what was discussed in which Slack threads. When an employee searches, results are filtered through real-time permissions to show only content the user has access to - a technically non-trivial problem that Glean has invested heavily in solving correctly.

Glean Assistant answers complex multi-step questions by reasoning over that graph. An on-call engineer can type an error signature and get back a synthesized answer pulling from past incident retrospectives, relevant runbooks, Slack threads where the bug was discussed, and the specific PR that introduced the regression. A new hire can ask about expense policy and get a direct answer with citations to the specific Confluence page, rather than spending 20 minutes navigating a wiki they don't know the shape of. Glean claims an average of 36 hours saved per new hire during onboarding and up to 110 hours saved per user per year.

Customers deploying Glean at scale include TIME Magazine, Databricks, Booking.com, T-Mobile, Duolingo, Wealthsimple, Grammarly, and Confluent, spanning technology, media, financial services, and professional services verticals.

"Glean helps you get work done, rather than just find information." - Tadeu Faedrick, Senior Engineer Manager, Booking.com

"Engineers solve incidents faster, leading to better experience." - Kathleen Cauley, Knowledge Manager, Wealthsimple

The September 2024 Series E round was a defining moment for the company. Glean raised $260M+ co-led by Altimeter and DST Global, pushing its valuation to $4.6B - double the $2.2B it had reached just seven months earlier in the February 2024 Series D. The round was notable for the investor roster: SoftBank Vision Fund 2, Craft Ventures, Sapphire Ventures, Coatue, General Catalyst, ICONIQ, IVP, Kleiner Perkins, Lightspeed, and Sequoia all participated. Concurrent with the funding, Glean launched its Next-Generation Prompting system: advanced multi-step RAG prompts, a Prompt Builder studio for creating and sharing prompts organization-wide, a Prompt Library, and embedded integrations with Zendesk and Salesforce Service Cloud.

Where Glean sits versus Hebbia and Microsoft Copilot

The two most mechanically distinct competitors are Hebbia and Microsoft 365 Copilot, and comparing them clarifies exactly what Glean is and is not optimized for.

Hebbia goes deep on document reasoning where Glean goes broad on enterprise search. Hebbia's Matrix interface is designed to process thousands of pages in parallel and extract structured insights, making it purpose-built for finance, legal, and consulting workflows where you need to pull specific data points from unstructured documents at scale (extract all revenue figures from 200 earnings calls; compare risk factors across 50 prospectuses). Glean does not have a document-level deep extraction mode. What Glean has that Hebbia lacks is the full enterprise knowledge graph: the ability to search across people, conversations, code, tickets, and documentation simultaneously, with personalization by role. Hebbia's estimated ARR of $13.7M (Sacra, early 2024) versus Glean's $250M+ suggests Hebbia remains a specialist tool for high-value professional research workflows, while Glean targets the broader enterprise productivity problem.

Microsoft 365 Copilot is the most frequently cited competitive reference for Glean in enterprise evaluations. The key mechanical difference is ecosystem scope. Copilot is deeply integrated into Word, Excel, PowerPoint, Teams, Outlook, and SharePoint - it knows what you were editing in Word, what meeting just ended in Teams, and what emails you've received. But its search and AI capabilities degrade sharply outside the Microsoft ecosystem. If your company uses Slack instead of Teams, Jira instead of Azure DevOps, or Salesforce as your CRM, Copilot produces thin, partial results. Glean's connectors cover 100+ apps regardless of vendor, making it a stronger fit for heterogeneous stacks. On model flexibility, Glean supports 15+ LLM backends; Copilot is tied to OpenAI via Azure only. The tradeoff is that for companies already standardized on M365, Copilot's depth inside those apps can outperform Glean's breadth.

For context on adjacent tools, AlphaSense occupies a similar "enterprise search with AI synthesis" position but focuses specifically on financial and market intelligence (SEC filings, earnings transcripts, broker research), not internal company knowledge. Snowflake Cortex addresses the structured-data end of enterprise knowledge search - querying databases and data warehouses in natural language - where Glean focuses on unstructured document and communication content. Teams wanting a self-hosted or open-source alternative to Glean's cloud-only model often evaluate AnythingLLM, which trades breadth of connectors for full local deployment.

What the daily workflow reality looks like

For most employees, Glean surfaces as a search bar available from a browser extension, web app, desktop app (November 2025 release), or embedded in Slack. The most common pattern: someone needs to find something they know exists somewhere - a pricing doc, a policy decision, a past incident writeup, a customer conversation - and types a natural language query rather than navigating to the specific app. Glean returns a synthesized answer with source citations and relevant documents from whatever apps it's connected to.

For teams using Glean Assistant heavily, the value compounds over time. The third-generation assistant (September 2025) learns each employee's writing preferences per context. An engineer drafting a postmortem gets different tone suggestions than a sales rep writing a deal update. The system learns from patterns in past work across all 100+ connected apps - which is powerful when it works and occasionally surfacing the wrong past context when it does not.

The Agent and Apps layer (now generally available) lets teams build specialized assistants: an IT help desk bot that only answers from approved KB articles, an onboarding assistant that walks new hires through their first 30 days using approved content, a sales assistant that knows every deal in Salesforce plus product documentation. Over 1,000 custom apps were deployed by 100+ customers during the preview period. The Agent Builder is no-code, using drag-and-drop and natural language configuration - meaning non-technical users in HR or operations can build their own agents without engineering involvement.

Who Glean is built for

Glean is best suited for enterprises with 200+ employees, heterogeneous SaaS stacks (20+ tools), and a documented knowledge discovery problem. The highest-ROI teams are typically IT (incident response, knowledge base), HR and People Ops (onboarding, policy queries), engineering (codebase context, runbooks), sales enablement (deal prep, competitive intelligence), and customer support (product knowledge retrieval). Companies where a significant portion of institutional knowledge lives in Slack threads, Confluence spaces, and historical Jira tickets - places employees rarely know to look - see the fastest time-to-value.

Specific customer outcomes reported by Glean: SafetyCulture cut search times by 50%. TIME Magazine was live in three weeks ("never integrated anything faster," per CIO Sharon Milz). Grammarly's support team found documents they didn't know existed. Wealthsimple's engineering team reduced incident resolution times.

The Gartner Emerging Leaders recognition (2025 Innovation Guide for Generative AI Knowledge Management) and Fast Company World's Most Innovative Companies 2025 listing reflect real market validation, not marketing positioning.

What Glean is not

Glean is not a workflow automation platform in the sense of tools like Zapier or Make. Until the 2025-2026 Agents layer, it found and synthesized information but did not take action. Even with Agents now live, the action surface is maturing - teams with complex approval workflows, multi-step human-in-the-loop processes, or deep ERP integration needs should evaluate purpose-built orchestration tools in addition to or instead of Glean.

Glean is not a fit for teams already standardized on Microsoft 365 with minimal non-Microsoft tooling. For those teams, Microsoft Copilot's depth inside Office apps outweighs Glean's breadth. Glean is also not a fit for teams under 100 people, given the 100-seat minimum contract floor (approximately $60,000/year minimum commitment). Teams wanting to pilot enterprise search with less financial exposure should consider AnythingLLM or similar open-source tools before committing to a Glean contract.

For organizations that need AI to reason deeply over specific external document corpora - financial filings, legal contracts, scientific literature - rather than internal company knowledge, Hebbia or AlphaSense are better fits.