Glean is an enterprise "Work AI" platform founded in 2019 by Arvind Jain (a former Google distinguished engineer and Rubrik co-founder) and headquartered in Palo Alto. It began as an enterprise search engine — indexing a company's SaaS apps, documents, and messages into a single, permissions-aware knowledge graph — and has since expanded into an assistant and an AI agent platform. As of mid-2026 the company reported roughly $300M ARR (roughly tripling in about 16 months) and a $7.2B valuation following its Series F, with customers including large enterprises across finance, tech, and services.
The technical core is a permissions-aware knowledge graph combined with hybrid retrieval — keyword, vector, and retrieval-augmented generation — and a dual-graph design that separates enterprise knowledge from each user's personal context. Crucially, results respect existing access controls: Glean never surfaces a document a user couldn't already open, which is the property that makes it deployable in regulated environments. The 2026 strategic pivot positions that graph as the moat for an agents platform, where no-code agents run multi-step workflows across connected tools, and Glean markets the graph as a way to cut enterprise LLM token spend by around 30%.
The main friction is commercial. Glean publishes no pricing; deals are custom, with base search licensing commonly around $40–50+ per user per month and AI/agent capabilities frequently an add-on (reported near $15/user/month). First-year totals often land between $300K and over $1M depending on size and scope, and standing up the platform requires real integration and change-management effort. It is firmly an enterprise product, not a self-serve tool.
Key Benefits
- Security-first retrieval: Every answer honors source-system permissions, so sensitive documents stay invisible to users who lack access.
- Breadth of coverage: 100+ connectors unify search across the whole SaaS stack, reducing the "where did I see that?" tax.
- From search to action: The no-code agent builder turns retrieval into multi-step workflows across the same connected tools.
- Grounded, cited answers: The assistant answers and drafts using company knowledge with citations, reducing hallucination risk.
Use Cases
- Enterprise knowledge search — Employees find documents, tickets, and messages across every connected app from one permissions-aware search box.
- Grounded AI assistant — Ask questions or draft content and get answers cited to internal sources rather than the open web.
- Cross-app agents — Build no-code agents that execute multi-step workflows (e.g., onboarding, support triage) across Slack, Jira, Salesforce, and more.
- Engineering and support enablement — Surface runbooks, prior tickets, and code context to speed resolution while respecting access controls.
