Glean

Glean is an enterprise Work AI platform built by ex-Google search engineers that indexes your entire internal knowledge base across 100+ tools and makes it searchable and actionable through AI. It goes beyond a chat interface by enforcing your existing permissions, so Slack DMs don't surface to the wrong person, and Salesforce data stays with sales. The platform includes Glean Search for finding anything, Glean Assistant for answering questions grounded in real company context, Glean Agents for automating multi-step workflows, and Glean Apps for building internal AI tools without managing your own retrieval infrastructure. Pricing is enterprise-only with custom quotes. Realistically suited for companies with 500 or more employees that feel the daily cost of scattered institutional knowledge.

When your company reaches a certain size, you realize that having ChatGPT is not the same as having AI that actually knows your company. It doesn’t know what your Q3 OKRs say, can’t find the architecture decision record buried in Confluence from 2023, and has no idea that the Salesforce deal in question was already escalated last Tuesday on Slack. Glean was built specifically to solve that gap. It’s an enterprise AI platform from ex-Google search engineers that indexes your entire internal knowledge base across 100+ business tools and makes it searchable and actionable through a natural-language interface.

Quick verdict

Glean is the most serious attempt to date at making enterprise knowledge actually accessible through AI. If your organization runs on a sprawling stack of disconnected tools and employees spend real hours every week hunting for information, Glean will pay for itself. But it’s priced and scoped exclusively for large organizations. If you’re under 300 employees, close this tab and look at something else.

What is Glean, exactly?

Glean was founded in 2019 by Arvind Jain and other engineers who had spent their careers building search infrastructure at Google. The founding thesis was straightforward: the same technology that makes the web searchable should be applied to the internal knowledge graph of a company. Jain watched enterprises pay millions for software licenses and then watch employees waste hours looking for answers that already existed somewhere in those systems.

The company is valued at over $7 billion as of 2025 and has attracted customers across financial services, healthcare, retail, manufacturing, and government. It’s Gartner-recognized and named to Fast Company’s Most Innovative Companies in 2025. That’s not background noise. It reflects the fact that Glean is a credible, well-funded, category-defining product.

At its core, Glean is a Work AI platform with four distinct layers. There’s search, which is the foundation. There’s the assistant, which is conversational AI grounded in real internal context. There are agents, which are automated AI workers that can handle workflows. And there’s an app platform, which lets teams build custom internal tools on top of Glean’s retrieval infrastructure.

What separates Glean from “ChatGPT for your company” experiments is the permissions layer. Glean doesn’t just pull content from your tools. It inherits and respects the access controls that already exist in those tools. If someone doesn’t have access to a document in Google Drive, they won’t see that content surface in Glean either. That’s not a trivial engineering problem, and it’s one reason the platform takes weeks to deploy properly rather than hours.

The company claims a three-week implementation timeline for initial deployment, 93% adoption rates within two years of rollout, and an average of 110 hours saved per user annually. These are enterprise sales figures so take them with appropriate skepticism, but the underlying model is sound.

The features that earn the enterprise budget

Universal search across work tools

This is where Glean starts. The platform connects to over 100 data sources and builds a unified, continuously updated index of all your internal content. Slack threads, Confluence pages, Google Drive documents, Salesforce records, GitHub code and pull requests, Jira tickets, ServiceNow incidents, Zendesk conversations, and dozens more.

The search itself uses hybrid retrieval, combining keyword matching with semantic similarity, which means you can search by exact term or by concept. You can ask “what did we decide about the data residency requirements for the EU expansion” and get an answer that pulls from the right meeting notes or Slack thread, even if none of those exact words appear together in a single document.

The relevance model also personalizes results based on your role, your team, and your recent activity patterns. An engineer searching for “deployment process” gets different results than a recruiter running the same query. That personalization is one of the features that earns Glean its adoption numbers.

Glean Assistant

Glean Assistant is the conversational interface built on top of the search and retrieval layer. You ask a question in natural language, and it synthesizes an answer from real internal sources, with citations back to the original documents or conversations. It doesn’t hallucinate details about your company because it’s grounded in what it can actually retrieve.

This is meaningfully different from general-purpose AI assistants. When an employee at a 2,000-person company asks “what’s our parental leave policy in Germany,” they don’t want a synthesized guess from training data. They want the exact current policy document, ideally with a pointer to the People team page where it lives. Glean Assistant delivers that.

The assistant also handles content generation tasks. Writing a draft based on existing internal context, summarizing a long thread, creating a structured report from scattered documents. It has a Canvas feature for longer-form collaborative content creation.

Glean Agents

Agents are where the platform moves from retrieval to action. Glean Agents are configurable AI workers that can execute multi-step workflows using your connected tools and internal knowledge. They’re not chatbots. They can be triggered by schedules, by events in connected systems, or by other agents.

A customer support team might deploy an agent that automatically pulls relevant documentation and past ticket history when a new support request comes in. An IT team might configure an agent that handles employee onboarding questions by pulling from HR policy documents, answering them in Slack, and routing escalations to the right person. Engineering teams can build agents that summarize pull request activity, generate incident reports from PagerDuty events, or brief the on-call engineer before their shift.

Glean supports agent orchestration, meaning agents can delegate to other agents for complex multi-step tasks.

Glean Apps platform

Glean Apps lets teams build custom internal AI applications on top of Glean’s retrieval and agent infrastructure without managing their own RAG stack. An engineering team that wants a dedicated tool for architecture decision lookup can build it on Glean. A sales team that wants a deal-briefing tool that pulls from CRM, email, and call transcripts can build that without standing up their own vector database and embedding pipeline.

This is a significant differentiator for IT and platform teams that want to offer AI capabilities across the organization without building and maintaining separate backend infrastructure for every use case. The governance controls in Glean Protect extend to all apps built on the platform.

Permissions-aware retrieval

This deserves its own heading because it’s the feature that makes everything else credible at the enterprise level. Glean doesn’t store copies of your data in a flat index that ignores access controls. It mirrors the permissions from each source system, in real time.

When you connect Salesforce, Glean learns who has access to which accounts and respects those boundaries in search results. When someone leaves a confidential Slack channel, their Glean results stop surfacing that channel’s content. This is what allows an enterprise with 5,000 employees to deploy a shared search interface without creating a compliance nightmare.

Glean Protect extends this with real-time data classification, activity monitoring, and audit trails. For regulated industries like financial services and healthcare, these aren’t nice-to-haves.

Pricing

Glean does not publish pricing on its website. You will not find a pricing page. There’s no trial, no freemium tier, and no self-serve path.

Based on what’s publicly known from analyst reports and community discussions, Glean contracts are structured per seat and run on annual terms. For a mid-sized enterprise deployment of 500 to 1,000 employees, expect contracts in the range of tens of thousands of dollars annually, potentially six figures depending on feature tier and connector scope. Very large deployments with custom requirements can go higher.

The sales process starts with a demo request. From there, Glean typically proposes a pilot program, often with a defined subset of your org, before full rollout. The claimed three-week implementation timeline applies to getting core search functional. Getting agents and apps built out to full organizational value takes longer, requires internal technical resources, and is an ongoing investment, not a one-time project.

There’s no way to evaluate Glean’s fit without engaging with their sales team. For budget-conscious evaluators: if you’re not prepared to spend at minimum five figures annually and dedicate internal headcount to the implementation, the conversation probably ends at the demo stage.

What you’re buying is not just software. You’re buying the index, the permissions sync infrastructure, the connector maintenance, the model updates, and the enterprise support that comes with a vendor at this price point.

Where Glean wins and where it doesn’t

Glean wins in organizations where the cost of not finding information is measurable and frequent. When engineers spend an hour a week hunting for context before they can write code, when customer support agents have to ping three different Slack channels before they can answer a ticket, when new hires feel lost for months because onboarding knowledge is scattered across wikis and old email threads. At that scale, the ROI math on Glean works.

It also wins in regulated industries where the alternative to Glean is either no AI at all or a compliance team’s nightmare: employees using general-purpose AI tools with no data governance.

Where it doesn’t win is anywhere the problem hasn’t reached critical mass yet. A 50-person startup with tight communication and a well-maintained Notion workspace doesn’t need Glean. A 200-person company that has most of its knowledge in one or two tools will find the cost-benefit math hard to justify.

It also doesn’t win in situations where your underlying data is a mess. Glean can index garbage, but it can’t fix it. If your Confluence is full of outdated pages, your Slack is an unstructured firehose, and your Salesforce records are inconsistently maintained, Glean will surface that chaos more efficiently. Data hygiene before deployment matters.

Who Glean is built for

Glean is built for companies with 500 or more employees that feel the daily friction of fragmented internal knowledge. Specifically:

Engineering organizations at scale where technical context is spread across GitHub, Jira, Confluence, and Slack, and new engineers spend weeks just figuring out how things work.

Customer-facing teams in sales and support where account context, product knowledge, and institutional memory need to come together quickly to serve customers well.

People and IT teams that spend significant time answering repetitive internal questions and want to deflect that volume to an AI that can give accurate, policy-grounded answers.

Organizations in regulated industries like financial services, healthcare, or government where data governance controls aren’t optional.

If your company is generating knowledge faster than any individual can track, Glean is worth the serious evaluation. If you’re not at that scale, you’re paying enterprise prices for a problem you don’t have yet.

Glean vs the alternatives

Glean vs Notion AI

These tools occupy different positions. Notion AI enhances the workspace you’ve built inside Notion. It helps you write, summarize, and find things within Notion. Glean treats Notion as one source among many and connects it alongside Slack, Salesforce, GitHub, and the rest. If your company lives inside Notion and that’s genuinely where your knowledge lives, Notion AI is faster, cheaper, and easier. If Notion is just one of a dozen tools, Glean is the right layer to put on top.

Notion AI’s pricing is per user at a fraction of Glean’s cost, which matters for smaller teams. But Notion AI has no meaningful permissions model, no agents framework, and no cross-app search. At 50 people: Notion AI. At 1,000 people with a heterogeneous stack: Glean.

Glean vs Perplexity Enterprise

Perplexity’s enterprise offering gives you an AI search interface with some ability to connect internal sources. It’s significantly cheaper and comes with a faster evaluation path. For teams that primarily need better external research with some internal file access bolted on, it’s a reasonable option.

Glean goes deeper on internal knowledge, permissions, agents, and custom app development. Perplexity Enterprise doesn’t have a comparable governance layer or the breadth of enterprise connectors. If your primary use case is internal knowledge at scale with compliance requirements, Glean is the more complete answer.

Glean vs Cody

These aren’t direct competitors. Cody, from Sourcegraph, is a coding assistant that uses your codebase as context. It’s built for developers and focused on code comprehension, generation, and review. Glean touches code sources like GitHub but isn’t a coding assistant.

The comparison matters for engineering orgs that are trying to decide where to spend their AI budget. Cody solves a specific, high-value developer problem. Glean solves the broader organizational knowledge problem, including for developers. Some large engineering organizations run both. If you have to pick one, identify whether your bigger pain is “we can’t write code efficiently” or “we can’t find company context efficiently.” Those are different problems with different tools. See also the best AI agents for coding if the developer productivity use case is what you’re evaluating.

Getting started

You start at glean.com and request a demo. There’s no trial you can spin up and no sandbox to poke around in before talking to sales.

Expect the sales conversation to include a discovery call to scope your connector list, a pilot proposal targeting a specific team or department, and a security review process that will involve your IT and compliance teams. The security review is real work, not a checkbox. Glean handles enterprise-grade authentication, SSO, data residency requirements, and SOC 2 compliance, but your team will need to validate all of that.

The pilot phase is where you find out if Glean delivers for your specific context. The company claims a three-week implementation for initial search functionality. Use the pilot to test the quality of retrieval across your most important data sources, to validate that permissions sync works correctly, and to measure actual adoption among the pilot group.

Full deployment including agents and apps is a longer engagement. Plan for dedicated internal resources to configure workflows and build out the app layer. Treat it as an ongoing product investment, not a one-time rollout.

If the pilot numbers are good and the budget is there, Glean compounds in value as more employees use it and more data sources are connected.

The bottom line

Glean is the most mature and most complete answer to a real enterprise problem: your company generates knowledge constantly, it’s scattered across a dozen tools, and most of it is effectively invisible to the people who need it. The platform is well-engineered, genuinely permissions-aware, and backed by a company with the funding and technical credibility to be a long-term partner.

The catch is the price and the minimum viable scale. You need to be large enough, messy enough, and resourced enough to justify and implement it. The sales process is a commitment before you spend a dollar. But for the companies that fit the profile, Glean isn’t a luxury purchase. It’s the layer that makes everything else your company has built actually findable.

Key features

• Universal search across 100+ connectors: Slack, Google Workspace, Confluence, Salesforce, GitHub, Jira, ServiceNow, and more
• Permissions-aware retrieval that respects your existing ACLs so people only see what they're allowed to see
• Glean Assistant for natural-language Q&A grounded in your actual internal knowledge
• Glean Agents for building automated multi-step workflows on top of your company data
• Glean Apps platform: build internal AI applications without standing up your own RAG infrastructure
• Deep Research mode for synthesizing information across sources into structured reports
• Glean Protect for real-time governance, data classification, and audit trails

Pros and cons

Who is Glean for?

• Engineering teams that need to find answers across GitHub, Confluence, and Slack without switching between five tabs
• Customer support organizations that want agents to pull from product docs and past ticket history to resolve issues faster
• People and IT departments that need to deflect repetitive internal questions by surfacing accurate policy documents automatically
• Sales and revenue teams that need account context from Salesforce, email, and internal notes without manual digging

Alternatives to Glean

If Glean isn't quite the right fit, the closest alternatives are notion-ai , perplexity , and cody . See our full Glean alternatives page for side-by-side comparisons.