Glean

What is Glean?

Glean is an enterprise Work AI platform that connects company knowledge, systems and context so that AI can actually do useful work inside the organisation rather than answering from the open internet. The problem it addresses is specific to large organisations: the information an employee needs to do their job is scattered across Slack threads, Google Drive documents, Jira tickets, Confluence pages, SharePoint sites, GitHub repositories and Salesforce records, and no general-purpose chatbot has access to any of it. Glean indexes that material, understands who is allowed to see what, and puts a search box, an assistant and a fleet of agents on top.

The platform is operated by Glean Technologies, Inc. and its subsidiaries, and it is worth noting that the privacy statement published on the marketing site covers the website and business operations only, explicitly stating that it does not apply to your use of the Solutions themselves. Founder and CEO Arvind Jain spent over a decade at Google as a distinguished engineer leading teams across Search, Maps and YouTube, and previously co-founded Rubrik, while co-founder and CTO Vishwanath T R held technical leadership roles at Facebook for close to a decade. That pedigree matters here because enterprise search is fundamentally an indexing and ranking problem before it is an AI problem.

The company's own timeline traces a deliberate progression: founded in 2019 building enterprise search in stealth, public launch in 2021 already running at more than 40 companies, unicorn status in 2022, a conversational Assistant in 2023, Agents alongside a 7.2 billion dollar valuation in 2025, and AI coworkers in 2026. Understanding that sequence explains the product: the retrieval and permissions layer came first, and the generative features were built on top of it. This is the opposite of the path taken by consumer AI search tools, which start with a model and later try to bolt on enterprise data access.

Core Features

• A connector layer covering the enterprise application estate: The connector layer is the foundation of the whole product, with more than 275 out-of-the-box integrations spanning native connectors, MCP-based connectors and a Push API for systems that need to send data in. The catalogue is organised by function — sales and marketing, engineering and analytics, HR, documents, project management — and includes the systems most companies actually run on.
• Permission inheritance enforced at query time: All data permissions are inherited from the source systems and strictly enforced, so users only ever see what they are already allowed to see, and permission changes propagate immediately rather than waiting for the next crawl. This is the hardest part of enterprise search and the single most important thing to evaluate in any competing product.
• A knowledge graph built over indexed content: The platform constructs indexes and knowledge graphs across your data so it can understand how the business actually works — who owns what, which projects relate to which teams, which documents are authoritative — rather than treating every document as an isolated blob of text.
• An assistant with access to real company context: The conversational layer answers questions grounded in internal material and can draw on more than 35 different large language models, letting organisations choose models by cost, capability or vendor policy rather than being locked to one provider.
• Agents and Actions that execute work: Beyond retrieval, where AI agents move from answering to doing, Actions and Agents let you invoke tasks in natural language, trigger agents on events or schedules, and constrain what agents may do through explicit boundaries and human-in-the-loop approval before execution.
• Token economics as an explicit product feature: The headline commercial argument has shifted from findability to cost control, with Glean claiming a 30 percent reduction in token usage compared with off-the-shelf MCP tools. The logic is that pre-connected, well-structured context means the model performs fewer operations to reach an answer.
• A governance layer spanning platform, data and agents: Glean Protect groups the security controls into three tiers, covering deployment isolation, sensitive-data detection and remediation, and guardrails on what autonomous agents are permitted to do.

Use Cases

1. Reducing the internal support burden on expert teams: In most large companies a small number of people answer the same questions repeatedly because they are the only ones who know where the answer lives. Pointing employees at a permission-aware search layer moves those answers into self-service. One PeerSpot reviewer working in support described connecting Confluence with Jira tickets to get a materially faster perspective than querying each system separately, and reported roughly a five percent improvement in ticket closure times.
2. Onboarding new employees into a large codebase or org structure: New joiners lose weeks discovering which document is current, which team owns a service and who to ask. A knowledge graph over the company's actual systems compresses that discovery, and because permissions are inherited, a new hire sees exactly the subset of material their role entitles them to.
3. Grounding AI initiatives without a bespoke RAG build: Many enterprises attempt to build their own retrieval-augmented generation stack and discover that connectors, permission synchronisation and freshness are where the effort actually goes. Buying that layer converts a multi-quarter engineering project into a deployment.
4. Cross-department agent deployment: Because the same context layer serves every team, organisations deploy agents across functions rather than in a single department. Glean reports that more than 85 percent of customers deploy across five or more departments, and customer stories cite thousands of agents built inside individual companies (vendor-reported figures).
5. Controlling AI spend as usage scales: For organisations whose model bills are growing faster than their AI value, a context layer that reduces the number of model operations per task is an argument the finance function understands. This has become Glean's primary commercial pitch rather than a secondary benefit.
6. Making archival content usable: Organisations sitting on decades of material — research, editorial archives, historical contracts — can bring that corpus into the same searchable surface as live systems, turning dormant content into something staff can actually query.

How to use Glean

1. Engage the sales team through the demo request on the website. There is no self-service signup, so the process begins with scoping: which systems you need connected, how many users, which deployment model and which regions.
2. Choose a deployment model. The platform can run in a Glean-hosted single-tenant environment or inside your own AWS, Azure or GCP account, with regional placement chosen to satisfy data residency obligations.
3. Connect source systems, starting narrow. Enable the connectors for the handful of systems that hold the majority of the knowledge your pilot group needs, and confirm that permission inheritance behaves as expected before broadening the scope.
4. Validate permissions with deliberately adversarial testing. Have users from different roles and seniority levels search for material they should not be able to see. This is the single most important acceptance test, and it is far cheaper to run during a pilot than to discover in production.
5. Turn on sensitive content policies and review what surfaces. Overshared documents that were technically accessible but practically invisible often become findable the moment good search arrives, so budget time to triage and remediate what the scan reveals.
6. Roll out the assistant to a pilot department, then introduce agents once search behaviour is trusted. Start agents in read-only or approval-gated configurations, and only grant write actions after the human-in-the-loop workflow has proven reliable.

Tips & Best Practices

• Treat oversharing discovery as a project phase, not a surprise. Deploying enterprise search reliably reveals that permissions in your source systems are looser than anyone believed. Glean states plainly that permissions alone are not enough to secure enterprise data for AI, which is why sensitive data protection is built in alongside a triage workflow for remediating overshared documents. Plan for that clean-up work rather than discovering it mid-rollout.
• Scope connectors by knowledge density, not by system count. Connecting twenty systems that hold little unique knowledge adds noise and cost. Connecting the three systems where decisions actually get recorded delivers most of the value.
• Insist on latency testing with realistic queries during the pilot. Reviewers report that response times can be noticeably slow, and a search tool that people find sluggish will lose to the habit of asking a colleague, regardless of answer quality.
• Constrain agents before you scale them. The vendor also concedes that agents are non-deterministic even under strong policies, and addresses this with alignment models that pre-scan every write action before it executes, plus protections against prompt injection, malicious code and toxic content. Use those controls deliberately rather than assuming defaults are appropriate.
• Budget for change management, not just deployment. The reported adoption figures come from organisations that invested in training and internal advocacy. A technically successful deployment that nobody uses returns nothing.
• Model the pricing against your actual usage curve. Consumption-based pricing means costs scale with adoption, which is the outcome you are working toward. Build the projection for the successful case, not the pilot.
• Keep an authoritative source of truth outside the search layer. Glean surfaces and ranks what already exists; it does not fix contradictory or outdated documents. Governance of the underlying content remains your responsibility.

Who is Glean for?

• Large enterprises with fragmented tool estates: The core constituency. Organisations running dozens of SaaS systems where no single person knows where anything lives.
• Companies in compliance-heavy sectors: Financial services is the most active industry researching the product on PeerSpot, and the compliance certification set is built for regulated environments.
• Organisations with data residency obligations: Teams that must keep data in specific regions and therefore need single-tenant or bring-your-own-cloud deployment.
• IT and internal support functions: Teams whose ticket volume is dominated by questions that have documented answers nobody can find.
• Enterprises formalising an AI strategy: By May 2026 the company reported reaching 300 million dollars in annual recurring revenue, a threefold increase from the 100 million dollar milestone just 15 months earlier, with customers including Databricks, Reddit, Pinterest and Samsung.
• Teams that considered building their own RAG stack: Engineering organisations that have scoped the connector and permissions work and concluded it is not their differentiator.
• Not suitable for individuals or small teams: There is no free tier, no self-service entry point and no individual plan. Small organisations should look at general-purpose assistants or lighter knowledge tools instead.

Platforms

• Web application: The primary interface, accessed through the browser.
• In-app surfaces across connected tools: The platform integrates into the systems where work happens rather than requiring users to visit a separate destination, with connectors spanning Slack, Google Workspace, Microsoft 365, Atlassian tools and many more.
• Single-tenant cloud deployment: Deployment runs in a fully isolated single-tenant environment, either hosted by Glean or inside your own AWS, Azure or GCP account, with regional placement across AMER, EMEA and APAC for data sovereignty requirements.
• MCP-based connectivity: Alongside native connectors, the platform uses Model Context Protocol connectors, allowing it to act as a context source for other AI tooling as well as consuming from it.
• Compliance certifications: The compliance posture advertised on the homepage covers ISO 42001, HIPAA, TX-RAMP Level 2, SOC 2 Type II, ISO 27001 and GDPR.

Pricing & Plans

Glean does not publish pricing. There is no pricing page, no free tier and no self-service signup; the only route into the product is a demo request handled by the sales organisation. This is standard for enterprise software sold at this scale, but it does mean you cannot evaluate cost without engaging commercially, and it makes direct comparison against competitors difficult without running parallel procurement processes.

What is known about the structure comes from the CEO rather than the website. Jain has described two structures to TechCrunch: a consumption-based model where clients pay per use, and a hybrid model combining a fixed monthly fee for active users with separate usage fees for model consumption. The practical implication is that costs scale with adoption, so the financial model should be built around your projected steady-state usage rather than pilot volumes. Any specific per-seat figure you find in third-party summaries should be treated as unverified. (To be verified.)

Alternatives

• Microsoft 365 Copilot: The most direct threat for organisations already standardised on Microsoft, with the advantage of native access to Microsoft Graph data and bundled commercial terms, though weaker across non-Microsoft systems.
• Google Gemini for Workspace: The equivalent argument for Google-centric organisations, strong within Workspace and correspondingly weaker outside it.
• Amazon Q Business: AWS's enterprise assistant, attractive where the data estate already sits in AWS and procurement runs through existing cloud commitments.
• ChatGPT Enterprise and Claude for Enterprise: Model-first offerings that have added connector and retrieval capabilities, generally stronger on raw reasoning and weaker on the depth of permission-aware indexing across a heterogeneous estate.
• Elastic Search and traditional search platforms: PeerSpot data puts Glean's mindshare in the Search as a Service category at 4.7 percent, up from 2.3 percent a year earlier but still well behind Elastic Search at 16.2 percent and Amazon OpenSearch Service at 12.3 percent. These give you more control and lower licence costs in exchange for building the connector, permissions and AI layers yourself.
• Building in-house on a vector database: Viable for organisations with a small number of systems and strong engineering capacity, but the connector maintenance and permission synchronisation burden is routinely underestimated.

Limitations & Considerations

• Reported revenue figures deserve careful reading: TechCrunch pointed out that this 300 million dollar figure cannot be fully described as traditional ARR, because a consumption model by definition has no strictly recurring component, making part of the total more accurately an annualised revenue run rate. This matters when assessing vendor stability, not because the business is weak, but because headline growth numbers and contracted recurring revenue are not the same thing.
• Connector setup and custom development have friction: An analyst at a financial services firm reviewing on PeerSpot raised three concrete complaints: enabling connectors required a lot of back and forth rather than being available by default, custom actions lacked a developer log for tracking requests to custom endpoints, and query latency was noticeably high. The same reviewer characterised the situation as a trade-off between speed and accuracy, noting that responses were usually very accurate.
• Agent workflow controls are still maturing: Another recurring theme is that workflow agents apply AI at every node with no per-node toggle, which reviewers say sometimes messes up the output, alongside requests for more model choices and better routing quality instead of exhaustive searching.
• Independent analysis flags adoption risk, not just technical risk: A signed research memo from Contrary Research raises three concerns a vendor page will not: that enterprise search is a difficult market in its own right, that connecting proprietary organisational data including internal chat carries data-security and regulatory exposure as the company pushes into regulated industries, and that adoption is uneven. Its most useful datapoint comes from a customer interview reporting that even after launch, employee adoption could be as low as 20-40 percent and varies considerably between teams. Treat that as the realistic planning assumption rather than the vendor's deployment-breadth figures, and note that the same page discloses its publisher holds an investment in Rubrik, a company Glean's CEO previously co-founded.
• Revenue milestones come from the company, reported by several outlets: Fortune reported in December 2025 that Jain disclosed 200 million dollars in ARR at its Brainstorm AI conference, double the 100 million figure from nine months earlier, and confirmed the June Series F of 150 million dollars lifting the valuation past 7 billion from 4.6 billion in 2024. These are consistent across TechCrunch, CNBC and Fortune, but each traces back to company disclosure rather than audited accounts, so read them as a credible growth trajectory rather than verified financials.
• The public review sample is small: On PeerSpot the platform holds 4.3 out of 5 across 12 reviews with 100 percent of reviewers willing to recommend it, a sample dominated by large enterprises but small enough that it should be read as directional rather than statistically firm. Enterprise software of this price point generates far fewer public reviews than consumer tools, so reference calls matter more than star ratings.
• The competitive position has changed fundamentally: Jain has acknowledged that the competitive picture changed fundamentally, saying that for the first four or five years of the company's existence there was no competition, whereas Google, Microsoft, OpenAI, Anthropic, Salesforce and Atlassian are now all building comparable tools. Several of those competitors can bundle the capability into contracts you already hold.
• Deployment quality depends on your source systems: Search quality is bounded by the state of the underlying content and permissions. Organisations with heavily overshared drives, duplicated documentation and stale content will surface exactly those problems faster.
• Scope of the published privacy statement: The marketing site's privacy statement explicitly excludes the product itself, so the terms governing your corporate data are contractual rather than published. Route those questions through legal and security review during procurement rather than inferring them from the website.

FAQ

Q1. How is Glean different from ChatGPT or other general AI assistants?

General assistants have no access to your internal systems and no model of who is permitted to see what. Glean indexes your company's actual content across connected systems and enforces the source permissions on every query, so answers are grounded in internal knowledge rather than public training data.

Q2. Does it respect existing file and channel permissions?

Yes. Permissions are inherited from each source system and strictly enforced, and permission changes are reflected in results immediately rather than at the next index refresh. Testing this behaviour with users at different access levels should still be part of your evaluation.

Q3. How much does it cost?

Glean does not publish pricing. Contracts are negotiated through sales, with the CEO describing both consumption-based pricing and a hybrid of fixed per-active-user fees plus model usage charges. Expect costs to scale with adoption.

Q4. How many systems can it connect to?

More than 275 out-of-the-box connectors are available, delivered as native integrations, MCP-based connectors or via a Push API for systems that need to push content in.

Q5. Can we deploy it in our own cloud?

Yes. The platform supports fully isolated single-tenant deployment either hosted by Glean or inside your own AWS, Azure or GCP environment, with regional placement across AMER, EMEA and APAC.

Q6. Is our data used to train models?

Glean states that it holds zero-retention agreements with model providers so that customer data is never stored or used for model training, and that its RAG architecture minimises what is exposed to the model in the first place. Confirm the specific contractual terms during procurement.

Q7. What happens if our documents are overshared internally?

Good search makes previously invisible oversharing visible. Glean provides sensitive content detection policies covering credentials, payment data and medical information, plus a triage workflow to investigate and remediate overshared documents, but the underlying permission hygiene remains your responsibility.

Q8. Can agents take actions that change data?

Yes, and this is why the guardrails matter. Agents can be constrained by explicit action boundaries, require human approval before execution, and are subject to alignment models that pre-scan write actions. Start with approval-gated configurations before granting autonomous write access.

Q9. Is there a free trial or a plan for small teams?

No. There is no free tier or self-service entry point, and the product is built and priced for large organisations. Smaller teams are better served by general-purpose assistants or lighter knowledge management tools.