Glean

What is Glean?

Glean is an enterprise-grade semantic search and knowledge discovery platform engineered to create a unified access layer for a company’s sprawling digital ecosystem. At its core, it addresses a fundamental scalability problem: as organizations grow, their data becomes fragmented across dozens of SaaS applications like Slack, Jira, Confluence, and Google Drive. Glean indexes these disparate sources and leverages advanced AI to provide a single, intelligent search interface. For technical teams, it acts as a queryable API over the entire organization’s knowledge base, translating natural language questions into contextually relevant, permission-aware results. It moves beyond simple keyword matching to understand user intent, making it a powerful tool for accelerating productivity and reducing the friction of information retrieval in complex corporate environments.

Key Features and How It Works

From a developer’s perspective, Glean’s architecture is built on several powerful, interconnected components that deliver its core value proposition.

• Vector Search with Deep Learning-Based LLMs: Instead of legacy keyword-based indexing, Glean uses vector embeddings to represent content. This means it understands the semantic meaning behind a query. When you search for “Q3 roadmap for the API gateway,” it finds documents that discuss the topic, even if they use different phrasing like “third-quarter plan for the ingress controller,” because it grasps the conceptual relationship between the terms.
• Continuous Training on Company Language: This feature operates much like a new engineer onboarding onto a complex project. Initially, they don’t know the internal codenames, acronyms, or the project’s history. Over time, through exposure, they learn that “Project Chimera” refers to the legacy monolith and “Triton” is the new microservices initiative. Glean does the same with your company’s data, continuously learning the unique lexicon, relationships, and context from documents and conversations to refine search relevance without manual configuration.
• Generative AI for Enterprise: This moves Glean from a search engine to an answer engine. It can synthesize information from multiple sources to provide direct answers, summaries, or analyses. For a developer, this is the equivalent of asking a senior architect a question and getting a concise summary rather than a list of 20 links to internal wikis and design documents.
• Personalized Results and Knowledge Graph: Glean builds a dynamic knowledge graph that maps relationships between content, people, and teams. It understands who reports to whom, which teams own which services, and who is the subject matter expert on a given topic. This allows it to personalize search results, prioritizing information from your immediate team or projects you’re actively involved in.
• Integration and Security: With over 100 pre-built connectors, Glean minimizes the engineering lift required for integration. Critically, it respects and enforces all data source permissions. This ensures that the unified search layer doesn’t inadvertently create a security vulnerability; users can only see results for content they already have access to in the source application.

Pros and Cons

Pros

• Reduced Context Switching: Provides a single interface to query all enterprise knowledge, drastically cutting down on time wasted searching individual platforms.
• Robust Security Model: Its strict adherence to source-level permissions makes it a safe choice for enterprises concerned with data governance and security.
• High-Quality Connectors: The extensive library of pre-built integrations reduces implementation time and maintenance overhead for engineering teams.
• Intelligent Semantic Search: The model’s ability to learn company-specific jargon provides highly relevant results for technical and business queries alike.

Cons

• Onboarding for Advanced Features: To fully leverage the platform, users may need time to understand how to formulate complex queries or interpret the knowledge graph’s insights.
• Garbage-In, Garbage-Out Principle: The utility of Glean is directly proportional to the quality of the underlying data. If source documentation is outdated or poorly maintained, the search results will reflect that.

Who Should Consider Glean?

Glean is particularly well-suited for organizations where information is highly distributed and knowledge management is a strategic priority.

• Enterprises with Diverse SaaS Stacks: Companies utilizing a wide array of tools (e.g., Slack, Microsoft 365, Jira, Salesforce) will see the highest ROI.
• Rapidly Scaling Startups: Helps new hires get up to speed quickly by providing a single source of truth for fragmented institutional knowledge.
• Engineering and Product Teams: Ideal for locating technical specifications, API documentation, post-mortems, and design documents scattered across Confluence, GitHub, and Jira.
• IT and Security Operations: Enables quick access to incident reports, security policies, and system architecture diagrams for faster response times.

Pricing and Plans

Glean operates on a freemium model, making it accessible for teams of various sizes to get started before committing to a larger rollout.

• Free Plan: A starter plan designed for small teams or for organizations wanting to trial the core search functionality with a limited set of integrations.
• Pro Plan: Starting at $15 per user/month, this plan unlocks a wider range of connectors, advanced administrative features, and enhanced support, making it suitable for growing businesses and larger departments that require more robust integration and control.

What makes Glean great?

How many hours a week do your teams lose just toggling between platforms to find a single piece of information? Glean’s primary value is its masterful execution in solving this pervasive and costly problem. What truly sets it apart is its ability to build and leverage a knowledge graph that understands your organization’s unique context. It’s not a generic search engine bolted onto your apps; it’s a bespoke intelligence layer that learns your company’s ontology—the people, the projects, and the language that connects them. For a technical professional, this is the difference between using a generic, off-the-shelf library and a purpose-built framework designed for your specific problem domain. This deep, contextual understanding, combined with a robust security model and seamless integration, makes Glean a powerful and scalable solution for modern enterprise knowledge management.

Frequently Asked Questions

How does Glean handle data security and access permissions?

Glean integrates directly with the identity provider (e.g., Okta, Azure AD) and enforces the native permissions of each connected application. A user’s search results will never include content they cannot access in the source system.

Can Glean search through code repositories like GitHub or GitLab?

Yes, Glean has connectors for popular code repositories. It can index code, pull requests, issues, and wikis, allowing developers to search for code snippets, discussions, and related documentation from one place.

How does the AI model train on our proprietary data? Is our data used for other customers?

Glean’s AI model is trained on a per-instance basis. Your company’s data is used solely to create and refine the language models and knowledge graph for your organization. Data is not shared or used to train models for any other customer, ensuring privacy and data isolation.

What is the integration process like?

Integration is streamlined via pre-built connectors that authenticate using OAuth. An administrator can typically connect a new application in minutes without requiring significant developer resources. The initial indexing time varies depending on the volume of data.