Glean for Enterprise Search and Knowledge Discovery

Overview

Glean is an AI-powered enterprise search and knowledge discovery platform that aims to centralize access to information scattered across an organization's many applications. Founded in 2019, Glean's core function is to allow employees to find relevant documents, conversations, and data points from a single search interface, rather than navigating disparate systems. The platform integrates with a range of enterprise tools, including collaboration suites, CRM systems, code repositories, and document management platforms, to build a comprehensive index of an organization's internal knowledge base.

The system utilizes natural language processing (NLP) and machine learning models to understand user queries, contextualize search results, and provide direct answers to questions. This approach is intended to move beyond traditional keyword-based search by interpreting the intent behind a user's request. For example, an employee searching for "Q4 marketing plan" might receive not just documents titled as such, but also relevant Slack discussions, project management tasks, or related presentations, all ranked by relevance. Glean's capabilities extend to personalized recommendations, suggesting information that might be relevant to an employee's role, projects, or recent activity, similar to how recommendation engines operate in consumer applications. This personalization is designed to proactively surface information before a user explicitly searches for it, potentially improving workflow efficiency.

Glean is best suited for organizations grappling with information fragmentation due to a large number of SaaS applications and internal data silos. Its target users include knowledge workers, developers, and technical buyers who need to quickly access accurate information to perform their roles. The platform is designed to improve internal productivity by reducing the time employees spend searching for information, answering common questions, and onboarding new team members by providing rapid access to institutional knowledge. It supports compliance requirements such as SOC 2 Type II, GDPR, CCPA, and ISO 27001, which addresses data security and privacy concerns for enterprise deployments. For technical teams, Glean offers APIs to integrate custom data sources and extend search functionality, allowing for programmatic interaction with the platform's capabilities.

Key features

• AI-powered Enterprise Search: Indexes and makes searchable content from various enterprise applications, using AI to understand query intent and provide relevant results.
• Unified Knowledge Discovery: Aggregates information from disparate internal systems into a single, searchable interface, eliminating the need to switch between applications.
• Personalized Answers: Employs natural language processing to directly answer employee questions, drawing information from indexed documents and conversations.
• Contextual Search: Ranks search results based on user context, role, team, and activity history to provide more relevant information.
• Custom Connectors: Provides APIs and tools for integrating with proprietary or niche internal data sources beyond standard SaaS applications.
• Security and Compliance: Adheres to enterprise security standards including SOC 2 Type II, GDPR, CCPA, and ISO 27001, ensuring data privacy and governance.
• Usage Analytics: Offers dashboards and reports on search queries, content gaps, and user behavior to help administrators optimize information access.

Pricing

Glean operates on a custom enterprise pricing model, which typically involves discussions with their sales team to determine specific organizational needs and user counts. As of June 2026, detailed public pricing tiers are not available on their website, indicating a negotiated approach for each client.

For specific pricing inquiries, prospective customers are directed to Glean's pricing page to request a demo and custom quote Glean pricing information.

Common integrations

Glean integrates with a range of enterprise applications to centralize knowledge. While the full list of connectors is extensive, common integrations often include platforms for communication, document management, project management, and developer tools. Developers can also use Glean's APIs to build custom connectors for internal systems not natively supported Glean API documentation.

• Collaboration & Communication: Google Workspace (Gmail, Drive, Calendar), Microsoft 365 (Outlook, SharePoint, Teams), Slack, Zoom.
• Document & Content Management: Confluence, Notion, Dropbox, Box, ServiceNow.
• CRM & Sales: Salesforce (via Salesforce Connect for data integration).
• Project Management: Jira, Asana, Trello.
• Code & Developer Tools: GitHub, GitLab.
• HR & Identity: Workday, Okta.

Alternatives

The enterprise search market includes several vendors offering AI-powered solutions to address information retrieval challenges. These alternatives provide varying feature sets, integration ecosystems, and deployment models.

• Coveo: Offers AI-powered search and recommendations for customer service, commerce, and workplace applications, focusing on personalized experiences.
• Elastic Enterprise Search: A suite of search products built on Elasticsearch, providing capabilities for workplace search, site search, and app search.
• Swiftype: Provides enterprise search and site search solutions, acquired by Elastic, often integrated within the Elastic ecosystem for content indexing and retrieval.

Getting started

While Glean's primary interaction is through its web interface, developers can extend its capabilities using the Glean API for custom integrations or data ingestion. The following example demonstrates a conceptual API call to search for information, assuming authentication has been handled and a client library or direct HTTP request is used. This Python example uses the requests library to make a POST request to a hypothetical Glean search endpoint.

import requests
import json

def glean_search(query_text, api_key, tenant_id):
    """
    Performs a search query against the Glean API.
    This is a conceptual example; actual endpoint and payload may vary.
    """
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
        "X-Glean-Tenant": tenant_id
    }
    payload = {
        "query": query_text,
        "limit": 5,
        "offset": 0
    }
    # Hypothetical API endpoint for search
    search_url = "https://api.glean.com/v1/search"

    try:
        response = requests.post(search_url, headers=headers, data=json.dumps(payload))
        response.raise_for_status()  # Raise an exception for HTTP errors
        return response.json()
    except requests.exceptions.HTTPError as err:
        print(f"HTTP error occurred: {err}")
        print(f"Response body: {response.text}")
        return None
    except Exception as err:
        print(f"An error occurred: {err}")
        return None

# Example usage (replace with actual API key and tenant ID)
GLEAN_API_KEY = "YOUR_GLEAN_API_KEY"
GLEAN_TENANT_ID = "YOUR_GLEAN_TENANT_ID"

if __name__ == "__main__":
    search_query = "latest marketing strategy document"
    search_results = glean_search(search_query, GLEAN_API_KEY, GLEAN_TENANT_ID)

    if search_results:
        print(f"Search results for '{search_query}':")
        for item in search_results.get("results", [])[:2]: # Display top 2 results
            print(f"  Title: {item.get('title')}")
            print(f"  URL: {item.get('url')}")
            print(f"  Snippet: {item.get('snippet', '')[:150]}...")
            print("---------------------------------")
    else:
        print("No results or an error occurred.")

This code snippet illustrates how to construct a request to search for a specific query. Developers would typically refer to the official Glean API documentation for exact endpoint details, required authentication methods, and payload structures for various operations, including data ingestion and custom connector development.