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Glean

Glean

Enterprise search that connects every SaaS tool and answers from your own content.

Enterprise AI Platforms & Agent Orchestration Active #EnterpriseSearch#Knowledge#AIAssistant#Enterprise

In short

Unified search across an organization's entire SaaS stack, with a knowledge assistant that answers from the real content rather than generic training data. Particularly valuable in organizations where institutional knowledge is fragmented across many tools.

What it is best at

  1. Finding anything across Slack, email, Confluence, Jira, Drive, and 100+ other sources
  2. Answering questions from internal documentation without knowing where it lives
  3. Reducing time spent finding information as a recurring daily tax

Built for: Enterprise Operations · Dev Teams · Marketing

Technical foundation

Base model
Proprietary retrieval models plus frontier LLMs for answer generation, grounded on company content.
Context and file handling
Connects to and indexes SaaS tools via read-only connectors.
Latency
Search is near-instant. Generated answers take a few seconds.
Output quality and limits
Strong when the source content is well-maintained. As with all RAG systems, the answers are only as current as the indexed content.

Pricing and access tiers

TierModelKey inclusionsLimits
EnterprisePer-seat, annualAll connectors, AI assistant, admin controlsContract-based

Pricing, version numbers, context-window sizes, and compliance certifications change frequently. Where stated they are accurate as of the as_of date and should be confirmed with the vendor before any procurement or compliance decision. Where they could not be stated confidently they are omitted rather than guessed.

Security, privacy and governance

Training data opt-out
Glean does not train its models on customer content. The data stays indexed in a dedicated tenant. Verify the current DPA.

The governance question this raises

The information-disclosure risk from Glean runs in the same direction as with Microsoft Copilot and Notion AI: it surfaces content the user already has permission to see, but it surfaces it faster and in response to natural language questions. If permission settings are permissive, previously obscure content becomes easily discoverable. Run a permission audit on the connected sources before deploying rather than after discovering that an AI answered a question with a confidential document the user technically had access to.

No compliance certifications are listed here. Certification status is vendor-specific and time-specific, so it is stated only where verified rather than assumed. Check the vendor’s trust centre and confirm it covers the specific tier you are buying.

Integrations and ecosystem

  • 100+ native SaaS connectors
  • Enterprise identity providers for permissions
  • Slack, Teams, and browser extensions

API and SDKs: API for custom integrations.

The verdict

Strengths

  • Genuinely useful for organizations with fragmented knowledge
  • Respects existing permissions from connected systems
  • Broad connector coverage

Drawbacks

  • Permissive legacy permissions become easily discoverable
  • Per-seat enterprise pricing at scale
  • Answer quality depends on documentation quality

Consider instead: Microsoft Copilot, Notion AI, Perplexity Enterprise Pro

Frequently asked questions

What is Glean used for?

Unified search across an organization's entire SaaS stack, with a knowledge assistant that answers from the real content rather than generic training data. Particularly valuable in organizations where institutional knowledge is fragmented across many tools.

What model does Glean run on?

Proprietary retrieval models plus frontier LLMs for answer generation, grounded on company content.

Does Glean train on your data?

Glean does not train its models on customer content. The data stays indexed in a dedicated tenant. Verify the current DPA.

What are the alternatives to Glean?

The closest comparable tools are Microsoft Copilot, Notion AI, Perplexity Enterprise Pro. Which fits depends on where the work already lives and what the organization's data terms require.

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