Why Glean Leads Enterprise Search: What Makes It So Powerful?

Background

Glean was founded in 2019 by former Google search engineer Arvind Jain, whose experience at Google and Rubrik highlighted the difficulty of finding information across siloed enterprise data. The initial goal was an “enterprise‑grade Google Search,” but the need to handle complex permissions and domain knowledge led to a broader Work AI Platform that adds AI‑driven search, content generation, task automation, and workflow optimization.

Market and Competition

In the past year Glean’s ARR grew three‑fold to $1 billion, with a daily‑active‑user to monthly‑active‑user ratio near 40 %. In December 2024 it closed a $260 million Series E round at a $4.6 billion valuation and holds over $5.5 billion in cash. The company targets the Enterprise AI Infrastructure market, estimated at $53‑55 billion in 2024 and projected to reach $117‑158 billion by 2033 (CAGR 9.2‑12.5 %). Competitors include large‑tech platform solutions (Microsoft 365 Copilot, Google Cloud Search), specialist enterprise‑search vendors (Coveo, Elastic, Lucidworks, Algolia), knowledge‑management platforms (Guru, ClickUp), and AI‑native search startups (Perplexity Enterprise, You.com Enterprise). Glean differentiates through deep enterprise integration, a dual knowledge‑graph architecture, and a focus on security and governance.

Users and Product

The core user base is any knowledge worker (engineers, sales, HR, finance, etc.). Glean’s product suite consists of:

Glean Search : secure, highly relevant search powered by >100 real‑time connectors and both enterprise and personal knowledge graphs.

Glean Assistant : an enterprise‑grade ChatGPT‑like assistant that can generate content, answer questions, and produce research reports (e.g., the Deep Research Agent demo).

Glean Agents : AI agents that automate tasks, integrate with tools such as Salesforce, Jira, and Workday, and execute multi‑step workflows. Annual agent actions exceed 100 million and are growing tenfold year‑over‑year.

Technical Challenges

Layer 1 – Data Layer

>100 real‑time data connectors handle extreme heterogeneity (documents, messages, CRM, code, custom apps) and dynamic permissions.

Identity‑graph unifies fragmented user identities for accurate permission mapping.

Signal‑driven ingestion captures metadata, version history, usage patterns, and collaboration signals to assess document authority, popularity, and freshness.

Real‑time or near‑real‑time sync ensures permission changes are instantly reflected in search and AI access control.

Scalable indexing supports hundreds of millions of documents for large multinational enterprises.

Layer 2 – Intelligence Layer

Fine‑tuned embedding models on enterprise‑specific jargon improve RAG retrieval recall and accuracy.

Task‑specific small models and knowledge distillation reduce latency and cost.

LLM‑agnostic routing selects the optimal foundation model per step, balancing performance and expense.

RAG combines keyword, semantic, and graph‑based retrieval (“dual‑graph” approach) with strict permission enforcement before feeding results to LLMs.

Agentic Reasoning Engine supports multi‑step workflows, conditional branching, loops, parallel execution, and sub‑agent calls, leveraging live context from both enterprise and personal graphs.

Layer 3 – Application & Interaction

Natural‑language task description or visual drag‑and‑drop builder lets non‑technical users create agents quickly.

Agents inherit deep enterprise context, enabling automatic understanding of projects, roles, and data permissions.

Agents are built, tested, deployed, monitored, and governed within a unified, secure environment.

Action libraries have grown from dozens to hundreds, and tight integrations with core business tools allow agents to perform real business operations.

Layer 4 – Security & Governance

Secure‑by‑design architecture meets SOC 2 Type II, GDPR, and HIPAA.

Encryption at rest (AES‑256, FIPS 140‑2) and in transit (TLS 1.2+); single‑tenant VPC deployment options.

Fine‑grained ACLs, group inheritance, and real‑time permission updates are enforced throughout indexing, graph construction, RAG, and agent execution.

Glean Protect adds prompt‑injection and jailbreak defenses; proactive data & AI governance scans >100 connectors to remediate over‑shared sensitive data.

Layer 5 – Open Operations

APIs expose search, conversational, and agent capabilities, delivering “Context‑as‑a‑Service” to ecosystem partners.

Glean positions itself as an “Enterprise System of Context Provider,” linking AI models and agents to heterogeneous, permission‑rich enterprise data.

Supports open standards (MCP, A2A, LangChain Agent Protocol) and hosts MCP servers to enable bi‑directional agent orchestration.

Hard Questions for Glean

How to keep innovating as foundational LLMs evolve? Glean maximizes LLM value while building higher‑order, context‑rich layers that move up the value chain.

How to tackle the cost and scale of enterprise knowledge‑graph construction? Glean uses a “use‑drives‑build,” signal‑driven, dual‑graph approach that embraces imperfect data.

How to ensure agent reliability and avoid black‑box risks in mission‑critical settings? Glean emphasizes observability, debugging tools, and a “human‑in‑the‑loop” co‑pilot model.

How to address security and trust when data complexity is extreme? Glean implements pixel‑level permission enforcement across indexing, graph, RAG, and agent execution (“security left‑shift”).

How to balance open ecosystem standards with deep customization for the “last mile”? Glean contributes to emerging standards, hosts MCP servers, and offers contextual intelligence via standardized APIs.

Key Takeaways

Deep, dynamic enterprise and personal graphs turn generic LLMs into valuable, contextual AI.

Low‑code agent builder democratizes automation, enabling every knowledge worker to become a “10X‑er.”

Investing in hard, unglamorous infrastructure—data connectors, permission handling, and graph construction—creates durable technical moats that competitors cannot quickly replicate.