Glean's 2026 enterprise search evaluation is definitive: human evaluators preferred Glean's answers 1.9x more often than ChatGPT's and 1.6x more than Claude's for enterprise knowledge queries. The platform's combination of hybrid search, knowledge graphs, and custom language models trained on company-specific data gives it a measurable advantage over general-purpose AI for workplace information retrieval. Companies like Databricks, Reddit, Intuit, and Samsung rely on Glean to make organizational knowledge accessible.

Glean's AI Agents extend beyond search into automated workflows — handling IT support tickets, answering HR policy questions, and synthesizing information from Slack, email, Confluence, and dozens of other enterprise tools. The platform deploys in weeks, not months, and integrates with the tools teams already use. For enterprises drowning in information, Glean is the closest thing to an AI that actually knows your company.

But there's a critical distinction between retrieving information and remembering it: Glean finds what your company already documented, but it doesn't learn from the interactions, patterns, and decisions that never make it into documents.

How Glean Wins at Enterprise Search

Glean's advantage comes from deep indexing of enterprise data sources. The platform connects to over 100 SaaS applications — Slack, Microsoft 365, Google Workspace, Salesforce, GitHub, Jira, Confluence, ServiceNow, and more. It builds a knowledge graph that maps relationships between people, documents, projects, and concepts within the organization. When a user asks "What's the status of Project Mercury?", Glean synthesizes information from project documents, Slack conversations, Jira tickets, and relevant emails.

The custom language models are trained on company-specific terminology, acronyms, and patterns. Where ChatGPT might misinterpret industry jargon or internal project names, Glean understands the company's language. This domain adaptation is what drives the 1.9x preference rate — Glean's answers are more accurate, more relevant, and more contextual.

The Agent capabilities extend this further. Glean Agents can automate multi-step workflows: an employee asks about parental leave policy, the agent retrieves the policy document, identifies the employee's region and tenure, and provides a personalized answer with the correct details. For IT support, an agent can diagnose issues by cross-referencing the user's system configuration with known bugs and resolution guides.

What Enterprise Search Doesn't Capture

Despite its power, Glean operates on a fundamental assumption: the information exists somewhere in the enterprise's documented knowledge. But the most valuable organizational knowledge often exists nowhere in writing. It lives in the heads of experienced employees, in the context of decisions that were made but never documented, in the patterns that emerge from thousands of interactions.

A senior engineer knows that the payments API has a subtle timeout issue under high load — not because it's documented, but because they've debugged it three times. A sales manager knows that Enterprise Customer X always renegotiates in Q4 — not from CRM data, but from years of relationship context. An operations lead knows that deploying on Fridays requires extra monitoring — not from a runbook, but from hard-won experience.

Glean can search across everything your company has written down. It can't search across the knowledge that your company has experienced but never captured. This is the gap between enterprise search and enterprise memory — and it's where the most valuable organizational intelligence resides.

From Search to Memory: The Enterprise Evolution

Enterprise search is retrieval: find existing information and present it. Enterprise memory is learning: capture knowledge from interactions, accumulate understanding over time, and make that knowledge available proactively. Search answers questions when asked. Memory anticipates needs based on accumulated context.

The difference matters for AI agents. A Glean Agent handling IT support tickets can search the knowledge base for solutions. But a memory-enabled agent would also know that this user had a similar issue last month, that the previous solution didn't fully resolve it, and that the root cause was eventually traced to a configuration change in the network team's last deployment. That context exists in interaction history, not in documented knowledge.

For enterprises running hundreds of AI agents across departments, the accumulated experiential knowledge is enormous. Every customer interaction, every support resolution, every engineering decision generates context that could inform future actions. Without memory, all of that contextual intelligence is generated and discarded continuously.

How MemU Complements Enterprise Search

MemU adds the memory layer that transforms enterprise search into enterprise intelligence. While Glean excels at finding what your organization has documented, MemU captures what your AI agents have experienced. Every agent interaction, every resolution pattern, every learned preference is persisted in MemU's memory system and available for future retrieval.

The integration is complementary, not competitive. Glean searches your documents. MemU remembers your experiences. Together, they give AI agents access to both documented knowledge and experiential wisdom — the full spectrum of organizational intelligence.

For enterprises building AI agent platforms, Glean plus MemU provides the complete knowledge architecture. Agents that can search the knowledge base and remember their interactions deliver dramatically better outcomes than agents limited to one or the other. It's the difference between an employee on their first day and one who's been with the company for years.

Get Started

Give your enterprise AI agents the memory they need alongside the search they have. Explore MemU at memu.pro and on GitHub.