A few years ago, the idea of a single search bar that could query your entire tech stack seemed like a luxury. It is now considered the norm. Modern enterprises expect systems that understand context, connect data across tools, and help move work forward.
This shift is changing how organizations evaluate tools like Glean. Glean is a widely adopted enterprise search platform that excels at finding documents across various enterprise tools, but it remains a search-first solution.
At a functional level, Glean reads data, doesn’t transform it. Glean agents are mostly read-only and bring in actions only via model context protocol (MCP); they do not have create, read, update, and delete (CRUD) access and have limited cross-system intelligence.
Not to mention, Glean requires separate licensing on top of your existing platform investments.
For teams that require autonomous AI that takes action across your entire tech stack, Glean quickly falls short.
This guide explores top Glean alternatives and helps enterprises understand when search-first tools are sufficient – and when platforms that combine search with autonomous action are the better long-term choice.
TL;DR
- Enterprise search software is essential for businesses to quickly and easily find necessary information, enhancing productivity and knowledge-sharing within organizations.
- The top five Glean alternatives are Microsoft Copilot, Computer by DevRev, Notion AI, and Gemini Enterprise.
- Computer by DevRev and Copilot stand out with their low-friction pilots; Glean requires a complex setup process, which often necessitates a paid proof of concept.
What is an enterprise search tool?
An enterprise search, when boiled down, is like having a private Google for your company. It connects internal documents, knowledge bases, and tools, indexes them in a single system, and allows employees to search for information from one place.
Unlike public web search, enterprise search requires permissions. Google didn’t have to think about access controls to index the web; enterprise search must respect role-based access, data sensitivity, and compliance requirements. The right tools show users only what they are authorized to see.
In recent years, enterprise search has evolved beyond simple keyword matching. Today, modern platforms follow this progression.
Search → Answer → Reason → Insight → Actions
(Search) is where AI-powered natural language processing (NLP) understands context and surfaces relevant results conversationally.
(Answer) delivers precise, context-aware responses instead of document lists.
(Reason) explains why results matter by connecting them to business context and relationships.
(Insight) surface patterns, trends, and root causes across your data.
(Actions) goes beyond discovery to autonomously execute workflows, update records, and drive outcomes.
The most advanced platforms don’t stop at finding information alone; it goes beyond and takes action on your behalf.
Why look for Glean alternatives
Glean offers two distinct products, i.e., Glean Search and Glean Assistant. Glean search is for semantic search across documents, while Glean Assistant is an AI assistant platform that indexes unstructured data across a wide range of tools in an enterprise tech stack.
Enterprises have got to a point where they require a tool that essentially does both. Glean offer a fragmented approach where as the alternatives mentioned below are more cohesive.
For organizations primarily focused on discovery and knowledge retrieval, Glean works well.
However, as enterprises push beyond search toward AI-driven execution and automation, many teams begin to encounter limitations. These gaps are often the reason organizations start evaluating alternatives to Glean.
Read-only agents, not autonomous action
Glean only finds answers but does not help you act on them, meaning agents analyze documents and draft responses, but can’t execute decisions across your systems. Limited CRUD access means no true workflow automation – your team still handles execution manually.
Limited agentic capabilities
Agents access data via read-only MCP integrations. They can’t autonomously route tickets, update records, or escalate intelligently without manual intervention.
Shallow cross-system intelligence
Without object schema understanding, agents miss business context and can’t coordinate across support, product, and engineering. This siloed approach limits value.
Deployment delays
Getting started with Glean requires 8-12 weeks for production due to extended indexing, permission mapping, and testing cycles. It requires a significant amount of developer time. Faster alternatives get teams value in weeks, not months.
Field-level access limitations
Glean applies permissions only at an object level rather than field level. This means teams can either see entire records or none at all; there is no customization here. For example, support agents might see entire customer profiles that include data such as revenue, when they are only looking for contact information; on the flip-side, they may be blocked from viewing the relevant record entirely.
Separate licensing burden
Glean Assistant charges additional per-seat costs, effectively doubling your software investment and straining budgets.
Feature comparison: at-a-glance
Here is a top-level look at how the competitors stack up against Glean and each other in terms of the factors mentioned below. The list has been curated after researching a wide range of reputable sources.
Top Glean alternatives compared
Let’s take a closer look at each of the alternatives and find out why they were chosen.
➜ Computer by DevRev (Top Glean alternative)
Best for: Support, product, and engineering teams that need unified workflows and autonomous AI agents capable of taking actions across your entire tech stack.
Computer is an AI platform by DevRev. It is a product that unifies support, product, and engineering data into a single workspace. It connects both structured and unstructured data through bidirectional sync known as AirSync, then organizes it into a knowledge graph, also known as Computer Memory, giving teams rich business context. It operates beyond search. Computer can triage, resolve, update, and trigger workflows with full CRUD access across your work tools.
Key features:
Unified enterprise search
- Searches across structured and unstructured data, connecting documents, tickets, customer records, and other internal documents through Computer Memory – a patented knowledge graph.
- One of the standout feature is the ability to preserve relationships between documentation and data, so results come with rich context instead of isolated records.
Autonomous AI agents with full CRUD
- Agents can create, read, update, and delete records to triage tickets, route issues, update CRM, and execute workflows end-to-end.
- Workflows run under clear guardrails, combining deterministic processes with AI reasoning.
Deep product ontology and object schema
- Understands business objects such as tickets, accounts, issues, and opportunities instead of treating everything as flat text.
- Maps data onto a unified schema so agents reason over how entities relate across systems.
Synced permissions and governance
- Role- and attribute-based access control (RBAC/ABAC) ensures every action respects existing permissions.
- Central policies apply consistently across all agents and connected tools.
Search, analytics, and actions in one interface
- Conversational UI that lets users search, ask analytical questions, and trigger actions without switching tools.
- In-app analytics engine runs live queries on top of synced data.
Recommended read: Find out how Computer works
Strengths vs. Glean:
True cross-team intelligence and faster decisions
- Unified context across support, product, and engineering means agents coordinate intelligently instead of operating in silos.
- Leaders see how customer issues connect to product gaps and engineering work, enabling faster, better prioritization.
From search to action: tangible productivity gains
- Instead of stopping at “here’s the answer,” agents take the next steps – updating tickets, syncing CRMs, triggering workflows – reducing manual follow-up.
- Teams recover hours per week that previously went into repetitive coordination work.
Higher ROI through a single, action-first platform
- One platform for search, analytics, and AI actions removes the need for separate search and assistant licenses.
- Avoids Glean’s double-licensing model, improving unit economics as usage scales.
Faster time-to-value than search-only deployments
- AirSync’s deep sync and object mapping reduce implementation overhead compared with surface-level metadata scraping.
- Organizations move from pilot to live workflows in weeks instead of long search-only rollouts.
More reliable automation with governance built in
- Full CRUD is coupled with policy-based approvals, audit trails, and permission inheritance, so AI can safely operate on production data.
- Stakeholders gain confidence to push more work to agents without sacrificing control.
Limitations:
Steeper learning curve with a developer-friendly interface. Requires understanding of agent architecture.
Best scenario:
Choose Computer if you need autonomous AI that searches, analyzes, and acts across support, product, and engineering – with full visibility and governance.
Why Computer wins:
“Glean searches; Computer acts. You get unified context plus autonomous actions across your entire tech stack.”
➜ Microsoft Copilot
Ideal for: Microsoft 365-heavy enterprises (Teams, SharePoint, Outlook, OneDrive).
Key features:
Deep integration with Microsoft 365 ecosystem. Built-in AI leveraging Copilot Pro. Works seamlessly with Teams, Outlook, SharePoint, OneDrive, Word, and Excel. Familiar interface for M365 users. Real-time sync with M365 permissions.
Strengths vs. Glean:
Deep M365 integration means the AI lives where your team already works. Built-in AI costs nothing extra – it’s part of your M365 subscription. Deployment takes just 2-4 weeks. Users are familiar with the Microsoft interface, reducing adoption friction.
Limitations:
Locked to the Microsoft ecosystem – doesn’t bridge to Jira, Salesforce, or custom systems. Can’t orchestrate workflows outside M365. M365 licensing costs add up quickly across enterprise scale. Customization is limited to what Microsoft’s AI model supports.
Best scenario:
Choose Copilot if your organization is Microsoft 365-first and happy staying within that ecosystem.
Limited free access through Microsoft 365 subscriptions.
➜ Notion AI
Ideal for: SMBs and product teams using Notion as their primary workspace.
Key features:
All-in-one workspace (docs, databases, AI chat, search). Easy knowledge base management. Quick implementation (~1 week). Built-in AI for summarization and Q&A.
Strengths vs. Glean :
Live in one week. Intuitive interface with minimal learning curve. No expensive per-seat licensing. Everything in one place – workspace, search, and collaboration.
Limitations:
Limited to Notion ecosystem; no external integrations. Search is shallow compared to enterprise-grade solutions. No autonomous actions or workflow automation. Not suitable for complex enterprise architectures.
Best scenario:
Choose Notion if you’re SMB-focused, primarily use Notion for documentation, and want quick AI search without external integrations.
➜ Gemini Enterprise
Ideal for: Google Workspace-native enterprises that want AI-powered search and intelligence embedded across Workspace and Google Cloud.
Gemini Enterprise brings generative AI and enterprise search together across Gmail, Drive, Docs, Meet, and connected Google Cloud systems. It’s designed for organizations already standardized on Google Workspace.
Key features:
Unified search and AI assistance across Google Workspace data (mail, docs, chats, meetings). Generative answers and summaries grounded in your organization’s content.
Native integrations with Vertex AI and Google Cloud services for extending search into custom apps.
Real-time sync with Google Workspace permissions.
Strengths vs. Glean:
Deep workspace integration means AI lives where your team works.
Built into the Google Workspace ecosystem, it is simpler to deploy.
Familiar interface for Google Workspace users.
Automatic permission sync with the workspace directory.
Limitations:
Primarily focused on Google Workspace data; limited bridging to third-party systems like Salesforce, Jira, or custom apps. Can’t orchestrate autonomous workflows across non-Google systems. Search is constrained to the Workspace context; it lacks deep object schema understanding. No autonomous actions or full CRUD capabilities across the tech stack.
Best scenario:
Choose Gemini Enterprise if your company is standardized on Google Workspace and wants “built-in” AI search. However, if you need cross-system agentic workflows and autonomous actions beyond Workspace, Computer is the stronger choice.
Why choose Computer over Glean?
Employee productivity Outcomes, not just answers
Computer focuses on outcomes and not just answers, it turns search into completed work: agents resolve tickets, sync CRM records, and trigger workflows autonomously, reducing manual coordination and time-to-resolution for support and product teams, leading to better employee productivity.
Faster decision-making
Powered by Computer Memory, agents understand customers, tickets, issues, and revenue objects in relation to each other. This ensures decisions align with support, product, and engineering instead of staying locked in silos.
Secure, governed AI at scale
Full CRUD access is wrapped in role-based controls, approvals, and audit trails, letting AI safely touch production systems while security and compliance teams retain control and visibility.
Total cost of ownership
Search, analytics, and AI actions are integrated into a single product, eliminating the extra licensing and operational overhead of purchasing and managing a separate assistant layer on top of search.
Time saved with faster migration
Deep, bidirectional sync creates a single source of truth, so organizations can move from legacy search tools to Computer while keeping systems in sync and minimizing disruption during rollout.
Uniphore: building a proactive support culture with DevRev
Talking about features is one thing, but hearing it from an active user showcases how useful DevRev is to enterprises.
One such example is Uniphore, a client of DevRev, which is an AI and analytics company serving 1,500 enterprise clients across various industries, including telecom and banking. Their platform handles billions of engagements weekly. They view support not just as ticket resolution, but as a core part of their “one big team” and “customer first” culture.
The challenge for Uniphore’s previous support tools was transactional, creating silos between support and engineering.
Issues:
- Their old “ticketing system” only measured volume and resolution, rather than fostering a “system of action” to prevent recurring issues.
- Fragmented data: Support engineers had to toggle between multiple tools (Rocketlane, PagerDuty, Jira) to get a complete picture.
- There was no seamless way for support to collaborate with product and engineering teams to fix root causes.
Solution:
Uniphore migrated to DevRev to create a unified, tool-agnostic environment.
- Unified “system of action”: DevRev integrated Rocketlane, PagerDuty, and Jira into a single interface. Support engineers now reside in a single environment where alerts and customer updates are centralized.
- Seamless data migration (AirSync): They used AirSync to migrate 16,000 tickets, 72,000 comments, and 17,000 attachments in just 6 hours, a process that typically takes weeks.
- Bidirectional Sync: Support can now create Jira issues directly within DevRev, and updates sync automatically both ways.
Results:
- Unified visibility: A single source of truth for support, engineering, and product teams, enabling the “steel thread” from customer feedback directly to code changes.
- Proactive support: Alerts from monitoring tools now flow directly into DevRev as issues, allowing the team to identify and fix problems before customers even report them.
- Cultural shift: Support has transformed from a transactional function into a strategic ally for product and engineering, providing direct insights into product performance.
- Migration Speed: Legacy data migration completed in 6 hours (vs. an estimated 6 weeks).
Case study: Read more on how Uniphore built a proactive support culture
Final verdict: which Glean Assistant alternative is right for you?
Choose Microsoft Copilot if:
Your organization is Microsoft 365-first. You want AI built into tools your team already uses. You’re comfortable staying within the M365 ecosystem.
Choose Notion if:
You’re SMB-focused and use Notion as primary workspace. You prioritize ease of use and speed over enterprise scale. You don’t need autonomous actions or workflow automation.
Choose Gemini Enterprise if:
Your organization is standardized on Google Workspace (Gmail, Drive, Docs, and Meet) and wants AI-native search inside those tools. The generative answers and summaries grounded in Google Workspace content can be helpful as well.
Choose Computer if:
You need autonomous AI agents that take action across your tech stack. You want support, product, and engineering unified and coordinated. You value faster deployment and no double licensing. You need cross-system intelligence and object schema understanding.
Work is broken. A better search bar won’t fix it. But an AI teammate might.
Book a demo to find out how you can.
