Customer-facing AI is no longer the shiny experiment on the side. It is quietly becoming the default way employees and customers ask questions, find knowledge, and get work done. The question for most teams is not whether to use AI, but where it should live: in a separate interface, in every app, or in the browser layer, stitching it all together. Glean has emerged as one of the most prominent “Work AI” platforms in this space, combining enterprise search, AI assistants, and agents on top of a graph of company knowledge. It promises a single place to find, summarize, and act on information across hundreds of systems.
But like any central brain, Glean lives primarily around your tools, not inside the moment of action. It excels at helping people discover and understand information, yet often stops one step before the workflow: updating records, triggering downstream processes, and guiding frontline agents inside their existing interfaces.
PixieBrix takes a different stance. Instead of making people go to a new AI destination, PixieBrix pushes AI into the browser, where work already happens. Our platform orchestrates AI, data, and human context directly in tools like Zendesk, Salesforce, Jira, and internal web apps, so agents don’t just find the right answer - they execute the right workflow in a couple of clicks.
Glean is a Work AI platform that combines enterprise search, AI assistants, and AI agents on top of an “Enterprise Graph” of your company’s data. It connects to 100+ applications and uses identity, permissions, and content signals to let employees search, ask questions, and automate tasks across tools like Google Workspace, Slack, Jira, Salesforce, and more.
The product family includes:
Glean was founded in 2019 by Arvind Jain, previously a Distinguished Engineer at Google and co-founder of Rubrik. The founding team included senior leaders from Google Search, Google Apps, and Facebook, bringing experience in large-scale search, indexing, security, and distributed systems.
From the beginning, Glean was designed around several principles:
These principles led to the development of Glean’s Enterprise Graph, which underpins search relevance, AI answers, and agent functionality.
Glean has received sustained investment from leading venture firms, reflecting market confidence in enterprise search and Work AI as a long-term category.
Publicly reported funding includes:
Funding momentum has aligned with growing enterprise adoption and measurable customer impact. Case studies from organizations including Super.com, Confluent, Sony Electronics, and TIME describe improved onboarding efficiency, reduced time spent searching for information, and greater cross-team visibility.
The company’s funding trajectory mirrors its evolution from an enterprise search product into a broader Work AI platform combining search, answers, and agents.
Glean positions itself as a Work AI platform and enterprise AI search foundation rather than just another chatbot. Its messaging emphasizes:
Glean pitches itself less as “just search” and more as the AI operating layer for knowledge work.
Publicly available data and case studies highlight some of the gains customers attribute to Glean:
The broad pattern: when employees can actually find and reuse what the company already knows, time saved, faster onboarding, and higher satisfaction tend to follow.
Glean uses an Enterprise Graph to model relationships between people, content, and systems. Identity, permissions, and usage signals all feed into a “system of context” that powers more relevant search and AI responses.
At the core is enterprise AI search that lets employees query across cloud storage, wikis, chats, tickets, and more with natural language instead of exact keywords. Search results are personalized and permission-aware, so people see only what they’re allowed to see.
Glean Assistant acts as an AI work companion:
Glean Agents automate repetitive tasks like triaging requests, summarizing metrics, updating records, and routing information. They can be embedded into systems such as Service Cloud, Zendesk, or internal apps, orchestrated over the Enterprise Graph.
Glean offers 100+ native connectors plus a framework for custom data sources:
Security is positioned as a core feature:
Glean’s dashboards and insights help teams understand:
Glean centralizes knowledge scattered across docs, tickets, and chats, making it easier for new hires and existing teams to get up to speed.
Top use cases include:
Support and go-to-market teams use Glean to quickly surface:
This reduces time spent switching between tools and improves answer consistency across teams.
AI agents and the assistant help “explain” metrics and automate low-value work:
Glean’s value depends heavily on how deeply it connects into your stack:
Most Glean deployments follow a pattern:
Case studies suggest time-to-value can be relatively fast; TIME, for example, reports being “up and running in three weeks” when indexing over 100 years of content.
Onboarding Glean enabled Sigma to significantly ease the learning curve for end-users, while simultaneously reducing administration overhead for managers, content creators, and enablement teams.
By centralizing knowledge and making it instantly accessible, Glean eliminated frustrating search cycles, enabling Webflow employees to focus on high-impact work and save 300+ hours per month.
Super.com saves 1,500+ hours monthly and onboards employees 20% faster with Glean.
With Glean, Grammarly’s support team spends less time searching and more time helping customers. Agents can confidently provide fast, accurate answers - reducing response times and improving customer satisfaction.
Duolingo achieves 5x ROI, saving 500+ hours monthly with Glean’s AI-powered knowledge tools.
Glean does not publish list pricing, but third-party benchmarking suggests:
As with many enterprise AI platforms, cost depends on:
Buyers should treat these numbers as directional and expect custom pricing for larger or more complex deployments.
Glean’s security story is a major part of its positioning:
Every powerful enterprise platform comes with trade-offs. Based on public commentary and market analysis:
Analyst write-ups and pricing explainers describe Glean as a premium-priced solution, with deal sizes that may be out of reach for smaller teams or early-stage companies.
Glean is excellent at search, summarization, and analysis, but its primary surfaces are search, assistant panels, and lightweight UI embeds. Many operational workflows (updating records, triggering complex automations, multi-step decision flows) still depend on separate tools or manual work inside each app.
Security reviews note that while Glean’s certifications are strong, any system that centralizes access across 100+ tools can amplify misconfigurations. If permissions are too broad in source systems or connectors are not properly scoped, more people may see more than intended.
Like other enterprise search platforms, Glean’s value depends on behavior change: people must learn to ask the assistant and search bar first. Driving that adoption across roles and regions requires ongoing enablement, analytics-driven nudges, and leadership sponsorship.
Where Glean focuses on search and work assistants, PixieBrix focuses on agent assist and workflow orchestration directly in the browser.
PixieBrix:
Support teams using PixieBrix’s Support Flow module have reported outcomes such as a 40% reduction in mean time to resolution (MTTR) and a 20% increase in CSAT, by giving agents in-flow guidance and automation instead of another destination app. If your top priority is agent productivity, reduced escalations, and consistent workflows inside tools like Zendesk, PixieBrix can be a more direct lever than a central knowledge hub alone.
Organizations evaluating Glean typically compare it to a mix of enterprise search platforms, knowledge management systems, and workflow-focused AI tools. The alternatives fall into several clear categories, each addressing different parts of the Work AI stack. The goal is to help teams understand which tools focus on search discovery, which emphasize knowledge governance, and which extend into workflow automation and in-flow AI execution.
These platforms compete most directly with Glean’s enterprise search capabilities. They offer relevance tuning, ingestion pipelines, knowledge indexing, and analytics for internal search experiences. Companies often evaluate these options when they need a search engine that spans content repositories, knowledge bases, and structured systems, particularly in support and service environments.
These tools overlap with Glean’s search foundation, but typically do not include AI assistants or agent automation built on top of their search graph.
Guru emphasizes knowledge verification, structured “cards,” and maintaining trusted internal documentation. Organizations often evaluate Guru when the primary goal is to centralize knowledge governance rather than unify search across many SaaS tools. Guru works well for sales enablement, customer support, and internal communications teams that need a clear source of truth for policies, product information, and process documentation. Compared with Glean, Guru is more opinionated and structured, but offers less breadth in search across heterogeneous systems.
For Microsoft-centric organizations, Copilot and Viva Topics are natural comparisons. They integrate deeply with Microsoft 365 content and identity, offering AI summarization, recommendations, and knowledge extraction.
Compared with Glean, Microsoft’s tools excel in document-centric and meeting-centric workflows but may require multiple add-ons to approximate Glean’s unified search across non-Microsoft tools.
Teams with strong internal engineering resources sometimes compare Glean with self-managed or cloud-managed search engines like Elastic or OpenSearch. These platforms offer full control over indexing, pipelines, and search logic, but require significant engineering work to build the equivalent of Glean’s permission model, connectors, and Enterprise Graph.
Companies consider these options when they are already operating a centralized search engineering practice or need deeply customized search behavior.
PixieBrix is not a direct search engine competitor, but it is often evaluated alongside Glean because it addresses a different, complementary challenge: turning knowledge into action inside the tools where work happens.
PixieBrix enables:
Where Glean centralizes discovery, PixieBrix focuses on execution. Many CX and operations teams use the two together: Glean for knowledge discovery and analytics, PixieBrix for frontline workflow orchestration and agent performance.
If you’re evaluating Glean, it’s natural to think first about knowledge discovery. The next question is what your agents actually do with that knowledge under pressure.
PixieBrix embeds AI directly into your support workspace, turning guidance into action:
Teams using PixieBrix have seen measurable improvements like a 40% reduction in MTTR and 20% higher CSAT, because agents no longer juggle a dozen apps to resolve a single interaction.
Used together, Glean can be your enterprise brain, while PixieBrix becomes your hands in the browser - the layer that operationalizes AI and knowledge into consistent, scalable support workflows.
