Glean (company)

Glean is an American enterprise AI software company headquartered in Palo Alto, California, that develops an AI-powered work platform combining enterprise search, an AI assistant, and AI agents. Founded in 2019 by former Google distinguished engineer Arvind Jain and three co-founders whose backgrounds included Google, Microsoft, Meta, and Uber, the company builds software that indexes and understands an organization's information across dozens of connected applications, then surfaces relevant knowledge and automates tasks for employees.^[1][2]

As of June 2025, Glean was valued at $7.2 billion following a $150 million Series F funding round led by Wellington Management.^[3][4] By December 2025, the company reported surpassing $200 million in annual recurring revenue (ARR), doubling from $100 million in roughly nine months.^[6][7] In May 2026, Glean said it had crossed $300 million in ARR, tripling the figure in 15 months.^[21][22] Its customers include Booking.com, Confluent, Databricks, Reddit, and Samsung, and it has been recognized on the Forbes AI 50, the CNBC Disruptor 50, and Fast Company's World's Most Innovative Companies lists.^[3][14][15]

As of 2026 the platform offers more than 275 connectors to enterprise applications^[40] and builds a knowledge graph of each organization's documents, people, and relationships. Its core proposition is that enterprise workers lose significant time searching for information scattered across SaaS tools, and that an AI layer sitting above all those tools can recover that productivity. Founder and CEO Arvind Jain has summarized the mission as building "the platform that brings AI into the fabric of everyday work, connecting people to knowledge, automating tasks, and enabling smarter decisions across the enterprise."^[5]

History

Who founded Glean and what is Arvind Jain's background?

Arvind Jain grew up in Jaipur, India, and developed an early interest in computers after his older brother brought a PC home. He earned his degree from the Indian Institute of Technology Delhi, then worked at Microsoft and at networking companies Akamai and Riverbed before joining Google in the mid-2000s.^[2]

At Google, Jain led significant infrastructure projects. He redesigned the web crawler system, worked on YouTube's video serving infrastructure, and developed search functionality for Google Maps. He spent roughly a decade at Google as a distinguished engineer, reporting his work on systems that operated at a scale few companies had to manage at the time.^[2]

In 2013, Jain left Google after being convinced by entrepreneur Bipul Sinha to co-found Rubrik, a cloud data security and management company. As co-founder and head of R&D, Jain helped build Rubrik from scratch into a company employing nearly 1,000 people by 2018. Rubrik later went public on the New York Stock Exchange in 2024.^[2][12]

It was the experience of scaling Rubrik that gave Jain the specific insight that became Glean's founding thesis. Rubrik grew so quickly that the company accumulated roughly 300 SaaS products, and employees regularly spent hours looking for information that should have been easy to find. An engineer would search Slack, then Confluence, then email, then a shared drive, and still not be sure they had found the right answer. Jain believed this problem was universal across every fast-growing company and that no adequate solution existed.^[2]

When was Glean founded?

Jain founded Glean in 2019 along with three co-founders: T.R. Vishwanath, who had held technical leadership roles at Facebook for close to a decade after working at Microsoft; Tony Gentilcore, who spent a decade at Google working on the search results page and founded its Chrome Speed Team; and Piyush Prahladka, who had worked at Google and Uber.^[1][5][12]

The company incorporated in Palo Alto under the legal name Glean Technologies, Inc.^[1] The founding team's shared Google background was deliberate. Building a system that could index, understand, and serve enterprise information at scale required deep expertise in distributed systems, search infrastructure, and machine learning, all of which the team had developed over their careers.

Glean's initial product focus was enterprise search, a market that had attracted multiple companies over the years but had largely failed to produce a tool that employees actually used consistently. The founding team believed that advances in natural language processing and machine learning made it newly possible to build a search experience good enough to change daily behavior at large organizations.

Early product development

Glean spent its first two years building its connector infrastructure and the underlying knowledge graph before releasing its first commercial product. The platform launched publicly in 2021 with connectors for a set of commonly used enterprise tools and the ability to return permission-respecting search results across all of them in a single query.

In September 2021, Glean introduced an assistive search capability that aggregated multi-application data and began returning results as more than a list of links. The early product used natural language processing to understand the intent behind a query and return the most relevant content rather than keyword matches.

The company grew steadily through 2022 and into 2023, adding connectors, refining relevance, and building out its knowledge graph technology. By spring 2023, Glean had integrated generative AI capabilities, enabling the system to synthesize answers from across connected documents rather than merely retrieving them.

How much funding has Glean raised?

Glean has raised $765 million across six rounds from a total of 53 investors, growing from an early-stage startup to a multi-billion-dollar company in roughly five years.^[13]

The Series A, closed in March 2019, raised $15 million with Kleiner Perkins and Lightspeed Venture Partners as lead investors. The round provided the capital needed to hire engineers and begin building the connector infrastructure.

The Series B in March 2021, led by General Catalyst, raised $40 million and was announced in September 2021, when Glean emerged from stealth and launched its commercial product.^[12] At this stage, the company was signing its first enterprise customers and proving that the search quality was sufficient to drive meaningful adoption inside organizations.

The Series C in May 2022, led by Sequoia Capital, raised $100 million and valued Glean at $1 billion, making it a unicorn.^[2] This round came as enterprise interest in AI-powered tools was accelerating and as Glean was beginning to show that customers would pay and renew for the product.

Glean raised two large rounds in 2024 in quick succession. The Series D in February 2024 raised more than $200 million at a $2.2 billion valuation, co-led by Kleiner Perkins and Lightspeed Venture Partners.^[34] Seven months later, in September 2024, the Series E raised $260 million at a $4.6 billion valuation, led by Altimeter Capital and DST Global. The rapid successive rounds reflected the speed at which the company was growing revenue after the emergence of large language models transformed enterprise interest in AI.

The Series F in June 2025 raised $150 million at a $7.2 billion valuation, led by Wellington Management.^[3][4][5] The round, announced June 10, 2025, lifted Glean's valuation by $2.6 billion in less than a year.^[4] New investors in this round included Khosla Ventures, Bicycle Capital, Geodesic Capital, and Archerman Capital. Returning investors included Altimeter, Capital One Ventures, Citi, Coatue, DST Global, General Catalyst, ICONIQ, IVP, Kleiner Perkins, Latitude Capital, Lightspeed Venture Partners, Sapphire Ventures, and Sequoia Capital.^[5] Glean said it intended to use the capital for product development, expanding its partner ecosystem, and international growth.^[5] Jain told Crunchbase News that the company had not needed to raise the capital but that the funding gave it the flexibility to move faster and execute on its long-term vision.^[23]

In total, from the beginning of 2024 through the Series F in June 2025, Glean raised $610 million and grew its valuation from $2.2 billion to $7.2 billion.

The Series F came roughly nine months after the Series E.^[23] At the time of the Series F the company reported having more than 800 employees; by December 2025 the company said it had more than 1,000 employees.^[7]

What is the Glean platform?

Glean's platform has three primary components: Glean Search, Glean Assistant, and Glean Agents. All three are built on top of a shared infrastructure that includes a connector layer, a knowledge graph, and a permissions enforcement engine.

Glean Search

Glean Search is the foundational layer of the platform. It connects to enterprise applications through pre-built connectors (more than 100 by late 2025 and more than 275 native and MCP-based connectors by 2026) and indexes the content of each application in near real time.^[7][40] Connectors capture changes through webhooks where available and through incremental crawling otherwise.

The platform supports integrations across the major categories of enterprise software: document storage (Google Drive, SharePoint, Dropbox), messaging (Slack, Microsoft Teams), project management (Jira, Asana, Linear), wikis and documentation (Confluence, Notion, Guru), customer relationship management (Salesforce), ticketing (Zendesk, ServiceNow), code repositories (GitHub, GitLab), email, and many others.

Critically, Glean mirrors the permission settings of each connected application. When a user searches, they receive results only from documents and data they are already authorized to see in the source application. This permission-aware architecture is a technical requirement for enterprise adoption, where different teams often have access to confidential documents that must not be surfaced to the wrong people.

Search results are ranked using the knowledge graph, which maps relationships between documents, people, and interactions. A document written by someone on the same team as the user, recently edited, and already opened by the user's colleagues will rank higher than an older document from an unrelated team, even if both are textually relevant to the query. This contextual ranking is one of the central differentiators Glean has pointed to over competing products.

Glean Assistant

Glean Assistant is an AI assistant that answers questions, summarizes documents, drafts content, and reasons across information retrieved from connected applications. Glean described its original 2021 search product as a work assistant; its generative AI chat assistant launched in 2023, and the assistant has since gone through multiple generations.^[2]

The assistant uses retrieval-augmented generation (RAG) to answer questions. When a user asks a question, the system retrieves relevant documents from the knowledge graph, uses those documents as context, and passes them along with the query to a large language model to generate a response. The response includes citations pointing back to the source documents.

Because the retrieval step is permission-aware, answers generated by the assistant are grounded only in documents the user has access to. This prevents the assistant from inadvertently revealing confidential information from documents the user does not have permission to view.

In September 2025, Glean introduced the third generation of the assistant, described as the most deeply personalized version to date.^[10][11] The third-generation assistant incorporates an Enterprise Graph (described in more detail below) that gives the assistant a model of each individual employee's role, projects, collaborators, and writing style.^[10] The assistant adapts its responses based on this personal context without requiring the user to specify it in every query.

The third-generation assistant also introduced a feature called Canvas, an interactive workspace inside the assistant interface where users can edit generated content inline and build on it iteratively.^[10][11] Glean described Canvas as designed to support writing tasks where a user wants to develop and refine content rather than simply receive a one-shot answer.

In May 2026, Glean repositioned the assistant as an "AI coworker," adding the ability to surface work proactively through personalized activity cards and to provide first drafts automatically using the personal graph rather than waiting for a prompt.^[25][26] The update introduced Skills, reusable units of execution logic for repetitive tasks built on the open Agent Skills standard, along with adaptive reasoning that selects the depth of reasoning and the underlying model to match a task's complexity, real-time voice conversations grounded in company context, and a Library that stores AI-generated outputs in a searchable repository.^[25][26] The release also added write tools that let the assistant make changes across Microsoft 365, Outlook, Teams, Gmail, Google Drive, and Slack, with a preview shown before any action is executed.^[26]

Glean Agents

In February 2025, Glean announced Glean Agents, which became generally available in May 2025.^[9] Agents are automated workflows that can be built and deployed by employees using natural language instructions, without requiring engineering skills.

An agent in Glean's terminology is a system that receives a goal, accesses the knowledge graph and connected applications to gather context, and then executes a multi-step workflow. Agents can read from and write to connected applications, trigger actions in third-party systems, and call external APIs.

Glean said at its Series F announcement in June 2025 that agents built on the platform were powering more than 100 million agent actions per year and that the company was targeting 1 billion agent actions by end of year.^[5] Common agent use cases include automatically routing IT support tickets to the right team, drafting customer-facing responses by pulling from knowledge base articles, summarizing new contracts and surfacing relevant precedents, and answering new employee onboarding questions by pulling from HR policies.

The agent infrastructure supports MCP (Model Context Protocol) and exposes SDKs and APIs that allow engineering teams to build custom agents or extend existing ones. Glean Protect, the platform's security layer, enforces permission boundaries and validates agent actions at runtime to prevent agents from accessing or modifying data outside what a user is authorized to affect.

In a February 2026 product update, Glean added more than 85 new agent actions across connected applications, an "insights" chat for administrators to query adoption and manage deployments in natural language, and eight engineering-focused agents aimed at code review, fixes, and releases.^[27]

Agent Development Lifecycle

On May 12, 2026, Glean introduced the Enterprise Agent Development Lifecycle (ADLC), a framework and set of platform capabilities meant to help organizations build, govern, and measure AI agents consistently.^[28][29] The framework defines seven phases: opportunity, design, performance, input, develop, launch, and monitor and improve.^[29] Glean positioned the ADLC as a response to enterprises building large numbers of agents without consistent oversight or success metrics; chief product officer Emrecan Dogan said agents are software that need "a disciplined way to be defined, built, launched, governed, and improved over time."^[28][29]

The release made several capabilities generally available, including an Auto Mode agent builder that generates agents from a natural-language description through guided questions, debug and trace views that expose an agent's inputs, tool calls, and model decisions step by step, an expanded agent sandbox providing a secure file system and code execution inside the customer's virtual private cloud, and new agent library controls such as verification badges and departmental categories.^[28][29] Content triggers and agent access policies were released in beta, and the release also described support for modular sub-agents that a parent agent can coordinate at runtime.^[28][29]

Enterprise Graph

The Enterprise Graph, announced in September 2025, is an expansion of Glean's earlier knowledge graph.^[10][11] Where the original knowledge graph mapped relationships between documents, the Enterprise Graph adds two layers: a company-wide graph tracking relationships between people, teams, workflows, and products; and a personal graph for each employee that captures their projects, collaborators, responsibilities, and communication patterns.^[10][11]

The personal graph is built automatically from signals across connected applications: documents the employee has authored or edited, colleagues they communicate with frequently, meetings they attend, and tasks they are assigned. This personal graph allows the assistant and agents to tailor responses and outputs to the specific context of each user.

Glean builds these graphs within each customer's single-tenant cloud environment, a deployment architecture that keeps each organization's data isolated. The single-tenant model was an architectural decision made early in the company's history and has remained a competitive differentiator given enterprise security requirements.

Who uses Glean?

In February 2025 Glean said it had more than doubled its customer base over the prior year, though it did not publish a customer count.^[8] Customers span technology, financial services, telecommunications, media, healthcare, and other industries.

Confluent, the data streaming company, has reported that more than 70 percent of its employees actively use Glean, a higher adoption rate than most enterprise SaaS tools see in practice.^[16] Duolingo and Grammarly, both software companies with large engineering and content organizations, have also been cited as customers.^[23] Other named customers include Booking.com, Comcast, eBay, Intuit, LinkedIn, Pinterest, Samsung, Zillow, Databricks, and Reddit.^[7][21][23]

Citi and Capital One Ventures joined Glean's Series D round in February 2024 as strategic investors.^[34] Citigroup and Capital One Ventures are both investors, reflecting fintech and financial services adoption.^[5]

The company held its first user conference, called Glean:GO, which Glean reported drew more than 10,000 attendees in person and online in May 2025.^[5][30]

In its December 2025 disclosure Glean reported that a 40 percent weekly DAU/MAU ratio, which it said was more than twice the SaaS industry benchmark, and that the average employee performed about five queries a day.^[7] By May 2026 the company said its Fortune 500 customer count had nearly doubled year over year and that more than 85 percent of its customers used Glean across five or more departments.^[21][22]

How does Glean's technology work?

Knowledge graph architecture

Glean's knowledge graph is built from three types of data. The first is content: documents, messages, tickets, code, and all other artifacts indexed from connected applications. The second is identity: users, roles, teams, organizational hierarchies, and the relationships between them. The third is behavioral signals: which documents users have opened, edited, or shared; which colleagues they interact with most; and how often documents are viewed.

The graph is built using machine learning rather than manual curation. Glean's systems infer relationships between entities automatically by analyzing data structures in enterprise applications. For example, a Jira ticket connected to a Confluence page, written by a person who is also in a Slack channel where the same topic is discussed, would be linked in the graph without a human having to annotate those connections.

Vector embeddings are used to represent documents and queries in a way that captures semantic meaning rather than just keywords. When a user submits a query, the system retrieves documents whose embeddings are semantically close to the query's embedding, in addition to keyword matches. This retrieval layer feeds into the RAG pipeline that generates assistant responses.

By the end of 2025, Glean reported that it had indexed more than 27 billion documents since inception and that its platform processed more than 20 trillion tokens annually, a figure it said had doubled in the prior quarter.^[7]

Document intelligence

In March 2026, Glean made its first publicly disclosed acquisition, buying Aryn, an agentic document intelligence company.^[24][31] Aryn's technology converts unstructured documents such as PDFs, contracts, and research reports into schema-driven structured data, extending Glean's reach beyond the SaaS application data it had previously indexed.^[24][31] Glean said the Aryn team would join the company; Aryn's hosted cloud service was scheduled to be sunset, with its APIs and website remaining available until April 15, 2026, while its Sycamore document-processing project was to remain open source under the Apache 2.0 license.^[31] Aryn described its mission as answering questions from all of an organization's data and characterized the deal as joining "a bigger pitch" rather than winding down.^[31]

Multi-model architecture

Glean operates a Model Hub that supports multiple large language models simultaneously. The platform is model-agnostic by design, allowing customers to use different models for different tasks and allowing Glean to swap in newer models as they become available. Glean integrates with Amazon Bedrock, enabling customers to access models available through AWS's managed inference service alongside models from other providers.^[17]

This multi-model approach is distinct from products like Microsoft 365 Copilot, which is powered mainly by OpenAI models (Microsoft added Anthropic's Claude models as an option in September 2025),^[37] or ChatGPT Enterprise, which runs exclusively on OpenAI's models. Glean's position is that enterprises should not be locked into a single model provider, particularly as the model landscape continues to evolve rapidly.

Security and deployment

Glean operates in a single-tenant cloud model, meaning each customer's data is stored and processed in an isolated environment rather than a shared infrastructure. Data is encrypted at rest and in transit. The platform passes SOC 2 Type II and ISO 27001 certifications.

The permission mirroring system is implemented per connector and updates in near real time. If a document's sharing permissions change in Confluence or Google Drive, the change is reflected in Glean search results and assistant responses within minutes rather than waiting for a full re-index cycle.

Glean Protect, introduced alongside the agent capabilities, adds an additional layer of security specifically for agentic workflows. It validates agent actions before they are executed and enforces that agents cannot access or modify data outside the bounds of what the initiating user is authorized to affect.

Partner ecosystem

At its first Glean:GO conference in May 2025, Glean announced strategic partnerships with Dell Technologies, Palo Alto Networks, Snowflake, and Workday.^[30][32] The Dell partnership was described as delivering a first on-premises deployment architecture for Glean's platform.^[30] The Snowflake collaboration integrates Glean with Snowflake Cortex Analyst, allowing users and agents to query structured data in a Snowflake environment using natural language or SQL.^[30] The Palo Alto Networks partnership integrates Prisma AIRS and the Prisma Access Browser to give customers more visibility and control over how agents interact with sensitive data.^[32] The Workday partnership lets Workday Illuminate agents interact with Glean agents.^[30]

How does Glean compare with Microsoft 365 Copilot and ChatGPT Enterprise?

Glean competes primarily with Microsoft 365 Copilot and ChatGPT Enterprise in enterprise procurement conversations, though the three products have meaningfully different architectures and use cases.

Microsoft 365 Copilot's primary advantage is deep integration with Office applications that many organizations have used for years. Copilot is embedded directly inside Word, Excel, PowerPoint, Outlook, and Teams, which means employees encounter it within their existing workflows without needing to open a separate tool. Copilot's meeting intelligence in Teams, which can summarize meetings and track action items, is widely cited as a standout capability for organizations that run on Teams.

However, Copilot's scope is largely bounded by the Microsoft ecosystem. Microsoft offers Copilot connectors that can index some third-party systems, including Salesforce, Confluence, Jira, and ServiceNow, but administrators must configure each one, and coverage of the long tail of SaaS products is narrower.^[39] An organization whose work is distributed across many different tools gets limited value from Copilot's retrieval capabilities.

ChatGPT Enterprise provides a capable general-purpose AI assistant with strong reasoning and writing capabilities, and since 2025 it has been able to search connected services such as Google Drive, SharePoint, and Slack within each user's existing permissions.^[38] Glean argues that its own connector catalog and knowledge graph reach deeper into an organization's systems than these integrations. ChatGPT Enterprise is well suited to tasks where a user provides the context themselves or where the task does not depend on specific organizational information.

Glean argues that its advantage over both competitors is its breadth of integration and the depth of its organizational knowledge model. Because it indexes data across all connected applications and builds a knowledge graph of the entire organization, it can surface relevant information that neither Copilot nor ChatGPT Enterprise would have access to. This breadth is particularly valuable for knowledge work that spans multiple systems.

Copilot adoption has faced challenges in practice. A 2024 Gartner survey found that only about 6 percent of organizations piloting Copilot had finished their pilots and were planning large-scale deployment,^[35] and in January 2026 Microsoft reported about 15 million paid Copilot seats against roughly 450 million paid commercial Microsoft 365 seats.^[36]

By 2026 Glean framed its competitive positioning increasingly around cost. Jain argued that connecting an external AI system to Glean lets that system retrieve the specific context it needs and therefore consume far fewer tokens than if it were pointed directly at an organization's raw systems.^[21] The company said internal testing showed its approach used about 30 percent fewer tokens than generic alternatives and that users preferred its output roughly 2.5 times more often.^[22] Jain also acknowledged the changing landscape, saying that for the first four or five years of the company's existence it had effectively no direct competition, but that as search became central to enterprise AI, "every single company in the world wants to be in this space," naming Google, Microsoft, OpenAI, Anthropic, Salesforce, and Atlassian among the competitors.^[21]

How fast is Glean's revenue growing?

Glean reached $100 million in annual recurring revenue in the fourth quarter of its fiscal year 2025 (which ended January 31, 2025), making it three years from product launch to that milestone. The company reported this in February 2025.^[8]

By December 2025, Glean announced it had doubled ARR to $200 million, a milestone reached nine months after the $100 million threshold.^[6][7]

Within the revenue base, the segment of contracts valued at $1 million or more nearly tripled over the same period, suggesting that Glean was not only signing more customers but also expanding within existing accounts.^[7] Company-wide deployments, meaning accounts where Glean was rolled out to the full employee base rather than a subset, more than doubled.^[7]

In May 2026, Glean said it had crossed $300 million in ARR, tripling the figure in 15 months.^[21][22] The company offers both a consumption-based model, in which customers pay per use, and a hybrid model that combines a fixed monthly fee for active users with usage-based fees for model consumption.^[21] Because a pure consumption model has no strictly recurring component, press coverage noted that part of the $300 million figure is more accurately described as an annualized revenue run rate than traditional ARR.^[21][22]

Glean does not publish its pricing publicly, operating a quote-based model typical of enterprise software. Glean has not published per-seat prices, and actual pricing varies by deal size and contract structure.^[21]

What is Glean used for?

Glean's customers use the platform across a range of departmental functions.

In IT and help desk contexts, Glean reduces the volume of internal support tickets by allowing employees to find answers to technical questions through search and the assistant before submitting a ticket. The platform can surface relevant runbooks, documentation, and previous ticket resolutions.

Engineering teams use Glean to reduce context-switching when debugging or reviewing code. An engineer investigating a production incident can search across Slack conversations, Jira tickets, Confluence documentation, and GitHub history in a single query rather than switching between applications. Glean agents can be configured to automatically gather this context when a new incident ticket is created.

Sales teams use the platform to prepare for customer meetings by pulling together account history, recent communications, product documentation, and competitive intelligence from across connected systems. Sales agents can draft customer-facing materials by pulling from approved templates and previous successful proposals.

For HR and employee onboarding, Glean allows new employees to self-serve answers to questions about policies, processes, benefits, and company resources rather than asking a colleague or submitting a ticket to HR. This is particularly useful in the first weeks of employment when a new hire has many questions and may not know which colleague to ask.

Customer support teams use Glean to find relevant knowledge base articles, past case resolutions, and product documentation while handling a case, reducing the time to resolution and the need to escalate to more specialized colleagues.

Reception

Glean has received broadly positive coverage from technology press and industry analysts since its commercial launch, with particular attention to the speed of its revenue growth.

In June 2025, TechCrunch reported on the Series F and noted that since early 2024 Glean had raised $610 million and grown its valuation from $2.2 billion to $7.2 billion.^[3] CNBC covered the same round, noting that Glean's ARR had passed $100 million in its fiscal year ending January 31, 2025, less than three years after launch.^[4]

Fast Company named Glean to its World's Most Innovative Companies list for 2025, where it was the only enterprise AI company to rank in the top 10.^[14] The company appeared on the Forbes AI 50 and Forbes Cloud 100 lists for 2025.^[15] Gartner recognized Glean as a Tech Innovator in Agentic AI.^[7]

Bloomberg identified Glean as one of 24 AI startups to watch in 2026, according to the company.^[7]

Sam Altman, CEO of OpenAI, reportedly asked OpenAI's investors in October 2024 not to back five rival companies, one of which was Glean, according to a Business Insider report citing Reuters.^[20] This comment was cited in coverage discussing the competitive overlap between Glean's enterprise knowledge layer and OpenAI's ChatGPT Enterprise product.

Glean was named to the CNBC Disruptor 50 list for the second consecutive year in 2025. It appeared on the list again in 2026, ranking number 41.^[33]

On the question of going public, Jain has indicated that an eventual listing is the expected outcome but has not committed to a timeline; the company had not filed publicly for an initial public offering as of mid-2026.^[23]

What are the limitations of Glean?

Reviews and analyst coverage have identified several limitations of Glean's platform.

Setup and implementation can be complex. Deploying Glean across an organization requires connecting each application through its connector system, configuring permissions, and completing an initial index. For large organizations with dozens of applications, this process can take weeks or months and requires sustained involvement from IT.

Glean's pricing is not publicly available, which makes it difficult for smaller companies to evaluate its cost relative to alternatives. The enterprise quote model creates friction for organizations that want to understand costs before initiating a sales conversation.

Connector coverage, while broad, is not universal. Organizations using less common or highly customized internal applications may find that those systems are not supported by a pre-built connector, requiring custom development to include them in the index.

At general availability in May 2025, Glean Agents were oriented mainly toward single-agent workflows; Glean later added agent-to-agent interaction with Workday agents and, with Agentic Engine 2, parallel sub-agent orchestration.^[18][30] This limits the complexity of workflows that can be automated compared to more specialized AI agent orchestration platforms. Glean addressed parts of this gap in 2026 with modular sub-agents coordinated by a parent agent and an expanded agent sandbox for larger data-processing tasks.^[28][29]

The platform also requires that source data be adequately organized and up to date. If an organization's documentation is fragmented, outdated, or inconsistently maintained, Glean will surface that fragmented content, and the quality of assistant answers will reflect the quality of the underlying data.

Continue reading

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Topics:AI AgentsAI CompaniesEnterprise AIInformation Retrieval

References

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