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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 Arvind Jain and three co-founders, all veterans of Google, 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.
As of June 2025, Glean was valued at $7.2 billion following a $150 million Series F funding round led by Wellington Management. By December 2025, the company reported surpassing $200 million in annual recurring revenue (ARR), doubling from $100 million in roughly nine months. Glean serves more than 400 enterprise customers, including Sony, Confluent, and Duolingo, and has been recognized on the Forbes AI 50, the CNBC Disruptor 50, and Fast Company's World's Most Innovative Companies lists.
The platform connects to more than 100 enterprise applications 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.
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.
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.
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.
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.
Jain founded Glean in 2019 along with three co-founders he had known from his time at Google: T.R. Vishwanath, who had worked on Google's distributed systems infrastructure; Tony Gentilcore, who had subsequently led engineering teams at Twitter and Pinterest; and Piyush Prahladka, who had also worked in Google engineering.
The company incorporated in Palo Alto under the legal name Glean Technologies, Inc. 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.
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.
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.
| Round | Date | Amount | Valuation | Lead Investor |
|---|---|---|---|---|
| Series A | March 2019 | $15M | N/A | Kleiner Perkins, Lightspeed |
| Series B | March 2021 | $40M | N/A | General Catalyst |
| Series C | May 2022 | $100M | $1B | Sequoia Capital |
| Series D | February 2024 | $200M+ | $2.2B | Kleiner Perkins |
| Series E | September 2024 | $260M | $4.6B | Altimeter Capital, DST Global |
| Series F | June 2025 | $150M | $7.2B | Wellington Management |
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 coincided with the launch of Glean's commercial product. 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. 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, led by Kleiner Perkins. 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. 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. Glean said it intended to use the capital for product development, expanding its partner ecosystem, and international growth.
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.
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 is the foundational layer of the platform. It connects to more than 100 enterprise applications through pre-built connectors and indexes the content of each application in near real time. 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 is an AI assistant that answers questions, summarizes documents, drafts content, and reasons across information retrieved from connected applications. The assistant was first introduced in 2021 and has gone through multiple generations.
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. 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. 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. 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 February 2025, Glean announced Glean Agents, which became generally available in May 2025. 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. 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.
The Enterprise Graph, announced in September 2025, is an expansion of Glean's earlier knowledge graph. 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.
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.
As of June 2025, Glean reported more than 400 enterprise customers, up from roughly 250 at the beginning of 2024. 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. Sony Electronics, a 50,000-person organization with decades of accumulated documentation, adopted Glean to modernize knowledge access for global teams. Duolingo and Grammarly, both software companies with large engineering and content organizations, have also been cited as customers.
Citigroup invested in Glean during the Series E round, signaling interest from financial services as both a potential customer and strategic partner. Citigroup and Capital One Ventures are both investors, reflecting fintech and financial services adoption.
The company held its first user conference, called Glean:GO, which Glean reported drew more than 10,000 attendees, suggesting a growing practitioner community around the platform.
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.
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.
This multi-model approach is distinct from products like Microsoft 365 Copilot, which is built on a specific set of Microsoft-managed Azure OpenAI models, 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.
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.
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.
| Feature | Glean | Microsoft 365 Copilot | ChatGPT Enterprise |
|---|---|---|---|
| Primary use case | Cross-system enterprise search and knowledge | Productivity within Microsoft 365 apps | General-purpose AI assistant |
| Data sources | 100+ enterprise app connectors | Microsoft 365 ecosystem; limited third-party | Web and custom uploads; no live enterprise data sync |
| Knowledge graph | Yes, built per customer | Graph API (limited cross-system) | No |
| Permission-aware results | Yes, per-connector mirroring | Yes, within Microsoft ecosystem | No |
| AI agent capability | Yes (Glean Agents, GA May 2025) | Yes (Copilot Studio) | Limited (GPT actions, third-party integrations) |
| Model flexibility | Multi-model (model hub) | Azure OpenAI (GPT-4 series) | OpenAI models only |
| Deployment model | Single-tenant cloud | Microsoft-managed cloud | OpenAI-managed cloud |
| Pricing structure | Enterprise quote (not public) | $30/user/month (M365 E3/E5 required) | $30/user/month (150-user minimum) |
| Best suited for | Organizations using 50+ SaaS tools | Organizations heavily using Microsoft 365 | General knowledge work, research, writing |
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. It does not natively search Salesforce, Slack, Confluence, Jira, Zendesk, or the hundreds of other SaaS products that most enterprises use. 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, but it does not connect to live enterprise data. It cannot retrieve a specific contract, search a Jira backlog, or pull from a proprietary knowledge base in the way Glean can. 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's 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. Reports from early 2026 indicated that only about 6 percent of organizations that piloted Copilot had moved to broader deployment, and roughly 15 million users had purchased full licenses out of Microsoft's 450 million Microsoft 365 subscribers.
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.
By December 2025, Glean announced it had doubled ARR to $200 million, a milestone reached nine months after the $100 million threshold. In a year-over-year comparison, ARR grew approximately 89 percent, from roughly $110 million to $208 million.
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. Company-wide deployments, meaning accounts where Glean was rolled out to the full employee base rather than a subset, more than doubled.
Glean does not publish its pricing publicly, operating a quote-based model typical of enterprise software. Industry estimates and customer reports have suggested per-seat pricing in the range of $10 to $30 per user per month depending on volume and contract structure, though actual pricing varies significantly by deal size.
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. Glean has reported that some customers have seen internal IT and HR support requests fall by around 20 percent after deployment.
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.
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 described Glean as one of the fastest-growing enterprise software companies in recent years. CNBC covered the same round and noted the company's doubling of ARR as unusual in the enterprise software market, where growth at that pace is uncommon past the $100 million threshold.
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. The company appeared on the Forbes AI 50 and Forbes Cloud 100 lists for 2025. Gartner recognized Glean as a Tech Innovator in Agentic AI.
Bloomberg identified Glean as one of 24 AI startups to watch in 2026, citing its enterprise graph technology and the growth of its agent platform.
Sam Altman, CEO of OpenAI, is reported to have described Glean as a threat to OpenAI's enterprise business, according to a Business Insider report from 2024. 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.
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. Some Gartner reviews have cited unexpected data disclosure risks if connectors are misconfigured.
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.
The agent capabilities, while available as of May 2025, support single-agent workflows. Agents cannot communicate with each other, maintain persistent memory across separate sessions, or handle workflows that require truly parallel execution across multiple independent agents. This limits the complexity of workflows that can be automated compared to more specialized AI agent orchestration platforms.
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.